From 499b59319fa99fbc38f57ec2c5ed456ca12dfba8 Mon Sep 17 00:00:00 2001
From: Rakesh Joshi <rakeshjoshi21021369@gmail.com>
Date: Thu, 16 May 2024 01:21:37 +0530
Subject: [PATCH 1/5] EDA anlaysis of the dataset

---
 .../Liver_disease_EDA.ipynb                   | 1063 +++++++++++++++++
 Liver DIsease prediction/README.md            |   27 +
 2 files changed, 1090 insertions(+)
 create mode 100644 Liver DIsease prediction/Liver_disease_EDA.ipynb
 create mode 100644 Liver DIsease prediction/README.md

diff --git a/Liver DIsease prediction/Liver_disease_EDA.ipynb b/Liver DIsease prediction/Liver_disease_EDA.ipynb
new file mode 100644
index 00000000..fac86e31
--- /dev/null
+++ b/Liver DIsease prediction/Liver_disease_EDA.ipynb	
@@ -0,0 +1,1063 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Liver Disease EDA(Exploratory Data Analysis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(r\"C:\\Users\\rakes\\health_proj\\Liver Disease Prediction\\Dataset\\indian_liver_patient.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Set Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(583, 11)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>65</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>16</td>\n",
+       "      <td>18</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>62</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>699</td>\n",
+       "      <td>64</td>\n",
+       "      <td>100</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>62</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>490</td>\n",
+       "      <td>60</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>58</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>182</td>\n",
+       "      <td>14</td>\n",
+       "      <td>20</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>72</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>195</td>\n",
+       "      <td>27</td>\n",
+       "      <td>59</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "0   65  Female              0.7               0.1                   187   \n",
+       "1   62    Male             10.9               5.5                   699   \n",
+       "2   62    Male              7.3               4.1                   490   \n",
+       "3   58    Male              1.0               0.4                   182   \n",
+       "4   72    Male              3.9               2.0                   195   \n",
+       "\n",
+       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "0                        16                          18             6.8   \n",
+       "1                        64                         100             7.5   \n",
+       "2                        60                          68             7.0   \n",
+       "3                        14                          20             6.8   \n",
+       "4                        27                          59             7.3   \n",
+       "\n",
+       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
+       "0      3.3                        0.90        1  \n",
+       "1      3.2                        0.74        1  \n",
+       "2      3.3                        0.89        1  \n",
+       "3      3.4                        1.00        1  \n",
+       "4      2.4                        0.40        1  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>60</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>500</td>\n",
+       "      <td>20</td>\n",
+       "      <td>34</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>40</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>98</td>\n",
+       "      <td>35</td>\n",
+       "      <td>31</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>52</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>245</td>\n",
+       "      <td>48</td>\n",
+       "      <td>49</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>184</td>\n",
+       "      <td>29</td>\n",
+       "      <td>32</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>216</td>\n",
+       "      <td>21</td>\n",
+       "      <td>24</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Age Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "578   60   Male              0.5               0.1                   500   \n",
+       "579   40   Male              0.6               0.1                    98   \n",
+       "580   52   Male              0.8               0.2                   245   \n",
+       "581   31   Male              1.3               0.5                   184   \n",
+       "582   38   Male              1.0               0.3                   216   \n",
+       "\n",
+       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "578                        20                          34             5.9   \n",
+       "579                        35                          31             6.0   \n",
+       "580                        48                          49             6.4   \n",
+       "581                        29                          32             6.8   \n",
+       "582                        21                          24             7.3   \n",
+       "\n",
+       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
+       "578      1.6                        0.37        2  \n",
+       "579      3.2                        1.10        1  \n",
+       "580      3.2                        1.00        1  \n",
+       "581      3.4                        1.00        1  \n",
+       "582      4.4                        1.50        2  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>579.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>44.746141</td>\n",
+       "      <td>0.756432</td>\n",
+       "      <td>3.298799</td>\n",
+       "      <td>1.486106</td>\n",
+       "      <td>290.576329</td>\n",
+       "      <td>80.713551</td>\n",
+       "      <td>109.910806</td>\n",
+       "      <td>6.483190</td>\n",
+       "      <td>3.141852</td>\n",
+       "      <td>0.947064</td>\n",
+       "      <td>1.286449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>16.189833</td>\n",
+       "      <td>0.429603</td>\n",
+       "      <td>6.209522</td>\n",
+       "      <td>2.808498</td>\n",
+       "      <td>242.937989</td>\n",
+       "      <td>182.620356</td>\n",
+       "      <td>288.918529</td>\n",
+       "      <td>1.085451</td>\n",
+       "      <td>0.795519</td>\n",
+       "      <td>0.319592</td>\n",
+       "      <td>0.452490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>63.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>2.700000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>175.500000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>208.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>3.100000</td>\n",
+       "      <td>0.930000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>58.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>1.300000</td>\n",
+       "      <td>298.000000</td>\n",
+       "      <td>60.500000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>7.200000</td>\n",
+       "      <td>3.800000</td>\n",
+       "      <td>1.100000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>19.700000</td>\n",
+       "      <td>2110.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>4929.000000</td>\n",
+       "      <td>9.600000</td>\n",
+       "      <td>5.500000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Age      Gender  Total_Bilirubin  Direct_Bilirubin  \\\n",
+       "count  583.000000  583.000000       583.000000        583.000000   \n",
+       "mean    44.746141    0.756432         3.298799          1.486106   \n",
+       "std     16.189833    0.429603         6.209522          2.808498   \n",
+       "min      4.000000    0.000000         0.400000          0.100000   \n",
+       "25%     33.000000    1.000000         0.800000          0.200000   \n",
+       "50%     45.000000    1.000000         1.000000          0.300000   \n",
+       "75%     58.000000    1.000000         2.600000          1.300000   \n",
+       "max     90.000000    1.000000        75.000000         19.700000   \n",
+       "\n",
+       "       Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
+       "count            583.000000                583.000000   \n",
+       "mean             290.576329                 80.713551   \n",
+       "std              242.937989                182.620356   \n",
+       "min               63.000000                 10.000000   \n",
+       "25%              175.500000                 23.000000   \n",
+       "50%              208.000000                 35.000000   \n",
+       "75%              298.000000                 60.500000   \n",
+       "max             2110.000000               2000.000000   \n",
+       "\n",
+       "       Aspartate_Aminotransferase  Total_Protiens     Albumin  \\\n",
+       "count                  583.000000      583.000000  583.000000   \n",
+       "mean                   109.910806        6.483190    3.141852   \n",
+       "std                    288.918529        1.085451    0.795519   \n",
+       "min                     10.000000        2.700000    0.900000   \n",
+       "25%                     25.000000        5.800000    2.600000   \n",
+       "50%                     42.000000        6.600000    3.100000   \n",
+       "75%                     87.000000        7.200000    3.800000   \n",
+       "max                   4929.000000        9.600000    5.500000   \n",
+       "\n",
+       "       Albumin_and_Globulin_Ratio     Dataset  \n",
+       "count                  579.000000  583.000000  \n",
+       "mean                     0.947064    1.286449  \n",
+       "std                      0.319592    0.452490  \n",
+       "min                      0.300000    1.000000  \n",
+       "25%                      0.700000    1.000000  \n",
+       "50%                      0.930000    1.000000  \n",
+       "75%                      1.100000    2.000000  \n",
+       "max                      2.800000    2.000000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# To see if there are null values and target class distribution, correlation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                           0\n",
+       "Gender                        0\n",
+       "Total_Bilirubin               0\n",
+       "Direct_Bilirubin              0\n",
+       "Alkaline_Phosphotase          0\n",
+       "Alamine_Aminotransferase      0\n",
+       "Aspartate_Aminotransferase    0\n",
+       "Total_Protiens                0\n",
+       "Albumin                       0\n",
+       "Albumin_and_Globulin_Ratio    4\n",
+       "Dataset                       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "416 167\n"
+     ]
+    }
+   ],
+   "source": [
+    "# target class distribution in data set, 1 represent its a liver patient and 2 represent it is not a liver patient\n",
+    "true_count=len(df.loc[df['Dataset']==1])\n",
+    "false_count=len(df.loc[df['Dataset']==2])\n",
+    "print(true_count,false_count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "numerical_vars = ['Age', 'Gender','Total_Bilirubin', 'Direct_Bilirubin', 'Alkaline_Phosphotase', \n",
+    "                  'Alamine_Aminotransferase', 'Aspartate_Aminotransferase', 'Total_Protiens', \n",
+    "                  'Albumin', 'Albumin_and_Globulin_Ratio']\n",
+    "df[numerical_vars].hist(figsize=(12, 10))\n",
+    "plt.suptitle('Histograms of Numerical Variables', fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#bar chart for gender distribution in the dataset and also for the liver patient and non liver patient\n",
+    "categorical_vars = ['Gender', 'Dataset']\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "for i, var in enumerate(categorical_vars, 1):\n",
+    "    plt.subplot(1, 2, i)\n",
+    "    sns.countplot(x=var, data=df)\n",
+    "    plt.title(f'Bar Chart of {var}', fontsize=14)\n",
+    "    plt.xlabel(var)\n",
+    "    plt.ylabel('Count')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#to find the correlation we change the genders to numeric\n",
+    "df['Gender']=df['Gender'].replace({'Male':1,'Female':0})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rqaeeUqVKlbRy5UrVrl1bO3fuVLNmzZScnGw6Im6hRYsWunLliurWratRo0YpISFBRYsW1erVq/Xqq6/q0KFDpiPiNkJCQjRjxgw99thjCg0N1QcffKDmzZtr//79euihh/T777+bjojb+PeI4n+yWCw6evRoNqbBnbh06ZL27Nmj06dPy2q1ZjrWokULQ6lgr7CwMG3fvl0FChTItH7u3DlVr16d70En8euvv6pBgwZyd3fXzz//rBo1aujnn39WwYIFtWHDBgUFBZmOCMBB8NgwgCwrVqyYZs2apTFjxmRanzVrlooXLy5JOnv2LBv1OoFSpUqpVKlSpmMgi55//nlVrFhRO3bsyPg++/3339WlSxe9+OKL2rJli+GEsBedXc5r6NCh6tChg/r27avHHntMtWvXliStXr1a1apVM5wOt/P+++/r5Zdf1pIlSzRt2rSM/fG+/fZbNW7c2HA62KNr165q166dgoODZbFY1LBhQ0nSjz/+qPLlyxtOB3skJCSYjoC7sHLlSnXu3Flnzpy57hh7BDmHY8eO3fA6Xb58WSdPnjSQCHeiSJEiio2N1cKFC7Vz505ZrVZ1795dHTt2VO7cuU3HA+BA6AgCkGXLly9X27ZtVb58ef3nP/+RxWLR9u3bFR8fr88//1zNmzfXtGnT9PPPPysqKsp0XPx//fr1s/tcrptjy507t3bs2KGKFStmWt+3b5/+85//6OLFi4aSISvo7HJ+ycnJSkpKUpUqVeTm5iZJ2rZtm/z8/LgRDWSDJUuW6MSJE2rbtq2KFSsm6a8RnAEBAWrZsqXhdLDXlStXlJCQoFKlSvEghBMpXbq0nnjiCQ0dOlSFChUyHQdZsHz5cklSq1atNHfuXPn7+2ccS09P19q1a7VmzRodPHjQVEQAwH1AIQjAHTl+/LimTZumQ4cOyWazqXz58nrppZd07tw5Va1a1XQ83ECDBg0yvd65c6fS09Mz3YB2d3fXAw88oJiYGBMRYaeqVasqKipKjz76aKb1mJgY9e7dW3v37jWUDFlRq1YtBQUFae7cudd1dp0+fZrOLiCbXLx4UVevXs205ufnZygNkHOkpaWpV69eGXvmHTp0SGFhYYqMjFSRIkU0ePBgwwlxK35+ftq9ezfTBZzQ3w+vWCwW/fuWoKenp0qWLKnx48erefPmJuIhi+bOnauCBQuqWbNmkqSBAwfqo48+Unh4uD799FOFhIQYTgjAUVAIAnDXzp07pwULFig6OlqxsbGMAXACUVFR+v7776+7Ad21a1c9/PDD6t+/v+GE+Ld/bnq9ceNGDRw4UMOHD1etWrUkSVu3btXIkSM1ZswYNW3a1FRMZAGdXc5v+/bt+uyzz5SYmKgrV65kOrZ06VJDqWCP1NRUDRo0SIsXL9bZs2evO857GeeQmpqq9evX3/B7MDIy0lAq2Kt3797atGmTJk6cqMaNG2vPnj0KCwvT8uXLNWzYMO3evdt0RNxCt27dVLduXXXv3t10FNyh0NBQbd++XQULFjQdBXehXLlymjZtmh599FFt2bJFjz32mCZOnKivvvpKHh4evCcFkIFCEIA7FhMTo+joaC1dulQhISFq06aN2rRpw94ITuDvDbFvdAO6UaNG+vXXXw0lw824ubnJYrFkvP77n++/1/75mhuYzoHOLue2cOFCde7cWY0aNdKaNWvUqFEj/fzzz0pOTtZTTz2l2bNnm46IW3jllVe0bt06jRw5Up07d9bUqVP1yy+/aPr06RozZow6duxoOiJuY/fu3WratKnS0tKUmpqq/Pnz68yZM/Lx8VFQUBCbnDuBkJAQLVq0SLVq1ZKvr6/i4uIUFhamw4cPq3r16pkegoHjSUtLU9u2bRUYGKiIiAh5enpmOk4xFsgePj4+OnDggEqUKKFBgwYpKSlJ8+bN0/79+1W/fn399ttvpiMCcBAM4AWQJSdPntScOXMUHR2t1NRUtWvXTlevXtXnn3+u8PBw0/FgpwsXLujUqVPXFYJOnz6tP/74w1Aq3Mq6detMR8A98M+bWu+8844iIyNv2Nn17rvvmooIO73zzjuaMGGCXnnlFfn6+mrSpEkKDQ3VSy+9pODgYNPxcBsrVqzQvHnzVL9+fXXr1k0PP/ywSpcurZCQEC1YsIBCkBPo27evnnzySU2bNk0BAQHaunWrPD099eyzz6p3796m48EOv/32m4KCgq5bT01NzfTwCxzTJ598olWrVil37tz6/vvvM10zi8VCIchJ0Fnp/PLmzauzZ8+qRIkSWr16tfr27StJypUrFxMGAGRCRxAAuzVt2lQbN25U8+bN1bFjRzVu3Fju7u7y9PRUXFwchSAn0rlzZ61fv17jx4/PdAN6wIABqlevXsasdgD3Fp1driNPnjzav3+/SpYsqYIFC2rdunWKiIhQfHy8Hn30USUlJZmOiFvImzev9u/fr5CQEBUrVkxLly5VzZo1lZCQoIiICP3555+mI+I2AgIC9OOPP6pcuXIKCAjQli1bVKFCBf3444967rnndODAAdMRcRuPPPKI/vvf/6pXr17y9fXVnj17FBoaqldffVWHDx/WypUrTUfELRQuXFiRkZEaPHhwxp4zcC50VrqGjh076sCBA6pWrZo+/fRTJSYmqkCBAlq+fLneeOMN7du3z3REAA6CjiAAdlu9erUiIyPVs2dPlSlTxnQc3IUPP/xQr732mp599tmMDbI9PDzUvXt3vffee4bT4XY2bNhwy+P16tXLpiTIKjq7XEf+/PkzOiiLFi2qffv2KSIiQufOnVNaWprhdLidsLAwHTt2TCEhIQoPD9fixYtVs2ZNrVixQgEBAabjwQ6enp4ZRfRChQopMTFRFSpUkL+/vxITEw2ngz1Gjx6txo0b66efftK1a9c0adIk7d+/X1u2bNH69etNx8NtXLlyRe3bt6cI5MTorHQNU6dO1ZtvvqkTJ07o888/V4ECBSRJO3fu1DPPPGM4HQBHQkcQALtt2bJF0dHRWrx4scqXL69OnTqpffv2KlKkCB1BTio1NVVHjhyRzWZT6dKllSdPHtORYIcb/cL9zy4TOkmA+69Dhw6qUaOG+vXrp7fffluTJk1Sy5YttWbNGlWvXp2NeR3chAkT5O7ursjISK1bt07NmjVTenq6rl27pqioKG6AOYFGjRqpS5cu6tChg3r06KHdu3crMjJS8+fP1++//64ff/zRdETYYe/evRo3bpx27twpq9Wq6tWra9CgQYqIiDAdDbfRt29fBQYG6o033jAdBXeIzkoAyFkoBAHIsrS0NC1cuFDR0dHatm2b0tPTFRUVpW7dusnX19d0PMDlnT9/PtPrq1evavfu3RoyZIjefvttPfbYY4aSISvo7HJuKSkpunTpkooUKSKr1apx48Zp48aNKl26tIYMGaJ8+fKZjogsSExM1I4dO1SqVClVqVLFdBzYYceOHfrjjz/UoEED/fbbb3ruuecyvgdnz57NdQTus8jISM2bN09VqlRR5cqV5enpmel4VFSUoWSwV2BgoDZt2qSyZcuqXLlymjx5sp544gkdOHBA1atXp8PZyaSlpd1wr6fKlSsbSgTA0VAIAnBXDh48qFmzZmn+/Pk6d+6cGjZsqOXLl5uOhdtITU3VmDFjtHbtWp0+fVpWqzXTceZBO6cNGzaob9++2rlzp+kosAOdXYAZV69eVaNGjTR9+nSVLVvWdBzcAZvNpsTERAUFBSl37tym4yALLly4ID8/v4zPb+Xv8+CYGjRocNNjFotFMTEx2ZgGd4LOStfw22+/qUuXLjfdV43fKQD8jUIQgHsiPT1dK1asUHR0NIUgJ/DMM89o/fr16tSpk4KDgzPdfJbESBwnFR8fr//85z9scu4k6Oxybu7u7kpKSlJQUFCm9bNnzyooKIhfuh1cYGCgNm/ezJ6HTspqtSpXrlzav38/19DJ/PNnp5ub23XvQaW/Cn0Wi4Wfo8B9dqvOyujoaFWtWtV0RNihY8eOOnbsmCZOnKgGDRpo2bJlOnXqlN566y2NHz9ezZo1Mx0RgIOgEAQAOVBAQIC+/vpr1a1b13QU3IE9e/Zkem2z2ZSUlKQxY8bo6tWr2rRpk6FkuBfo7HIObm5uSk5Ovq4Q9Ouvv6pUqVK6ePGioWSwR//+/eXp6akxY8aYjoI7VLFiRc2aNUu1atUyHQVZsH79etWtW1ceHh5av379Lc995JFHsikVADiv4OBgffnll6pZs6b8/Py0Y8cOlS1bVsuXL9fYsWO1ceNG0xEBOAgP0wEAANkvX758yp8/v+kYuENVq1aVxWLRv5/lqFWrlqKjow2lwr0SGBiogwcPmo6Bm5g8ebKkv8bezJw5U3nz5s04lp6erg0bNqh8+fKm4sFOV65c0cyZM7VmzRrVqFFDefLkyXScvS0c39ixYzVgwABNmzZNlSpVMh0HdvpncSc0NFTFixe/rivIZrPpxIkT2R0NWdSgQYMbdnT9jdFwzmvXrl0aOnSovvrqK9NRYIfU1NSMB5Py58+v3377TWXLllVERIR27dplOB0AR0IhCAByoFGjRmno0KGaO3eufHx8TMdBFiUkJGR67ebmpsDAQOXKlctQItyJW3V2scm545owYYKkv67Xhx9+KHd394xjXl5eKlmypD788ENT8WCnffv2qXr16pKkQ4cOZTp2qxubcBzPPvus0tLSVKVKFXl5eV23V1BKSoqhZLBXaGjoDUdspqSkKDQ0lNFwDu7fY8OuXr2q2NhY7du3T88995yZULDbmjVrtHr1anl6eur5559XWFiYDhw4oMGDB2vFihVq2LCh6YiwU7ly5XTw4EGVLFlSVatW1fTp0zPejwYHB5uOB8CBMBoOAHKgatWq6ciRI7LZbCpZsqQ8PT0zHefJIcfFJueu4++9EW7W2UVXiWNr0KCBli5dqnz58pmOAuRIc+fOveVxbkQ7Pjc3N506dUqBgYGZ1o8fP67w8HClpqYaSoa7MXz4cP35558aN26c6Si4iblz56pr167Knz+/UlJSVLBgQUVFRenll19WmzZt1L9/fzotnciCBQt09epVdenSRbt379YTTzyhs2fPysvLS3PmzFH79u1NRwTgICgEAUAONGLEiFseHzZsWDYlwZ1gk3PXcPz48Uyv6exyXn+/naaTxPkcPnxYR44cUb169ZQ7d+6MTeoB3D/9+vWTJE2aNEkvvPBCpu709PR0/fjjj3J3d2fPQyd1+PBh1axZk648B1a1alU9/fTTGjx4sBYvXqynn35a1apV0+LFi1WqVCnT8XCX0tLSdODAAZUoUUIFCxY0HQeAA6EQBACAk2GTc+dHZ5drmDdvnt577z39/PPPkqSyZctqwIAB6tSpk+FkuJ2zZ8+qXbt2WrdunSwWi37++WeFhYWpe/fuCggI0Pjx401HhB2OHDmi2bNn68iRI5o0aZKCgoK0cuVKFS9eXBUrVjQdDzfRoEEDSdL69etVu3ZteXl5ZRz7e8Tma6+9xgMvTmr+/PkaNGiQfv31V9NRcBO+vr7as2ePQkNDZbVa5e3tre+++y7THl5wHiNHjtRrr7123cj3ixcv6r333tPQoUMNJQPgaCgEAUAOde7cOS1ZskRHjhzRgAEDlD9/fu3atUuFChVS0aJFTcfDLfTq1Uvz5s1T6dKl2eTcidHZ5dyioqI0ZMgQvfrqq6pbt65sNps2bdqkqVOn6q233lLfvn1NR8QtdO7cWadPn9bMmTNVoUIFxcXFKSwsTKtXr1bfvn21f/9+0xFxG+vXr1eTJk1Ut25dbdiwQfHx8QoLC9PYsWO1bds2LVmyxHRE3EbXrl01adIk+fn5mY6CO9C6detMr//e63DHjh0aMmQIEwYcmJubm5KTkzP25/L19c34dxDOx93d/Yb7rZ09e1ZBQUHstwYgg4fpAACA7Ldnzx49/vjj8vf317Fjx/TCCy8of/78WrZsmY4fP6558+aZjohbYJNz19C5c2fNmjWLzi4nNWXKFE2bNk2dO3fOWGvZsqUqVqyo4cOHUwhycKtXr9aqVatUrFixTOtlypS5bmwjHNPgwYP11ltvqV+/fvL19c1Yb9CggSZNmmQwGew1e/bsjM9Pnjwpi8XCw0hOxN/fP9NrNzc3lStXTiNHjlSjRo0MpYK9Vq1alXENrVar1q5dq3379mU6p0WLFiaiIYtuNtY2Li5O+fPnN5AIgKOiEAQAOVC/fv3UpUsXjR07NtPNkyZNmqhDhw4Gk8Ee69atMx0B98CVK1c0c+ZMrVmzhs4uJ5SUlKQ6depct16nTh0lJSUZSISsSE1NvW6EiiSdOXNG3t7eBhIhq/bu3atPPvnkuvXAwECdPXvWQCJkldVq1VtvvaXx48frzz//lPRXZ0L//v31v//9T25uboYT4lb+WciD83nuuecyvX7ppZcyvbZYLHSSOLh8+fLJYrHIYrGobNmymYpB6enp+vPPP9WjRw+DCQE4GgpBAJADbd++XdOnT79uvWjRokpOTjaQCMh56OxybqVLl9bixYv1xhtvZFpftGgR4/6cQL169TRv3jyNGjVK0l/fc1arVe+9917G/iVwbAEBAUpKSlJoaGim9d27d9NV4iT+97//ZXTG/nPE5vDhw3Xp0iW9/fbbpiPCDjt27FB8fLwsFosqVKigBx54wHQk3IbVajUdAffAxIkTZbPZ1K1bN40YMSJTl97f+63Vrl3bYEIAjoZCEADkQLly5dKFCxeuWz948KACAwMNJMLttG7dWnPmzJGfn991M9n/benSpdmUCneDzi7nNmLECLVv314bNmxQ3bp1ZbFYtHHjRq1du1aLFy82HQ+38d5776l+/frasWOHrly5ooEDB2r//v1KSUnRpk2bTMeDHTp06KBBgwbps88+yyjkbdq0Sa+99lqmkY1wXHPnztXMmTMzjZ+qUqWKihYtqpdffplCkIM7efKknnnmGW3atEkBAQGS/tqDtE6dOvr0009VvHhxswFxzzRr1kwzZ85UcHCw6Sj4h7+7ukJDQ1WnTh15enoaTgTA0dFrDQA5UMuWLTVy5EhdvXpV0l9PQicmJmrw4MFq06aN4XS4EX9//4wuEX9//1t+ALj/2rRpox9//FEFCxbUF198oaVLl6pgwYLatm2bnnrqKdPxcBvh4eHas2ePatasqYYNGyo1NVWtW7fW7t27VapUKdPxYIe3335bJUqUUNGiRfXnn38qPDxc9erVU506dfTmm2+ajgc7pKSkqHz58tetly9fXikpKQYSISu6deumq1evKj4+XikpKUpJSVF8fLxsNpu6d+9uOh7uoQ0bNujixYumY+AmHnnkkYwi0MWLF3XhwoVMHwDwN4vNZrOZDgEAyF4XLlxQ06ZNtX//fv3xxx8qUqSIkpOTVatWLX377bfX7VUC4N6gswsw55/ff/PmzVP79u3ZD8jJXLhwQX5+fpnWjh49ql27dslqtapatWqMZnQiDz74oB588EFNnjw503qvXr20fft2bd261VAy2CN37tzavHmzqlWrlml9165dqlu3LoUDF+Lr66u4uDiFhYWZjoIbSEtL08CBA7V48eIb7pHHXk8A/sZoOADIgfz8/LRx40atW7dOO3fulNVqVfXq1fX444+bjga4tH93dsG5Wa1WHT58WKdPn75u3n69evUMpcLNfPXVV0pNTZWfn5+6du2qxo0bKygoyHQsZEG+fPmUlJSkoKAgPfroo1q6dKnCwsK4Oemkxo4dq2bNmum7775T7dq1ZbFYtHnzZp04cULffPON6Xi4jRIlSmRMF/ina9eusU8XkI0GDBigdevW6YMPPlDnzp01depU/fLLL5o+fbrGjBljOh4AB0JHEADkIBcvXtTatWvVvHlzSdLrr7+uy5cvZxz38PDQyJEjlStXLlMRcRPVqlXLKCDczq5du+5zGgBbt25Vhw4ddPz4cf377bTFYuHpSwdUuXJlVa9eXQ0aNFDXrl01efLk67pL/sYeM47J399fW7duVYUKFeTm5qZTp06xt6GT+/XXXzV16lQdOHBANptN4eHhevnll1WkSBHT0XAbX375pd555x1NnTpVDzzwgCwWi3bs2KFevXpp0KBBatWqlemIuEfoCHJsJUqU0Lx581S/fn35+flp165dKl26tObPn69PP/2UwjqADBSCACAHmT59ur766iutWLFC0l9v6itWrKjcuXNLkg4cOKCBAweqb9++JmPiBkaMGGH3ucOGDbuPSQBIUtWqVVW2bFmNGDFCwcHB1xVq6fhyPJs3b1a/fv105MgRpaSkyNfX94YFdovFwv4kDqpNmzbatGmTKlSooPXr16tOnTry8vK64bkxMTHZnA5ZcfXqVTVq1EjTp09X2bJlTceBnfLly5fp52ZqaqquXbsmD4+/hs38/XmePHn4OepCKAQ5trx582r//v0KCQlRsWLFtHTpUtWsWVMJCQmKiIjQn3/+aToiAAfBaDgAyEEWLFhwXZHnk08+yXhT//HHH2vq1KkUghwQxR3XQGeX6/j555+1ZMkSlS5d2nQU2KlOnToZe464ubnp0KFDjIZzMh9//LHmzp2rI0eOaP369apYsaJ8fHxMx8Id8PT01L59++z+NxGOYeLEiaYjAPiXsLAwHTt2TCEhIQoPD9fixYtVs2ZNrVixQgEBAabjAXAgFIIAIAc5dOhQpqcuc+XKJTc3t4zXNWvW1CuvvGIiGpAjMCbFdTz44IM6fPgwhSAnlZCQwEgxJ5Q7d2716NFDkrRjxw69++673ORyYp07d9asWbPYw8KJPPfcc6YjwIA33nhD+fPnNx0DN9G1a1fFxcXpkUce0euvv65mzZppypQpunbtmqKiokzHA+BAGA0HADlI7ty5FRsbq3Llyt3w+IEDB1S1alVdunQpm5PhdvLnz69Dhw6pYMGC143l+DdGcQD3x549ezI+P3LkiN58800NGDBAERER8vT0zHRu5cqVszsesmDlypXKmzevHnroIUnS1KlTNWPGDIWHh2vq1KnKly+f4YTIqvT0dO3du1chISFcPyfRq1cvzZs3T6VLl1aNGjWUJ0+eTMe5gel4Lly4YPe5N9uDDY7l0KFD+v7773X69GlZrdZMx4YOHWooFe5GYmKiduzYoVKlSqlKlSqm4wBwIHQEAUAOUqxYMe3bt++mhaA9e/aoWLFi2ZwK9pgwYYJ8fX0lMZYDMKVq1aqyWCz653NU3bp1y/j872MWi0Xp6ekmIsJOAwYM0LvvvitJ2rt3r/r3769+/fopJiZG/fr10+zZsw0nxO306dNHERER6t69u9LT01WvXj1t2bJFPj4++uqrr1S/fn3TEXEb+/btU/Xq1SX9dTP6nxgZ55gCAgJue234d9B5zJgxQz179lTBggVVuHDhTNfWYrFQCHICVqtVc+bM0dKlS3Xs2DFZLBaFhobqv//9Lw8lAbgOHUEAkIP07t1b3333nXbu3KlcuXJlOnbx4kXVqFFDjz/+uCZNmmQoIeDa6OxybsePH7f73JCQkPuYBHcrb9682rdvn0qWLKnhw4dr3759WrJkiXbt2qWmTZsqOTnZdETcRtGiRfXll1+qRo0a+uKLL/TKK69o3bp1mjdvntatW6dNmzaZjgi4nPXr19t13u7du9WnT5/7GwZ3LSQkRC+//LIGDRpkOgrugM1m05NPPqlvvvlGVapUUfny5WWz2RQfH6+9e/eqRYsW+uKLL0zHBOBAKAQBQA5y6tQpVa1aVV5eXnr11VdVtmxZWSwWHThwQO+//76uXbum3bt3q1ChQqajwk42m03r1q3TxYsXVadOHcbhOLi5c+fq6aeflre3t+bOnXvLc5nDD9w/+fPn18aNGxUeHq6HHnpInTt31osvvqhjx44pPDxcaWlppiPiNnLlyqXDhw+rWLFievHFF+Xj46OJEycqISFBVapUydIIK2S/zz77TF988YWuXr2qxx9/XC+++KLpSLhL58+f14IFCzRz5kzFxcXREeQE/Pz8FBsbq7CwMNNRcAdmz56t3r1768svv1SDBg0yHYuJiVGrVq30/vvvq3PnzoYSAnA0jIYDgBykUKFC2rx5s3r27KnBgwdnjDeyWCxq2LChPvjgA4pADuzcuXPq3bu3du3apVq1amn8+PFq2rSpNm/eLEkKDAzUmjVrGAPgwP5Z3KHQ4/wOHjyoKVOmKD4+XhaLReXLl1evXr1uOn4TjuOhhx5Sv379VLduXW3btk2LFi2S9Nd4KkakOodChQrpp59+UnBwsFauXKkPPvhAkpSWliZ3d3fD6XArH330kXr06KEyZcooV65c+vzzz5WQkKDRo0ebjoY7EBMTo+joaC1dulQhISFq06aNZs2aZToW7NC2bVutXr1aPXr0MB0Fd+DTTz/VG2+8cV0RSJIeffRRDR48WAsWLKAQBCADHUEAkEOlpKTo8OHDkqTSpUsrf/78hhPhdp5//nlt2LBBnTt31ldffSU3NzfZbDZNnDhRbm5uGjhwoPLmzasVK1aYjoosorPL+SxZskTPPPOMatSoodq1a0uStm7dqu3bt+uTTz5R27ZtDSfErSQmJurll1/WiRMnFBkZqe7du0uS+vbtq/T0dE2ePNlwQtzO8OHDNXHiRAUHBystLU2HDh2St7e3oqOjNWPGDG3ZssV0RNxERESEWrVqpVGjRkmS5syZo169eumPP/4wnAz2OnnypObMmaPo6GilpqaqXbt2+vDDDxUXF6fw8HDT8WCn0aNHKyoqSs2aNVNERIQ8PT0zHY+MjDSUDPYoXLiwVq5cqapVq97w+O7du9WkSRPG3QLIQCEIAAAnUbRoUX3yySd65JFH9Msvv6h48eKKiYnJ2BB727ZtatGiBW/2HRydXa4hLCxMzz77rEaOHJlpfdiwYZo/f76OHj1qKBmQcyxZskQnTpxQ27ZtMzq55s6dq4CAALVs2dJwOtxMnjx5tHfv3oxxVOnp6cqdO7cSExNVuHBhw+lwO02bNtXGjRvVvHlzdezYUY0bN5a7u7s8PT0pBDmZ0NDQmx6zWCy8l3FwXl5eOn78uIKDg294/Ndff1VoaKguX76czckAOCoKQQAAOAkPDw+dOHEi482+j4+P9u7dq1KlSkmSkpOTVbRoUWayOzg6u1yDj4+P9uzZo9KlS2da//nnn1WlShX2mHECVqtVhw8f1unTp2W1WjMdq1evnqFUgOtzc3NTcnKygoKCMtZ8fX0VFxfHXiVOwMPDQ5GRkerZs6fKlCmTsU4hCMhe7u7uSk5OVmBg4A2Pnzp1SkWKFOF3QwAZ2CMIAAAnYbVaM+174O7uLovFkvH6n5/DcX377bcZnV1du3bN6Ox68MEHJUnvvvuuWrRoYTglbqd+/fr64YcfrisEbdy4UQ8//LChVLDX1q1b1aFDBx0/flz/fi7OYrFw08RJrF27VmvXrr1hMS86OtpQKthj5syZyps3b8bra9euac6cOSpYsGDGGmOpHNMPP/yg6Oho1ahRQ+XLl1enTp3Uvn1707GAHMdms6lLly7y9va+4XE6gQD8Gx1BAAA4CTc3N7311lsZN04GDRqkAQMGZNw0+eOPPzR06FBuYDo4Ortcw4cffqihQ4eqXbt2qlWrlqS/igufffaZRowYoSJFimScS2HP8VStWlVly5bViBEjFBwcfF0h3d/f31Ay2GvEiBEaOXKkatSoccNruGzZMkPJcDslS5a87cMrjKVyfGlpaVq4cKGio6O1bds2paenKyoqSt26dZOvr6/peLiJfv36adSoUcqTJ4/69et3y3OjoqKyKRXuRNeuXe06b/bs2fc5CQBnQSEIAAAnYc+NE0lKSEjIhjS4U/8eifPvcTiMcXAObm5udp1Hd4ljypMnj+Li4q7r6ILzCA4O1tixY9WpUyfTUYAc7+DBg5o1a5bmz5+vc+fOqWHDhlq+fLnpWLiBBg0aaNmyZQoICFCDBg1uep7FYlFMTEw2JgMA3G+MhgMAwEkcO3bMdATcI/8cifPvcTh//PGHyWiw07/HUMG5PPjggzp8+DCFICd25coV1alTx3QMZIOIiAh98803Kl68uOkouIly5cpp7NixGj16tFasWMFoRge2bt26G34OAHB9dAQBAOCiuHHimOjscl3nzp1TQECA6Riww7Jly/Tmm29qwIABioiIkKenZ6bjlStXNpQM9ho0aJDy5s2rIUOGmI6C++zfnbMAAADIOjqCAABwUceOHdPVq1dNx8C/0NnlGt59912VLFkyY4Pstm3b6vPPP1dwcLC++eYbValSxXBC3EqbNm0kSd26dctYs1gsstlsjPNzEpcuXdJHH32k7777TpUrV76umMfeFgBwvdatW9t97tKlS+9jEgBAdqMQBAAA4MDo7HJM06dP18cffyxJWrNmjb777jutXLlSixcv1oABA7R69WrDCXErdNw5vz179qhq1aqSpH379mU6Zk/XJQDkRP7+/qYjAAAMoRAEAADgwOjsckxJSUkZxbmvvvpK7dq1U6NGjVSyZEk9+OCDhtPhdkJCQkxHwF1ibwsAyLrZs2ebjgAAMIRCEAAAAJBF+fLl04kTv4Qk9AAAMqRJREFUJ1S8eHGtXLlSb731liTJZrMxVsyJ/PTTT0pMTNSVK1cyrbdo0cJQIgAAAAC49ygEAQAAAFnUunVrdejQQWXKlNHZs2fVpEkTSVJsbKxKly5tOB1u5+jRo3rqqae0d+/ejL2BpP8bKUYxzzls375dn3322Q2LeextAQC3FhoaestRmkePHs3GNACA+41CEAAAAJBFEyZMUMmSJXXixAmNHTtWefPmlfTXyLiXX37ZcDrcTu/evRUaGqrvvvtOYWFh2rZtm86ePav+/ftr3LhxpuPBDgsXLlTnzp3VqFEjrVmzRo0aNdLPP/+s5ORkPfXUU6bj4R6aPn26ChUqZDoG4HL69OmT6fXVq1e1e/durVy5UgMGDDATCgBw31hsfz/+BgAAXMonn3yili1bKk+ePKaj4C74+voqLi5OYWFhpqMALqNgwYKKiYlR5cqV5e/vr23btqlcuXKKiYlR//79tXv3btMRcRuVK1fWSy+9pFdeeSXj52RoaKheeuklBQcHa8SIEaYj4gYmT55s97mRkZH3MQmAm5k6dap27NjBfkIA4GIoBAEA4AS4cZJzUQhyXEeOHNHEiRMVHx8vi8WiChUqqE+fPlwrJ5AvXz7t3LlTYWFhKlWqlGbOnKkGDRroyJEjioiIUFpamumIuI08efJo//79KlmypAoWLKh169YpIiJC8fHxevTRR5WUlGQ6Im4gNDTUrvMsFgtjqQBDjh49qqpVq+rChQumowAA7iFGwwEA4AQmTJhg13kWi4VCkJOYN2+e2rdvL29v70zrV65cyRh5JDESx1GtWrVKLVq0UNWqVVW3bl3ZbDZt3rxZ4eHhWrFihRo2bGg6Im6hUqVK2rNnj8LCwvTggw9q7Nix8vLy0kcffUQhz0nkz59ff/zxhySpaNGi2rdvnyIiInTu3DkKeQ4sISHBdAQAt7FkyRLlz5/fdAwAwD1GRxAAAIAB7u7uSkpKUlBQUKb1s2fPKigoiM3qHVy1atX0xBNPaMyYMZnWBw8erNWrV2vXrl2GksEeq1atUmpqqlq3bq2jR4+qefPmOnDggAoUKKBFixbp0UcfNR0Rt9GhQwfVqFFD/fr109tvv61JkyapZcuWWrNmjapXr66lS5eajggADq1atWqyWCwZr202m5KTk/Xbb7/pgw8+0IsvvmgwHQDgXqMQBAAAYICbm5tOnTqlwMDATOtxcXFq0KCBUlJSDCWDPXLlyqW9e/eqTJkymdYPHTqkypUr69KlS4aS4U6lpKQoX758mW6KwXGlpKTo0qVLKlKkiKxWq8aNG6eNGzeqdOnSGjJkiPLly2c6Iuxw8uRJLV++XImJibpy5UqmY1FRUYZSATnDv/dSc3NzU2BgoOrXr6/y5csbSgUAuF8YDQcAgBPixonz+vvpS4vFoscee0weHv/3diw9PV0JCQlq3LixwYSwR2BgoGJjY68rBMXGxl7X5QXHcu3aNeXKlUuxsbGqVKlSxjpjcJzHtWvXtGLFCj3xxBOS/rp5OXDgQA0cONBwMmTF2rVr1aJFC4WGhurgwYOqVKmSjh07JpvNpurVq5uOB7i8YcOGmY4AAMhGFIIAAHAy3Dhxbq1atZL0V8HgiSeeUN68eTOOeXl5qWTJkmrTpo2hdLDXCy+8oBdffFFHjx5VnTp1ZLFYtHHjRr377rvq37+/6Xi4BQ8PD4WEhDB+0Yl5eHioZ8+eio+PNx0Fd+H1119X//79NXLkSPn6+urzzz9XUFCQOnbsyAMRQDZJT0/XsmXLFB8fL4vFogoVKqhly5aZHlQCALgGRsMBAOBkatasqcaNG2fcOImLi8t046Rnz56mI8IOc+fO1dNPPy1vb2/TUXAHbDabJk6cqPHjx+vXX3+VJBUpUkQDBgxQZGQk48Uc3OzZs/XZZ5/p448/phPISTVo0EC9e/fOKK7D+fj6+io2NlalSpVSvnz5tHHjRlWsWFFxcXFq2bKljh07Zjoi4NL27dunli1bKjk5WeXKlZP014jbwMBALV++XBEREYYTAgDuJQpBAAA4GW6cuIbt27fLarXqwQcfzLT+448/yt3dXTVq1DCUDFn1xx9/SPrrexPOoVq1ajp8+LCuXr2qkJAQ5cmTJ9PxXbt2GUoGe3322WcaPHiw+vbtqwceeOC6a1i5cmVDyWCvwoULKyYmRuHh4apYsaJGjx6tFi1aKC4uTnXr1tWff/5pOiLg0mrVqqWgoCDNnTs3Y1+133//XV26dNHp06e1ZcsWwwkBAPcSvZ4AADiZPHny6PLly5L+6kA4cuSIKlasKEk6c+aMyWjIgldeeUUDBw68rhD0yy+/6N1339WPP/5oKBmyigKQ82nZsiVdW06uffv2kqTIyMiMNYvFIpvNJovFwug/J1CrVi1t2rRJ4eHhatasmfr376+9e/dq6dKlqlWrlul4gMuLi4vTjh07MopAkpQvXz69/fbb+s9//mMwGQDgfqAQBACAk+HGiWv46aefbrinU7Vq1fTTTz8ZSISsOHXqlF577TWtXbtWp0+f1r+b7LkJ7diGDx9uOgLuUkJCgukIuEtRUVEZXT/Dhw/Xn3/+qUWLFql06dKaMGGC4XSA6ytXrpxOnTqV8UDZ306fPq3SpUsbSgUAuF8oBAEA4GS4ceIavL29derUKYWFhWVaT0pKYoNeJ9ClSxclJiZqyJAhCg4OprvEyYSFhWn79u0qUKBApvVz586pevXqOnr0qKFksNfx48dVp06d635eXrt2TZs3b1ZISIihZLDXP//98/Hx0QcffGAwDZAzXLhwIePzd955R5GRkRo+fHjGw2Rbt27VyJEj9e6775qKCAC4T9gjCAAAwICnn35aycnJ+vLLL+Xv7y/pr5vQrVq1UlBQkBYvXmw4IW7F19dXP/zwg6pWrWo6Cu6Am5ubkpOTFRQUlGn91KlTKl68uK5cuWIoGezl7u6upKSk667h2bNnFRQURFeeE6AgC2Q/Nze3TA+v/H1L8O+1f77m5ygAuBYeNwUAwMlw48Q1jB8/XvXq1VNISIiqVasmSYqNjVWhQoU0f/58w+lwO8WLF79uHBwc3/LlyzM+X7VqVUYRVvprnN/atWsVGhpqIhqy6O+9gP7t7NmzypMnj4FEyKpjx47d8Ebz5cuX9csvvxhIBLi+devWmY4AADCEQhAAAE6GGyeuoWjRotqzZ48WLFiguLg45c6dW127dtUzzzwjT09P0/FwGxMnTtTgwYM1ffp0lSxZ0nQc2KlVq1aS/nrS+bnnnst0zNPTUyVLltT48eMNJIO9WrduLemva9ilSxd5e3tnHEtPT9eePXtUp04dU/FgB3sKsvxcBe6PRx55xHQEAIAhFIIAAHAS3DhxPXny5NGLL75oOgbslC9fvkwdCKmpqSpVqpR8fHyuK96lpKRkdzzYwWq1SpJCQ0O1fft2FSxY0HAiZNXf//bZbDb5+voqd+7cGce8vLxUq1YtvfDCC6biwQ4UZAHHce7cOc2aNUvx8fGyWCwKDw9Xt27dMv2eAQBwDewRBACAk3Bzc5P0142Tf//z/c8bJ82bNzcRD3dg/vz5mj59uo4ePaotW7YoJCREEyZMUFhYmFq2bGk6Hv5l7ty5dp/775ubAO6tESNG6LXXXmMMnBOjIAuYtWPHDj3xxBPKnTu3atasKZvNph07dujixYtavXq1qlevbjoiAOAeohAEAICT4caJa5g2bZqGDh2qPn366K233tL+/fsVFhamOXPmaO7cucxwB+6z9evXa9y4cRlPQVeoUEEDBgzQww8/bDoasuC3337TwYMHZbFYVLZsWQUGBpqOBABO4eGHH1bp0qU1Y8YMeXj8NTDo2rVrev7553X06FFt2LDBcEIAwL3kZjoAAADImoSEBIpALmDKlCmaMWOG/ve//2X88i1JNWrU0N69ew0mw63YbDa99957qlu3rmrWrKk33nhDly5dMh0LWfTxxx/r8ccfl4+PjyIjI/Xqq68qd+7ceuyxx/TJJ5+Yjgc7pKWlqVu3bgoODla9evX08MMPq0iRIurevbvS0tJMx4Od1q9fryeffFKlS5dWmTJl1KJFC/3www+mYwE5wo4dOzRo0KBM70M9PDw0cOBA7dixw2AyAMD9QCEIAAAnxI0T55eQkKBq1apdt+7t7a3U1FQDiWCPMWPGaPDgwcqTJ4+Cg4MVFRWlyMhI07GQRW+//bbGjh2rRYsWKTIyUr1799aiRYs0ZswYjRo1ynQ82KFv375av369VqxYoXPnzuncuXP68ssvtX79evXv3990PNiBgixglp+fnxITE69bP3HihHx9fQ0kAgDcT4yGAwDAyXz88cfq2rWrWrdurbp168pms2nz5s1atmyZ5syZow4dOpiOCDuEh4dr9OjRatmypXx9fRUXF6ewsDBNnjxZc+fO1c6dO01HxA2UK1dOvXv31ssvvyxJWrlypVq1aqWLFy/KYrEYTgd7eXt7a//+/SpdunSm9cOHD6tSpUp0eTmBggULasmSJapfv36m9XXr1qldu3b67bffzASD3SpUqKAXX3xRffv2zbQeFRWlGTNmKD4+3lAyIGeIjIzUsmXLNG7cONWpU0cWi0UbN27UgAED1KZNG02cONF0RADAPeRx+1MAAIAj+ftJ9n/eOOndu7eioqI0atQoCkFOYsCAAXrllVd06dIl2Ww2bdu2TZ9++qlGjx6tmTNnmo6Hmzh+/LiaN2+e8fqJJ56QzWbTr7/+qqJFixpMhqwoXry41q5de10haO3atSpevLihVMiKtLQ0FSpU6Lr1oKAgRsM5iaNHj+rJJ5+8br1FixZ64403DCQCcpZx48bJYrGoc+fOunbtmiTJ09NTPXv21JgxYwynAwDcaxSCAABwMtw4cQ1du3bVtWvXNHDgQKWlpalDhw4qWrSoJk2apKefftp0PNzElStXlDt37ozXFotFXl5eunz5ssFUyKr+/fsrMjJSsbGxmZ6CnjNnjiZNmmQ6HuxQu3ZtDRs2TPPmzVOuXLkkSRcvXtSIESNUu3Ztw+lgDwqygFleXl6aNGmSRo8erSNHjshms6l06dLy8fExHQ0AcB9QCAIAwMlw48T5Xbt2TQsWLNCTTz6pF154QWfOnJHValVQUJDpaLDDkCFDMt0kuXLlit5++235+/tnrEVFRZmIBjv17NlThQsX1vjx47V48WJJf42pWrRokVq2bGk4HewxadIkNW7cWMWKFVOVKlVksVgUGxurXLlyadWqVabj4Ra6deumSZMmUZAFHISPj48iIiJMxwAA3GfsEQQAgJP4+8bJxx9/rD59+qhbt243vHHy0ksvmY4KO/j4+Cg+Pl4hISGmoyAL6tevf9u9gCwWi2JiYrIpEZBzXbx4UR9//LEOHDggm82m8PBwdezYMVPXHhyPu7u7kpKSFBQUpGXLlmn8+PEZ+wFVqFBBAwYMoCAL3CetW7e2+9ylS5fexyQAgOxGIQgAACfBjRPX0qBBA/Xu3VutWrUyHQXIsa5cuaLTp0/LarVmWi9RooShRIDrc3NzU3JyMl2wgAFdu3a1+9zZs2ffxyQAgOxGIQgAACfBjRPX8tlnn2nw4MHq27evHnjgAeXJkyfT8cqVKxtKhnvJz89PsbGxCgsLMx0F//Dzzz+rW7du2rx5c6Z1m80mi8Wi9PR0Q8mQFYcOHdL3339/w2Le0KFDDaXC7bi5uenUqVMKDAw0HQUAACDHoBAEAICT4MaJa3Fzc7tuzWKxcCPaxfj6+iouLo5CkIOpW7euPDw8NHjwYAUHB1837q9KlSqGksFeM2bMUM+ePVWwYEEVLlw40zW0WCzatWuXwXS4FTc3N/n7+992zGZKSko2JQIAAHB9HqYDAAAA+5UtW5YbJy4iISHBdAQgx4qNjdXOnTtVvnx501Fwh9566y29/fbbGjRokOkouAMjRoyQv7+/6RhAjnTkyBG9/fbbio6OlvTXONQ///wz47i7u7s2btyocuXKmYoIALgPKAQBAOBEuHHiOkJCQkxHAHKs8PBwnTlzxnQM3IXff/9dbdu2NR0Dd+jpp59m1C1gyJQpU1S4cOGM17///ruGDh2a8T25aNEiTZgwQR9++KGpiACA+4BCEAAAToQbJ85t+fLlatKkiTw9PbV8+fJbntuiRYtsSgXkPO+++64GDhyod955RxEREfL09Mx03M/Pz1Ay2Ktt27ZavXq1evToYToKsuh2nc0A7q/vvvtOU6ZMybTWpk2bjDG2JUuW1PPPP28iGgDgPqIQBACAk+DGifNr1aqVkpOTFRQUpFatWt30PPYIch183zqmxx9/XJL02GOPZVpnjy7nUbp0aQ0ZMkRbt269YTEvMjLSUDLcDtsUA2YdP35coaGhGa+ff/75TBMHSpYsqZMnT5qIBgC4jyw23oUBAOAU3NzcMooIAJyDr6+v4uLiMp6yhWNYv379LY8/8sgj2ZQEd+qfNzH/zWKx6OjRo9mYBgCch7+/v9asWaOaNWve8Pi2bdv0+OOP68KFC9mcDABwP9ERBACAk7BaraYj4B6xWq2aM2eOli5dqmPHjslisSgsLExt2rRRp06d6CJxIleuXFFCQoJKlSolD4/r31p/++23Klq0qIFkuBUKPc4vISHBdAQAcEoVK1bUd999d9NC0KpVq1SpUqVsTgUAuN/oCAIAAMhGNptNTz75pL755htVqVJF5cuXl81mU3x8vPbu3asWLVroiy++MB0Tt5GWlqZevXpp7ty5kqRDhw4pLCxMkZGRKlKkiAYPHmw4If5tz549qlSpktzc3LRnz55bnlu5cuVsSgUAQPaaMWOG+vTpo8WLF6tZs2aZjq1YsUJPP/20Jk6cqBdeeMFQQgDA/UAhCAAAIBvNnj1bvXv31pdffqkGDRpkOhYTE6NWrVrp/fffV+fOnQ0lhD169+6tTZs2aeLEiWrcuLH27NmjsLAwLV++XMOGDdPu3btNR8S//HO8ppubmywWyw33KmGPIMfVr18/jRo1Snny5FG/fv1ueW5UVFQ2pQIA5/PMM89o0aJFKl++vMqVKyeLxaIDBw7o4MGDatOmjRYvXmw6IgDgHmM0HAAAQDb69NNP9cYbb1xXBJKkRx99VIMHD9aCBQsoBDm4L774QosWLVKtWrUyjfILDw/XkSNHDCbDzSQkJCgwMDDjczif3bt36+rVqxmf3wzjNQHg1j799FO1bNlSCxcu1MGDByVJZcqU0dChQ/X0008bTgcAuB/oCAIAAMhGhQsX1sqVK1W1atUbHt+9e7eaNGmi5OTk7A2GLPHx8dG+ffsUFhYmX19fxcXFKSwsTHFxcapXr57Onz9vOiIAAMBdGzNmjHr06KGAgADTUQAAd4GOIAAAgGyUkpKiQoUK3fR4oUKF9Pvvv2djItyJ//znP/r666/Vq1cvSf/XgTBjxgzVrl3bZDTY6ZdfftGmTZt0+vRpWa3WTMciIyMNpQIAwLG88847ateuHYUgAHByFIIAAACyUXp6ujw8bv4WzN3dXdeuXcvGRLgTo0ePVuPGjfXTTz/p2rVrmjRpkvbv368tW7Zo/fr1puPhNmbPnq0ePXrIy8tLBQoUyDRKzGKxUAhyApcuXdKUKVO0bt26Gxbzdu3aZSgZALgWBgkBgGugEAQAAJCNbDabunTpIm9v7xsev3z5cjYnwp2oU6eONm3apHHjxqlUqVJavXq1qlevri1btigiIsJ0PNzG0KFDNXToUL3++utyc3MzHQd3oFu3blqzZo3++9//qmbNmuwLBAAAANwCewQBAABko65du9p13uzZs+9zEiDnKlCggLZt26ZSpUqZjoI75O/vr2+++UZ169Y1HQUAXNo/90IEADgvOoIAAACyEQUe12G1WnX48OEbjqWqV6+eoVSwR/fu3fXZZ59p8ODBpqPgDhUtWlS+vr6mYwAAAABOgY4gAAAAIIu2bt2qDh066Pjx49fNzrdYLEpPTzeUDPZIT09X8+bNdfHiRUVERMjT0zPT8aioKEPJYK9vv/1WkydP1ocffqiQkBDTcQDAZdERBACugY4gAAAAIIt69OihGjVq6Ouvv1ZwcDD7kziZd955R6tWrVK5cuUkKdP141o6hxo1aujSpUsKCwuTj4/PdcW8lJQUQ8kAwLU8/PDDyp07t+kYAIC7REcQAAAAkEV58uRRXFycSpcubToK7kC+fPk0YcIEdenSxXQU3KHHH39ciYmJ6t69uwoVKnRdAe+5554zlAwAHNeFCxfsPtfPz+8+JgEAZDc6ggAAAIAsevDBB3X48GEKQU7K29tbdevWNR0Dd2Hz5s3asmWLqlSpYjoKADiNgIAAuztfGXMLAK6FQhAAAACQRb169VL//v2VnJx8wz1mKleubCgZ7NG7d29NmTJFkydPNh0Fd6h8+fK6ePGi6RgA4FTWrVuX8fmxY8c0ePBgdenSRbVr15YkbdmyRXPnztXo0aNNRQQA3CeMhgMAAACyyM3N7bo1i8Uim80mi8XCU7QO7qmnnlJMTIwKFCigihUrXlfIW7p0qaFksNfq1as1YsQIvf322zcsxjLSCABu7bHHHtPzzz+vZ555JtP6J598oo8++kjff/+9mWAAgPuCQhAAAACQRcePH7/l8ZCQkGxKgjvRtWvXWx6fPXt2NiXBnfq7GPvvEUcUYwHAPj4+PoqLi1OZMmUyrR86dEhVq1ZVWlqaoWQAgPuB0XAAAABAFlHocW4UepzfP8cbAQCyrnjx4vrwww81fvz4TOvTp09X8eLFDaUCANwvdAQBAAAAdli+fLmaNGkiT09PLV++/JbntmjRIptS4V75/fff9fHHH2vWrFmKjY01HQd3ITY2VlWrVjUdAwAc2jfffKM2bdqoVKlSqlWrliRp69atOnLkiD7//HM1bdrUcEIAwL1EIQgAAACwg5ubm5KTkxUUFHTDPYL+xlgq5/Ldd99p1qxZ+uKLL1SwYEG1bt1akyZNMh0LWXT+/HktWLBAM2fOVFxcHN+DAGCHkydP6oMPPtCBAwdks9kUHh6uHj160BEEAC6IQhAAAACAHCUxMVGzZ8/W7Nmz9eeff+r333/X4sWL1aZNG9PRkEUxMTGKjo7W0qVLFRISojZt2qhNmzaqVq2a6WgAAACAw2CPIAAAAAA5wuLFizVz5kxt2rRJTZs21aRJk9SkSRPlyZNHFSpUMB0Pdjp58qTmzJmj6Ohopaamql27drp69ao+//xzhYeHm44HAE7j3Llz2rZtm06fPi2r1ZrpWOfOnQ2lAgDcD3QEAQAAAHaYPHmy3edGRkbexyS4Ux4eHho4cKBef/11+fr6Zqx7enoqLi6OIoITaNq0qTZu3KjmzZurY8eOaty4sdzd3bmGAJBFK1asUMeOHZWamipfX19ZLJaMYxaLRSkpKQbTAQDuNQpBAAAAgB1CQ0PtOs9isejo0aP3OQ3uxIsvvqjFixerYsWK6tSpk9q3b698+fJRRHAiHh4eioyMVM+ePVWmTJmMda4hAGRN2bJl1bRpU73zzjvy8fExHQcAcJ9RCAIAAACQY1y8eFGLFy9WdHS0fvzxRz3xxBP6+uuvFRsbq0qVKpmOh9vYsmWLoqOjtXjxYpUvXz6joFekSBEKQQCQBXny5NHevXsVFhZmOgoAIBu4mQ4AAAAAANkld+7ceu6557R+/Xrt3btX4eHhKlSokOrWrasOHTpo6dKlpiPiFmrXrq0ZM2YoKSlJL730khYuXKiiRYvKarVqzZo1+uOPP0xHBACn8MQTT2jHjh2mYwAAsgkdQQAAAMAdOHnypJYvX67ExERduXIl07GoqChDqXAnrFarvv76a82aNUvffvutLl++bDoSsuDgwYOaNWuW5s+fr3Pnzqlhw4Zavny56VgA4NBmzZqlkSNHqmvXroqIiJCnp2em4y1atDCUDABwP1AIAgAAALJo7dq1atGihUJDQ3Xw4EFVqlRJx44dk81mU/Xq1RUTE2M6Iu7Q6dOnFRQUJElq1qyZZs6cqeDgYMOpYI/09HStWLFC0dHRGYWgkydPqkiRInJzYxgGAPzTrX4uWiwWpaenZ2MaAMD9RiEIAAAAyKKaNWuqcePGGjlypHx9fRUXF6egoCB17NhRjRs3Vs+ePU1HxD3w97Vl/wTn5efnp9jYWK4hAAAAcjQeiwIAAACyKD4+Xs8995wkycPDQxcvXlTevHk1cuRIvfvuu4bTAfgbzz0CAAAAkofpAAAAAICzyZMnT8Y+MkWKFNGRI0dUsWJFSdKZM2dMRgMAALBLamqq1q9ff8P9DiMjIw2lAgDcDxSCAAAAgCyqVauWNm3apPDwcDVr1kz9+/fX3r17tXTpUtWqVct0PAAAgFvavXu3mjZtqrS0NKWmpip//vw6c+aMfHx8FBQURCEIAFwMo+EAAACALIqKitKDDz4oSRo+fLgaNmyoRYsWKSQkRLNmzTKcDgAA4Nb69u2rJ598UikpKcqdO7e2bt2q48eP64EHHtC4ceNMxwMA3GMWG0OTAQAAgCy5cuWKvLy8bnjszJkzKliwYDYnwv3g6+uruLg4hYWFmY6CO+Tn56fY2FiuIQD8S0BAgH788UeVK1dOAQEB2rJliypUqKAff/xRzz33nA4cOGA6IgDgHqIjCAAAAMiidu3ayWq1Xrd+6tQp1a9fP/sD4b544403lD9/ftMxcBd47hEAbszT01MWi0WSVKhQISUmJkqS/P39Mz4HALgOCkEAAABAFiUlJal79+7XrdWvX1/ly5c3lApZMX/+fNWtW1dFihTR8ePHJUkTJ07Ul19+mXHO66+/roCAAEMJYY/Dhw9r1apVunjxoqTrCz8//fSTQkJCTEQDAIdWrVo17dixQ5LUoEEDDR06VAsWLFCfPn0UERFhOB0A4F6jEAQAAABk0TfffKNt27apb9++kqRffvlF9evXV0REhBYvXmw4HW5n2rRp6tevn5o2bapz584pPT1d0l9jciZOnGg2HOxy9uxZPf744ypbtqyaNm2qpKQkSdLzzz+v/v37Z5xXvHhxubu7m4oJAA7rnXfeUXBwsCRp1KhRKlCggHr27KnTp0/ro48+MpwOAHCvsUcQAAAAcAdOnjyphx56SE899ZS+/vprVa9eXQsWLOCmsxMIDw/XO++8o1atWmXaB2jfvn2qX7++zpw5YzoibqNz5846ffq0Zs6cqQoVKmRcw9WrV6tv377av3+/6YgA4BI2bdqkGjVqyNvb23QUAMBd8DAdAAAAAHBGxYoV05o1a/TQQw+pYcOGmj9/fsasfTi2hIQEVatW7bp1b29vpaamGkiErFq9erVWrVqlYsWKZVovU6ZMxqg/AMDda9KkiWJjYxUWFmY6CgDgLlAIAgAAAOyQL1++GxZ60tLStGLFChUoUCBjLSUlJTujIYtCQ0MVGxt73d4x3377rcLDww2lQlakpqbKx8fnuvUzZ87w1DoA3EMMEgIA10AhCAAAALADe8e4jgEDBuiVV17RpUuXZLPZtG3bNn366acaPXq0Zs6caToe7FCvXj3NmzdPo0aNkiRZLBZZrVa99957atCggeF0AAAAgGNhjyAAAAAAOc6MGTP01ltv6cSJE5KkokWLavjw4erevbvhZLDHTz/9pPr16+uBBx5QTEyMWrRoof379yslJUWbNm1SqVKlTEcEAJfwz730AADOi0IQAAAAYIcLFy7Yfa6fn999TIJ76cyZM7JarQoKCjIdBVmUnJysadOmaefOnbJarapevbpeeeUVBQcHm44GAC6DQhAAuAYKQQAAAIAd3NzcbrhH0D/ZbDZZLBalp6dnUyogZ0pMTFTx4sVv+D2ZmJioEiVKGEgFAK7Hz89PsbGxFIIAwMmxRxAAAABgh3Xr1tl13u7du+9zEtytU6dO6bXXXtPatWt1+vTp6zbCppDn+EJDQ5WUlHRdJ9fZs2cVGhrKNQSAe4TnxwHANdARBAAAANyl8+fPa8GCBZo5c6bi4uK4Ce3gmjRposTERL366qsKDg6+rqukZcuWhpLBXm5ubjp16pQCAwMzrR8/flzh4eFKTU01lAwAAABwPHQEAQAAAHcoJiZG0dHRWrp0qUJCQtSmTRvNmjXLdCzcxsaNG/XDDz+oatWqpqMgi/r16ydJslgsGjJkiHx8fDKOpaen68cff+S6AoAd6I4FgJyFQhAAAACQBSdPntScOXMUHR2t1NRUtWvXTlevXtXnn3+u8PBw0/Fgh+LFizPqxkn9PXrRZrNp79698vLyyjjm5eWlKlWq6LXXXjMVDwCcRpcuXZSYmKghQ4bcsDsWAOBaGA0HAAAA2Klp06bauHGjmjdvro4dO6px48Zyd3eXp6en4uLiKAQ5idWrV2v8+PGaPn26SpYsaToO7kDXrl01adIk+fn5mY4CAE7J19eX7lgAyEEoBAEAAAB28vDwUGRkpHr27KkyZcpkrFMIci758uVTWlqarl27Jh8fH3l6emY6npKSYigZAADZIzw8XAsWLFC1atVMRwEAZANGwwEAAAB2+uGHHxQdHa0aNWqofPny6tSpk9q3b286FrJo4sSJpiPgHti+fbs+++wzJSYm6sqVK5mOLV261FAqAHAOEydO1ODBg+mOBYAcgo4gAAAAIIvS0tK0cOFCRUdHa9u2bUpPT1dUVJS6desmX19f0/EAl7dw4UJ17txZjRo10po1a9SoUSP9/PPPSk5O1lNPPaXZs2ebjggADo3uWADIWSgEAQAAAHfh4MGDmjVrlubPn69z586pYcOGWr58uelY+JcLFy5k7Cdz4cKFW57LvjOOr3LlynrppZf0yiuvyNfXV3FxcQoNDdVLL72k4OBgjRgxwnREAHBoc+fOveXx5557LpuSAACyA4UgAAAA4B5IT0/XihUrFB0dTSHIAbm7uyspKUlBQUFyc3OTxWK57hybzSaLxaL09HQDCZEVefLk0f79+1WyZEkVLFhQ69atU0REhOLj4/Xoo48qKSnJdEQAAADAYbBHEAAAAHAPuLu7q1WrVmrVqpXpKLiBmJgY5c+fX5K0bt06w2lwt/Lnz68//vhDklS0aFHt27dPEREROnfunNLS0gynAwDHRHcsAORcFIIAAAAAuLxHHnnkhp/DOT388MNas2aNIiIi1K5dO/Xu3VsxMTFas2aNHnvsMdPxAMAh5cuXL6M7NiAggO5YAMhBKAQBAAAAyHEuXbqkPXv26PTp07JarZmOtWjRwlAq2Ov999/XpUuXJEmvv/66PD09tXHjRrVu3VpDhgwxnA4AHBPdsQCQc7FHEAAAAIAcZeXKlercubPOnDlz3TGeggYAAADgaigEAQAAAMhRSpcurSeeeEJDhw5VoUKFTMfBHXB3d88Yb/RPZ8+eVVBQEMU8ALAD3bEAkHMwGg4AAABAjnL69Gn169ePIpATu9nzjJcvX5aXl1c2pwEA50N3LADkLBSCAAAAAOQo//3vf/X999+rVKlSpqMgiyZPnizpr5uUM2fOVN68eTOOpaena8OGDSpfvrypeADgNF599VW1bduW7lgAyCEYDQcAAAAgR0lLS1Pbtm0VGBioiIgIeXp6ZjoeGRlpKBluJzQ0VJJ0/PhxFStWTO7u7hnHvLy8VLJkSY0cOVIPPvigqYgA4BT8/Py0e/duHooAgByCQhAAAACAHGXmzJnq0aOHcufOrQIFCshisWQcs1gsOnr0qMF0sEeDBg20bNkyBQQEmI4CAE6pW7duqlu3rrp37246CgAgG1AIAgAAAJCjFC5cWJGRkRo8eLDc3NxMx0EWXb16VeXKldNXX32l8PBw03EAwCnRHQsAOQt7BAEAAADIUa5cuaL27dtTBHJSnp6eunz5cqZOLgBA1nzyySdatWqVcufOre+///667lgKQQDgWugIAgAAAJCj9O3bV4GBgXrjjTdMR8EdGjNmjA4cOKCZM2fKw4PnGwEgq+iOBYCchXfMAAAAAHKU9PR0jR07VqtWrVLlypWvG4cTFRVlKBns9eOPP2rt2rVavXq1IiIilCdPnkzHly5daigZADgHumMBIGehEAQAAAAgR9m7d6+qVasmSdq3b1+mY4wbcw4BAQFq06aN6RgA4LSee+45LVq0iO5YAMghGA0HAAAAAAAA5CCRkZGaN2+eqlSpQncsAOQAFIIAAAAAAACAHKRBgwY3PWaxWBQTE5ONaQAA9xuFIAAAAAA5zvbt2/XZZ58pMTFRV65cyXSM/WWcw5IlS7R48eIbXsNdu3YZSgUAAAA4HnaEAwAAAJCjLFy4UHXr1tVPP/2kZcuW6erVq/rpp58UExMjf39/0/Fgh8mTJ6tr164KCgrS7t27VbNmTRUoUEBHjx5VkyZNTMcDAAAAHAodQQAAAABylMqVK+ull17SK6+8Il9fX8XFxSk0NFQvvfSSgoODNWLECNMRcRvly5fXsGHD9Mwzz2Rcw7CwMA0dOlQpKSl6//33TUcEAIfWoEEDWSyWmx5nNBwAuBY6ggAAAADkKEeOHFGzZs0kSd7e3kpNTZXFYlHfvn310UcfGU4HeyQmJqpOnTqSpNy5c+uPP/6QJHXq1EmffvqpyWgA4BSqVq2qKlWqZHyEh4frypUr2rVrlyIiIkzHAwDcYx6mAwAAAABAdsqfP39G4aBo0aLat2+fIiIidO7cOaWlpRlOB3sULlxYZ8+eVUhIiEJCQrR161ZVqVJFCQkJYugFANzehAkTbrg+fPhw/fnnn9mcBgBwv9ERBAAAACBHefjhh7VmzRpJUrt27dS7d2+98MILeuaZZ/TYY48ZTgd7PProo1qxYoUkqXv37urbt68aNmyo9u3b66mnnjKcDgCc17PPPqvo6GjTMQAA9xh7BAEAAADIUVJSUnTp0iUVKVJEVqtV48aN08aNG1W6dGkNGTJE+fLlMx0Rt2G1WmW1WuXh8deQi8WLF2dcwx49esjLy8twQgBwTvPnz9egQYP066+/mo4CALiHKAQBAAAAAAAAOUjr1q0zvbbZbEpKStKOHTs0ZMgQDRs2zFAyAMD9wB5BAAAAAFzehQsX7D7Xz8/vPibBvfL7779r1qxZio+Pl8ViUYUKFdS1a1flz5/fdDQAcHj+/v6ZXru5ualcuXIaOXKkGjVqZCgVAOB+oSMIAAAAgMtzc3OTxWK55Tk2m00Wi0Xp6enZlAp3av369WrZsqX8/PxUo0YNSdLOnTt17tw5LV++XI888ojhhAAAAIDjoBAEAAAAwOWtX7/ervN2796tPn363N8wuGuVKlVSnTp1NG3aNLm7u0uS0tPT9fLLL2vTpk3at2+f4YQA4Bx27NiRqbPygQceMB0JAHAfUAgCAAAAkKOdP39eCxYs0MyZMxUXF0dHkBPInTu3YmNjVa5cuUzrBw8eVNWqVXXx4kVDyQDAOZw8eVLPPPOMNm3apICAAEnSuXPnVKdOHX366acqXry42YAAgHvKzXQAAAAAADAhJiZGzz77rIKDgzVlyhQ1bdpUO3bsMB0Ldqhevbri4+OvW4+Pj1fVqlWzPxAAOJlu3brp6tWrio+PV0pKilJSUhQfHy+bzabu3bubjgcAuMfoCAIAAACQY5w8eVJz5sxRdHS0UlNT1a5dO3344YeKi4tTeHi46Xiw06JFizRw4ED16tVLtWrVkiRt3bpVU6dO1ZgxY1ShQoWMcytXrmwqJgA4rNy5c2vz5s2qVq1apvVdu3apbt26dFYCgIuhEAQAAAAgR2jatKk2btyo5s2bq2PHjmrcuLHc3d3l6elJIcjJuLnderiFxWKRzWaTxWJh1B8A3EC5cuU0f/581axZM9P6tm3b1KFDBx0+fNhQMgDA/eBhOgAAAAAAZIfVq1crMjJSPXv2VJkyZUzHwV1ISEgwHQEAnNrYsWPVq1cvTZ06VQ888IAsFot27Nih3r17a9y4cabjAQDuMTqCAAAAAOQIW7ZsUXR0tBYvXqzy5curU6dOat++vYoUKUJHEADA5eXLl08WiyXjdWpqqq5duyYPj7+eE//78zx58iglJcVUTADAfUAhCAAAAECOkpaWpoULFyo6Olrbtm1Tenq6oqKi1K1bN/n6+pqOBzvMnTtXBQsWVLNmzSRJAwcO1EcffaTw8HB9+umnCgkJMZwQABzP3Llz7T73ueeeu49JAADZjUIQAAAAgBzr4MGDmjVrlubPn69z586pYcOGWr58uelYuI1y5cpp2rRpevTRR7VlyxY99thjmjhxor766it5eHho6dKlpiMCAAAADoNCEAAAAIAcLz09XStWrFB0dDSFICfg4+OjAwcOqESJEho0aJCSkpI0b9487d+/X/Xr19dvv/1mOiIAOJwLFy7Yfa6fn999TAIAyG4epgMAAAAAgGnu7u5q1aqVWrVqZToK7JA3b16dPXtWJUqU0OrVq9W3b19JUq5cuXTx4kXD6QDAMQUEBGTaI+hGbDabLBaL0tPTsykVACA7UAgCAAAAADiVhg0b6vnnn1e1atV06NChjL2C9u/fz/5AAHAT69ats+u83bt33+ckAIDsxmg4AAAAAIBTOXfunN58802dOHFCPXv2VOPGjSVJw4YNk6enp958803DCQHAuZw/f14LFizQzJkzFRcXR0cQALgYCkEAAAAAAKfGDUwAuDMxMTGKjo7W0qVLFRISojZt2qhNmzaqVq2a6WgAgHuI0XAAAAAAAKd0oxuYs2bNMh0LABzayZMnNWfOHEVHRys1NVXt2rXT1atX9fnnnys8PNx0PADAfUAhCAAAAADgNLiBCQB3rmnTptq4caOaN2+uKVOmqHHjxnJ3d9eHH35oOhoA4D5yMx0AAAAAAAB7NG3aVOHh4frpp580ZcoU/frrr5oyZYrpWADgNFavXq3nn39eI0aMULNmzeTu7m46EgAgG1AIAgAAAAA4BW5gAsDd+eGHH/THH3+oRo0aevDBB/X+++/rt99+Mx0LAHCfUQgCAAAAADgFbmACwN2pXbu2ZsyYoaSkJL300ktauHChihYtKqvVqjVr1uiPP/4wHREAcB9YbDabzXQIAAAAAADslZaWpoULFyo6Olrbtm1Tenq6oqKi1K1bN/n6+pqOBwBO5eDBg5o1a5bmz5+vc+fOqWHDhlq+fLnpWACAe4hCEAAAAADAaXEDEwDujfT0dK1YsULR0dH8HAUAF0MhCAAAAADg9LiBCQAAANwYhSAAAAAAAAAAAAAX5WY6AAAAAAAAAAAAAO4PCkEAAAAAAAAAAAAuikIQAAAAAAAAAACAi6IQBAAAAAAAAAAA4KIoBAEAAAAAAAAAALgoCkEAAAAAAAAAAAAuikIQAAAAAAAAAACAi/p/DVERdzXMkZoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coormat=df.corr()#correlation between features in the dataset\n",
+    "top_corr_features=coormat.index\n",
+    "plt.figure(figsize=(20,20))\n",
+    "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\",fmt=\".2f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.056560</td>\n",
+       "      <td>0.011763</td>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.080425</td>\n",
+       "      <td>-0.086883</td>\n",
+       "      <td>-0.019910</td>\n",
+       "      <td>-0.187461</td>\n",
+       "      <td>-0.265924</td>\n",
+       "      <td>-0.216408</td>\n",
+       "      <td>-0.137351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <td>0.056560</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.089291</td>\n",
+       "      <td>0.100436</td>\n",
+       "      <td>-0.027496</td>\n",
+       "      <td>0.082332</td>\n",
+       "      <td>0.080336</td>\n",
+       "      <td>-0.089121</td>\n",
+       "      <td>-0.093799</td>\n",
+       "      <td>-0.003424</td>\n",
+       "      <td>-0.082416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <td>0.011763</td>\n",
+       "      <td>0.089291</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.874618</td>\n",
+       "      <td>0.206669</td>\n",
+       "      <td>0.214065</td>\n",
+       "      <td>0.237831</td>\n",
+       "      <td>-0.008099</td>\n",
+       "      <td>-0.222250</td>\n",
+       "      <td>-0.206267</td>\n",
+       "      <td>-0.220208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.100436</td>\n",
+       "      <td>0.874618</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.234939</td>\n",
+       "      <td>0.233894</td>\n",
+       "      <td>0.257544</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.228531</td>\n",
+       "      <td>-0.200125</td>\n",
+       "      <td>-0.246046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <td>0.080425</td>\n",
+       "      <td>-0.027496</td>\n",
+       "      <td>0.206669</td>\n",
+       "      <td>0.234939</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.125680</td>\n",
+       "      <td>0.167196</td>\n",
+       "      <td>-0.028514</td>\n",
+       "      <td>-0.165453</td>\n",
+       "      <td>-0.234166</td>\n",
+       "      <td>-0.184866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <td>-0.086883</td>\n",
+       "      <td>0.082332</td>\n",
+       "      <td>0.214065</td>\n",
+       "      <td>0.233894</td>\n",
+       "      <td>0.125680</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.791966</td>\n",
+       "      <td>-0.042518</td>\n",
+       "      <td>-0.029742</td>\n",
+       "      <td>-0.002375</td>\n",
+       "      <td>-0.163416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <td>-0.019910</td>\n",
+       "      <td>0.080336</td>\n",
+       "      <td>0.237831</td>\n",
+       "      <td>0.257544</td>\n",
+       "      <td>0.167196</td>\n",
+       "      <td>0.791966</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.025645</td>\n",
+       "      <td>-0.085290</td>\n",
+       "      <td>-0.070040</td>\n",
+       "      <td>-0.151934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <td>-0.187461</td>\n",
+       "      <td>-0.089121</td>\n",
+       "      <td>-0.008099</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.028514</td>\n",
+       "      <td>-0.042518</td>\n",
+       "      <td>-0.025645</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.784053</td>\n",
+       "      <td>0.234887</td>\n",
+       "      <td>0.035008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin</th>\n",
+       "      <td>-0.265924</td>\n",
+       "      <td>-0.093799</td>\n",
+       "      <td>-0.222250</td>\n",
+       "      <td>-0.228531</td>\n",
+       "      <td>-0.165453</td>\n",
+       "      <td>-0.029742</td>\n",
+       "      <td>-0.085290</td>\n",
+       "      <td>0.784053</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.689632</td>\n",
+       "      <td>0.161388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <td>-0.216408</td>\n",
+       "      <td>-0.003424</td>\n",
+       "      <td>-0.206267</td>\n",
+       "      <td>-0.200125</td>\n",
+       "      <td>-0.234166</td>\n",
+       "      <td>-0.002375</td>\n",
+       "      <td>-0.070040</td>\n",
+       "      <td>0.234887</td>\n",
+       "      <td>0.689632</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.163131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dataset</th>\n",
+       "      <td>-0.137351</td>\n",
+       "      <td>-0.082416</td>\n",
+       "      <td>-0.220208</td>\n",
+       "      <td>-0.246046</td>\n",
+       "      <td>-0.184866</td>\n",
+       "      <td>-0.163416</td>\n",
+       "      <td>-0.151934</td>\n",
+       "      <td>0.035008</td>\n",
+       "      <td>0.161388</td>\n",
+       "      <td>0.163131</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Age    Gender  Total_Bilirubin  \\\n",
+       "Age                         1.000000  0.056560         0.011763   \n",
+       "Gender                      0.056560  1.000000         0.089291   \n",
+       "Total_Bilirubin             0.011763  0.089291         1.000000   \n",
+       "Direct_Bilirubin            0.007529  0.100436         0.874618   \n",
+       "Alkaline_Phosphotase        0.080425 -0.027496         0.206669   \n",
+       "Alamine_Aminotransferase   -0.086883  0.082332         0.214065   \n",
+       "Aspartate_Aminotransferase -0.019910  0.080336         0.237831   \n",
+       "Total_Protiens             -0.187461 -0.089121        -0.008099   \n",
+       "Albumin                    -0.265924 -0.093799        -0.222250   \n",
+       "Albumin_and_Globulin_Ratio -0.216408 -0.003424        -0.206267   \n",
+       "Dataset                    -0.137351 -0.082416        -0.220208   \n",
+       "\n",
+       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "Age                                 0.007529              0.080425   \n",
+       "Gender                              0.100436             -0.027496   \n",
+       "Total_Bilirubin                     0.874618              0.206669   \n",
+       "Direct_Bilirubin                    1.000000              0.234939   \n",
+       "Alkaline_Phosphotase                0.234939              1.000000   \n",
+       "Alamine_Aminotransferase            0.233894              0.125680   \n",
+       "Aspartate_Aminotransferase          0.257544              0.167196   \n",
+       "Total_Protiens                     -0.000139             -0.028514   \n",
+       "Albumin                            -0.228531             -0.165453   \n",
+       "Albumin_and_Globulin_Ratio         -0.200125             -0.234166   \n",
+       "Dataset                            -0.246046             -0.184866   \n",
+       "\n",
+       "                            Alamine_Aminotransferase  \\\n",
+       "Age                                        -0.086883   \n",
+       "Gender                                      0.082332   \n",
+       "Total_Bilirubin                             0.214065   \n",
+       "Direct_Bilirubin                            0.233894   \n",
+       "Alkaline_Phosphotase                        0.125680   \n",
+       "Alamine_Aminotransferase                    1.000000   \n",
+       "Aspartate_Aminotransferase                  0.791966   \n",
+       "Total_Protiens                             -0.042518   \n",
+       "Albumin                                    -0.029742   \n",
+       "Albumin_and_Globulin_Ratio                 -0.002375   \n",
+       "Dataset                                    -0.163416   \n",
+       "\n",
+       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "Age                                          -0.019910       -0.187461   \n",
+       "Gender                                        0.080336       -0.089121   \n",
+       "Total_Bilirubin                               0.237831       -0.008099   \n",
+       "Direct_Bilirubin                              0.257544       -0.000139   \n",
+       "Alkaline_Phosphotase                          0.167196       -0.028514   \n",
+       "Alamine_Aminotransferase                      0.791966       -0.042518   \n",
+       "Aspartate_Aminotransferase                    1.000000       -0.025645   \n",
+       "Total_Protiens                               -0.025645        1.000000   \n",
+       "Albumin                                      -0.085290        0.784053   \n",
+       "Albumin_and_Globulin_Ratio                   -0.070040        0.234887   \n",
+       "Dataset                                      -0.151934        0.035008   \n",
+       "\n",
+       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
+       "Age                        -0.265924                   -0.216408 -0.137351  \n",
+       "Gender                     -0.093799                   -0.003424 -0.082416  \n",
+       "Total_Bilirubin            -0.222250                   -0.206267 -0.220208  \n",
+       "Direct_Bilirubin           -0.228531                   -0.200125 -0.246046  \n",
+       "Alkaline_Phosphotase       -0.165453                   -0.234166 -0.184866  \n",
+       "Alamine_Aminotransferase   -0.029742                   -0.002375 -0.163416  \n",
+       "Aspartate_Aminotransferase -0.085290                   -0.070040 -0.151934  \n",
+       "Total_Protiens              0.784053                    0.234887  0.035008  \n",
+       "Albumin                     1.000000                    0.689632  0.161388  \n",
+       "Albumin_and_Globulin_Ratio  0.689632                    1.000000  0.163131  \n",
+       "Dataset                     0.161388                    0.163131  1.000000  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Filling the null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#we can also fill the nan values in the column Albumin_and_Globulin_Ratio\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "imputer=SimpleImputer(strategy='mean')\n",
+    "imputer.fit(df[['Albumin_and_Globulin_Ratio']])\n",
+    "df['Albumin_and_Globulin_Ratio']=imputer.transform(df[['Albumin_and_Globulin_Ratio']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                           0\n",
+       "Gender                        0\n",
+       "Total_Bilirubin               0\n",
+       "Direct_Bilirubin              0\n",
+       "Alkaline_Phosphotase          0\n",
+       "Alamine_Aminotransferase      0\n",
+       "Aspartate_Aminotransferase    0\n",
+       "Total_Protiens                0\n",
+       "Albumin                       0\n",
+       "Albumin_and_Globulin_Ratio    0\n",
+       "Dataset                       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Liver DIsease prediction/README.md b/Liver DIsease prediction/README.md
new file mode 100644
index 00000000..94b1c541
--- /dev/null
+++ b/Liver DIsease prediction/README.md	
@@ -0,0 +1,27 @@
+# Liver Disease Prediction  Dataset 📊
+
+Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors.
+
+- [Liver Disease Prediction Dataset](https://www.kaggle.com/datasets/uciml/indian-liver-patient-records)
+
+## Dataset Features 📋
+
+This data set contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India. The "Dataset" column is a class label used to divide groups into liver patient (liver disease) or not (no disease). This data set contains 441 male patient records and 142 female patient records.
+Columns:
+
+- **Age**: Older age may indicate a higher risk of liver disease.
+- **Gender**: Certain liver diseases have gender-specific prevalence.
+- **Total Bilirubin**: Elevated levels suggest liver dysfunction.
+- **Direct Bilirubin**: Elevated levels may indicate bile duct obstruction.
+- **Alkaline Phosphotase**: Elevated levels can signal liver or bone disease.
+- **Alamine Aminotransferase (ALT)**: Elevated levels indicate liver damage.
+- **Aspartate Aminotransferase (AST)**: Elevated levels suggest liver inflammation.
+- **Total Proteins**: Changes may occur in liver disease.
+- **Albumin**: Decreased levels suggest liver dysfunction.
+- **Albumin and Globulin Ratio**: Provides additional insight into liver function.
+- **Dataset**: Indicates whether the patient has liver disease.
+- **Diagnosis**: Indicates whether the patient has diabetes.
+
+## Inspiration 💡
+
+This dataset can inspire research and analysis aimed at predicting liver patient based on various clinical indicators. By examining the relationship between various parameters.

From 16fa4a5c810e9b662b94701316813e2722c9e259 Mon Sep 17 00:00:00 2001
From: Rakesh Joshi <rakeshjoshi21021369@gmail.com>
Date: Sun, 19 May 2024 23:02:26 +0530
Subject: [PATCH 2/5] Delete Liver DIsease prediction/Liver_disease_EDA.ipynb

---
 .../Liver_disease_EDA.ipynb                   | 1063 -----------------
 1 file changed, 1063 deletions(-)
 delete mode 100644 Liver DIsease prediction/Liver_disease_EDA.ipynb

diff --git a/Liver DIsease prediction/Liver_disease_EDA.ipynb b/Liver DIsease prediction/Liver_disease_EDA.ipynb
deleted file mode 100644
index fac86e31..00000000
--- a/Liver DIsease prediction/Liver_disease_EDA.ipynb	
+++ /dev/null
@@ -1,1063 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Liver Disease EDA(Exploratory Data Analysis)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df=pd.read_csv(r\"C:\\Users\\rakes\\health_proj\\Liver Disease Prediction\\Dataset\\indian_liver_patient.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Set Exploration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(583, 11)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>65</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>187</td>\n",
-       "      <td>16</td>\n",
-       "      <td>18</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>0.90</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>62</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>10.9</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>699</td>\n",
-       "      <td>64</td>\n",
-       "      <td>100</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>0.74</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>62</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>7.3</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>490</td>\n",
-       "      <td>60</td>\n",
-       "      <td>68</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>0.89</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>58</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>182</td>\n",
-       "      <td>14</td>\n",
-       "      <td>20</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>72</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>3.9</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>195</td>\n",
-       "      <td>27</td>\n",
-       "      <td>59</td>\n",
-       "      <td>7.3</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
-       "0   65  Female              0.7               0.1                   187   \n",
-       "1   62    Male             10.9               5.5                   699   \n",
-       "2   62    Male              7.3               4.1                   490   \n",
-       "3   58    Male              1.0               0.4                   182   \n",
-       "4   72    Male              3.9               2.0                   195   \n",
-       "\n",
-       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
-       "0                        16                          18             6.8   \n",
-       "1                        64                         100             7.5   \n",
-       "2                        60                          68             7.0   \n",
-       "3                        14                          20             6.8   \n",
-       "4                        27                          59             7.3   \n",
-       "\n",
-       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
-       "0      3.3                        0.90        1  \n",
-       "1      3.2                        0.74        1  \n",
-       "2      3.3                        0.89        1  \n",
-       "3      3.4                        1.00        1  \n",
-       "4      2.4                        0.40        1  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>578</th>\n",
-       "      <td>60</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>500</td>\n",
-       "      <td>20</td>\n",
-       "      <td>34</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.37</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>579</th>\n",
-       "      <td>40</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>98</td>\n",
-       "      <td>35</td>\n",
-       "      <td>31</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.10</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>580</th>\n",
-       "      <td>52</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>245</td>\n",
-       "      <td>48</td>\n",
-       "      <td>49</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>581</th>\n",
-       "      <td>31</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>184</td>\n",
-       "      <td>29</td>\n",
-       "      <td>32</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>582</th>\n",
-       "      <td>38</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>216</td>\n",
-       "      <td>21</td>\n",
-       "      <td>24</td>\n",
-       "      <td>7.3</td>\n",
-       "      <td>4.4</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Age Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
-       "578   60   Male              0.5               0.1                   500   \n",
-       "579   40   Male              0.6               0.1                    98   \n",
-       "580   52   Male              0.8               0.2                   245   \n",
-       "581   31   Male              1.3               0.5                   184   \n",
-       "582   38   Male              1.0               0.3                   216   \n",
-       "\n",
-       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
-       "578                        20                          34             5.9   \n",
-       "579                        35                          31             6.0   \n",
-       "580                        48                          49             6.4   \n",
-       "581                        29                          32             6.8   \n",
-       "582                        21                          24             7.3   \n",
-       "\n",
-       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
-       "578      1.6                        0.37        2  \n",
-       "579      3.2                        1.10        1  \n",
-       "580      3.2                        1.00        1  \n",
-       "581      3.4                        1.00        1  \n",
-       "582      4.4                        1.50        2  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>579.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>44.746141</td>\n",
-       "      <td>0.756432</td>\n",
-       "      <td>3.298799</td>\n",
-       "      <td>1.486106</td>\n",
-       "      <td>290.576329</td>\n",
-       "      <td>80.713551</td>\n",
-       "      <td>109.910806</td>\n",
-       "      <td>6.483190</td>\n",
-       "      <td>3.141852</td>\n",
-       "      <td>0.947064</td>\n",
-       "      <td>1.286449</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>16.189833</td>\n",
-       "      <td>0.429603</td>\n",
-       "      <td>6.209522</td>\n",
-       "      <td>2.808498</td>\n",
-       "      <td>242.937989</td>\n",
-       "      <td>182.620356</td>\n",
-       "      <td>288.918529</td>\n",
-       "      <td>1.085451</td>\n",
-       "      <td>0.795519</td>\n",
-       "      <td>0.319592</td>\n",
-       "      <td>0.452490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>63.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>2.700000</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>33.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>175.500000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>5.800000</td>\n",
-       "      <td>2.600000</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>45.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>208.000000</td>\n",
-       "      <td>35.000000</td>\n",
-       "      <td>42.000000</td>\n",
-       "      <td>6.600000</td>\n",
-       "      <td>3.100000</td>\n",
-       "      <td>0.930000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>58.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.600000</td>\n",
-       "      <td>1.300000</td>\n",
-       "      <td>298.000000</td>\n",
-       "      <td>60.500000</td>\n",
-       "      <td>87.000000</td>\n",
-       "      <td>7.200000</td>\n",
-       "      <td>3.800000</td>\n",
-       "      <td>1.100000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>90.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>75.000000</td>\n",
-       "      <td>19.700000</td>\n",
-       "      <td>2110.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>4929.000000</td>\n",
-       "      <td>9.600000</td>\n",
-       "      <td>5.500000</td>\n",
-       "      <td>2.800000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              Age      Gender  Total_Bilirubin  Direct_Bilirubin  \\\n",
-       "count  583.000000  583.000000       583.000000        583.000000   \n",
-       "mean    44.746141    0.756432         3.298799          1.486106   \n",
-       "std     16.189833    0.429603         6.209522          2.808498   \n",
-       "min      4.000000    0.000000         0.400000          0.100000   \n",
-       "25%     33.000000    1.000000         0.800000          0.200000   \n",
-       "50%     45.000000    1.000000         1.000000          0.300000   \n",
-       "75%     58.000000    1.000000         2.600000          1.300000   \n",
-       "max     90.000000    1.000000        75.000000         19.700000   \n",
-       "\n",
-       "       Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
-       "count            583.000000                583.000000   \n",
-       "mean             290.576329                 80.713551   \n",
-       "std              242.937989                182.620356   \n",
-       "min               63.000000                 10.000000   \n",
-       "25%              175.500000                 23.000000   \n",
-       "50%              208.000000                 35.000000   \n",
-       "75%              298.000000                 60.500000   \n",
-       "max             2110.000000               2000.000000   \n",
-       "\n",
-       "       Aspartate_Aminotransferase  Total_Protiens     Albumin  \\\n",
-       "count                  583.000000      583.000000  583.000000   \n",
-       "mean                   109.910806        6.483190    3.141852   \n",
-       "std                    288.918529        1.085451    0.795519   \n",
-       "min                     10.000000        2.700000    0.900000   \n",
-       "25%                     25.000000        5.800000    2.600000   \n",
-       "50%                     42.000000        6.600000    3.100000   \n",
-       "75%                     87.000000        7.200000    3.800000   \n",
-       "max                   4929.000000        9.600000    5.500000   \n",
-       "\n",
-       "       Albumin_and_Globulin_Ratio     Dataset  \n",
-       "count                  579.000000  583.000000  \n",
-       "mean                     0.947064    1.286449  \n",
-       "std                      0.319592    0.452490  \n",
-       "min                      0.300000    1.000000  \n",
-       "25%                      0.700000    1.000000  \n",
-       "50%                      0.930000    1.000000  \n",
-       "75%                      1.100000    2.000000  \n",
-       "max                      2.800000    2.000000  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(df.describe())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# To see if there are null values and target class distribution, correlation "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                           0\n",
-       "Gender                        0\n",
-       "Total_Bilirubin               0\n",
-       "Direct_Bilirubin              0\n",
-       "Alkaline_Phosphotase          0\n",
-       "Alamine_Aminotransferase      0\n",
-       "Aspartate_Aminotransferase    0\n",
-       "Total_Protiens                0\n",
-       "Albumin                       0\n",
-       "Albumin_and_Globulin_Ratio    4\n",
-       "Dataset                       0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "416 167\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target class distribution in data set, 1 represent its a liver patient and 2 represent it is not a liver patient\n",
-    "true_count=len(df.loc[df['Dataset']==1])\n",
-    "false_count=len(df.loc[df['Dataset']==2])\n",
-    "print(true_count,false_count)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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0V1dXxMTE4MMPP8T06dOxd+9eoy0LKH99nz59utzpV65cwd27d6V9+/jx4wAeJlRl3fDr8uXLAIC//vpLZ1p5+1xZNDfm6ty5s97vuXLlCiIiInD27Nky65S1/1bFc889BycnJ1y5cgX79++XfmDZvn07bt68CQ8PD50kNT4+Hu+//z6Ki4sr3caqrEfg4Y0kX3vtNdy/f7/SywwICICVle49fmUyGQICAnDlyhWcO3euwmS8KvuQtbU13nrrLcybNw9PPPEEunbtil69eqF79+7o1q0b7Ozsyl0mEVF1wrupExGVoX///ti6dStCQ0Nx7tw5fPrpp3jhhRegVCoxZMiQUhOQ8mgSz/LO3nt6egKAdCdyALh37x4AlHpmqbzykhwdHUstLyoqwpAhQ3D9+nX069cPaWlp+Pfff/HgwQMIIaQfAtRqdanvf/QsrIYmAa5oesmzlwCwatUqTJ8+HfXr10dycjLee+89dO/eHd7e3vjkk0/KTWZKquq6Nobo6Gi4ubkhNTXV6Mm4IbfHnTt3AAAXLlzAzz//XOqf5u72msfBlVTWPlcezciVOnXq6P2e0aNH4+zZswgJCcGOHTuQlZWFwsJCCCGk/dZQd1QHHsb13HPPAdB+5rjm/0OHDoVcLpfK9+3bh/feew8ymQzx8fE4efIk7t69i+LiYgghMG3aNABlf8aqsh7/+OMPvPrqq7h//z4mTpyII0eOIDc3V1rmV199Ve4yDfW5qeo+NHv2bCxatAhNmzbF/v378dFHHyE8PByenp6YOnWqNGKHiKi6YzJORFSOfv364eeff8Y///yDTZs2YcKECahTpw6+//57DBgwoMyD2dI4OTkB+N8Q0dL8/fffALQTbM3BeMmzyCU9TjL566+/4sKFC2jUqBE2bNiAJ598Eu7u7rC2tgYAXL16tcrzrgo7OzvExsbi2rVrOH36NL744gsMGDAAN2/exDvvvIMFCxboNZ+qrmtjcHZ2xsSJEwFU/Nzxsn6k0ND8MGMKmnX41VdfQTy8x0yZf7GxsQZZpmZb3L59W6/6169fx969e+Hg4IBt27ahT58+8PT0lJJhY+2/mmHoP/zwA1QqFXJzc7F582ataRpr1qwBALzzzjuYMmUKWrVqBUdHR2lbG6ON69evh1qtxtChQ/HJJ5+gXbt2cHZ21nuZ//zzT5nTNJ8pfT43Vd2HrKys8Pbbb+PcuXO4ePEiEhMTMXToUNy/fx+zZ8+WPk9ERNUdk3EiIj24ubnh2WefxWeffYYTJ07A1dUVR44cQWZmplSnrOHQGs2bNwfwcChuWcmW5pnDmrol///777+X+h7NUNCq0DxrvEOHDlAoFDrTDXmtbWW1aNEC//d//4effvoJS5cuBQDpjF5FqrqujeWtt96Ch4cH9u/fj127dpVZT/PDS1nJ0IULF4zSvtK0atUKwMPnVZtK69atAaDc556XpBni3KJFC7i5uelMN9b+GxYWBh8fH+Tk5GDbtm344YcfcP/+fTRv3hydOnXSqqv5jJV2Pbix2vi4yzx79mypo1CEENLlAPp8bgyxDzVu3BijRo3CunXr8NNPPwEAvvnmG71HyRARWTIm40REleTp6Qk/Pz8A0LrO2t7eHkDpQ3YBoFu3bnBwcMDVq1fx3//+V2d6ZmYmDh48CJlMhvDwcKlc8//Vq1ejqKhI530JCQlVjkXTZs1Z4pLUajUWLVpU5XkbkuYa4vKuay+pquvaWJycnPDOO+8AAD788MMy6zVp0gQAkJGRoTMtMzPTpD+OlByKffPmTZMss1+/fpDL5Th06BB+/vnnCutr9t/s7OxSf3SZO3euwdsIPDxzq7nx2erVq6Uh6qXduK28z1hycrJRtml5yzxz5ox0Fr8s165dK7XO1q1bcfnyZTg6OqJr164VtsPQ+5CmHygoKEBOTs5jz4+IyNyYjBMRlWHo0KHYunWrdAdijR9++AHHjx+HTCZD+/btpfKSiVR+fr7O/FxcXPD6668DAN58800cOXJEmvbHH38gKioKwMM7D5e80dawYcOgVCpx6tQprRsyCSGwbNkyrF27tsoxam6M9vPPP2PVqlVS+Z07dzBixIhSD+aNZffu3XjnnXdw6tQprfK7d+9i3rx5AKD33bmruq6N6Y033kD9+vVx8OBB6czlo/r27Qvg4QiAX3/9VSo/f/48oqKiKnUTu8cVHByMIUOG4ObNmwgPD9dah8DD+w2kpqZixIgRBruG18vLC2+++SYAYNCgQUhOTtaafv36dXz00UfS69atW6Nu3bq4du0aPv74Yykhv3//Pt5++22dNhuSJvHesmUL0tLSIJPJMGLECJ163bp1A/DwOuiLFy9K5RkZGXj55ZeNckMyzTKXLl2Ko0ePSuXnzp3DCy+8AFtb23Lfb2NjgwkTJmiNujl16pS0bV577TW9hqlXZR86deoUxo0bh4yMDK0fWFQqFT7++GMAQKNGjap0E00iIotj/KenEREZl+Z5wY8+e/lRmmc46/uccVdXV+k5v4GBgaJjx47Cy8tLeg7vBx98oFW/qKhI+Pv7CwDC3d1ddOnSRfTo0UO8/fbbUp38/HzRq1cvaR6tWrUSbdu2FdbW1gKAaNu2rfj333912p6SkiJsbW0FAOHq6io6duwovL29BQAxf/58AUBYWVnpvE+znPJMmjRJqtewYUPRoUMHYW9vL+RyuVi2bFmpzw+u6PnGZa1TjdKeyb5x40apHfXq1RPBwcGibdu20vPCXV1dy31u9aOquq4N+ZzxR33yySdaz7x/dBnFxcWid+/e0vYMCAgQgYGBwsrKSjz55JNi+PDh5T5nvKzPQEUxlfXM7by8PBEeHq61f4SEhIigoCBhb28vlRcUFEjvKW3blqasffP+/fvi2WeflaZ7e3uLjh07igYNGgiZTKbzniVLlkh1lUqlCA4OFi4uLkImk4mvvvqqzOVU9TnjJbVt21aaf/fu3Uutc+fOHdGkSRMBQNja2oqgoCAREBAg7ZMxMTGl9kua54w/Wl5SWZ9DtVotOnfuLAAIa2tr0bJlSxEYGChkMpnw8vISs2bNKnUbabbd0KFDRfv27YVMJhOBgYEiKChIWvcdO3YUd+/e1asdQlR+Hzpy5IhUVqdOHfHEE0+I9u3bS/2xra2t2LZtW5nrhIioOuGZcSKiMiQmJuL//u//4O/vj+vXr+P333+Hg4MDnnvuOaSlpWmdoQMeDl3dunUrBg8eDGtra/z6669IS0vTOjNlb2+PnTt34tNPP0VwcDAuX76Mc+fOoVWrVpg1axbS09NLPePTu3dvHDx4EP379wfw8OyRj48P1q1bh3HjxgGo+o3I5s6di0WLFqFFixbIysrC5cuX0bt3b+zfv7/CRxcZUvfu3fHZZ59hwIABcHJywqlTp3Dp0iU0a9YMkydPxpkzZyr13OqqrmtjGj9+PJRKZZnTZTIZNm7ciJiYGHh7e+PixYu4d+8epk6diuTkZK27dJuCk5MTduzYgTVr1qBPnz7Iz8+Xnlndpk0bvPvuu/j1118NenZXoVBg48aNWLNmDcLCwnD//n0cO3YMVlZW6Nevn9YIDuDhiIPVq1ejXbt2uHXrFi5cuIDg4GBs27YNr7zyisHaVZqSw9JfeumlUuu4uLjgwIEDGDVqFFxcXHD27FkUFhYiJiYGBw8eNMoNBG1sbLBz505MmDABnp6euHDhAm7fvo2xY8fi8OHD8PHxKff9CoUCaWlpePvtt5Gbm4uzZ8+iYcOGmDJlCvbu3VupO7xXdh/y9/fHV199hRdeeAH16tXDuXPncP78efj4+OC1117DqVOnpBEkRETVnUyIMu5sQ0RE1cLhw4cRHByMtm3baiX+RERERGS5eGaciKiaW7lyJQDodUMlIiIiIrIMTMaJiKqBvXv3IikpSetGWWq1GgsWLMCyZctgZWWFV1991YwtJCIiIqLKMN1tWYmIqMouX76MMWPGQC6Xw8/PDy4uLjh37hxyc3MBAPHx8WjXrp15G0lEREREeuM140RE1cAff/yBRYsWYe/evbh+/Try8vLg5uaGkJAQvPnmm4iIiDB3E4mIiIioEpiMExEREREREZkYrxknIiIiIiIiMjEm40REREREREQmxmSciIiIiIiIyMSYjBMRERERERGZGJNxIiIiIiIiIhNjMk5ERERERERkYkzGiYiIiIiIiEyMyTgRERERERGRiTEZJyIiIiIiIjIxJuNEREREREREJsZknIiIiIiIiMjEmIwTERERERERmRiTcSIiIiIiIiITYzJOREREREREZGJMxomIiIiIiIhMjMk4ERERERERkYkxGSciIiIiIiIyMSbjRERERERERCbGZJyIiIiIiIjIxJiMExEREREREZkYk3EiIiIiIiIiE2MyTkRERERERGRiTMaJiIiIiIiITIzJOBEREREREZGJMRknIiIiIiIiMjEm40REREREREQmxmSciIiIiIiIyMSYjBMRERERERGZGJNxIiIiIiIiIhNjMk5ERERERERkYkzGiYiIiIiIiEyMyTgRERERERGRiTEZJyIiIiIiIjIxJuNEREREREREJsZknIiIiIiIiMjEmIwTERERERERmRiTcSIiIiIiIiITYzJOREREREREZGJMxomIiIiIiIhMjMk4ERERERERkYkxGSciIiIiIiIyMSbjRERERERERCbGZJyIiIiIiIjIxJiMExEREREREZkYk3EiIiIiIiIiE2MyTkRERERERGRiTMaJiIiIiIiITIzJOBEREREREZGJMRknIiIiIiIiMjEm40REREREREQmxmSciIiIiIiIyMSYjBMRERERERGZGJNxIiIiIiIiIhNjMk5ERERERERkYkzGiYiIiIiIiEyMyTgRERERERGRiTEZJ4vy2WefQSaTITAw0NxNIaJaSCaT6fWXmppa4bzi4uKwadOmx25PbGxspd7TuHFjrbba2dmhWbNmiImJwb///qtVNzY2FjKZTKusZ8+e6Nmz52O3Qx8ymQxvvvlmhfVSU1P1Xu9EZBoJCQk6fY1SqUSvXr0QHx+P7Oxsrfql9Tem8jj9MftUMiYbczeAqKRvvvkGAHDy5En88ssvCAkJMXOLiKg2OXjwoNbrmTNnYu/evdizZ49WeatWrSqcV1xcHAYPHoyBAwcasol66dq1Kz755BMAQEFBATIzMxEbG4t9+/YhMzNTqvfKK6/g6aefrnB+Bw8eRIMGDYzW3oo88cQTOHjwoF7rnYhMa+XKlWjRogXUajWys7Nx4MABzJkzB5988gm+++479O7dG4D+/Y0xPG5/zD6VjIXJOFmMzMxMHDt2DP3798fWrVuxYsUKJuNEZFKdO3fWel2vXj1YWVnplFu6OnXqaLW5V69eyMvLw8yZM3Hu3Dk0b94cANCgQQO9Dgj1ib+goAB2dnZGOfPl4uJS7bYBUW0RGBiI4OBg6fXzzz+P//znP+jWrRsGDRqE8+fPw9PTU+/+pqCgAPb29sZscqWxTyVj4TB1shgrVqwAAMyePRuhoaFISkpCfn6+Vp1r165h8ODBcHZ2Rp06dTBixAhkZGRAJpMhISFBq25mZiaeeeYZuLm5wc7ODu3bt8f69etNFQ4R1VC3bt3C+PHj4ePjA1tbWzRp0gTTpk2DSqWS6shkMty7dw+JiYnS0EbNMMV//vkH48ePR6tWreDk5IT69evjqaeewv79+43abldXVwCAXC6XyvQdNvrokErN8NTk5GS8/PLLqFevHhwcHKBSqTB69Gg0btxYZx7lLeuLL75A8+bNoVAo0KpVKyQlJWlNL21I5ejRo+Hk5IQLFy6gX79+cHJygq+vLyZOnKi1LYjI9Bo2bIj58+cjLy8PX3zxBYDS+4DGjRsjMjISGzZsQPv27WFnZ4cZM2YAALKysjBu3Dg0aNAAtra28PPzw4wZM/DgwQOteahUKnz00Udo2bIl7Ozs4O7ujl69eiE9PR1A+f3x42CfSobAM+NkEQoKCrBu3Tp07NgRgYGBePnll/HKK6/g+++/R1RUFADg3r176NWrF27duoU5c+agWbNm2LFjB1588UWd+e3duxdPP/00QkJCsHz5cri6uiIpKQkvvvgi8vPzMXr0aBNHSEQ1wf3799GrVy/88ccfmDFjBtq0aYP9+/cjPj4eR48exdatWwE8HIL41FNPoVevXvjggw8APDwTATxM5gFg+vTpUCqVuHv3LjZu3IiePXti9+7dBjlIFEJIB6z3799HRkYGFi1ahK5du8LPz++x56/x8ssvo3///vj2229x7949rYNSff3000/Yu3cvPvroIzg6OmLp0qUYNmwYbGxsMHjw4HLfq1ar8cwzz2Ds2LGYOHEi9u3bh5kzZ8LV1RUffvhhVcMiIgPo168frK2tsW/fvnLr/fbbbzh9+jTef/99+Pn5wdHREVlZWejUqROsrKzw4YcfomnTpjh48CBmzZqFS5cuYeXKlQCABw8eoG/fvti/fz+io6Px1FNP4cGDBzh06BCuXLmC0NDQcvtjfbFPZZ9qLEzGySL88MMPuHPnDsaOHQsAePHFFxEdHY0VK1ZIyXhiYiIuXLiA7du3S9fjREREID8/X/rVVWP8+PFo3bo19uzZAxubh7t5nz598O+//+K9997DqFGjYGXFgSFEVDmJiYn4/fffsX79erzwwgsAgPDwcDg5OeHdd99FSkoKwsPD0blzZ1hZWaFevXo6QwEDAgKwdOlS6XVRURH69OmDS5cu4bPPPjNIMr5t2zadg7hOnTrhhx9+eOx5lxQWFqbT/1bWv//+i4yMDHh6egJ4eAAfGBiIqVOnVnjgWFhYiBkzZkjbIiwsDJmZmVi7di0PHInMzNHRER4eHrh+/Xq59bKzs3Hq1ClpqDcAvPbaa8jJycHJkyfRsGFDAA8/3/b29pg0aRLeeecdtGrVCuvWrcPevXvx1Vdf4ZVXXpHeP2DAAOn/5fXH+mKfyj7VWJiNkEVYsWIF7O3tMXToUACAk5MTXnjhBezfvx/nz58HAKSlpcHZ2VnnxhjDhg3Ten3hwgWcOXMGI0aMAPDwV1PNX79+/XDjxg2cPXvWBFERUU2zZ88eODo66hzQaEbb7N69W6/5LF++HE888QTs7OxgY2MDuVyO3bt34/Tp0wZpZ7du3ZCRkYGMjAz8/PPPWLFiBf755x889dRTOnf/fRzPP//8Y88jLCxMOmgEAGtra7z44ou4cOECrl27Vu57ZTKZ1kE3ALRp0waXL19+7HYR0eMTQlRYp02bNlqJOABs2bIFvXr1gre3t9ZxXN++fQE8PCYEgO3bt8POzg4vv/yy4RtfAvtU9qnGwmSczO7ChQvYt28f+vfvDyEEbt++jdu3b0sHu5o7rN+8eVOrc9F4tOzvv/8GAEyaNAlyuVzrb/z48QBg0I6TiGqPmzdvQqlU6lynV79+fdjY2ODmzZsVzmPBggV4/fXXERISgh9//BGHDh1CRkYGnn76aRQUFBikna6urggODkZwcDBCQ0Px8ssvY+3atTh9+jTmz59vkGUAgJeX12PPQ6lUlllW0fp0cHCAnZ2dVplCocD9+/cfu11E9Hju3buHmzdvwtvbu9x6pfUjf//9NzZv3qxzHNe6dWsA/zuO++eff+Dt7W300Y7sU9mnGguHqZPZffPNNxBC4Icffih1uE9iYiJmzZoFd3d3/PrrrzrTs7KytF57eHgAAKZOnYpBgwaVusyAgAADtJyIaht3d3f88ssvEEJoJeTZ2dl48OCB1P+UZ/Xq1ejZsyeWLVumVZ6Xl2fw9pbUpk0bAMCxY8cMNs/Sbh5kZ2dX6s1+yvoR9NE+vGSZu7v7Y7aQiMxl69atKCoqqvDSm9L6EQ8PD7Rp0wYff/xxqe/RJPj16tXDgQMHUFxcbPLLD9mnkiEwGSezKioqQmJiIpo2bYqvv/5aZ/qWLVswf/58bN++HT169MD69euxfft2aZgSAJ07RAYEBMDf3x/Hjh1DXFyc0WMgotojLCwM69evx6ZNm/Dcc89J5atWrZKmaygUilLPdMtkMigUCq2y33//HQcPHoSvr6+RWg4cPXoUwMOz+MbUuHFjZGdn4++//5ZGLhUWFmLnzp2l1t+9e7dW3aKiInz33Xdo2rSpWZ/DS0RVd+XKFUyaNAmurq4YN25cpd8fGRmJbdu2oWnTpqhbt26Z9fr27Yt169YhISGh3KHqZfXHj4N9KhkCk3Eyq+3bt+P69euYM2dOqb+cBgYGYsmSJVixYgVWr16NhQsX4qWXXsKsWbPQrFkzbN++XeqMSv4i+sUXX6Bv377o06cPRo8eDR8fH9y6dQunT5/Gb7/9hu+//95UIRJRDTJq1Ch8/vnniIqKwqVLlxAUFIQDBw4gLi4O/fr1Q+/evaW6QUFBSE1NxebNm+Hl5QVnZ2cEBAQgMjISM2fOxPTp09GjRw+cPXsWH330Efz8/HQe2VNVt2/fxqFDhwA8vDvu6dOnERcXB4VCgTfeeMMgyyjLiy++iA8//BBDhw7FO++8g/v37+Ozzz5DUVFRqfU9PDzw1FNP4YMPPpDu/HvmzBmdH1qJyDKdOHFCuqY7Ozsb+/fvx8qVK2FtbY2NGzeiXr16lZ7nRx99hJSUFISGhuKtt95CQEAA7t+/j0uXLmHbtm1Yvnw5GjRogGHDhmHlypV47bXXcPbsWfTq1QvFxcX45Zdf0LJlS+leRGX1x/pin0rGwmSczGrFihWwtbXFmDFjSp3u4eGB5557Dj/88APu3r2LPXv2IDo6GpMnT4ZMJkNERASWLl2Kfv36oU6dOtL7evXqhV9//RUff/wxoqOjkZOTA3d3d7Rq1QpDhgwxUXREVNPY2dlh7969mDZtGubNm4d//vkHPj4+mDRpEqZPn65V99NPP8Ubb7yBoUOHIj8/Hz169EBqaiqmTZuG/Px8rFixAnPnzkWrVq2wfPlybNy4UeuZr4/j559/RpcuXQA8vHmPj48POnXqhGnTpqFdu3YGWUZZ/Pz88N///hfvvfceBg8eDC8vL8TExOCff/6Rnh9c0jPPPIPWrVvj/fffx5UrV9C0aVOsWbOm1MdWEpHl0RzD2draok6dOmjZsiXeffddvPLKK1VKxIGH105nZmZi5syZmDdvHq5duwZnZ2f4+fnh6aefls6W29jYYNu2bYiPj8e6deuwaNEiODs7o23btlo3/C2rP9YX+1QyFpnQ5zaHRBYsLi5O6nA4/IaIiIiIiKoDnhmnamXJkiUAgBYtWkCtVmPPnj347LPP8NJLLzERJyIiIiKiaoPJOFUrDg4OWLhwIS5dugSVSoWGDRvi3Xffxfvvv2/uphERGVVF15NbWVmZ/G7CRETVFftUsgQcpk5ERFQNlPbIm5KioqKQkJBgmsYQEVVz7FPJEvDMOBERUTWQkZFR7nR9nnFOREQPsU8lS8Az40REREREREQmVi3PjBcXF+P69etwdnaucIgJEdVcQgjk5eXB29ub13Xpif0nEQHVq/+MjY3VeYSTp6cnsrKyADyMZcaMGfjyyy+Rk5ODkJAQfP7552jdurVUX6VSYdKkSVi3bh0KCgoQFhaGpUuXVurmr+w/icjQfWe1TMavX78OX19fczeDiCzE1atXeTd9PbH/JKKSqkv/2bp1a+zatUt6bW1tLf1/7ty5WLBgARISEtC8eXPMmjUL4eHhOHv2LJydnQEA0dHR2Lx5M5KSkuDu7o6JEyciMjIShw8f1ppXedh/EpGGofrOapmMazrWr7/+GgMHDoRcLjdziwxLrVYjOTkZERERNSq2mhoXwNjMJTc3F76+vlKfQBXTrKurV6/CxcWl3LqWvO2NjbEz9poee3XrP21sbKBUKnXKhRBYtGgRpk2bhkGDBgEAEhMT4enpibVr12LcuHG4c+cOVqxYgW+//Ra9e/cGAKxevRq+vr7YtWsX+vTpU+oyVSoVVCqV1rIA4OLFixWuN7Vajb1796JXr141fl+qTbECjLemqyjevLw8+Pn5GazvrJbJuGZokIODA1xcXGrcjqFWq2tkbDU1LoCxmRuHC+pPs65cXFz0SsYtfdsbC2Nn7LUl9urSf54/fx7e3t5QKBQICQlBXFwcmjRpgosXLyIrKwsRERFSXYVCgR49eiA9PR3jxo3D4cOHoVartep4e3sjMDAQ6enpZSbj8fHxOsPjAeDgwYNwcHCosM0ODg745ZdfqhBt9VObYgUYb01XXrz5+fkADNd3VstknIiIiIhqh5CQEKxatQrNmzfH33//jVmzZiE0NBQnT56Urhv39PTUeo+npycuX74MAMjKyoKtrS3q1q2rU0fz/tJMnToVMTEx0mvNaIKIiAi9fsxMSUlBeHh4jf9hpzbFCjDemq6ieHNzcw26PCbjRERERGSx+vbtK/0/KCgIXbp0QdOmTZGYmIjOnTsD0D1LJYSo8MxVRXUUCgUUCoVOuVwu1zspqUzd6q42xQow3pqurHgNvQ4s+/aZREREREQlODo6IigoCOfPn5euI3/0DHd2drZ0tlypVKKwsBA5OTll1iEiMgcm40RERERUbahUKpw+fRpeXl7w8/ODUqlESkqKNL2wsBBpaWkIDQ0FAHTo0AFyuVyrzo0bN3DixAmpDhGROXCYOhERERFZrEmTJmHAgAFo2LAhsrOzMWvWLOTm5iIqKgoymQzR0dGIi4uDv78//P39ERcXBwcHBwwfPhwA4OrqirFjx2LixIlwd3eHm5sbJk2ahKCgIOnu6kRE5sBknIiIiIgs1rVr1zBs2DD8+++/qFevHjp37oxDhw6hUaNGAIDJkyejoKAA48ePR05ODkJCQpCcnKz16KGFCxfCxsYGQ4YMQUFBAcLCwpCQkKD3M8aJiIyByTgRERERWaykpKRyp8tkMsTGxiI2NrbMOnZ2dli8eDEWL15s4NYREVUdk3FC4ylbjTbvS7P7G23eRGQagbE7oSoyzrOI2UcQUU3G/pOIysMbuBERERERERGZGJNxIiIiIiIiIhNjMk5ERERERERkYkzGiYiIiIiIiEyMyTgRERERERGRiTEZJyIygdjYWMhkMq0/pVIpTRdCIDY2Ft7e3rC3t0fPnj1x8uRJrXmoVCpMmDABHh4ecHR0xDPPPINr166ZOhQiIiIiMgAm40REJtK6dWvcuHFD+jt+/Lg0be7cuViwYAGWLFmCjIwMKJVKhIeHIy8vT6oTHR2NjRs3IikpCQcOHMDdu3cRGRmJoqIic4RDRERERI+BzxknIjIRGxsbrbPhGkIILFq0CNOmTcOgQYMAAImJifD09MTatWsxbtw43LlzBytWrMC3336L3r17AwBWr14NX19f7Nq1C3369DFpLERERET0eJiMU7XWeMpWo8370uz+Rps31U7nz5+Ht7c3FAoFQkJCEBcXhyZNmuDixYvIyspCRESEVFehUKBHjx5IT0/HuHHjcPjwYajVaq063t7eCAwMRHp6epnJuEqlgkqlkl7n5uYCANRqNdRqdbnt1UxXWIkqx1yRitpgLpp2WWr7jImx147Ya0OMRESWjsk4EZEJhISEYNWqVWjevDn+/vtvzJo1C6GhoTh58iSysrIAAJ6enlrv8fT0xOXLlwEAWVlZsLW1Rd26dXXqaN5fmvj4eMyYMUOnPDk5GQ4ODnq1fWZwsV71qmLbtm1Gm7chpKSkmLsJZsPYa7b8/HxzN4GIqNZjMk5EZAJ9+/aV/h8UFIQuXbqgadOmSExMROfOnQEAMplM6z1CCJ2yR1VUZ+rUqYiJiZFe5+bmwtfXFxEREXBxcSl33mq1GikpKfgg0wqq4vLbUVUnYi1zeL0m9vDwcMjlcnM3x6QYe+2IXTNKhoiIzIfJOBGRGTg6OiIoKAjnz5/HwIEDATw8++3l5SXVyc7Ols6WK5VKFBYWIicnR+vseHZ2NkJDQ8tcjkKhgEKh0CmXy+V6JxuqYhlURcZJxi094anMeqppGHvNjr2mx0dEVB3wbupERGagUqlw+vRpeHl5wc/PD0qlUmtobGFhIdLS0qREu0OHDpDL5Vp1bty4gRMnTpSbjBMRERGRZeKZcSIiE5g0aRIGDBiAhg0bIjs7G7NmzUJubi6ioqIgk8kQHR2NuLg4+Pv7w9/fH3FxcXBwcMDw4cMBAK6urhg7diwmTpwId3d3uLm5YdKkSQgKCpLurk5ERERE1QeTcSIiE7h27RqGDRuGf//9F/Xq1UPnzp1x6NAhNGrUCAAwefJkFBQUYPz48cjJyUFISAiSk5Ph7OwszWPhwoWwsbHBkCFDUFBQgLCwMCQkJMDa2tpcYRERERFRFTEZJyIygaSkpHKny2QyxMbGIjY2tsw6dnZ2WLx4MRYvXmzg1hERERGRqfGacSIiIiIiIiITYzJOREREREREZGJMxomIiIiIiIhMjMk4ERERERERkYkxGSciIiIiIiIysUon4/v27cOAAQPg7e0NmUyGTZs2aU0XQiA2Nhbe3t6wt7dHz549cfLkSa06KpUKEyZMgIeHBxwdHfHMM8/g2rVrjxUIERERERERUXVR6WT83r17aNu2LZYsWVLq9Llz52LBggVYsmQJMjIyoFQqER4ejry8PKlOdHQ0Nm7ciKSkJBw4cAB3795FZGQkioqKqh4JERERERERUTVR6eeM9+3bF3379i11mhACixYtwrRp0zBo0CAAQGJiIjw9PbF27VqMGzcOd+7cwYoVK/Dtt9+id+/eAIDVq1fD19cXu3btQp8+fXTmq1KpoFKppNe5ubnS/9VqdWVDsHiamEwVm8JaGG3eJWMwRlymaru+dbk/mpYltomIiIiISB+VTsbLc/HiRWRlZSEiIkIqUygU6NGjB9LT0zFu3DgcPnwYarVaq463tzcCAwORnp5eajIeHx+PGTNmlLrMlJQUQ4ZgUUwV29xOxpv3tm3bdMoMGZep214R7o+mlZ+fb+4mEBERERFViUGT8aysLACAp6enVrmnpycuX74s1bG1tUXdunV16mje/6ipU6ciJiZGep2bmwtfX18AQHh4OORyucFisARqtRopKSkmiy0wdqfR5n0i9n8/rhgjLlO1vSKm3mamZMmxlRwlQ0RERERUnRg0GdeQyWRar4UQOmWPKq+OQqGAQqEodZpcLre4BMFQTBWbqqj8bfM4Smu/IeMyddv1eQ/3R9OxtPYQEREREenLoI82UyqVAKBzhjs7O1s6W65UKlFYWIicnJwy6xARERERERHVZAZNxv38/KBUKrWuLS0sLERaWhpCQ0MBAB06dIBcLteqc+PGDZw4cUKqQ0RERERERFSTVXqY+t27d3HhwgXp9cWLF3H06FG4ubmhYcOGiI6ORlxcHPz9/eHv74+4uDg4ODhg+PDhAABXV1eMHTsWEydOhLu7O9zc3DBp0iQEBQVJd1cnIiIiIiIiqskqnYxnZmaiV69e0mvNjdWioqKQkJCAyZMno6CgAOPHj0dOTg5CQkKQnJwMZ2dn6T0LFy6EjY0NhgwZgoKCAoSFhSEhIQHW1tYGCIksSeMpW6X/K6wF5nZ6eNM1Y17rTURERDVTfHw83nvvPbz99ttYtGgRgIf3HZoxYwa+/PJL6djz888/R+vWraX3qVQqTJo0CevWrZOOPZcuXYoGDRqYKRIioioMU+/ZsyeEEDp/CQkJAB7evC02NhY3btzA/fv3kZaWhsDAQK152NnZYfHixbh58yby8/OxefNm6e7oRERERESPysjIwJdffok2bdpolc+dOxcLFizAkiVLkJGRAaVSifDwcOTl5Ul1oqOjsXHjRiQlJeHAgQO4e/cuIiMjUVRUZOowiIgkBr1mnIiIiIjI0O7evYsRI0bgq6++0no8rhACixYtwrRp0zBo0CAEBgYiMTER+fn5WLt2LQDgzp07WLFiBebPn4/evXujffv2WL16NY4fP45du3aZKyQiIuM82oyIiIiIyFDeeOMN9O/fH71798asWbOk8osXLyIrKwsRERFSmUKhQI8ePZCeno5x48bh8OHDUKvVWnW8vb0RGBiI9PR09OnTp9RlqlQqqFQq6XVubi4AQK1WQ61Wl9tezXSFlah8sHqqqA2mommHpbTH2BhvzVZRvIZeD0zGiYiIiMhiJSUl4bfffkNGRobONM3jdB99PK6npycuX74s1bG1tdU6o66p8+jjeEuKj4/HjBkzdMqTk5Ph4OCgV9tnBhfrVa8qtm3bZrR5V0XJJyXVBoy3Zisr3vz8fIMuh8k4EREREVmkq1ev4u2330ZycjLs7OzKrCeTad8YVgihU/aoiupMnTpVulEx8PDMuK+vLyIiIuDi4lLuvNVqNVJSUvBBphVUxca5ae2J2NLP6JuaJtbw8HDI5XJzN8foGG/NVlG8mhEyhsJknIiIiIgs0uHDh5GdnY0OHTpIZUVFRdi3bx+WLFmCs2fPAnh49tvLy0uqk52dLZ0tVyqVKCwsRE5OjtbZ8ezsbISGhpa5bIVCAYVCoVMul8v1TkpUxTKjPUHG0hKjyqyXmoDx1mxlxWvodcAbuBERERGRRQoLC8Px48dx9OhR6S84OBgjRozA0aNH0aRJEyiVSq0hpYWFhUhLS5MS7Q4dOkAul2vVuXHjBk6cOFFuMk5EZGw8M05EREREFsnZ2VnnEbmOjo5wd3eXyqOjoxEXFwd/f3/4+/sjLi4ODg4OGD58OADA1dUVY8eOxcSJE+Hu7g43NzdMmjQJQUFB6N27t8ljIiLSYDJORERERNXW5MmTUVBQgPHjxyMnJwchISFITk6Gs7OzVGfhwoWwsbHBkCFDUFBQgLCwMCQkJMDa2tqMLSei2o7JOBERERFVG6mpqVqvZTIZYmNjERsbW+Z77OzssHjxYixevNi4jSMiqgReM05ERERERERkYkzGiYiIiIiIiEyMyTgRERERERGRiTEZJyIyg/j4eMhkMkRHR0tlQgjExsbC29sb9vb26NmzJ06ePKn1PpVKhQkTJsDDwwOOjo545plncO3aNRO3noiIiIgeF5NxIiITy8jIwJdffok2bdpolc+dOxcLFizAkiVLkJGRAaVSifDwcOTl5Ul1oqOjsXHjRiQlJeHAgQO4e/cuIiMjUVRUZOowiIiIiOgxMBknIjKhu3fvYsSIEfjqq69Qt25dqVwIgUWLFmHatGkYNGgQAgMDkZiYiPz8fKxduxYAcOfOHaxYsQLz589H79690b59e6xevRrHjx/Hrl27zBUSEREREVUBH21GRGRCb7zxBvr374/evXtj1qxZUvnFixeRlZWFiIgIqUyhUKBHjx5IT0/HuHHjcPjwYajVaq063t7eCAwMRHp6Ovr06aOzPJVKBZVKJb3Ozc0FAKjVaqjV6nLbqpmusBJVC1YPFbXBXDTtstT2GRNjrx2x14YYiYgsHZNxIiITSUpKwm+//YaMjAydaVlZWQAAT09PrXJPT09cvnxZqmNra6t1Rl1TR/P+R8XHx2PGjBk65cnJyXBwcNCr3TODi/WqVxXbtm0z2rwNISUlxdxNMBvGXrPl5+ebuwlERLUek3EiIhO4evUq3n77bSQnJ8POzq7MejKZTOu1EEKn7FHl1Zk6dSpiYmKk17m5ufD19UVERARcXFzKna9arUZKSgo+yLSCqrj8NlTViVjds/mWQBN7eHg45HK5uZtjUoy9dsSuGSVDRETmw2SciMgEDh8+jOzsbHTo0EEqKyoqwr59+7BkyRKcPXsWwMOz315eXlKd7Oxs6Wy5UqlEYWEhcnJytM6OZ2dnIzQ0tNTlKhQKKBQKnXK5XK53sqEqlkFVZJxk3NITnsqsp5qGsdfs2Gt6fERE1QFv4EZEZAJhYWE4fvw4jh49Kv0FBwdjxIgROHr0KJo0aQKlUqk1PLawsBBpaWlSot2hQwfI5XKtOjdu3MCJEyfKTMaJiIiIyDLxzDgRkQk4OzsjMDBQq8zR0RHu7u5SeXR0NOLi4uDv7w9/f3/ExcXBwcEBw4cPBwC4urpi7NixmDhxItzd3eHm5oZJkyYhKCgIvXv3NnlMRERERFR1TMaJiCzE5MmTUVBQgPHjxyMnJwchISFITk6Gs7OzVGfhwoWwsbHBkCFDUFBQgLCwMCQkJMDa2tqMLSciIiKiymIyTkRkJqmpqVqvZTIZYmNjERsbW+Z77OzssHjxYixevNi4jSMiIiIio+I140REREREREQmZvBk/MGDB3j//ffh5+cHe3t7NGnSBB999BGKi//3nFohBGJjY+Ht7Q17e3v07NkTJ0+eNHRTiIiIiIiIiCySwZPxOXPmYPny5ViyZAlOnz6NuXPnYt68eVpDKufOnYsFCxZgyZIlyMjIgFKpRHh4OPLy8gzdHCIiIiIiIiKLY/Brxg8ePIhnn30W/fv3BwA0btwY69atQ2ZmJoCHZ8UXLVqEadOmYdCgQQCAxMREeHp6Yu3atRg3bpyhm0RUJY2nbNW7rsJaYG4nIDB2p17PY740u//jNI2IiIiIiKo5gyfj3bp1w/Lly3Hu3Dk0b94cx44dw4EDB7Bo0SIAwMWLF5GVlYWIiAjpPQqFAj169EB6enqpybhKpYJKpZJe5+bmSv9Xq9WGDsHsNDGZKjaFtTDNcqyE1r81SWVjq077ran3x8qwxDYREREREenD4Mn4u+++izt37qBFixawtrZGUVERPv74YwwbNgwAkJWVBQDw9PTUep+npycuX75c6jzj4+MxY8aMUqelpKQYsPWWxVSxze1kksVIZgYXV1ypmtI3tm3bthm5JYZniZ+1/Px8czeBiIiIiKhKDJ6Mf/fdd1i9ejXWrl2L1q1b4+jRo4iOjoa3tzeioqKkejKZ9lBeIYROmcbUqVMRExMjvc7NzYWvry8AIDw8HHK53NBhmJVarUZKSorJYguM3Wn0ZQAPzxrPDC7GB5lWUBVXPJS7OqlsbCdi+5igVYZh6v2xMkqOkiEiIiIiqk4Mnoy/8847mDJlCoYOHQoACAoKwuXLlxEfH4+oqCgolUoAD8+Qe3l5Se/Lzs7WOVuuoVAooFAoSp0ml8stLkEwFFPFps81zgZdXrHM5Ms0FX1jq477rCV+1iytPURERERE+jL43dTz8/NhZaU9W2tra+nRZn5+flAqlVpDXgsLC5GWlobQ0FBDN4eIiIiIiIjI4hj8zPiAAQPw8ccfo2HDhmjdujWOHDmCBQsW4OWXXwbwcHh6dHQ04uLi4O/vD39/f8TFxcHBwQHDhw83dHOIiIiIiIiILI7Bk/HFixfjgw8+wPjx45GdnQ1vb2+MGzcOH374oVRn8uTJKCgowPjx45GTk4OQkBAkJyfD2dnZ0M0hIiIiIiIisjgGT8adnZ2xaNEi6VFmpZHJZIiNjUVsbKyhF09ERERERERk8Qx+zTgRERERERERlY/JOBEREREREZGJMRknIiIiIiIiMjEm40REREREREQmxmSciIiIiIiIyMSYjBMRERGRxVq2bBnatGkDFxcXuLi4oEuXLti+fbs0XQiB2NhYeHt7w97eHj179sTJkye15qFSqTBhwgR4eHjA0dERzzzzDK5du2bqUIiItDAZJyIiIiKL1aBBA8yePRuZmZnIzMzEU089hWeffVZKuOfOnYsFCxZgyZIlyMjIgFKpRHh4OPLy8qR5REdHY+PGjUhKSsKBAwdw9+5dREZGoqioyFxhERExGSciIiIiyzVgwAD069cPzZs3R/PmzfHxxx/DyckJhw4dghACixYtwrRp0zBo0CAEBgYiMTER+fn5WLt2LQDgzp07WLFiBebPn4/evXujffv2WL16NY4fP45du3aZOToiqs1szN0AIiIiIiJ9FBUV4fvvv8e9e/fQpUsXXLx4EVlZWYiIiJDqKBQK9OjRA+np6Rg3bhwOHz4MtVqtVcfb2xuBgYFIT09Hnz59Sl2WSqWCSqWSXufm5gIA1Go11Gp1ue3UTFdYiSrHWpGK2mAqmnZYSnuMjfHWbBXFa+j1wGSciIiIiCza8ePH0aVLF9y/fx9OTk7YuHEjWrVqhfT0dACAp6enVn1PT09cvnwZAJCVlQVbW1vUrVtXp05WVlaZy4yPj8eMGTN0ypOTk+Hg4KBXu2cGF+tVryq2bdtmtHlXRUpKirmbYFKMt2YrK978/HyDLofJOBERERFZtICAABw9ehS3b9/Gjz/+iKioKKSlpUnTZTKZVn0hhE7ZoyqqM3XqVMTExEivc3Nz4evri4iICLi4uJQ7b7VajZSUFHyQaQVVcfntqKoTsaWf0Tc1Tazh4eGQy+Xmbo7RMd6araJ4NSNkDIXJOBERERFZNFtbWzRr1gwAEBwcjIyMDHz66ad49913ATw8++3l5SXVz87Ols6WK5VKFBYWIicnR+vseHZ2NkJDQ8tcpkKhgEKh0CmXy+V6JyWqYhlURcZJxi0tMarMeqkJGG/NVla8hl4HvIEbEREREVUrQgioVCr4+flBqVRqDSktLCxEWlqalGh36NABcrlcq86NGzdw4sSJcpNxIiJj45lxIiIiIrJY7733Hvr27QtfX1/k5eUhKSkJqamp2LFjB2QyGaKjoxEXFwd/f3/4+/sjLi4ODg4OGD58OADA1dUVY8eOxcSJE+Hu7g43NzdMmjQJQUFB6N27t5mjI6LajMk4EREREVmsv//+GyNHjsSNGzfg6uqKNm3aYMeOHQgPDwcATJ48GQUFBRg/fjxycnIQEhKC5ORkODs7S/NYuHAhbGxsMGTIEBQUFCAsLAwJCQmwtrY2V1hEREzGiYiIiMhyrVixotzpMpkMsbGxiI2NLbOOnZ0dFi9ejMWLFxu4dUREVcdrxomITGDZsmVo06YNXFxc4OLigi5dumD79u3SdCEEYmNj4e3tDXt7e/Ts2RMnT57UmodKpcKECRPg4eEBR0dHPPPMM7h27ZqpQyEiIiIiA2AyTkRkAg0aNMDs2bORmZmJzMxMPPXUU3j22WelhHvu3LlYsGABlixZgoyMDCiVSoSHhyMvL0+aR3R0NDZu3IikpCQcOHAAd+/eRWRkJIqKiswVFhERERFVEZNxIiITGDBgAPr164fmzZujefPm+Pjjj+Hk5IRDhw5BCIFFixZh2rRpGDRoEAIDA5GYmIj8/HysXbsWAHDnzh2sWLEC8+fPR+/evdG+fXusXr0ax48fx65du8wcHRERERFVFq8ZJyIysaKiInz//fe4d+8eunTpgosXLyIrKwsRERFSHYVCgR49eiA9PR3jxo3D4cOHoVartep4e3sjMDAQ6enp6NOnT6nLUqlUUKlU0uvc3FwAgFqthlqtLredmukKK1HlWCtSURvMRdMuS22fMTH22hF7bYiRiMjSMRknIjKR48ePo0uXLrh//z6cnJywceNGtGrVCunp6QAAT09Prfqenp64fPkyACArKwu2traoW7euTp2srKwylxkfH48ZM2bolCcnJ8PBwUGvds8MLtarXlVs27bNaPM2hJLPJa5tGHvNlp+fb+4mEBHVekzGiYhMJCAgAEePHsXt27fx448/IioqCmlpadJ0mUymVV8IoVP2qIrqTJ06FTExMdLr3Nxc+Pr6IiIiAi4uLuXOW61WIyUlBR9kWkFVXH47qupEbOln9M1NE3t4eDjkcrm5m2NSjL12xK4ZJUNERObDZJyIyERsbW3RrFkzAEBwcDAyMjLw6aef4t133wXw8Oy3l5eXVD87O1s6W65UKlFYWIicnByts+PZ2dkIDQ0tc5kKhQIKhUKnXC6X651sqIplUBUZJxm39ISnMuuppmHsNTv2mh4fEVF1wBu4ERGZiRACKpUKfn5+UCqVWkNjCwsLkZaWJiXaHTp0gFwu16pz48YNnDhxotxknIiIiIgsE8+MExGZwHvvvYe+ffvC19cXeXl5SEpKQmpqKnbs2AGZTIbo6GjExcXB398f/v7+iIuLg4ODA4YPHw4AcHV1xdixYzFx4kS4u7vDzc0NkyZNQlBQEHr37m3m6IiIiIiosoxyZvyvv/7CSy+9BHd3dzg4OKBdu3Y4fPiwNF0IgdjYWHh7e8Pe3h49e/aUnrVLRFQT/f333xg5ciQCAgIQFhaGX375BTt27EB4eDgAYPLkyYiOjsb48eMRHByMv/76C8nJyXB2dpbmsXDhQgwcOBBDhgxB165d4eDggM2bN8Pa2tpcYRERERFRFRn8zHhOTg66du2KXr16Yfv27ahfvz7++OMP1KlTR6ozd+5cLFiwAAkJCWjevDlmzZqF8PBwnD17VuvAk4ioplixYkW502UyGWJjYxEbG1tmHTs7OyxevBiLFy82cOuIiIiIyNQMnozPmTMHvr6+WLlypVTWuHFj6f9CCCxatAjTpk3DoEGDAACJiYnw9PTE2rVrMW7cOEM3iYiIiIiIiMiiGDwZ/+mnn9CnTx+88MILSEtLg4+PD8aPH49XX30VAHDx4kVkZWUhIiJCeo9CoUCPHj2Qnp5eajKuUqmgUqmk1yUfx6FWqw0dgtlpYjJVbAprYZrlWAmtf2uSysZWnfZbU++PlWGJbSIiIiIi0ofBk/E///wTy5YtQ0xMDN577z38+uuveOutt6BQKDBq1ChkZWUBgPS4Hg1PT09cvny51HnGx8djxowZpU4reWfhmsZUsc3tZJLFSGYGF5t2gSakb2zbtm0zcksMzxI/a/n5+eZuAhERERFRlRg8GS8uLkZwcDDi4uIAAO3bt8fJkyexbNkyjBo1Sqonk2k/s1YIoVOmMXXqVMTExEivc3Nz4evrCwAIDw+vcc/KVKvVSElJMVlsgbE7jb4M4OFZ45nBxfgg0wqqYuM8s9hcKhvbidg+JmiVYZh6f6yMkqNkiIiIiIiqE4Mn415eXmjVqpVWWcuWLfHjjz8CAJRKJQAgKysLXl5eUp3s7Gyds+UaCoUCCoWi1GlyudziEgRDMVVsqiLTJsaqYpnJl2kq+sZWHfdZS/ysWVp7iIiIiIj0ZfBHm3Xt2hVnz57VKjt37hwaNWoEAPDz84NSqdQa8lpYWIi0tDSEhoYaujlEREREREREFsfgZ8b/85//IDQ0FHFxcRgyZAh+/fVXfPnll/jyyy8BPByeHh0djbi4OPj7+8Pf3x9xcXFwcHDA8OHDDd0cIiIiIiIiIotj8GS8Y8eO2LhxI6ZOnYqPPvoIfn5+WLRoEUaMGCHVmTx5MgoKCjB+/Hjk5OQgJCQEycnJfMZ4GRpP2WruJhAREREREZEBGTwZB4DIyEhERkaWOV0mkyE2NhaxsbHGWDwRERERERGRRTP4NeNEREREREREVD4m40REREREREQmxmSciIiIiIiIyMSYjBMRERERERGZGJNxIiIiIiIiIhMzyt3Uiah8xn5c3aXZ/Y06fyIiIiIiejw8M05ERERERERkYkzGiYiIiIiIiEyMyTgRERERERGRiTEZJyIiIiIiIjIxJuNEREREREREJsZknIiIiIiIiMjEmIwTERERkcWKj49Hx44d4ezsjPr162PgwIE4e/asVh0hBGJjY+Ht7Q17e3v07NkTJ0+e1KqjUqkwYcIEeHh4wNHREc888wyuXbtmylCIiLQwGSciIiIii5WWloY33ngDhw4dQkpKCh48eICIiAjcu3dPqjN37lwsWLAAS5YsQUZGBpRKJcLDw5GXlyfViY6OxsaNG5GUlIQDBw7g7t27iIyMRFFRkTnCIiKCjbkbQERERERUlh07dmi9XrlyJerXr4/Dhw/jySefhBACixYtwrRp0zBo0CAAQGJiIjw9PbF27VqMGzcOd+7cwYoVK/Dtt9+id+/eAIDVq1fD19cXu3btQp8+fXSWq1KpoFKppNe5ubkAALVaDbVaXW6bNdMVVqLqgVegojaYiqYdltIeY2O8NVtF8Rp6PTAZJyIiIqJq486dOwAANzc3AMDFixeRlZWFiIgIqY5CoUCPHj2Qnp6OcePG4fDhw1Cr1Vp1vL29ERgYiPT09FKT8fj4eMyYMUOnPDk5GQ4ODnq1dWZwcaViq4xt27YZbd5VkZKSYu4mmBTjrdnKijc/P9+gy2EyTkRERETVghACMTEx6NatGwIDAwEAWVlZAABPT0+tup6enrh8+bJUx9bWFnXr1tWpo3n/o6ZOnYqYmBjpdW5uLnx9fREREQEXF5dy26lWq5GSkoIPMq2gKpZVLkg9nYjV/QHBHDSxhoeHQy6Xm7s5Rsd4a7aK4tWMkDEUJuNEREREVC28+eab+P3333HgwAGdaTKZdtIrhNApe1R5dRQKBRQKhU65XC7XOylRFcugKjJOMm5piVFl1ktNwHhrtrLiNfQ64A3ciIiIiMjiTZgwAT/99BP27t2LBg0aSOVKpRIAdM5wZ2dnS2fLlUolCgsLkZOTU2YdIiJTYzJORGQCfDQPEVHVCCHw5ptvYsOGDdizZw/8/Py0pvv5+UGpVGpd41lYWIi0tDSEhoYCADp06AC5XK5V58aNGzhx4oRUh4jI1JiMExGZAB/NQ0RUNW+88QZWr16NtWvXwtnZGVlZWcjKykJBQQGAh8PTo6OjERcXh40bN+LEiRMYPXo0HBwcMHz4cACAq6srxo4di4kTJ2L37t04cuQIXnrpJQQFBUl3VyciMjVeM05EZAJ8NE/5y7A0te1RLiUx9toRe3WKcdmyZQCAnj17apWvXLkSo0ePBgBMnjwZBQUFGD9+PHJychASEoLk5GQ4OztL9RcuXAgbGxsMGTIEBQUFCAsLQ0JCAqytrU0VChGRFibjRERmwEfzPGRpj+Z5VG17lEtJjL1mM/TjeYxJiIp/EJTJZIiNjUVsbGyZdezs7LB48WIsXrzYgK0jIqo6JuNERCbGR/P8j6U8mudRte1RLiUx9toRu6Efz0NERJXHZJyIyMT4aB7tdliy2vYol5IYe82OvabHR0RUHRj9Bm7x8fHSjTU09LljMBFRTcRH8xARERERYORkPCMjA19++SXatGmjVa7PHYOJiGoSPpqHiIiIiEoyWjJ+9+5djBgxAl999ZXW9Y2P3jE4MDAQiYmJyM/Px9q1a43VHCIis+KjeYiIiIioJKNdM/7GG2+gf//+6N27N2bNmiWV63PH4EeV9WgeoHo9mkNfjz5aRWFtvMcKmZLm8UjGfEySuVhabIb8XFjyo34ssU1l4aN5iIiIiKgkoyTjSUlJ+O2335CRkaEzTZ87Bj+qrEfzADX78SOa2OZ2MnNDDMyYj0kyN0uJzRiPi7LEzxofzUNERERE1ZXBk/GrV6/i7bffRnJyMuzs7MqsV5k7Bpf1aB4ANfLxI48+WiUwdqe5m2QQCiuBmcHFRn1MkrlYWmyGfFyUJT/qh4/mISIiIqLqyuDJ+OHDh5GdnY0OHTpIZUVFRdi3bx+WLFmCs2fPAnh4htzLy0uqU97dgMt6NA9Qsx8/oonNWI8UMhdjPibJ3CwlNmN8Jizxs2Zp7SEiIiIi0pfBk/GwsDAcP35cq2zMmDFo0aIF3n33XTRp0kS6Y3D79u0B/O+OwXPmzDF0c0ym8ZStBpuXwlpgbicgMHanRSR2REREREREZFgGT8adnZ0RGBioVebo6Ah3d3epXHPHYH9/f/j7+yMuLk7rjsFERERERERENZnR7qZeHn3uGExERERERERUU5kkGU9NTdV6rc8dg4mIiIiIiIhqKitzN4CIiIiIiIiotmEyTkRERERERGRiTMaJiIiIiIiITIzJOBEREREREZGJMRknIiIiIiIiMjEm40REREREREQmxmSciIiIiIiIyMSYjBMRERERERGZGJNxIiIiIiIiIhNjMk5ERERERERkYkzGiYiIiIiIiEyMyTgRERERERGRiTEZJyIiIiIiIjIxJuNEREREREREJsZknIiIiIiIiMjEmIwTERERERERmRiTcSIiIiIiIiITYzJOREREREREZGJMxomIiIjIYu3btw8DBgyAt7c3ZDIZNm3apDVdCIHY2Fh4e3vD3t4ePXv2xMmTJ7XqqFQqTJgwAR4eHnB0dMQzzzyDa9eumTAKIiJdTMaJiIiIyGLdu3cPbdu2xZIlS0qdPnfuXCxYsABLlixBRkYGlEolwsPDkZeXJ9WJjo7Gxo0bkZSUhAMHDuDu3buIjIxEUVGRqcIgItJhY+4GEBERERGVpW/fvujbt2+p04QQWLRoEaZNm4ZBgwYBABITE+Hp6Ym1a9di3LhxuHPnDlasWIFvv/0WvXv3BgCsXr0avr6+2LVrF/r06WOyWIiISmIyTkRERETV0sWLF5GVlYWIiAipTKFQoEePHkhPT8e4ceNw+PBhqNVqrTre3t4IDAxEenp6mcm4SqWCSqWSXufm5gIA1Go11Gp1ue3STFdYiSrHVpGK2mAqmnZYSnuMjfHWbBXFa+j1wGSciMgE9u3bh3nz5uHw4cO4ceMGNm7ciIEDB0rThRCYMWMGvvzyS+Tk5CAkJASff/45WrduLdVRqVSYNGkS1q1bh4KCAoSFhWHp0qVo0KCBGSIiIjK/rKwsAICnp6dWuaenJy5fvizVsbW1Rd26dXXqaN5fmvj4eMyYMUOnPDk5GQ4ODnq1b2ZwsV71qmLbtm1Gm3dVpKSkmLsJJsV4a7ay4s3PzzfocpiMExGZgOaaxzFjxuD555/Xma655jEhIQHNmzfHrFmzEB4ejrNnz8LZ2RnAw2seN2/ejKSkJLi7u2PixImIjIzE4cOHYW1tbeqQiIgshkwm03othNApe1RFdaZOnYqYmBjpdW5uLnx9fREREQEXF5dy561Wq5GSkoIPMq2gKi6/HVV1ItYyhtdrYg0PD4dcLjd3c4yO8dZsFcWrGSFjKEzGiYhMwFzXPHKYZdXUtmF5JTH22hF7TYlRqVQCeHj228vLSyrPzs6WzpYrlUoUFhYiJydH6+x4dnY2QkNDy5y3QqGAQqHQKZfL5XonJapiGVRFxknGLS0xqsx6qQkYb81WVryGXgcGT8bj4+OxYcMGnDlzBvb29ggNDcWcOXMQEBAg1dFnOCYRUW1hzGseOczy8dS2YXklMfaazdBDLc3Fz88PSqUSKSkpaN++PQCgsLAQaWlpmDNnDgCgQ4cOkMvlSElJwZAhQwAAN27cwIkTJzB37lyztZ2IyODJeFpaGt544w107NgRDx48wLRp0xAREYFTp07B0dERgH7DMYmIagtjXvPIYZZVU9uG5ZXE2GtH7IYeamlMd+/exYULF6TXFy9exNGjR+Hm5oaGDRsiOjoacXFx8Pf3h7+/P+Li4uDg4IDhw4cDAFxdXTF27FhMnDgR7u7ucHNzw6RJkxAUFCSNNCIiMgeDJ+M7duzQer1y5UrUr18fhw8fxpNPPqnXcMxHlTXMErCcYVYKa8MN49QMCTXm0FBzqKlxAZYXW8C0LQabl8JKYGYw0OGjHVAVyywqebKUz7+hGOOaRw6zfDy1bVheSYy9ZsdeneLLzMxEr169pNeaHxijoqKQkJCAyZMno6CgAOPHj5dGXCYnJ2ud4Fm4cCFsbGwwZMgQ6QaYCQkJvN8GEZmV0a8Zv3PnDgDAzc0NgH7DMR9V1jBLwHKGks3tZPh5GnNoqDnV1LiA2hGbJQ0rrinDLI15zSMRUXXXs2dPCFH2j90ymQyxsbGIjY0ts46dnR0WL16MxYsXG6GFRERVY9RkXAiBmJgYdOvWDYGBgQD0G475qLKGWQKwmKFkgbE7DTavh2cii406NNQcampcQO2KzZLOjFenYZbl4TWPRERERLWPUZPxN998E7///jsOHDigM60ywzHLGmYJWM5QMmMM4TTm0FBzqqlxAbUjNkv4vGlYUlsqwmseiYiIiKgkoyXjEyZMwE8//YR9+/ahQYMGUrk+wzGJiGoaXvNIRERERCUZPBkXQmDChAnYuHEjUlNT4efnpzVdn+GYREQ1Da95JCIiIqKSDJ6Mv/HGG1i7di3++9//wtnZWbpG3NXVFfb29pDJZBUOxyQiIiIiIiKqyQyejC9btgzAw7NAJa1cuRKjR48GAL2GYxIRERERERHVVEYZpl4RfYZjEhEREREREdVUVuZuABEREREREVFtw2SciIiIiIiIyMSYjBMRERERERGZGJNxIiIiIiIiIhNjMk5ERERERERkYkzGiYiIiIiIiEyMyTgRERERERGRiTEZJyIiIiIiIjIxJuNEREREREREJsZknIiIiIiIiMjEbMzdAFNpPGWruZtARERERGQQxj62vTS7v1HnT0Q8M05ERERERERkckzGiYiIiIiIiEyMyTgRERERERGRiTEZJyIiIiIiIjIxJuNEREREREREJsZknIiIiIiIiMjEmIwTERERERERmVitec44ERFZHj4nl4iIiGornhknIiIiIiIiMjEm40REREREREQmxmHqRERUY1V1GLzCWmBuJyAwdidURbJS63AIPBERET0OJuNERERERKRF3x8z9fnx8lHG/jHTmPcjOT8zwmjzptqHyTgRVQpvuEVERERE9PjMmowvXboU8+bNw40bN9C6dWssWrQI3bt3N2eTiIgsHvtOIqKqYf9pGYz9w74xBcburPRIgMrgSYnaxWzJ+HfffYfo6GgsXboUXbt2xRdffIG+ffvi1KlTaNiwobmaRURk0dh3Wg6OEiGqXth/EpGlMVsyvmDBAowdOxavvPIKAGDRokXYuXMnli1bhvj4eHM1i4jIorHvJCKqGvafVB0Y84de/shrecySjBcWFuLw4cOYMmWKVnlERATS09N16qtUKqhUKun1nTt3AAD5+fm4efMm5HJ5hcu0eXDvMVttOjbFAvn5xbBRW6Go2PDDX8ylpsYFMDZDunnzpt518/LyAABCCGM1x6JUtu8Eyu4/b926BbVaXe7y1Go18vPza+R+XRFL+Ew3m7TeaPP+ZWpYmdM0213f79eapDbFzv7zIfafj88S+ktTqs7xVuV7RWEl8H77YrSbtgGqCuIt77vlcYXE7zbavIH/tb2i7wFD951mScb//fdfFBUVwdPTU6vc09MTWVlZOvXj4+MxY8YMnXLNL5s10XBzN8BIampcAGMzFI/5lX9PXl4eXF1dDd8YC1PZvhMou//08/MzShtrkpr8ma7K54xqJvaf7D8NoSb3l6VhvKWrzt8tlW27ofpOs97ATSbT/nVFCKFTBgBTp05FTEyM9Lq4uBiXL19Gu3btcPXqVbi4uBi9raaUm5sLX1/fGhdbTY0LYGzmIoRAXl4evL29zd0Uk9K37wRK7z9v3boFd3f3Mt+jYcnb3tgYO2Ov6bGz/3yI/efjq02xAoy3pqsoXkP3nWZJxj08PGBtba3zS2R2drbOL5YAoFAooFAotMqsrKwAAC4uLjV2x6ipsdXUuADGZg614YyORmX7TqD0/rNOnTqVWq6lbntTYOyMvSZj/8n+05BqU6wA463pyovXkH2nlcHmVAm2trbo0KEDUlJStMpTUlIQGhpqjiYREVk89p1ERFXD/pOILJHZhqnHxMRg5MiRCA4ORpcuXfDll1/iypUreO2118zVJCIii8e+k4ioath/EpGlMVsy/uKLL+LmzZv46KOPcOPGDQQGBmLbtm1o1KiRXu9XKBSYPn26zvChmqCmxlZT4wIYG5nO4/adlVGbtz1jZ+xU87D/NI7aFCvAeGs6U8crE7XlmRZEREREREREFsIs14wTERERERER1WZMxomIiIiIiIhMjMk4ERERERERkYkxGSciIiIiIiIyMSbjRERERERERCZWbZPxpUuXws/PD3Z2dujQoQP2799v7iZVSnx8PDp27AhnZ2fUr18fAwcOxNmzZ7XqCCEQGxsLb29v2Nvbo2fPnjh58qSZWlw18fHxkMlkiI6Olsqqc1x//fUXXnrpJbi7u8PBwQHt2rXD4cOHpenVNbYHDx7g/fffh5+fH+zt7dGkSRN89NFHKC4ulupU19ioaqp7H6uP2tIPV6Sm9dP6qKl9OVmG2tB/AkBsbCxkMpnWn1KpNHezDGbfvn0YMGAAvL29IZPJsGnTJq3pNa2fqCje0aNH62zvzp07m6exj8mSvv+rZTL+3XffITo6GtOmTcORI0fQvXt39O3bF1euXDF30/SWlpaGN954A4cOHUJKSgoePHiAiIgI3Lt3T6ozd+5cLFiwAEuWLEFGRgaUSiXCw8ORl5dnxpbrLyMjA19++SXatGmjVV5d48rJyUHXrl0hl8uxfft2nDp1CvPnz0edOnWkOtU1tjlz5mD58uVYsmQJTp8+jblz52LevHlYvHixVKe6xkaVVxP6WH3Uhn64IjWtn9ZHTe7LyfxqS/+p0bp1a9y4cUP6O378uLmbZDD37t1D27ZtsWTJklKn17R+oqJ4AeDpp5/W2t7btm0zYQsNx6K+/0U11KlTJ/Haa69plbVo0UJMmTLFTC16fNnZ2QKASEtLE0IIUVxcLJRKpZg9e7ZU5/79+8LV1VUsX77cXM3UW15envD39xcpKSmiR48e4u233xZCVO+43n33XdGtW7cyp1fn2Pr37y9efvllrbJBgwaJl156SQhRvWOjyquJfaw+alo/XJGa2E/royb35WR+tan/nD59umjbtq25m2ESAMTGjRul1zW9n3g0XiGEiIqKEs8++6xZ2mNs5vz+r3ZnxgsLC3H48GFERERolUdERCA9Pd1MrXp8d+7cAQC4ubkBAC5evIisrCytOBUKBXr06FEt4nzjjTfQv39/9O7dW6u8Osf1008/ITg4GC+88ALq16+P9u3b46uvvpKmV+fYunXrht27d+PcuXMAgGPHjuHAgQPo168fgOodG1VOTe1j9VHT+uGK1MR+Wh81uS8n86qN/ef58+fh7e0NPz8/DB06FH/++ae5m2QStbWfSE1NRf369dG8eXO8+uqryM7ONneTDMKc3//VLhn/999/UVRUBE9PT61yT09PZGVlmalVj0cIgZiYGHTr1g2BgYEAIMVSHeNMSkrCb7/9hvj4eJ1p1TmuP//8E8uWLYO/vz927tyJ1157DW+99RZWrVoFoHrH9u6772LYsGFo0aIF5HI52rdvj+joaAwbNgxA9Y6NKqcm9rH6qGn9cEVqaj+tj5rcl5N51bb+MyQkBKtWrcLOnTvx1VdfISsrC6Ghobh586a5m2Z0tbGf6Nu3L9asWYM9e/Zg/vz5yMjIwFNPPQWVSmXupj0Wc3//2xh0biYkk8m0XgshdMqqizfffBO///47Dhw4oDOtusV59epVvP3220hOToadnV2Z9apbXABQXFyM4OBgxMXFAQDat2+PkydPYtmyZRg1apRUrzrG9t1332H16tVYu3YtWrdujaNHjyI6Ohre3t6IioqS6lXH2Khqatu2rkn9cEVqcj+tj5rcl5NlqC37Tt++faX/BwUFoUuXLmjatCkSExMRExNjxpaZTm3Z1gDw4osvSv8PDAxEcHAwGjVqhK1bt2LQoEFmbNnjMff3f7U7M+7h4QFra2udXyWys7N1fr2oDiZMmICffvoJe/fuRYMGDaRyzd0oq1uchw8fRnZ2Njp06AAbGxvY2NggLS0Nn332GWxsbKS2V7e4AMDLywutWrXSKmvZsqV0U5bqus0A4J133sGUKVMwdOhQBAUFYeTIkfjPf/4jnTWrzrFR5dS0PlYfNa0frkhN7qf1UZP7cjKv2th/luTo6IigoCCcP3/e3E0xOvYTD/vSRo0aVevtbQnf/9UuGbe1tUWHDh2QkpKiVZ6SkoLQ0FAztaryhBB48803sWHDBuzZswd+fn5a0/38/KBUKrXiLCwsRFpamkXHGRYWhuPHj+Po0aPSX3BwMEaMGIGjR4+iSZMm1TIuAOjatavOYw/OnTuHRo0aAai+2wwA8vPzYWWl3R1YW1tLjzarzrFR5dSUPlYfNbUfrkhN7qf1UZP7cjKv2tR/lkalUuH06dPw8vIyd1OMjv0EcPPmTVy9erVabm+L+v436O3gTCQpKUnI5XKxYsUKcerUKREdHS0cHR3FpUuXzN00vb3++uvC1dVVpKamihs3bkh/+fn5Up3Zs2cLV1dXsWHDBnH8+HExbNgw4eXlJXJzc83Y8soreZdeIapvXL/++quwsbERH3/8sTh//rxYs2aNcHBwEKtXr5bqVNfYoqKihI+Pj9iyZYu4ePGi2LBhg/Dw8BCTJ0+W6lTX2KjyakIfq4/a1A9XpKb00/qoyX05mV9t6T+FEGLixIkiNTVV/Pnnn+LQoUMiMjJSODs715hY8/LyxJEjR8SRI0cEALFgwQJx5MgRcfnyZSFEzesnyos3Ly9PTJw4UaSnp4uLFy+KvXv3ii5duggfH59qGa8lff9Xy2RcCCE+//xz0ahRI2FrayueeOIJ6Vb01QWAUv9Wrlwp1SkuLhbTp08XSqVSKBQK8eSTT4rjx4+br9FV9OhBXnWOa/PmzSIwMFAoFArRokUL8eWXX2pNr66x5ebmirfffls0bNhQ2NnZiSZNmohp06YJlUol1amusVHVVPc+Vh+1qR+uSE3qp/VRU/tysgy1of8UQogXX3xReHl5CblcLry9vcWgQYPEyZMnzd0sg9m7d2+p3xFRUVFCiJrXT5QXb35+voiIiBD16tUTcrlcNGzYUERFRYkrV66Yu9lVYknf/7L/3yAiIiIiIiIiMpFqd804ERERERERUXXHZJyIiIiIiIjIxJiMExEREREREZkYk3EiIiIiIiIiE2MyTkRERERERGRiTMaJiIiIiIiITIzJOBEREREREZGJMRknIiIiIiIiMjEm40REREREREQmxmSciIiIiIiIyMSYjBMRERERERGZGJNxIiIiIiIiIhNjMk5ERERERERkYkzGiYiIiIiIiEyMyTgRERERERGRiTEZJyIiIiIiIjIxJuNEREREREREJsZknIiIiIiIiMjEmIwTERERERERmRiTcSIiIiIiIiITYzJOREREREREZGJMxomIiIiIiIhMjMm4CXz22WeQyWQIDAwsdbpMJkNsbKz0OiEhATKZDJmZmQZZfmxsLGQymVZZz5490bNnT4PM/3E0btwYMplM+nNyckJISAhWrVqlUy8yMtJMrayYodt3/fp1xMbG4ujRowabJ5ExVbafMyVL6e801Go1lEolZDIZfvjhB6MsIzU1FTKZDKmpqUaZv0ZcXBw2bdpk1GUYyq1btzB06FDUr18fMpkMAwcONHeTqBapqI+0NNu2bXvsPnvt2rVYtGiRQdqjD1OsY1N8n6SnpyM2Nha3b9826nIM5bvvvkPr1q1hb28PmUzGY9dKYjJuAt988w0A4OTJk/jll1/M3JqHli5diqVLl5q7GQCArl274uDBgzh48KD0Q0RUVBSWLVtm7qaZzfXr1zFjxgx2aFRtWGI/p2FJ/R0AbNmyBX///TcAYMWKFUZZxhNPPIGDBw/iiSeeMMr8NapTMj5z5kxs3LgRCxcuxMGDBzF37lxzN4lqEUvuI0uzbds2zJgx47HmYepk3BTr2BTfJ+np6ZgxY0a1SMb/+ecfjBw5Ek2bNsWOHTtw8OBBNG/e3NzNqlaYjBtZZmYmjh07hv79+wMw3oFXZbVq1QqtWrUydzMAAHXq1EHnzp3RuXNnDB48GDt27ICLiwsWLFhg7qYRkR4stZ/TsKT+Dni4fmxtbREeHo7k5GRcu3bN4MtwcXFB586d4eLiYvB5V1VBQQGEEGZb/okTJ9C0aVOMGDECnTt3NsgBY0FBgQFaRjWdpfeRJeXn55u7CVViqnVsad8ngHn7oXPnzkGtVuOll15Cjx490LlzZzg4ODzWPM39XWFqTMaNTNMZzJ49G6GhoUhKSqpSR3fjxg106NAB/v7+OH/+PICHw0IiIiLg5eUFe3t7tGzZElOmTMG9e/cqnN+jw2wuXboEmUyGTz75BAsWLICfnx+cnJzQpUsXHDp0SOf9mZmZeOaZZ+Dm5gY7Ozu0b98e69evr3RcpalTpw4CAgJw+fJlnWk7duzAE088AXt7e7Ro0UL6FbSkEydO4Nlnn0XdunVhZ2eHdu3aITExUatOcXExZs2ahYCAANjb26NOnTpo06YNPv30U6mOZnj/kSNHMGjQILi4uMDV1RUvvfQS/vnnn1Lbboj2paamomPHjgCAMWPGSEP4NcPFMjMzMXToUDRu3Bj29vZo3Lgxhg0bprO+8vPzMWnSJPj5+cHOzg5ubm4IDg7GunXrtOoZc1tS7VCVfu6ff/7B+PHj0apVKzg5OaF+/fp46qmnsH//fq16mr5p3rx5mDNnjrTf9+zZUzoImDJlCry9veHq6ornnnsO2dnZWvOwpP7u+vXr2LFjBwYMGIB33nkHxcXFSEhI0Kk3evRoODk54cyZM+jTpw8cHR3h5eWF2bNnAwAOHTqEbt26wdHREc2bN9fp40obpq6Z54ULF9CvXz84OTnB19cXEydOhEql0nr/rVu3MH78ePj4+MDW1hZNmjTBtGnTtOrJZDLcu3cPiYmJUj+lWc+aUU7Jycl4+eWXUa9ePTg4OEClUuHChQsYM2YM/P394eDgAB8fHwwYMADHjx8vNYZ169Zh2rRp8Pb2houLC3r37o2zZ89q1T1y5AgiIyNRv359KBQKeHt7o3///rh27Zq0vXft2oXTp09LbdWsm8LCQsyaNQstWrSAQqFAvXr1MGbMGJ1+XnM50oYNG9C+fXvY2dlJZw4///xzPPnkk6hfvz4cHR0RFBSEuXPnQq1W691ODSEEli5dinbt2sHe3h5169bF4MGD8eeff+rsJ1Q96NNHLlu2DG3btoWTkxOcnZ3RokULvPfee9J0zWcqJSUFY8aMgZubGxwdHTFgwACdfSMlJQXPPvssGjRoADs7OzRr1gzjxo3Dv//+q1VPc5zz22+/YfDgwahbty6aNm2K0aNH4/PPPwcArUsJL126BEC//b1nz57YunUrLl++rDUPDX0/d4Zcx5bwffLTTz+hS5cucHBwgLOzM8LDw3Hw4EGtbfLOO+8AAPz8/HT6K0P0Qz179kRgYCAyMjLQvXt3ODg4oEmTJpg9ezaKi4ulehUdK48ePRrdunUDALz44ota3wGAft+dhviu0OeYHgDOnz+P4cOHS/1vy5Ytpf3crAQZTX5+vnB1dRUdO3YUQgjx9ddfCwAiISFBqx4AMX36dOn1ypUrBQCRkZEhhBDi+PHjwtfXV3Tp0kX8888/Ur2ZM2eKhQsXiq1bt4rU1FSxfPly4efnJ3r16qU1/+nTp4tHN3WPHj1Ejx49pNcXL14UAETjxo3F008/LTZt2iQ2bdokgoKCRN26dcXt27elunv27BG2traie/fu4rvvvhM7duwQo0ePFgDEypUrK7WOGjVqJPr3769VVlhYKOrXry+8vb216jVo0EC0atVKrFq1SuzcuVO88MILAoBIS0uT6p05c0Y4OzuLpk2bilWrVomtW7eKYcOGCQBizpw5Ur34+HhhbW0tpk+fLnbv3i127NghFi1aJGJjY3XWW6NGjcQ777wjdu7cKRYsWCAcHR1F+/btRWFhoVHad+fOHWkfeP/998XBgwfFwYMHxdWrV4UQQnz//ffiww8/FBs3bhRpaWkiKSlJ9OjRQ9SrV09r/xg3bpxwcHAQCxYsEHv37hVbtmwRs2fPFosXLzbKtqTaqar93JkzZ8Trr78ukpKSRGpqqtiyZYsYO3assLKyEnv37pXqafqmRo0aiQEDBogtW7aI1atXC09PT9G8eXMxcuRI8fLLL4vt27eL5cuXCycnJzFgwACtZVtKfyeEEB9//LEAILZu3SqKi4tFo0aNhJ+fnyguLtaqFxUVJWxtbUXLli3Fp59+KlJSUsSYMWMEADF16lTRvHlzsWLFCrFz504RGRkpAIjMzEzp/Xv37hUAtNZlyXl+8sknYteuXeLDDz8UMplMzJgxQ6pXUFAg2rRpIxwdHcUnn3wikpOTxQcffCBsbGxEv379pHoHDx4U9vb2ol+/flI/dfLkSSHE/77HfHx8xP/93/+J7du3ix9++EE8ePBApKWliYkTJ4offvhBpKWliY0bN4qBAwcKe3t7cebMGZ0YGjduLEaMGCG2bt0q1q1bJxo2bCj8/f3FgwcPhBBC3L17V7i7u4vg4GCxfv16kZaWJr777jvx2muviVOnTon79++LgwcPivbt24smTZpIbb1z544oKioSTz/9tHB0dBQzZswQKSkp4uuvvxY+Pj6iVatWIj8/X2pPo0aNhJeXl2jSpIn45ptvxN69e8Wvv/4qhBDiP//5j1i2bJnYsWOH2LNnj1i4cKHw8PAQY8aMkd5fUTs1Xn31VSGXy8XEiRPFjh07xNq1a0WLFi2Ep6enyMrKqvQ+R+alTx+5bt06AUBMmDBBJCcni127donly5eLt956S6qj+Uz5+vpKfd6XX34p6tevL3x9fUVOTo5Ud9myZSI+Pl789NNPIi0tTSQmJoq2bduKgIAArWOXksc57777rkhJSRGbNm0SFy5cEIMHDxYApM/LwYMHxf3794UQ+u3vJ0+eFF27dhVKpVJrHkKISn3uDLWOhTD/98maNWsEABERESE2bdokvvvuO9GhQwdha2sr9u/fL4QQ4urVq2LChAkCgNiwYYNWfyXE4/dDmhjc3d2Fv7+/WL58uUhJSRHjx48XAERiYqJUr6Jj5QsXLojPP/9cABBxcXFa3wH6fnca4rtCn2P6kydPCldXVxEUFCRWrVolkpOTxcSJE4WVlZVWPXNgMm5Eq1atEgDE8uXLhRBC5OXlCScnJ9G9e3eteuUl4ykpKcLFxUUMHjxYFBQUlLms4uJioVarRVpamgAgjh07Jk2rTDIeFBQkHeAIIcSvv/4qAIh169ZJZS1atBDt27cXarVaa56RkZHCy8tLFBUVVbxy/r9GjRqJfv36CbVaLdRqtbh48aKIiooSAMQ777yjVc/Ozk5cvnxZKisoKBBubm5i3LhxUtnQoUOFQqEQV65c0VpO3759hYODg9QpRkZGinbt2pXbNs16+89//qNVrulMV69ebbT2ZWRk6H2w/+DBA3H37l3h6OgoPv30U6k8MDBQDBw4sNz3GnJbUu1U1X7uUQ8ePBBqtVqEhYWJ5557TirX9E1t27bV2h8XLVokAIhnnnlGaz7R0dECgHTgIoTl9HfFxcWiWbNmwsfHR1qupp/ZvXu3Vl1NP/jjjz9KZWq1WtSrV08AEL/99ptUfvPmTWFtbS1iYmKksrKScQBi/fr1Wsvq16+fCAgIkF4vX7681Hpz5swRAERycrJU5ujoKKKionRi1XyPjRo1qsL18uDBA1FYWCj8/f21+ltNDCV/ABBCiPXr10tJghBCZGZmCgBi06ZN5S6nR48eonXr1lplmiSo5HoW4n998NKlS6WyRo0aCWtra3H27Nlyl1NUVCTUarVYtWqVsLa2Frdu3dK7nQcPHhQAxPz587XKr169Kuzt7cXkyZPLXTZZHn36yDfffFPUqVOn3PloPlMl+0chhPj5558FADFr1qxS36c5Prx8+bIAIP773/9K0zT9z4cffqjzvjfeeEPn2LE0Ze3vQgjRv39/0ahRI533VOZzpw99v4fM+X1SVFQkvL29RVBQkNay8/LyRP369UVoaKhUNm/ePAFAXLx4USfWx+2HNDEAEL/88ovWe1q1aiX69OkjvdbnWFnTT3///fda5fp+dxriu0Kfdvbp00c0aNBAa1sK8fCzZ2dnp7V+TI3D1I1oxYoVsLe3x9ChQwEATk5OeOGFF7B//35pqHl5EhMT0a9fP7zyyitYv3497OzstKb/+eefGD58OJRKJaytrSGXy9GjRw8AwOnTp6vU5v79+8Pa2lp63aZNGwCQhkBfuHABZ86cwYgRIwAADx48kP769euHGzdu6AwfrMi2bdsgl8shl8vh5+eH9evXY8KECZg1a5ZWvXbt2qFhw4bSazs7OzRv3lxrePaePXsQFhYGX19frfeOHj0a+fn50lCgTp064dixYxg/fjx27tyJ3NzcMtuniVVjyJAhsLGxwd69e43WvvLcvXsX7777Lpo1awYbGxvY2NjAyckJ9+7d09runTp1wvbt2zFlyhSkpqbqXFNkjG1Jtc/j9HPLly/HE088ATs7O9jY2EAul2P37t2l9l/9+vWDldX/vrJatmwJANL1gY+WX7lypcK2m7q/S0tLw4ULFxAVFSUtV3MpSmmXtMhkMvTr1096bWNjg2bNmsHLywvt27eXyt3c3FC/fv1SL+0pbZ4DBgzQKmvTpo1OP+Xo6IjBgwdr1Rs9ejQAYPfu3RUH+/89//zzOmUPHjxAXFwcWrVqBVtbW9jY2MDW1hbnz58vdds/88wzOu0F/redmjVrhrp16+Ldd9/F8uXLcerUKb3bt2XLFtSpUwcDBgzQ2r7t2rWDUqnUuRt9mzZtSr3W/MiRI3jmmWfg7u4ufR+PGjUKRUVFOHfunN7t3LJlC2QyGV566SWt9iiVSrRt29bod8cnw9Onj+zUqRNu376NYcOG4b///a/OcPKSHj0mCQ0NRaNGjbSOSbKzs/Haa6/B19dX6lsbNWoEoPTjw9I+p+XRZ38vT2U/dxWp7PeQOb5Pzp49i+vXr2PkyJFay3ZycsLzzz+PQ4cO6X0Z6+P0QxpKpRKdOnXSmW/J74LKHCuXVJXvzsf5rqionffv38fu3bvx3HPPwcHBQac99+/fL/WSAlNhMm4kFy5cwL59+9C/f38IIXD79m3cvn1bOrgp7cDrUUlJSbC3t8crr7yi82iyu3fvonv37vjll18wa9YspKamIiMjAxs2bABQ9Zs5uLu7a71WKBRa89PcAXjSpElSAq35Gz9+PACU+yVSmm7duiEjIwOZmZk4deoUbt++jc8++wy2trbltk3TvpKx3rx5E15eXjr1vL29pekAMHXqVHzyySc4dOgQ+vbtC3d3d4SFhZX6ODmlUqn12sbGBu7u7tK8jNG+8gwfPhxLlizBK6+8gp07d+LXX39FRkYG6tWrp7Wszz77DO+++y42bdqEXr16wc3NDQMHDpS+mIyxLal2eZx+bsGCBXj99dcREhKCH3/8EYcOHUJGRgaefvrpUvsvNzc3rdea/qGs8vv371fYflP3d5prGp977jlpXbm6uqJbt2748ccfde6c6+DgoPMjrK2trU7MmnJ9Yi5tngqFQuu9N2/elB69VlL9+vVhY2OjVz+lUVp/FxMTgw8++AADBw7E5s2b8csvvyAjIwNt27YtddtXtJ1cXV2RlpaGdu3a4b333kPr1q3h7e2N6dOn61wr+ai///4bt2/fhq2trc42zsrK0tm+pcVz5coVdO/eHX/99Rc+/fRT7N+/HxkZGdK1iJVp599//w0hBDw9PXXac+jQIfbJ1Yy+feTIkSPxzTff4PLly3j++edRv359hISEICUlRWeejx6TaMo0n8vi4mJERERgw4YNmDx5Mnbv3o1ff/1VSjZK+4yVtl+XRd/9vTyV/dyVpyrfQ+b4PtFsn7KOAYuLi5GTk1PhcsqaR2W3iz7HrJU5Vi6pKt+dj/NdUVE7b968iQcPHmDx4sU67dH84G3OvtXGbEuu4b755hsIIfDDDz+U+hzZxMREzJo1S+tXtEetWbMGH3zwAXr06IHk5GS0a9dOmrZnzx5cv34dqamp0tlwAEZ/DIKHhweAhzv+oEGDSq0TEBBQqXm6uroiODj4sdsGPOxcbty4oVN+/fp1AP9rv42NDWJiYhATE4Pbt29j165deO+999CnTx9cvXpV606QWVlZ8PHxkV4/ePAAN2/eLLUjM1T7ynLnzh1s2bIF06dPx5QpU6RylUqFW7duadV1dHTEjBkzMGPGDPz999/SWfIBAwbgzJkzRtmWVLs8Tj+3evVq9OzZU+cRhnl5eUZrb2UZ8jNy584d/PjjjwAg3aDxUWvXrpUOVMzJ3d0dv/zyC4QQWgl5dnY2Hjx4UGE/VdKjCT3wcNuPGjUKcXFxWuX//vsv6tSpU6U2BwUFISkpCUII/P7770hISMBHH30Ee3t7rb7yUR4eHnB3d8eOHTtKne7s7Kz1urR4Nm3ahHv37mHDhg3S2UcApT6asqJ2enh4QCaTYf/+/dLBfEmllZHlqkwfOWbMGIwZMwb37t3Dvn37MH36dERGRuLcuXNa+1VWVpbOfLKystCsWTMAD28Se+zYMSQkJCAqKkqqc+HChTLbWdp+XZbK7O9lqeznrjyGON42Bc0xY1nHgFZWVqhbt65e83rcfkhflTlWLqkq352P811RUTvr1q0La2trjBw5Em+88Uap7fHz8ytzPRgbk3EjKCoqQmJiIpo2bYqvv/5aZ/qWLVswf/58bN++HZGRkWXOx83NDbt27UJkZCR69eqF7du3o3PnzgD+t9M++sX8xRdfGDASXQEBAfD398exY8d0PhyWICwsDBs3bsT169els80AsGrVKjg4OEjrr6Q6depg8ODB+OuvvxAdHY1Lly5pPbZizZo16NChg/R6/fr1ePDggdYdIw3dvkd/UdWQyWQQQuhs96+//hpFRUVlLtfT0xOjR4/GsWPHsGjRIuTn51v8tiTL9rj9nEwm09mPf//9dxw8eFDnMg5zMeRnZO3atSgoKMDMmTOlu8+W9MILL+Cbb76xiGQ8LCwM69evx6ZNm/Dcc89J5atWrZKmazx6JkUfpW37rVu34q+//pISiqqSyWRo27YtFi5ciISEBPz222/l1o+MjERSUhKKiooQEhJS5WUC2t/HQgh89dVXlW5nZGQkZs+ejb/++gtDhgypUnvIMlS1j3R0dETfvn1RWFiIgQMH4uTJk1rJ1Zo1a7SG9Kanp+Py5ct45ZVXABju+LDkcYi9vb1UXpn9vaz+wRCfO8Bwx9umEBAQAB8fH6xduxaTJk2S1uO9e/fw448/SndYB8o+BixPVfqhyqjoWLkkQ313VuW7oqx29urVC0eOHEGbNm10Rt6aG5NxI9i+fTuuX7+OOXPmlJqwBQYGYsmSJVixYkWFnYOzszN27NiBQYMGITw8HD/99BN69eqF0NBQ1K1bF6+99hqmT58OuVyONWvW4NixY0aK6n+++OIL9O3bF3369MHo0aPh4+ODW7du4fTp0/jtt9/w/fffG70NZZk+fTq2bNmCXr164cMPP4SbmxvWrFmDrVu3Yu7cuXB1dQUADBgwAIGBgQgODka9evVw+fJlLFq0CI0aNYK/v7/WPDds2AAbGxuEh4fj5MmT+OCDD9C2bdsqHSjp276mTZvC3t4ea9asQcuWLeHk5ARvb294e3vjySefxLx58+Dh4YHGjRsjLS0NK1as0DmjFBISgsjISLRp0wZ169bF6dOn8e2332p1+Ja8LcmyPW4/FxkZiZkzZ2L69Ono0aMHzp49i48++gh+fn548OCBCSLQj6E+IytWrEDdunUxadIknWHiADBq1CgsWLAAx44dQ9u2bQ0dRqWMGjUKn3/+OaKionDp0iUEBQXhwIEDiIuLQ79+/dC7d2+pblBQEFJTU7F582Z4eXnB2dm5wtECkZGRSEhIQIsWLdCmTRscPnwY8+bNQ4MGDarU3i1btmDp0qUYOHAgmjRpAiEENmzYgNu3byM8PLzc9w4dOhRr1qxBv3798Pbbb6NTp06Qy+W4du0a9u7di2effVbrB4nShIeHw9bWFsOGDcPkyZNx//59LFu2TGfIqT7t7Nq1K/7v//4PY8aMQWZmJp588kk4Ojrixo0bOHDgAIKCgvD6669XaT2RaVWmj/zvf/8Le3t7dO3aFV5eXsjKykJ8fDxcXV11RtJkZmbilVdewQsvvICrV69i2rRp8PHxkX7Ia9GiBZo2bYopU6ZACAE3Nzds3ry51CHv5QkKCgIAzJkzB3379oW1tTXatGmj9/6umceGDRuwbNkydOjQAVZWVggODjbI566y69jcybiVlRXmzp2LESNGIDIyEuPGjYNKpcK8efNw+/Zt6bGVwP/W/aeffoqoqCjI5XIEBASUO2KgMttFX5U5Vn6UIb479f2u0Kedn376Kbp164bu3bvj9ddfR+PGjZGXl4cLFy5g8+bN2LNnT5XX02Mz8Q3jaoWBAwcKW1tbkZ2dXWadoUOHChsbG5GVlVXho82EEEKlUonnn39e2NnZia1btwohhEhPTxddunQRDg4Ool69euKVV14Rv/32m85duCtzN/V58+bptPXR9gkhxLFjx8SQIUNE/fr1hVwuF0qlUjz11FPSnSz1VdqjzSpT79E4hHj4KLgBAwYIV1dXYWtrK9q2batzV/L58+eL0NBQ4eHhIWxtbUXDhg3F2LFjxaVLl6Q6mvV2+PBhMWDAAOHk5CScnZ3FsGHDxN9//23U9gnx8G6jLVq0EHK5XGsbXLt2TTz//POibt26wtnZWTz99NPixIkTolGjRlp3NZ4yZYoIDg4WdevWFQqFQjRp0kT85z//Ef/++6/Wcgy1Lal2edx+TqVSiUmTJgkfHx9hZ2cnnnjiCbFp0yYRFRWldffdsvqmsu7gWlr/ae7+7tixYwKAiI6OLrPOmTNnpEcbCfHwzueOjo469Uq7I7gQun1QWXdTL22epX1H3Lx5U7z22mvCy8tL2NjYiEaNGompU6dKjzbSOHr0qOjatatwcHAQAKT1XNp20MjJyRFjx44V9evXFw4ODqJbt25i//79OtuprG2s2X6afvPMmTNi2LBhomnTpsLe3l64urqKTp066TzWqKx1p1arxSeffCLatm0r7OzshJOTk2jRooUYN26cOH/+fJnruKTNmzdL7/fx8RHvvPOO2L59u9Y20LedQgjxzTffiJCQEOHo6Cjs7e1F06ZNxahRo7QeX0eWrTJ9ZEJCgujVq5fw9PQUtra2wtvbWwwZMkT8/vvvUl3NZyo5OVmMHDlS1KlTR3q0YMn9VAghTp06JcLDw4Wzs7OoW7eueOGFF8SVK1d0+jfNZ7/kY1E1VCqVeOWVV0S9evWETCbTuru3Pvu7EELcunVLDB48WNSpU0eah4a+nztDreOsrCyL+D7ZtGmTCAkJEXZ2dsLR0VGEhYWJn3/+Wee9U6dOFd7e3sLKykprvT5uP6SJobS+8NHvX32Olctad0Lo991piO8KfdopxMNt9fLLLwsfHx8hl8tFvXr1RGhoaJlPIjAVmRBCGDPZJ6quYmNjMWPGDPzzzz+VukaSiIj+H3v3Hh5Vee7//zPkMJCYRBIkk2iEaANWghZBEbQShAQj4IFWVNSCxX7pBqnZgaJI3QyKiaQVsSBYLCURjHG3goeKmFAhlB1oQ5QKqBS3EaEmZouBAImTIVm/P/hlypADOcw579d1zQWz5pk1971m5sncaz3PWgBcKTc3Vw8++KBKS0tddp4dAN7H2dQBAAAAAPAw5ozDLc4357NHjx5O11kEAH9FfwcArtXY2KjGxsY22wQHU8bA/zFMHW5xvstkTJ06Vbm5uZ4JBgDciP4OAFyraapgW8rLy9W/f3/PBAS4CcU43GL37t1tPt50JnAA8Hf0dwDgWl999ZW++uqrNtv44mWqgI6iGAcAAAAAwMP8crJFY2OjvvrqK0VERJx3eCCAwGUYhk6cOKH4+Hjm5LYT/ScAif6zM+g/Abi87+zKddGysrIMScYjjzziWNbY2GgsXLjQiIuLM3r27GmMGjXK2Ldvn9PzvvvuO+Phhx82YmJijLCwMGPixInG4cOH2/26hw8fNiRx48aNmyGpQ/1Hd0f/yY0bt7Nv9J/tR//JjRu3ppur+s5OHxkvLS3V6tWrddVVVzktz8nJ0dKlS5Wbm6sBAwZo8eLFSk1N1YEDBxQRESFJysjI0Ntvv62CggLFxMRozpw5mjBhgsrKyhQUFHTe125az+HDhxUZGSlJstvtKiwsVFpamkJCQjqblk/rDjlK3SNPcnSNmpoaJSQkOPoEnF9L/WdrusPntAm5BiZybR39Z8d1p/6T+L3Ln+P359il88fv6r6zU8X4yZMndd999+mll17S4sWLHcsNw9CyZcu0YMECTZo0SZKUl5en2NhY5efna8aMGTp+/LjWrFmjdevWaezYsZKk9evXKyEhQVu2bNG4cePO+/pNQ4MiIyOdivGwsDBFRkb65RvfHt0hR6l75EmOrsVwwfZrqf9sTXf4nDYh18BErudH/9l+3an/JH7v8uf4/Tl2qf3xu6rv7FQxPmvWLI0fP15jx451KsbLy8tVWVmptLQ0xzKz2axRo0appKREM2bMUFlZmex2u1Ob+Ph4JScnq6SkpMVi3GazyWazOe7X1NRIOrOx7Ha74/9n/xuIukOOUvfIkxxd+xoAAACAv+lwMV5QUKAPPvhApaWlzR6rrKyUJMXGxjotj42N1aFDhxxtQkND1bt372Ztmp5/ruzs7BavNVhYWKiwsDCnZUVFRe1Pxk91hxyl7pEnOXZNbW2t29YNAAAAuFOHivHDhw/rkUceUWFhoXr27Nlqu3MP2xuGcd5D+W21mT9/vjIzMx33m8bqp6WlOQ1TLyoqUmpqql8OiWiP7pCj1D3yJEfXaBolAwAAAPibDhXjZWVlqqqq0tChQx3LGhoatH37dq1YsUIHDhyQdObod1xcnKNNVVWV42i5xWJRfX29qqurnY6OV1VVaeTIkS2+rtlsltlsbrY8JCSk2Y/8lpYFmu6Qo9Q98iTHrq8bAAAA8EcdujjamDFjtHfvXu3Zs8dxGzZsmO677z7t2bNHl112mSwWi9Ow1Pr6ehUXFzsK7aFDhyokJMSpTUVFhfbt29dqMQ4AAAAAQCDp0JHxiIgIJScnOy0LDw9XTEyMY3lGRoaysrKUlJSkpKQkZWVlKSwsTFOmTJEkRUVFafr06ZozZ45iYmIUHR2tuXPnavDgwY6zqwMAAAAAEMg6fZ3x1sybN091dXWaOXOmqqurNXz4cBUWFjpdi+25555TcHCwJk+erLq6Oo0ZM0a5ubntusY4AAAAAAD+rsvF+LZt25zum0wmWa1WWa3WVp/Ts2dPLV++XMuXL+/qywMAAAAA4HdcfmTcV/V/7B23rv+LZ8a7df0A4C3J1vdka2j7ihidRd8JIJDRfwJoS4dO4AYAAAAAALqOYhwAAAAAAA+jGAcAAAAAwMMoxgEAAAAA8DCKcQAAAAAAPIxiHAAAAAAAD6MYBwAAAADAwyjGAQAAAADwMIpxAAAAAAA8jGIcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEAAAAAADyMYhwAPMBqtcpkMjndLBaL43HDMGS1WhUfH69evXopJSVF+/fvd1qHzWbT7Nmz1adPH4WHh+u2227TkSNHPJ0KAAAAXIBiHAA8ZNCgQaqoqHDc9u7d63gsJydHS5cu1YoVK1RaWiqLxaLU1FSdOHHC0SYjI0MbN25UQUGBduzYoZMnT2rChAlqaGjwRjoAAADogmBvBwAA3UVwcLDT0fAmhmFo2bJlWrBggSZNmiRJysvLU2xsrPLz8zVjxgwdP35ca9as0bp16zR27FhJ0vr165WQkKAtW7Zo3LhxHs0FAAAAXUMxDgAecvDgQcXHx8tsNmv48OHKysrSZZddpvLyclVWViotLc3R1mw2a9SoUSopKdGMGTNUVlYmu93u1CY+Pl7JyckqKSlptRi32Wyy2WyO+zU1NZIku90uu93eZrxNj5t7GJ3O+XzOF4OnNMXhK/G4E7kGpo7m6k/bxGq1atGiRU7LYmNjVVlZKenMDs1FixZp9erVqq6u1vDhw/XCCy9o0KBBjvY2m01z587Vq6++qrq6Oo0ZM0YrV67UJZdc4tFcAOBsFOMA4AHDhw/Xyy+/rAEDBujrr7/W4sWLNXLkSO3fv9/xgzI2NtbpObGxsTp06JAkqbKyUqGhoerdu3ezNk3Pb0l2dnazH7GSVFhYqLCwsHbF/tSwxna164xNmza5bd2dUVRU5O0QPIZcA1N7c62trXVzJK41aNAgbdmyxXE/KCjI8f+maT65ubkaMGCAFi9erNTUVB04cEARERGSzkzzefvtt1VQUKCYmBjNmTNHEyZMUFlZmdO6AMCTKMYBwAPS09Md/x88eLBGjBihyy+/XHl5ebr++uslSSaTyek5hmE0W3au87WZP3++MjMzHfdramqUkJCgtLQ0RUZGtrluu92uoqIiPbG7h2yNbcfRWfusvjG8vinX1NRUhYSEeDsctyLXwNTRXJtGyfgLpvkACEQU4wDgBeHh4Ro8eLAOHjyoO+64Q9KZo99xcXGONlVVVY6j5RaLRfX19aqurnY6Ol5VVaWRI0e2+jpms1lms7nZ8pCQkHYXJ7ZGk2wN7inGfa1A6sh28XfkGpjam6u/bQ+m+bT+Gu5ctz9NZzgb8XuPP8cunT9+V+dFMQ4AXmCz2fTJJ5/ohz/8oRITE2WxWFRUVKQhQ4ZIkurr61VcXKwlS5ZIkoYOHaqQkBAVFRVp8uTJkqSKigrt27dPOTk5XssDANyNaT4t88Q0H3+f4kH83uPPsUutx+/qKT4U4wDgAXPnztXEiRN16aWXqqqqSosXL1ZNTY2mTp0qk8mkjIwMZWVlKSkpSUlJScrKylJYWJimTJkiSYqKitL06dM1Z84cxcTEKDo6WnPnztXgwYMdwy4BIBAxzadl7pzm4+9TPIjfe/w5dun88bt6ig/FOAB4wJEjR3Tvvffqm2++0UUXXaTrr79eu3btUr9+/SRJ8+bNU11dnWbOnOk4G3BhYaHj5EOS9Nxzzyk4OFiTJ092nA04NzeXkw8B6FaY5vPvONzN36d4EL/3+HPsUuvxuzqnHi5dGwCgRQUFBfrqq69UX1+vf/3rX3r99dd15ZVXOh43mUyyWq2qqKjQd999p+LiYiUnJzuto2fPnlq+fLmOHj2q2tpavf3220pISPB0KgDgVU3TfOLi4pym+TRpmubTVGifPc2nSdM0n7aKcQBwN46MAwAAwGcxzQdAoKIYBwAAgM9img+AQEUxDgAAAJ9VUFDQ5uNN03ysVmurbZqm+SxfvtzF0QFA5zFnHAAAAAAAD6MYBwAAAADAwyjGAQAAAADwMIpxAAAAAAA8jGIcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEAAAAAADyMYhwAAAAAAA+jGAcAAAAAwMMoxgEAAAAA8DCKcQAAAAAAPIxiHAAAAAAAD6MYBwAAAADAwzpUjK9atUpXXXWVIiMjFRkZqREjRujdd991PG4YhqxWq+Lj49WrVy+lpKRo//79Tuuw2WyaPXu2+vTpo/DwcN122206cuSIa7IBAAAAAMAPdKgYv+SSS/TMM89o9+7d2r17t26++WbdfvvtjoI7JydHS5cu1YoVK1RaWiqLxaLU1FSdOHHCsY6MjAxt3LhRBQUF2rFjh06ePKkJEyaooaHBtZkBAAAAAOCjOlSMT5w4UbfeeqsGDBigAQMG6Omnn9YFF1ygXbt2yTAMLVu2TAsWLNCkSZOUnJysvLw81dbWKj8/X5J0/PhxrVmzRs8++6zGjh2rIUOGaP369dq7d6+2bNnilgQBAAAAAPA1wZ19YkNDg/74xz/q1KlTGjFihMrLy1VZWam0tDRHG7PZrFGjRqmkpEQzZsxQWVmZ7Ha7U5v4+HglJyerpKRE48aNa/G1bDabbDab435NTY0kyW63y263O/5/9r/nMgcZnU21XVp7XXe8hidey5u6Q57k6NrXAAAAAPxNh4vxvXv3asSIEfruu+90wQUXaOPGjbryyitVUlIiSYqNjXVqHxsbq0OHDkmSKisrFRoaqt69ezdrU1lZ2eprZmdna9GiRc2WFxYWKiwszGlZUVFRi+vIue78uXXFpk2b3PsCZ2ktx0DTHfIkx66pra1127oBAAAAd+pwMT5w4EDt2bNHx44d0+uvv66pU6equLjY8bjJZHJqbxhGs2XnOl+b+fPnKzMz03G/pqZGCQkJSktLU2RkpKQzR8iKioqUmpqqkJCQZutItr7Xrvw6a5+15aP6rnS+HANFd8iTHF2jaZQMAAAA4G86XIyHhobqe9/7niRp2LBhKi0t1fPPP69HH31U0pmj33FxcY72VVVVjqPlFotF9fX1qq6udjo6XlVVpZEjR7b6mmazWWazudnykJCQZj/yW1omSbaGtncIdJUnC6rWcgw03SFPcuz6ugEAAAB/1OXrjBuGIZvNpsTERFksFqchqfX19SouLnYU2kOHDlVISIhTm4qKCu3bt6/NYhwAAAAAgEDSoSPjjz/+uNLT05WQkKATJ06ooKBA27Zt0+bNm2UymZSRkaGsrCwlJSUpKSlJWVlZCgsL05QpUyRJUVFRmj59uubMmaOYmBhFR0dr7ty5Gjx4sMaOHeuWBAEAAAAA8DUdOjL+9ddf64EHHtDAgQM1ZswY/e1vf9PmzZuVmpoqSZo3b54yMjI0c+ZMDRs2TP/6179UWFioiIgIxzqee+453XHHHZo8ebJuuOEGhYWF6e2331ZQUJBrMwMAH5adne3YidnEMAxZrVbFx8erV69eSklJ0f79+52eZ7PZNHv2bPXp00fh4eG67bbbdOTIEQ9HDwAAgK7q0JHxNWvWtPm4yWSS1WqV1WpttU3Pnj21fPlyLV++vCMvDQABo7S0VKtXr9ZVV13ltDwnJ0dLly5Vbm6uBgwYoMWLFys1NVUHDhxw7NTMyMjQ22+/rYKCAsXExGjOnDmaMGGCysrK2KkJAADgR7o8ZxwA0H4nT57Ufffdp5deesnpRJaGYWjZsmVasGCBJk2apOTkZOXl5am2tlb5+fmSpOPHj2vNmjV69tlnNXbsWA0ZMkTr16/X3r17tWXLFm+lBAAAgE7o8NnUAQCdN2vWLI0fP15jx47V4sWLHcvLy8tVWVmptLQ0xzKz2axRo0appKREM2bMUFlZmex2u1Ob+Ph4JScnq6SkROPGNb/Eos1mk81mc9xvuhyc3W6X3W5vM9amx809jM4l2w7ni8FTmuLwlXjciVwDU0dz9ddtkp2drccff1yPPPKIli1bJunMzsxFixZp9erVqq6u1vDhw/XCCy9o0KBBjufZbDbNnTtXr776qurq6jRmzBitXLlSl1xyiZcyAQCKcQDwmIKCAn3wwQcqLS1t9lhlZaUkOS4F2SQ2NlaHDh1ytAkNDXU6ot7Upun558rOztaiRYuaLS8sLFRYWFi74n5qWGO72nXGpk2b3Lbuzjj7ah+BjlwDU3tzra2tdXMkrscUHwCBhmIcADzg8OHDeuSRR1RYWKiePXu22s5kMjndNwyj2bJztdVm/vz5yszMdNyvqalRQkKC0tLSFBkZ2eZ67Xa7ioqK9MTuHrI1th1DZ+2zNj+a7w1Nuaampgb89evJNTB1NNemUTL+4uwpPmePKjp3io8k5eXlKTY2Vvn5+ZoxY4Zjis+6descV+9Zv369EhIStGXLlhZHFUnde2SRv48qIX7v8efYpfPH7+q8KMYBwAPKyspUVVWloUOHOpY1NDRo+/btWrFihQ4cOCDpzNHvuLg4R5uqqirH0XKLxaL6+npVV1c7HR2vqqrSyJEjW3xds9kss9ncbHlISEi7ixNbo0m2BvcU475WIHVku/g7cg1M7c3V37aHp6f4SIwskvx/VAnxe48/xy61Hr+rRxVRjAOAB4wZM0Z79+51Wvbggw/qiiuu0KOPPqrLLrtMFotFRUVFGjJkiCSpvr5excXFWrJkiSRp6NChCgkJUVFRkSZPnixJqqio0L59+5STk+PZhADAQ7wxxUfq3iOL/H1UCfF7jz/HLp0/flePKqIYBwAPiIiIUHJystOy8PBwxcTEOJZnZGQoKytLSUlJSkpKUlZWlsLCwjRlyhRJUlRUlKZPn645c+YoJiZG0dHRmjt3rgYPHuwYegkAgcRbU3wkRhY1vYY/FlRNiN97/Dl2qfX4XZ0TxTgA+Ih58+aprq5OM2fOdJwRuLCw0HECIkl67rnnFBwcrMmTJzvOCJybm8sJiAAEJG9N8QEAT+A64wDgJdu2bXNcmkc6c2THarWqoqJC3333nYqLi5sdTe/Zs6eWL1+uo0ePqra2Vm+//bYSEhI8HDkAeEbTFJ89e/Y4bsOGDdN9992nPXv2OE3xadI0xaep0D57ik+Tpik+FOMAvIkj4wAAAPBJTPEBEMgoxgEAAOC3mOIDwF9RjAMAAMBvbNu2zel+0xQfq9Xa6nOapvgsX77cvcEBQAcwZxwAAAAAAA+jGAcAAAAAwMMoxgEAAAAA8DCKcQAAAAAAPIxiHAAAAAAAD6MYBwAAAADAwyjGAQAAAADwMIpxAAAAAAA8jGIcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEAAAAAADyMYhwAAAAAAA+jGAcAAAAAwMMoxgEAAAAA8DCKcQAAAAAAPIxiHAAAAAAAD6MYBwAAAADAwyjGAQAAAADwMIpxAAAAAAA8jGIcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEA8IBVq1bpqquuUmRkpCIjIzVixAi9++67jscNw5DValV8fLx69eqllJQU7d+/32kdNptNs2fPVp8+fRQeHq7bbrtNR44c8XQqAAAAcAGKcQDwgEsuuUTPPPOMdu/erd27d+vmm2/W7bff7ii4c3JytHTpUq1YsUKlpaWyWCxKTU3ViRMnHOvIyMjQxo0bVVBQoB07dujkyZOaMGGCGhoavJUWAAAAOoliHAA8YOLEibr11ls1YMAADRgwQE8//bQuuOAC7dq1S4ZhaNmyZVqwYIEmTZqk5ORk5eXlqba2Vvn5+ZKk48ePa82aNXr22Wc1duxYDRkyROvXr9fevXu1ZcsWL2cHAO7DyCIAgSrY2wEAQHfT0NCgP/7xjzp16pRGjBih8vJyVVZWKi0tzdHGbDZr1KhRKikp0YwZM1RWVia73e7UJj4+XsnJySopKdG4ceNafC2bzSabzea4X1NTI0my2+2y2+1txtn0uLmH0elcz+d8MXhKUxy+Eo87kWtg6miu/rRNmkYWfe9735Mk5eXl6fbbb9eHH36oQYMGOUYW5ebmasCAAVq8eLFSU1N14MABRURESDozsujtt99WQUGBYmJiNGfOHE2YMEFlZWUKCgryZnoAujGKcQDwkL1792rEiBH67rvvdMEFF2jjxo268sorVVJSIkmKjY11ah8bG6tDhw5JkiorKxUaGqrevXs3a1NZWdnqa2ZnZ2vRokXNlhcWFiosLKxdcT81rLFd7Tpj06ZNblt3ZxQVFXk7BI8h18DU3lxra2vdHInrTJw40en+008/rVWrVmnXrl268sornUYWSWeK9djYWOXn52vGjBmOkUXr1q3T2LFjJUnr169XQkKCtmzZ0urOTABwN4pxAPCQgQMHas+ePTp27Jhef/11TZ06VcXFxY7HTSaTU3vDMJotO9f52syfP1+ZmZmO+zU1NUpISFBaWpoiIyPbXLfdbldRUZGe2N1Dtsa24+isfVbf+BHclGtqaqpCQkK8HY5bkWtg6miuTaNk/A0ji5q/hjvX7U8jKM5G/N7jz7FL54/f1Xl1qBjPzs7Whg0b9Omnn6pXr14aOXKklixZooEDBzraGIahRYsWafXq1aqurtbw4cP1wgsvaNCgQY42NptNc+fO1auvvqq6ujqNGTNGK1eu1CWXXOK6zADAx4SGhjqGWQ4bNkylpaV6/vnn9eijj0o6c/Q7Li7O0b6qqspxtNxisai+vl7V1dVOR8erqqo0cuTIVl/TbDbLbDY3Wx4SEtLu4sTWaJKtwT3FuK8VSB3ZLv6OXANTe3P1t+3ByKLmPDGyyN9HlRC/9/hz7FLr8bt6VFGHivHi4mLNmjVL1157rU6fPq0FCxYoLS1NH3/8scLDwyWJeTsA0E6GYchmsykxMVEWi0VFRUUaMmSIJKm+vl7FxcVasmSJJGno0KEKCQlRUVGRJk+eLEmqqKjQvn37lJOT47UcAMATGFnUnDtHFvn7qBLi9x5/jl06f/yuHlXUoWJ88+bNTvfXrl2rvn37qqysTDfddFOzMwJLzNsBAEl6/PHHlZ6eroSEBJ04cUIFBQXatm2bNm/eLJPJpIyMDGVlZSkpKUlJSUnKyspSWFiYpkyZIkmKiorS9OnTNWfOHMXExCg6Olpz587V4MGDHX0pAAQqRhY154lCx99HlRC/9/hz7FLr8bs6py7NGT9+/LgkKTo6WpLcNm+nPXN2zje+3xzkvjk7bb2uO17DX+dgtFd3yJMcXfsa/uDrr7/WAw88oIqKCkVFRemqq67S5s2blZqaKkmaN2+e6urqNHPmTMcUn8LCQseIIkl67rnnFBwcrMmTJzum+OTm5jKiCEC3w8giAIGg08W4YRjKzMzUjTfeqOTkZElyzLtx9bydjszZaW18f8517UiqCzx5RmB/n4PRXt0hT3LsGn86G/CaNWvafNxkMslqtcpqtbbapmfPnlq+fLmWL1/u4ugAwHcxsghAoOp0Mf7www/ro48+0o4dO5o95up5O+2Zs3O+8f3J1vfOm1NXeOKMwP4+B6O9ukOe5Oga/no2YABA+zGyCECg6lQxPnv2bL311lvavn270xnQLRaLJNfP2+nInJ3Wxve7a77O2a/rKf4+B6O9ukOe5Nj1dQMAAhsjiwAEqh4daWwYhh5++GFt2LBB77//vhITE50eP3veTpOmeTtNhfbZ83aaNM3baeskGgAAAAAABIoOHRmfNWuW8vPz9eabbyoiIsIxxzsqKkq9evVi3g4AAAAAAO3QoWJ81apVkqSUlBSn5WvXrtW0adMkMW8HAAAAAIDz6VAxbhjnvzwY83YAAAAAAGhbh+aMAwAAAACArqMYBwAAAADAwyjGAQAAAADwMIpxAAAAAAA8jGIcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEAAAAAADyMYhwAAAAAAA+jGAcAAAAAwMMoxgEAAAAA8DCKcQAAAAAAPIxiHAAAAAAAD6MYBwAAAADAwyjGAQAAAADwMIpxAAAAAAA8jGIcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEAAAAAADyMYhwAPCA7O1vXXnutIiIi1LdvX91xxx06cOCAUxvDMGS1WhUfH69evXopJSVF+/fvd2pjs9k0e/Zs9enTR+Hh4brtttt05MgRT6YCAAAAF6AYBwAPKC4u1qxZs7Rr1y4VFRXp9OnTSktL06lTpxxtcnJytHTpUq1YsUKlpaWyWCxKTU3ViRMnHG0yMjK0ceNGFRQUaMeOHTp58qQmTJighoYGb6QFAACATqIYBwAP2Lx5s6ZNm6ZBgwbp6quv1tq1a/Xll1+qrKxM0pmj4suWLdOCBQs0adIkJScnKy8vT7W1tcrPz5ckHT9+XGvWrNGzzz6rsWPHasiQIVq/fr327t2rLVu2eDM9AHAbRhYBCFTB3g4AALqj48ePS5Kio6MlSeXl5aqsrFRaWpqjjdls1qhRo1RSUqIZM2aorKxMdrvdqU18fLySk5NVUlKicePGNXsdm80mm83muF9TUyNJstvtstvtbcbY9Li5h9HJLM/vfDF4SlMcvhKPO5FrYOporv60TZpGFl177bU6ffq0FixYoLS0NH388ccKDw+X9O+RRbm5uRowYIAWL16s1NRUHThwQBEREZLOjCx6++23VVBQoJiYGM2ZM0cTJkxQWVmZgoKCvJkigG6KYhwAPMwwDGVmZurGG29UcnKyJKmyslKSFBsb69Q2NjZWhw4dcrQJDQ1V7969m7Vpev65srOztWjRombLCwsLFRYW1q54nxrW2K52nbFp0ya3rbszioqKvB2Cx5BrYGpvrrW1tW6OxHU2b97sdH/t2rXq27evysrKdNNNNzUbWSRJeXl5io2NVX5+vmbMmOEYWbRu3TqNHTtWkrR+/XolJCRoy5Yt7MxsZd3+tNPmbMTvPf4cu3T++F2dF8U4AHjYww8/rI8++kg7duxo9pjJZHK6bxhGs2XnaqvN/PnzlZmZ6bhfU1OjhIQEpaWlKTIyss312u12FRUV6YndPWRrbDuGztpnbf4D2Buack1NTVVISIi3w3Ercg1MHc21qbD0R54aWcTOTP/fkUX83uPPsUutx+/qHZkU4wDgQbNnz9Zbb72l7du365JLLnEst1gsks4c/Y6Li3Msr6qqchwtt1gsqq+vV3V1tdPR8aqqKo0cObLF1zObzTKbzc2Wh4SEtLs4sTWaZGtwTzHuawVSR7aLvyPXwNTeXP11e3hyZFF33pnp7zuyiN97/Dl26fzxu3pHJsU4AHiAYRiaPXu2Nm7cqG3btikxMdHp8cTERFksFhUVFWnIkCGSpPr6ehUXF2vJkiWSpKFDhyokJERFRUWaPHmyJKmiokL79u1TTk6OZxMCAC/w5Mgidmb6/44s4vcef45daj1+V+dEMQ4AHjBr1izl5+frzTffVEREhONITFRUlHr16iWTyaSMjAxlZWUpKSlJSUlJysrKUlhYmKZMmeJoO336dM2ZM0cxMTGKjo7W3LlzNXjwYMccSAAIVJ4eWQQA7salzQDAA1atWqXjx48rJSVFcXFxjttrr73maDNv3jxlZGRo5syZGjZsmP71r3+psLDQcSZgSXruued0xx13aPLkybrhhhsUFhamt99+mzMBAwhYhmHo4Ycf1oYNG/T++++3ObKoSdPIoqZC++yRRU2aRhZRjAPwFo6MA4AHGMb5z6hrMplktVpltVpbbdOzZ08tX75cy5cvd2F0AOC7GFkEIFBRjAMAAMBnrVq1SpKUkpLitHzt2rWaNm2apDMji+rq6jRz5kxVV1dr+PDhLY4sCg4O1uTJk1VXV6cxY8YoNzeXkUUAvIZiHAAAAD6LkUUAAhXFuIv0f+wdt637i2fGu23dAAAAAADP4wRuAAAAAAB4GMU4AAAAAAAeRjEOAAAAAICHUYwDAAAAAOBhFOMAAAAAAHgYxTgAAAAAAB5GMQ4AAAAAgIdRjAMAAAAA4GEdLsa3b9+uiRMnKj4+XiaTSW+88YbT44ZhyGq1Kj4+Xr169VJKSor279/v1MZms2n27Nnq06ePwsPDddttt+nIkSNdSgQAAAAAAH/R4WL81KlTuvrqq7VixYoWH8/JydHSpUu1YsUKlZaWymKxKDU1VSdOnHC0ycjI0MaNG1VQUKAdO3bo5MmTmjBhghoaGjqfCQAAAAAAfiK4o09IT09Xenp6i48ZhqFly5ZpwYIFmjRpkiQpLy9PsbGxys/P14wZM3T8+HGtWbNG69at09ixYyVJ69evV0JCgrZs2aJx48Z1IR0AAAAAAHxfh4vxtpSXl6uyslJpaWmOZWazWaNGjVJJSYlmzJihsrIy2e12pzbx8fFKTk5WSUlJi8W4zWaTzWZz3K+pqZEk2e122e12x//P/vdc5iCj6wl6SXtzDBTdIU9ydO1rAAAAAP7GpcV4ZWWlJCk2NtZpeWxsrA4dOuRoExoaqt69ezdr0/T8c2VnZ2vRokXNlhcWFiosLMxpWVFRUYvryLmufTn4ok2bNjndby3HQNMd8iTHrqmtrXXbugEAAAB3cmkx3sRkMjndNwyj2bJztdVm/vz5yszMdNyvqalRQkKC0tLSFBkZKenMEbKioiKlpqYqJCSk2TqSre91NA2fsc96ZrTA+XIMFN0hT3J0jaZRMgAAAIC/cWkxbrFYJJ05+h0XF+dYXlVV5ThabrFYVF9fr+rqaqej41VVVRo5cmSL6zWbzTKbzc2Wh4SENPuR39IySbI1tL0zwJe1N8dA0x3yJMeurxsAAADwRy69znhiYqIsFovTsNT6+noVFxc7Cu2hQ4cqJCTEqU1FRYX27dvXajEOAAAAAEAg6fCR8ZMnT+qzzz5z3C8vL9eePXsUHR2tSy+9VBkZGcrKylJSUpKSkpKUlZWlsLAwTZkyRZIUFRWl6dOna86cOYqJiVF0dLTmzp2rwYMHO86uDgAAAABAIOtwMb57926NHj3acb9pLvfUqVOVm5urefPmqa6uTjNnzlR1dbWGDx+uwsJCRUREOJ7z3HPPKTg4WJMnT1ZdXZ3GjBmj3NxcBQUFuSAlAAAAAAB8W4eL8ZSUFBlG65cJM5lMslqtslqtrbbp2bOnli9fruXLl3f05QEAAAAA8HsunTMOAAAAAADOj2IcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEA8IDt27dr4sSJio+Pl8lk0htvvOH0uGEYslqtio+PV69evZSSkqL9+/c7tbHZbJo9e7b69Omj8PBw3XbbbTpy5IgHswAAAICrUIwDgAecOnVKV199tVasWNHi4zk5OVq6dKlWrFih0tJSWSwWpaam6sSJE442GRkZ2rhxowoKCrRjxw6dPHlSEyZMUENDg6fSAACPY2cmgEBFMQ4AHpCenq7Fixdr0qRJzR4zDEPLli3TggULNGnSJCUnJysvL0+1tbXKz8+XJB0/flxr1qzRs88+q7Fjx2rIkCFav3699u7dqy1btng6HQDwGHZmAghUHb7OOADAtcrLy1VZWam0tDTHMrPZrFGjRqmkpEQzZsxQWVmZ7Ha7U5v4+HglJyerpKRE48aNa3HdNptNNpvNcb+mpkaSZLfbZbfb24yr6XFzD6PTuZ3P+WLwlKY4fCUedyLXwNTRXP1pm6Snpys9Pb3Fx87dmSlJeXl5io2NVX5+vmbMmOHYmblu3TqNHTtWkrR+/XolJCRoy5YtrfafAOBuFOMA4GWVlZWSpNjYWKflsbGxOnTokKNNaGioevfu3axN0/Nbkp2drUWLFjVbXlhYqLCwsHbF99Swxna164xNmza5bd2dUVRU5O0QPIZcA1N7c62trXVzJJ7Bzkz3rtufdtqcjfi9x59jl84fv6vzohgHAB9hMpmc7huG0WzZuc7XZv78+crMzHTcr6mpUUJCgtLS0hQZGdnmuu12u4qKivTE7h6yNbYdR2fts/rGEammXFNTUxUSEuLtcNyKXANTR3NtKiz9HTsz3cvfd2QRv/f4c+xS6/G7ekcmxTgAeJnFYpF05gdjXFycY3lVVZXjB6bFYlF9fb2qq6udflBWVVVp5MiRra7bbDbLbDY3Wx4SEtLu4sTWaJKtwT3FuK8VSB3ZLv6OXANTe3MNtO3BzkzX8vcdWcTvPf4cu3T++F29I5NiHAC8LDExURaLRUVFRRoyZIgkqb6+XsXFxVqyZIkkaejQoQoJCVFRUZEmT54sSaqoqNC+ffuUk5PjtdgBwJvYmele/r4ji/i9x59jl1qP39U5cTZ1APCAkydPas+ePdqzZ4+kM/Mc9+zZoy+//FImk0kZGRnKysrSxo0btW/fPk2bNk1hYWGaMmWKJCkqKkrTp0/XnDlz9Je//EUffvih7r//fg0ePNhxQiIA6G7O3pnZpGlnZlOhffbOzCZNOzPbKsYBwN04Mg4AHrB7926NHj3acb9p6OPUqVOVm5urefPmqa6uTjNnzlR1dbWGDx+uwsJCRUREOJ7z3HPPKTg4WJMnT1ZdXZ3GjBmj3NxcBQUFeTwfAPCUkydP6rPPPnPcb9qZGR0drUsvvdSxMzMpKUlJSUnKyspqdWdmTEyMoqOjNXfuXHZmAvA6inEA8ICUlBQZRutn1TWZTLJarbJara226dmzp5YvX67ly5e7IUIA8E3szAQQqCjGAQAA4LPYmQkgUDFnHAAAAAAAD6MYBwAAAADAwyjGAQAAAADwMIpxAAAAAAA8jGIcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEAAAAAADyMYhwAAAAAAA+jGAcAAAAAwMOCvR0Azq//Y+9IksxBhnKuk5Kt78nWYHLZ+r94ZrzL1gUAAAAAOD+OjAMAAAAA4GEU4wAAAAAAeBjFOAAAAAAAHkYxDgAAAACAh1GMAwAAAADgYRTjAAAAAAB4GMU4AAAAAAAeRjEOAAAAAICHUYwDAAAAAOBhFOMAAAAAAHgYxTgAAAAAAB4W7O0A4H39H3vHbev+4pnxbls3AAAAAPgrinEAgNe4c2egxA5BAIHLnf2nOchQznVuWz2A/x/D1AEAAAAA8DCvFuMrV65UYmKievbsqaFDh+qvf/2rN8MBAL9A3wkAnUP/CcCXeG2Y+muvvaaMjAytXLlSN9xwg373u98pPT1dH3/8sS699FJvhQUX68wQqqahUcnW92RrMLXZliGo6G7oOwGgc+g/AfgarxXjS5cu1fTp0/XQQw9JkpYtW6b33ntPq1atUnZ2trfCAjwi2fpeu3c4dAY7KQIXfWfHtHeHYEd2Ajbhewb4F/pPAL7GK8V4fX29ysrK9NhjjzktT0tLU0lJSbP2NptNNpvNcf/48eOSpG+//VZ2u12SZLfbVVtbq6NHjyokJKTZOoJPn3JlCl4R3GiotrZRwfYeamh0fQHnKzqS5/fm/reHonItcw/3vpfu3C5/mz+mXe3O9510hRMnTkiSDMNwy/p9TUf7Tql9/Wdrmt7DQO9zpM71r/7c//xqSKNbv5vDs//ilvVK7e+DpM71Q74Se0d1NFf6zzPoP1vW1Cf+YMEG2fww/h1zb3L7bxB38sRvKHfx59il88fv6r7TK8X4N998o4aGBsXGxjotj42NVWVlZbP22dnZWrRoUbPliYmJbovRV03xdgAe0h3y9Ncc+zzr7QiaO3HihKKiorwdhtt1tO+U6D87wl+/k53hz7n6Yh/UXr4YO/0n/Wdr/LmfiPPB7xoCi6v6Tq9e2sxkct7TZhhGs2WSNH/+fGVmZjruNzY26ttvv1VMTIyjfU1NjRISEnT48GFFRka6N3Av6Q45St0jT3J0DcMwdOLECcXHx7tl/b6qvX2n1L7+szXd4XPahFwDE7m2jv7zDPrPlhG/d/lz/P4cu3T++F3dd3qlGO/Tp4+CgoKa7YmsqqpqtsdSksxms8xms9OyCy+8sMV1R0ZG+uUb3xHdIUepe+RJjl3XHY7oNOlo3yl1rP9sTXf4nDYh18BEri2j/6T/PB/i9y5/jt+fY5fajt+VfadXLm0WGhqqoUOHqqioyGl5UVGRRo4c6Y2QAMDn0XcCQOfQfwLwRV4bpp6ZmakHHnhAw4YN04gRI7R69Wp9+eWX+vnPf+6tkADA59F3AkDn0H8C8DVeK8bvvvtuHT16VE8++aQqKiqUnJysTZs2qV+/fp1an9ls1sKFC5sNJwok3SFHqXvkSY7oLFf3nW3pTu8huQYmcsXZ6D/bj/i9y5/j9+fYJc/HbzK6yzUtAAAAAADwEV6ZMw4AAAAAQHdGMQ4AAAAAgIdRjAMAAAAA4GEU4wAAAAAAeBjFOAAAAAAAHhYQxfjKlSuVmJionj17aujQofrrX//q7ZDazWq1ymQyOd0sFovjccMwZLVaFR8fr169eiklJUX79+93WofNZtPs2bPVp08fhYeH67bbbtORI0c8nYrD9u3bNXHiRMXHx8tkMumNN95wetxVOVVXV+uBBx5QVFSUoqKi9MADD+jYsWNuzu7fzpfntGnTmr23119/vVMbX88zOztb1157rSIiItS3b1/dcccdOnDggFObQHk/4cyf+1UpMPvWs3WXflbqHn2tRH8baHyhD/XnfsLfvw+rVq3SVVddpcjISEVGRmrEiBF69913/SL2c2VnZ8tkMikjI8Mv4vfU33+XxW74uYKCAiMkJMR46aWXjI8//th45JFHjPDwcOPQoUPeDq1dFi5caAwaNMioqKhw3KqqqhyPP/PMM0ZERITx+uuvG3v37jXuvvtuIy4uzqipqXG0+fnPf25cfPHFRlFRkfHBBx8Yo0ePNq6++mrj9OnT3kjJ2LRpk7FgwQLj9ddfNyQZGzdudHrcVTndcsstRnJyslFSUmKUlJQYycnJxoQJEzyV5nnznDp1qnHLLbc4vbdHjx51auPreY4bN85Yu3atsW/fPmPPnj3G+PHjjUsvvdQ4efKko02gvJ/4N3/vVw0jMPvWs3WXftYwukdfaxj0t4HEV/pQf+4n/P378NZbbxnvvPOOceDAAePAgQPG448/boSEhBj79u3z+djP9ve//93o37+/cdVVVxmPPPKIY7kvx++pv/+uit3vi/HrrrvO+PnPf+607IorrjAee+wxL0XUMQsXLjSuvvrqFh9rbGw0LBaL8cwzzziWfffdd0ZUVJTx4osvGoZhGMeOHTNCQkKMgoICR5t//etfRo8ePYzNmze7Nfb2OLfzd1VOH3/8sSHJ2LVrl6PNzp07DUnGp59+6uasmmvtB+Ltt9/e6nP8Mc+qqipDklFcXGwYRuC+n92dv/erhhH4fevZuks/axjdp681DPpbf+aLfai/9xOB8H3o3bu38fvf/95vYj9x4oSRlJRkFBUVGaNGjXIU474evyf+/rsydr8epl5fX6+ysjKlpaU5LU9LS1NJSYmXouq4gwcPKj4+XomJibrnnnv0+eefS5LKy8tVWVnplJ/ZbNaoUaMc+ZWVlclutzu1iY+PV3Jysk9uA1fltHPnTkVFRWn48OGONtdff72ioqJ8Ku9t27apb9++GjBggH72s5+pqqrK8Zg/5nn8+HFJUnR0tKTu9352B4HSr0rdq289W3f8XgZaXyvR3/orf+lD/e3z5M/fh4aGBhUUFOjUqVMaMWKE38Q+a9YsjR8/XmPHjnVa7g/xu/vvvytj9+ti/JtvvlFDQ4NiY2OdlsfGxqqystJLUXXM8OHD9fLLL+u9997TSy+9pMrKSo0cOVJHjx515NBWfpWVlQoNDVXv3r1bbeNLXJVTZWWl+vbt22z9ffv29Zm809PT9corr+j999/Xs88+q9LSUt18882y2WyS/C9PwzCUmZmpG2+8UcnJyY74pO7xfnYXgdCvSt2vbz1bd/teBlpfK9Hf+jN/6UP96fPkr9+HvXv36oILLpDZbNbPf/5zbdy4UVdeeaVfxF5QUKAPPvhA2dnZzR7z9fg98ffflbEHd6i1jzKZTE73DcNotsxXpaenO/4/ePBgjRgxQpdffrny8vIcJ6DpTH6+vg1ckVNL7X0p77vvvtvx/+TkZA0bNkz9+vXTO++8o0mTJrX6PF/N8+GHH9ZHH32kHTt2NHusO7yf3Y0/96tS9+1bz9ZdvpeB1tdK9LeBwF/6UH/4PPnr92HgwIHas2ePjh07ptdff11Tp05VcXGxz8d++PBhPfLIIyosLFTPnj1bbeer8Xvq77+rYvfrI+N9+vRRUFBQsz0QVVVVzfZ4+Ivw8HANHjxYBw8edJz5r638LBaL6uvrVV1d3WobX+KqnCwWi77++utm6/+///s/n8xbkuLi4tSvXz8dPHhQkn/lOXv2bL311lvaunWrLrnkEsfy7vx+BqpA7FelwO9bz9bdv5f+3NdK9Lf+zl/6UH/5PPnz9yE0NFTf+973NGzYMGVnZ+vqq6/W888/7/Oxl5WVqaqqSkOHDlVwcLCCg4NVXFys3/72twoODnas21fjP5c7/v67Mna/LsZDQ0M1dOhQFRUVOS0vKirSyJEjvRRV19hsNn3yySeKi4tTYmKiLBaLU3719fUqLi525Dd06FCFhIQ4tamoqNC+fft8chu4KqcRI0bo+PHj+vvf/+5o87e//U3Hjx/3ybwl6ejRozp8+LDi4uIk+UeehmHo4Ycf1oYNG/T+++8rMTHR6fHu/H4GqkDsV6XA71vP1t2/l/7Y10r0t4HCX/pQX/88BeL3wTAM2Ww2n499zJgx2rt3r/bs2eO4DRs2TPfdd5/27Nmjyy67zKfjP5c7/v67NPYOne7NBzVdPmLNmjXGxx9/bGRkZBjh4eHGF1984e3Q2mXOnDnGtm3bjM8//9zYtWuXMWHCBCMiIsIR/zPPPGNERUUZGzZsMPbu3Wvce++9LZ5+/5JLLjG2bNlifPDBB8bNN9/s1cvvnDhxwvjwww+NDz/80JBkLF261Pjwww8dl/RwVU633HKLcdVVVxk7d+40du7caQwePNijl2ZpK88TJ04Yc+bMMUpKSozy8nJj69atxogRI4yLL77Yr/L8j//4DyMqKsrYtm2b0yUiamtrHW0C5f3Ev/l7v2oYgdm3nq279LPnyzVQ+lrDoL8NJL7Sh/pzP+Hv34f58+cb27dvN8rLy42PPvrIePzxx40ePXoYhYWFPh97S84+m7qvx++pv/+uit3vi3HDMIwXXnjB6NevnxEaGmpcc801jsse+IOma9uFhIQY8fHxxqRJk4z9+/c7Hm9sbDQWLlxoWCwWw2w2GzfddJOxd+9ep3XU1dUZDz/8sBEdHW306tXLmDBhgvHll196OhWHrVu3GpKa3aZOnWoYhutyOnr0qHHfffcZERERRkREhHHfffcZ1dXVHsqy7Txra2uNtLQ046KLLjJCQkKMSy+91Jg6dWqzHHw9z5byk2SsXbvW0SZQ3k848+d+1TACs289W3fpZw2je/S1hkF/G2h8oQ/1537C378PP/3pTx3v/0UXXWSMGTPGUYj7euwtObcY9+X4PfX331WxmwzDMDp2LB0AAAAAAHSFX88ZBwAAAADAH1GMAwAAAADgYRTjAAAAAAB4GMU4AAAAAAAeRjEOAAAAAICHUYwDAAAAAOBhFOMAAAAAAHgYxTgAAAAAAB5GMQ4AAAAAgIdRjAMAAAAA4GEU4wAAAAAAeBjFOAAAAAAAHkYxDgAAAACAh1GMAwAAAADgYRTjAAAAAAB4GMU4AAAAAAAeRjEOAAAAAICHUYwDAAAAAOBhFOMAAAAAAHgYxTgAAAAAAB5GMQ4AAAAAgIdRjAMAAAAA4GEU4wHKZDK167Zt27bzrisrK0tvvPFGl+OxWq0dek7//v2dYr3gggs0fPhwvfzyy12K5VwlJSWyWq06duxYs8dSUlKUkpLi0tcDgCa//e1vZTKZlJyc3OLj5/adubm5MplM2r17t4cibJ/+/ftr2rRp3g4D8Gl8333LtGnT1L9//049989//rNuv/12xcfHKzQ0VBERERoyZIgWLlyoL7/80qltV35LmkwmPfzww516bmtaiqczv9Pbq+lz3HQLDg5WXFyc7rnnHh08eLDT622tPtm2bVu7axxfEOztAOAeO3fudLr/1FNPaevWrXr//fedll955ZXnXVdWVpZ+/OMf64477nBliO1yww036De/+Y0k6ciRI/rNb36jqVOn6tSpU/qP//gPl7xGSUmJFi1apGnTpunCCy90emzlypUueQ0AaMkf/vAHSdL+/fv1t7/9TcOHD/dyRJ2zceNGRUZGejsMwKfxffd/jY2NevDBB/Xyyy8rPT1d2dnZ6t+/v+rq6lRaWqq1a9fqD3/4gw4fPuztUDtk586duuSSS9z6GmvXrtUVV1yh7777Tv/zP/+jp59+Wlu3btWnn36q3r17d3h9rdUn11xzjXbu3NmuGscXUIwHqOuvv97p/kUXXaQePXo0W+7rLrzwQqeYx44dq379+mnp0qWtFuMNDQ06ffq0zGZzl1/fX77IAPzP7t279Y9//EPjx4/XO++8ozVr1vjtj/MhQ4Z4OwTAp/F9DwxLlizRyy+/rOzsbD322GNOj91yyy2aP3++fve733kpus7zRH2QnJysYcOGSTpzdL6hoUELFy7UG2+8oQcffNBlrxMZGelX9Q7D1Luxb7/9VjNnztTFF1+s0NBQXXbZZVqwYIFsNpujjclk0qlTp5SXl+cYXtI0tOX//u//NHPmTF155ZW64IIL1LdvX918883661//6raYL7zwQg0cOFCHDh2SJH3xxRcymUzKycnR4sWLlZiYKLPZrK1bt0qS3nrrLY0YMUJhYWGKiIhQamqq06gBq9WqX/7yl5KkxMTEZsP3WxrKU19fr8WLF+uKK66Q2WzWRRddpAcffFD/93//59Suf//+mjBhgjZv3qxrrrlGvXr10hVXXOHYM96ktrZWc+fOVWJionr27Kno6GgNGzZMr776qis3HQAfs2bNGknSM888o5EjR6qgoEC1tbXtem51dbUefPBBRUdHKzw8XBMnTtTnn3/u1Ka1oaTn9mtNQ/ry8/P16KOPKi4uThdccIEmTpyor7/+WidOnND/+3//T3369FGfPn304IMP6uTJk22+VtM6X331VS1YsEDx8fGKjIzU2LFjdeDAgfZtICCA8H1vn/b+tmz6/feb3/xGS5cuVWJioi644AKNGDFCu3btarbe3NxcDRw4UGazWd///vc7NeWxvr5eOTk5Sk5OblaINwkODtasWbPOu672/AY/2+9+9zsNGDBAZrNZV155pQoKCpwet1qtMplMzZ7XNET8iy++aDOe1qZIbN26Vf/xH/+hPn36KCYmRpMmTdJXX3113vzao6kw//rrrx3LvvvuO82ZM0c/+MEPFBUVpejoaI0YMUJvvvlms3hbq09aG6Z+vprAWyjGu6nvvvtOo0eP1ssvv6zMzEy98847uv/++5WTk6NJkyY52u3cuVO9evXSrbfeqp07d2rnzp2OodvffvutJGnhwoV65513tHbtWl122WVKSUlx2zwNu92uQ4cO6aKLLnJa/tvf/lbvv/++fvOb3+jdd9/VFVdcofz8fN1+++2KjIzUq6++qjVr1qi6ulopKSnasWOHJOmhhx7S7NmzJUkbNmxw5HjNNde0+PqNjY26/fbb9cwzz2jKlCl655139Mwzz6ioqEgpKSmqq6tzav+Pf/xDc+bM0X/+53/qzTff1FVXXaXp06dr+/btjjaZmZlatWqVfvGLX2jz5s1at26d7rrrLh09etSVmw6AD6mrq9Orr76qa6+9VsnJyfrpT3+qEydO6I9//GO7nj99+nT16NFD+fn5WrZsmf7+978rJSWlxXNftNfjjz+uqqoq5ebm6tlnn9W2bdt077336kc/+pGioqL06quvat68eVq3bp0ef/zxdq/z0KFD+v3vf6/Vq1fr4MGDmjhxohoaGjodJ+Bv+L63//ve0d+WL7zwgoqKirRs2TK98sorOnXqlG699VYdP37c0SY3N1cPPvigvv/97+v111/Xr371Kz311FPNpm6ez+7du3Xs2DFNnDixQ887V3t/gzd566239Nvf/lZPPvmk/vSnP6lfv36699579ac//alLcbTHQw89pJCQEOXn5ysnJ0fbtm3T/fff75J1l5eXS5IGDBjgWGaz2fTtt99q7ty5euONN/Tqq6/qxhtv1KRJk5x2oLRVn7SkPTWB1xjoFqZOnWqEh4c77r/44ouGJOO///u/ndotWbLEkGQUFhY6loWHhxtTp04972ucPn3asNvtxpgxY4w777zT6TFJxsKFCzsUc79+/Yxbb73VsNvtht1uN8rLy42pU6cakoxf/vKXhmEYRnl5uSHJuPzyy436+nrHcxsaGoz4+Hhj8ODBRkNDg2P5iRMnjL59+xojR450LPv1r39tSDLKy8ubxTBq1Chj1KhRjvuvvvqqIcl4/fXXndqVlpYakoyVK1c6xd+zZ0/j0KFDjmV1dXVGdHS0MWPGDMey5ORk44477ujQtgHg315++WVDkvHiiy8ahnGmb7rggguMH/7wh07tzu07165da0hq1sf+z//8jyHJWLx4sWNZv379Wuy7z+3Xtm7dakgyJk6c6NQuIyPDkGT84he/cFp+xx13GNHR0U7Lzn2tpnXeeuutTu3++7//25Bk7Ny5s1lcQKDi+97573trvy2bfv8NHjzYOH36tGP53//+d0OS8eqrrxqG8e/fg9dcc43R2NjoaPfFF18YISEhRr9+/dodS0FBgdP7eLam36pNt7Od+x505De4JKNXr15GZWWl0za54oorjO9973uOZQsXLjRaKuuaPkNn/8Y9N56m12npszdz5kyndjk5OYYko6KiotlrtaZpXbt27TLsdrtx4sQJY/PmzYbFYjFuuummZtvrbE3v//Tp040hQ4Y4PdZafdL0edy6dathGB2rCbyBI+Pd1Pvvv6/w8HD9+Mc/dlreNOzoL3/5S7vW8+KLL+qaa65Rz549FRwcrJCQEP3lL3/RJ5984pI4N23apJCQEIWEhCgxMVH//d//rdmzZ2vx4sVO7W677TaFhIQ47h84cEBfffWVHnjgAfXo8e+P+QUXXKAf/ehH2rVrV7uHh53tz3/+sy688EJNnDhRp0+fdtx+8IMfyGKxNNtr+4Mf/ECXXnqp437Pnj01YMAAxzB7Sbruuuv07rvv6rHHHtO2bduaHV0HEHjWrFmjXr166Z577pF0pm+666679Ne//rVdZ5e97777nO6PHDlS/fr1c0zR6YwJEyY43f/+978vSRo/fnyz5d9++22zoastue2225zuX3XVVZLk1AcCgY7ve8e+7x35bTl+/HgFBQW1+ppNvwenTJniNIy7X79+GjlyZIfias2xY8ccv1Wbbm2dAb+jv8HHjBmj2NhYx/2goCDdfffd+uyzz3TkyBGX5NAaV/bh119/vUJCQhQREaFbbrlFvXv31ptvvqngYOdTmP3xj3/UDTfcoAsuuMDx/q9Zs6bTtYW7agJXoRjvpo4ePSqLxdJsfknfvn0VHBzcriHSTSdRGz58uF5//XXt2rVLpaWluuWWW1xWUN54440qLS3V7t279fHHH+vYsWP67W9/q9DQUKd2cXFxTveb4j93uSTFx8ersbFR1dXVHY7n66+/1rFjxxQaGtqs462srNQ333zj1D4mJqbZOsxms9P2+e1vf6tHH31Ub7zxhkaPHq3o6GjdcccdXbrcAwDf9dlnn2n79u0aP368DMPQsWPHdOzYMccPs3PPK9ESi8XS4rKuTG+Jjo52ut/Uz7a2/LvvvjvvOs/tA5tOrMlOR3QXfN879n3v6G/L871m0zZqbRt2RNPBlXML0YiICJWWlqq0tFQLFy4873o6+hu8rdjdPaXRlX34yy+/rNLSUr3//vuaMWOGPvnkE917771ObTZs2KDJkyfr4osv1vr167Vz506Vlpbqpz/9abs+gy1xV03gKpxNvZuKiYnR3/72NxmG4dQZVFVV6fTp0+rTp89517F+/XqlpKRo1apVTstPnDjhsjijoqIcJ3hoy7kdWlPnUVFR0aztV199pR49enTqMgpNJ7DYvHlzi49HRER0eJ3h4eFatGiRFi1apK+//tpxlHzixIn69NNPO7w+AL7tD3/4gwzD0J/+9KcW5/zl5eVp8eLFTkd7zlVZWdnisu9973uO+z179mzxZEDffPNNu/p4AF3H971jXP3bsun3YGvbsCOGDh2q3r176+2331ZWVpZjeVBQkOO36r59+9oVU0d+g7cVe1N+PXv2lHRmzvXZVxM69yCRN33/+993bKfRo0eroaFBv//97/WnP/3JsXNq/fr1SkxM1Guvvea0bVo7sV17uKsmcBWOjHdTY8aM0cmTJ/XGG284LW86OcKYMWMcy849ktvEZDI1u3zYRx995BNnJhw4cKAuvvhi5efnyzAMx/JTp07p9ddfd5xNUerYXr4JEybo6NGjamho0LBhw5rdBg4c2KW4Y2NjNW3aNN177706cOCAV4fNAHC9hoYG5eXl6fLLL9fWrVub3ebMmaOKigq9++67ba7nlVdecbpfUlKiQ4cOOZ01uX///vroo4+c2v3zn//kbOaAh/B97zhX/7YcOHCg4uLi9Oqrrzr9Hjx06JBKSko6tK7Q0FD98pe/1L59+7RkyZJOxSN17De4dGbY+tlnHG9oaNBrr72myy+/3HFt8P79+0tSs8/A22+/3ek43S0nJ0e9e/fWf/3Xf6mxsVHSmfc/NDTUqRCvrKxsdjZ1qfX65FwdqQm8gSPj3dRPfvITvfDCC5o6daq++OILDR48WDt27FBWVpZuvfVWjR071tF28ODB2rZtm95++23FxcUpIiJCAwcO1IQJE/TUU09p4cKFGjVqlA4cOKAnn3xSiYmJOn36tBezk3r06KGcnBzdd999mjBhgmbMmCGbzaZf//rXOnbsmJ555hlH28GDB0uSnn/+eU2dOlUhISEaOHBgi0e577nnHr3yyiu69dZb9cgjj+i6665TSEiIjhw5oq1bt+r222/XnXfe2aFYhw8frgkTJuiqq65S79699cknn2jdunVe7xwAuN67776rr776SkuWLGl22UTpzHVYV6xYoTVr1jSb03m23bt366GHHtJdd92lw4cPa8GCBbr44os1c+ZMR5sHHnhA999/v2bOnKkf/ehHOnTokHJycppdjQKAe/B97zhX/7bs0aOHnnrqKT300EO688479bOf/UzHjh2T1Wrt8DB1SXr00Uf16aef6rHHHtP27dt19913q3///rLZbPr888/1+9//XkFBQW3+fuvIb3DpzKjMm2++WU888YTCw8O1cuVKffrpp06XN7v11lsVHR2t6dOn68knn1RwcLByc3N1+PDhDufoKb1799b8+fM1b9485efn6/7779eECRO0YcMGzZw5Uz/+8Y91+PBhPfXUU4qLi2s2fbO1+uRcHakJvIFivJvq2bOntm7dqgULFujXv/61/u///k8XX3yx5s6d22y+y/PPP69Zs2bpnnvuUW1trUaNGqVt27ZpwYIFqq2t1Zo1a5STk6Mrr7xSL774ojZu3Oi2S5t1xJQpUxQeHq7s7GzdfffdCgoK0vXXX6+tW7c6nbQjJSVF8+fPV15enl566SU1NjZq69atLf7hDAoK0ltvvaXnn39e69atU3Z2toKDg3XJJZdo1KhRjsK+I26++Wa99dZbeu6551RbW6uLL75YP/nJT7RgwYKupA/AB61Zs0ahoaF68MEHW3y8T58+uvPOO/WnP/3J6UhIS+tZt26d7rnnHtlsNo0ePVrPP/+803zPKVOm6KuvvtKLL76otWvXKjk5WatWrdKiRYtcnheA5vi+d5w7fltOnz5dkrRkyRJNmjRJ/fv31+OPP67i4uIOr7NHjx7Ky8vTj3/8Y7300kuaN2+ejh49ql69eunyyy/XmDFjtH79+jZHSnbkN7h05iRqgwYN0q9+9St9+eWXuvzyy/XKK6/o7rvvdrSJjIzU5s2blZGRofvvv18XXnihHnroIaWnp+uhhx7qUI6eNHv2bK1YsUJPPvmk7r33Xj344IOqqqrSiy++qD/84Q+67LLL9Nhjj+nIkSPNPsut1SctaW9N4A0m4+zj9QAAAAAAwO2YMw4AAAAAgIcxTB0ed745Pz169HC6DiAAAAD8l2EYamhoaLNNUFBQs6vjeEpDQ4PaGixsMpnaPON9d9TY2Og48Vprzr2GOJqj4oHHnXt97nNvP/3pT70dIgAAAFwkLy/vvL//iouLvRbfmDFj2ozt8ssv91psvurJJ58873v6xRdfeDtMn8eccXjc7t2723y8T58+jks0AAAAwL8dPXpU5eXlbbZp7Uo2nnDgwIE2r2VuNps7dZLeQPbVV1/pq6++arPNVVddpdDQUA9F5J8oxgEAAAAA8DC/HMjf2Nior776ShEREV6bWwLA+wzD0IkTJxQfH895BtqJ/hOARP/ZGfSfAFzedxodVFxcbEyYMMGIi4szJBkbN250enzq1KmGJKfb8OHDndp89913xsMPP2zExMQYYWFhxsSJE43Dhw+3O4bDhw83ew1u3Lh131tH+o/ujv6TGzduZ9/oP9uP/pMbN25NN1f1nR0+Mn7q1CldffXVevDBB/WjH/2oxTa33HKL1q5d67h/7lyBjIwMvf322yooKFBMTIzmzJmjCRMmqKysrF1nKmyaT3L48GFFRkZ2NAWfZ7fbVVhYqLS0NIWEhHg7HK9jezhje/xbTU2NEhISvDbHzB+5ov8MpM8gufgmcnE/+s+O8+bvT1/9HLWHv8bur3FLxO5Oru47O1yMp6enKz09vc02ZrNZFoulxceOHz+uNWvWaN26dRo7dqwkaf369UpISNCWLVs0bty488bQNDQoMjIyYIvxsLAwRUZG+uSH0NPYHs7YHs0xXLD9XNF/BtJnkFx8E7l4Dv1n+3nz96evf47a4q+x+2vcErF7gqv6TrfMGd+2bZv69u2rCy+8UKNGjdLTTz+tvn37SpLKyspkt9uVlpbmaB8fH6/k5GSVlJS0WIzbbDbZbDbH/ZqaGkln3iy73e6OFLyqKadAzK0z2B7O2B7/xjYAgMCXnZ2tDRs26NNPP1WvXr00cuRILVmyRAMHDnS0mTZtmvLy8pyeN3z4cO3atctx32azae7cuXr11VdVV1enMWPGaOXKlbrkkks8lgsAnM3lxXh6erruuusu9evXT+Xl5XriiSd08803q6ysTGazWZWVlQoNDVXv3r2dnhcbG6vKysoW15mdna1FixY1W15YWKiwsDBXp+AzioqKvB2CT2F7OGN7SLW1td4OAQDgZsXFxZo1a5auvfZanT59WgsWLFBaWpo+/vhjhYeHO9q5e5okALiay4vxu+++2/H/5ORkDRs2TP369dM777yjSZMmtfo8wzBaPdw/f/58ZWZmOu43jdVPS0sL2GHqRUVFSk1N9enhGZ7C9nDG9vi3plEyAIDAtXnzZqf7a9euVd++fVVWVqabbrrJsdzV0yR9aWSmP4+K89fY/TVuidjdydVxuf3SZnFxcerXr58OHjwoSbJYLKqvr1d1dbXT0fGqqiqNHDmyxXWYzWaZzeZmy0NCQgK6GAn0/DqK7eGM7aFunz8AdEfHjx+XJEVHRzstd/U0SV8cmenPo+L8NXZ/jVsidndw9ahMtxfjR48e1eHDhxUXFydJGjp0qEJCQlRUVKTJkydLkioqKrRv3z7l5OS4OxwA8Irt27fr17/+tcrKylRRUaGNGzfqjjvukHRmL+uvfvUrbdq0SZ9//rmioqI0duxYPfPMM4qPj3esg/mOALo7wzCUmZmpG2+8UcnJyY7l7pgm6UsjM/15VJy/xu6vcUvE7k6uHpXZ4WL85MmT+uyzzxz3y8vLtWfPHkVHRys6OlpWq1U/+tGPFBcXpy+++EKPP/64+vTpozvvvFOSFBUVpenTp2vOnDmKiYlRdHS05s6dq8GDBzuGDQFAoGnrspC1tbX64IMP9MQTT+jqq69WdXW1MjIydNttt2n37t2Odsx3BNDdPfzww/roo4+0Y8cOp+XumCbpiyMz/XlUnL/G7q9xS8TuDq6OqcPF+O7duzV69GjH/aY9hlOnTtWqVau0d+9evfzyyzp27Jji4uI0evRovfbaa07XYnvuuecUHBysyZMnO47u5Obm8mMSQMBq67KQUVFRzYZjLV++XNddd52+/PJLXXrppS65LCQA+LPZs2frrbfe0vbt2887IsgV0yQBwN06XIynpKTIMIxWH3/vvffOu46ePXtq+fLlWr58eUdfHgC6hePHj8tkMunCCy+U1Ln5jpJ7TkDk6ydX6Qhy8U3k4n6+Fk9bDMPQ7NmztXHjRm3btk2JiYnnfQ7TJAH4A7fPGQf8Vf/H3nHbur94Zrzb1g3/99133+mxxx7TlClTHPMSOzPfUXLvCYh89eQqnUEuvolc3MefLg05a9Ys5efn680331RERISjz4uKilKvXr108uRJpkm6gat+B5mDDOVcJyVb35Ot4cyUAH4HAWdQjAOAD7Hb7brnnnvU2NiolStXnrd9W/MdJfecgMjXT67SEb6cS7L1/CPNzmbuYeipYY16YncP2Rpb/0xI0j6rb09r8OX3paN8NRd/ujTkqlWrJJ0ZnXm2tWvXatq0aQoKCmKaJAC/RDEOAD7Cbrdr8uTJKi8v1/vvv+9ULHd2vqM7T0DkqydX6QxfzKXpCFKHn9doOu9zfS3X1vji+9JZvpaLL8VyPm1Nj5SkXr16MU0SgF/q4e0AAAD/LsQPHjyoLVu2KCYmxunxs+c7Nmma78jJhwAAAPwPR8YBwAPauixkfHy8fvzjH+uDDz7Qn//8ZzU0NDjmREZHRys0NJT5jgAAAAGGYhwAPKCty0JarVa99dZbkqQf/OAHTs/bunWrY54k8x0BAAACB8U4AHjA+S4Leb45kRLzHQEAAAIJc8YBAAAAAPAwinEAAAAAADyMYhwAAAAAAA9jzjgAAHCp/o+906Xnm4MM5VwnJVvfa3bN9C+eGd+ldQMA4Cs4Mg4AAAAAgIdRjAMAAAAA4GEU4wAAAAAAeBjFOAAAAAAAHkYxDgAAAACAh1GMAwAAAADgYRTjAAAAAAB4GNcZBwCgm+nqdcABAEDXcWQcAAAAAAAPoxgHAAAAAMDDGKYOv8ZQSwAAAAD+iCPjAAAAAAB4GMU4AAAAAAAe1uFifPv27Zo4caLi4+NlMpn0xhtvOB6z2+169NFHNXjwYIWHhys+Pl4/+clP9NVXXzmtIyUlRSaTyel2zz33dDkZAAAAAAD8QYeL8VOnTunqq6/WihUrmj1WW1urDz74QE888YQ++OADbdiwQf/85z912223NWv7s5/9TBUVFY7b7373u85lAAAAAACAn+nwCdzS09OVnp7e4mNRUVEqKipyWrZ8+XJdd911+vLLL3XppZc6loeFhclisXT05QEAAAAA8HtuP5v68ePHZTKZdOGFFzotf+WVV7R+/XrFxsYqPT1dCxcuVERERIvrsNlsstlsjvs1NTWSzgyLt9vtbovdW5pyCsTcOqOt7WEOMjwdjkt05b3l8/FvbAMAAAD4K7cW4999950ee+wxTZkyRZGRkY7l9913nxITE2WxWLRv3z7Nnz9f//jHP5odVW+SnZ2tRYsWNVteWFiosLAwt8Xvba1tj+6qpe2Rc50XAnGBTZs2dXkdfD7OTI0BAAAA/JHbinG73a577rlHjY2NWrlypdNjP/vZzxz/T05OVlJSkoYNG6YPPvhA11xzTbN1zZ8/X5mZmY77NTU1SkhIUFpamlORHyjsdruKioqUmpqqkJAQb4fjdW1tj2Tre16Kqmv2Wcd1+rl8Pv6taZQMAAAA4G/cUozb7XZNnjxZ5eXlev/9989bMF9zzTUKCQnRwYMHWyzGzWazzGZzs+UhISEBXYwEen4d1dL2sDWYvBRN17jifeXz4Zrt6Cnbt2/Xr3/9a5WVlamiokIbN27UHXfc4XjcMAwtWrRIq1evVnV1tYYPH64XXnhBgwYNcrSx2WyaO3euXn31VdXV1WnMmDFauXKlLrnkEi9kBAAAgK5w+XXGmwrxgwcPasuWLYqJiTnvc/bv3y+73a64uDhXhwMAPqGtK1FIUk5OjpYuXaoVK1aotLRUFotFqampOnHihKNNRkaGNm7cqIKCAu3YsUMnT57UhAkT1NDQ4Kk0AAAA4CIdPjJ+8uRJffbZZ4775eXl2rNnj6KjoxUfH68f//jH+uCDD/TnP/9ZDQ0NqqyslCRFR0crNDRU//u//6tXXnlFt956q/r06aOPP/5Yc+bM0ZAhQ3TDDTe4LjP4hP6PvdPldZiDDOVcd2ZIur8eCQfauhKFYRhatmyZFixYoEmTJkmS8vLyFBsbq/z8fM2YMUPHjx/XmjVrtG7dOo0dO1aStH79eiUkJGjLli0aN67zUx8AAADgeR0uxnfv3q3Ro0c77jfN5Z46daqsVqveeustSdIPfvADp+dt3bpVKSkpCg0N1V/+8hc9//zzOnnypBISEjR+/HgtXLhQQUFBXUgFAPxTeXm5KisrlZaW5lhmNps1atQolZSUaMaMGSorK5PdbndqEx8fr+TkZJWUlLRajLvjahSBdEZ/X86lo1eLMPcwnP71Z23l4ovvVVt89TPma/G0JTs7Wxs2bNCnn36qXr16aeTIkVqyZIkGDhzoaMNUHwD+qMPFeEpKigyj9T/0bT0mSQkJCSouLu7oywJAwGoaQRQbG+u0PDY2VocOHXK0CQ0NVe/evZu1aXp+S9x5NYpAOqO/L+bS2atFPDWs0bWBeFFLubjiahTe4GufMX+6GkVxcbFmzZqla6+9VqdPn9aCBQuUlpamjz/+WOHh4ZL+PdUnNzdXAwYM0OLFi5WamqoDBw44Lp2bkZGht99+WwUFBYqJidGcOXM0YcIElZWVcUAIgFe4/TrjAID2MZmcp2EYhtFs2bnO18YdV6MIpDP6+3IuHb1ahLmHoaeGNeqJ3T1ka/TvKT1t5dKVq1F4g69+xvzpahSbN292ur927Vr17dtXZWVluummm5jqA8BvUYwDgJdZLBZJZ45+n30iy6qqKsfRcovFovr6elVXVzsdHa+qqtLIkSNbXbc7r0YRSGf098VcOnuODFujKWDOr9FSLr72PrWXr33GfCmWjjp+/LikM+cjktw31ccd03w6yxvTHTo6VabV9bQw7cQfpkn46hST9iB293F1XBTjAOBliYmJslgsKioq0pAhQyRJ9fX1Ki4u1pIlSyRJQ4cOVUhIiIqKijR58mRJUkVFhfbt26ecnByvxd6dueIElQA6xjAMZWZm6sYbb1RycrIk9031cec0n87y5HSHzk6Vac3Z0078abqJr00x6Qhidz1XT/GhGAcAD2jrShSXXnqpMjIylJWVpaSkJCUlJSkrK0thYWGaMmWKJCkqKkrTp0/XnDlzFBMTo+joaM2dO1eDBw92DLkEgED38MMP66OPPtKOHTuaPebqqT7umObTWd6Y7tDRqTKtaWnaiT9MN/HVKSbtQezu4+opPhTjAOABbV2JIjc3V/PmzVNdXZ1mzpzpOBNwYWGh48RDkvTcc88pODhYkydPdpwJODc3lxMPAegWZs+erbfeekvbt293OgO6u6b6uHOaT2d58rVdPd3l7GknvlhktcbXpph0BLG7nqtj6uHStQEAWtR0JYpzb7m5uZLOHNGxWq2qqKjQd999p+LiYscQzCY9e/bU8uXLdfToUdXW1urtt99WQkKCF7IBAM8xDEMPP/ywNmzYoPfff1+JiYlOj5891adJ01SfpkL77Kk+TZqm+rR13g0AcCeOjAMAAMBnzZo1S/n5+XrzzTcVERHhmOMdFRWlXr16yWQyMdUHgF+iGAcAAIDPWrVqlaQzI4zOtnbtWk2bNk2SmOoDwC9RjAMAAMBnGcb5L7HVNNXHarW22qZpqs/y5ctdGB0AdB5zxgEAAAAA8DCKcQAAAAAAPIxiHAAAAAAAD6MYBwAAAADAwyjGAQAAAADwMIpxAAAAAAA8jGIcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEAAAAAADyMYhwAAAAAAA+jGAcAAAAAwMMoxgEAAAAA8DCKcQAAAAAAPIxiHAAAAAAAD+twMb59+3ZNnDhR8fHxMplMeuONN5weNwxDVqtV8fHx6tWrl1JSUrR//36nNjabTbNnz1afPn0UHh6u2267TUeOHOlSIgAAAAAA+IsOF+OnTp3S1VdfrRUrVrT4eE5OjpYuXaoVK1aotLRUFotFqampOnHihKNNRkaGNm7cqIKCAu3YsUMnT57UhAkT1NDQ0PlMAAAAAADwE8EdfUJ6errS09NbfMwwDC1btkwLFizQpEmTJEl5eXmKjY1Vfn6+ZsyYoePHj2vNmjVat26dxo4dK0lav369EhIStGXLFo0bN64L6QAAAAAA4Ps6XIy3pby8XJWVlUpLS3MsM5vNGjVqlEpKSjRjxgyVlZXJbrc7tYmPj1dycrJKSkpaLMZtNptsNpvjfk1NjSTJbrfLbre7MgWf0JRTIORmDjK6vo4ehtO/gaAr720gfT66im0AAAAAf+XSYryyslKSFBsb67Q8NjZWhw4dcrQJDQ1V7969m7Vpev65srOztWjRombLCwsLFRYW5orQfVJRUZG3Q+iynOtct66nhjW6bmVetmnTpi6vIxA+H11VW1vr7RAAAACATnFpMd7EZDI53TcMo9myc7XVZv78+crMzHTcr6mpUUJCgtLS0hQZGdn1gH2M3W5XUVGRUlNTFRIS4u1wuiTZ+l6X12HuYeipYY16YncP2Rrb/hz5i33Wzk/HCKTPR1c1jZIJFKdPn5bVatUrr7yiyspKxcXFadq0afrVr36lHj3OnOLDMAwtWrRIq1evVnV1tYYPH64XXnhBgwYN8nL0AAAA6AiXFuMWi0WSHD8im1RVVTmOllssFtXX16u6utrp6HhVVZVGjhzZ4nrNZrPMZnOz5SEhIQFdjARCfrYG1xXPtkaTS9fnTa54XwPh89FVgZb/kiVL9OKLLyovL0+DBg3S7t279eCDDyoqKkqPPPKIpH+fJDM3N1cDBgzQ4sWLlZqaqgMHDigiIsLLGQAAAKC9XFqMJyYmymKxqKioSEOGDJEk1dfXq7i4WEuWLJEkDR06VCEhISoqKtLkyZMlSRUVFdq3b59ycnJcGQ4A+JWdO3fq9ttv1/jx4yVJ/fv316uvvqrdu3dLat9JMs/ljnNuBNJ5C7qSiyvOieFKgXR+jbZy8bfPna9+X3wtHgDojjpcjJ88eVKfffaZ4355ebn27Nmj6OhoXXrppcrIyFBWVpaSkpKUlJSkrKwshYWFacqUKZKkqKgoTZ8+XXPmzFFMTIyio6M1d+5cDR482HF2dQDojm688Ua9+OKL+uc//6kBAwboH//4h3bs2KFly5ZJat9JMs/lznNuBNJ5CzqTiyvPieFKgXR+jZZyccU5N7zB174vnHMDALyvw8X47t27NXr0aMf9prncU6dOVW5urubNm6e6ujrNnDnTMZ+xsLDQafjkc889p+DgYE2ePFl1dXUaM2aMcnNzFRQU5IKUAMA/Pfroozp+/LiuuOIKBQUFqaGhQU8//bTuvfdeSe07Sea53HHOjUA6b0FXcnHFOTFcKZDOr9FWLl0554Y3+Or3JdDOuQEA/qjDxXhKSooMo/UhcCaTSVarVVartdU2PXv21PLly7V8+fKOvjwABKzXXntN69evV35+vgYNGqQ9e/YoIyND8fHxmjp1qqNdR06S6c5zbgTSeQs6k4uvnsMikM6v0VIu/vqZ87Xviy/FAgDdlVvOpg4A6Lhf/vKXeuyxx3TPPfdIkgYPHqxDhw4pOztbU6dObddJMgEAAOAfeng7AADAGbW1tY5LmDUJCgpSY+OZebNnnySzSdNJMlu7GgUAAAB8E0fGAcBHTJw4UU8//bQuvfRSDRo0SB9++KGWLl2qn/70p5LODE8/30kyAQAA4B8oxgHARyxfvlxPPPGEZs6cqaqqKsXHx2vGjBn6r//6L0eb9pwkEwAAAL6PYhwAfERERISWLVvmuJRZS9pzkkwAAAD4PuaMAwAAwGdt375dEydOVHx8vEwmk9544w2nx6dNmyaTyeR0u/76653a2Gw2zZ49W3369FF4eLhuu+02HTlyxINZAEBzFOMAAADwWadOndLVV1+tFStWtNrmlltuUUVFheO2adMmp8czMjK0ceNGFRQUaMeOHTp58qQmTJighoYGd4cPAK1imDoAAAB8Vnp6utLT09tsYzabHZd/PNfx48e1Zs0arVu3TmPHjpUkrV+/XgkJCdqyZYvGjRvn8pgBoD0oxgEAAODXtm3bpr59++rCCy/UqFGj9PTTT6tv376SpLKyMtntdqWlpTnax8fHKzk5WSUlJa0W4zabTTabzXG/pqZGkmS322W3292YTXNNr+fJ1zUHGa5ZTw/D6V/Js3l0lje2uasQu/u4Oi6KcQAAAPit9PR03XXXXerXr5/Ky8v1xBNP6Oabb1ZZWZnMZrMqKysVGhqq3r17Oz0vNjZWlZWVra43OztbixYtara8sLBQYWFhLs+jPYqKijz2WjnXuXZ9Tw1rdPz/3GkEvsyT29zViN31amtrXbo+inEAAAD4rbvvvtvx/+TkZA0bNkz9+vXTO++8o0mTJrX6PMMwZDKZWn18/vz5yszMdNyvqalRQkKC0tLSFBkZ6Zrg28lut6uoqEipqakKCQnxyGsmW99zyXrMPQw9NaxRT+zuIVvjme29z+r7UwO8sc1dhdjdp2mEjKtQjAMAACBgxMXFqV+/fjp48KAkyWKxqL6+XtXV1U5Hx6uqqjRy5MhW12M2m2U2m5stDwkJ8VqR4MnXtjW0vqOiU+trNDnW6YtFVmu8+X53FbG7nqtj4mzqAAAACBhHjx7V4cOHFRcXJ0kaOnSoQkJCnIa9VlRUaN++fW0W4wDgbhwZBwAAgM86efKkPvvsM8f98vJy7dmzR9HR0YqOjpbVatWPfvQjxcXF6YsvvtDjjz+uPn366M4775QkRUVFafr06ZozZ45iYmIUHR2tuXPnavDgwY6zqwOAN1CMAwAAwGft3r1bo0ePdtxvmsc9depUrVq1Snv37tXLL7+sY8eOKS4uTqNHj9Zrr72miIgIx3Oee+45BQcHa/Lkyaqrq9OYMWOUm5uroKAgj+cDAE0oxgEAAOCzUlJSZBitX2brvffOf6Kxnj17avny5Vq+fLkrQwOALmHOOAAAAAAAHkYxDgAAAACAh1GMAwAAAADgYRTjAAAAAAB4GMU4AAAAAAAeRjEOAAAAAICHUYwDAAAAAOBhFOMAAAAAAHiYy4vx/v37y2QyNbvNmjVLkjRt2rRmj11//fWuDgMAAAAAAJ8V7OoVlpaWqqGhwXF/3759Sk1N1V133eVYdsstt2jt2rWO+6Ghoa4OAwAAAAAAn+XyYvyiiy5yuv/MM8/o8ssv16hRoxzLzGazLBaLq18aAAAAAAC/4PJi/Gz19fVav369MjMzZTKZHMu3bdumvn376sILL9SoUaP09NNPq2/fvq2ux2azyWazOe7X1NRIkux2u+x2u/sS8JKmnAIhN3OQ0fV19DCc/g0EXXlvA+nz0VVsAwAAAPgrtxbjb7zxho4dO6Zp06Y5lqWnp+uuu+5Sv379VF5erieeeEI333yzysrKZDabW1xPdna2Fi1a1Gx5YWGhwsLC3BW+1xUVFXk7hC7Luc5163pqWKPrVuZlmzZt6vI6AuHz0VW1tbXeDsHl/vWvf+nRRx/Vu+++q7q6Og0YMEBr1qzR0KFDJUmGYWjRokVavXq1qqurNXz4cL3wwgsaNGiQlyMHAABAR7i1GF+zZo3S09MVHx/vWHb33Xc7/p+cnKxhw4apX79+eueddzRp0qQW1zN//nxlZmY67tfU1CghIUFpaWmKjIx0XwJeYrfbVVRUpNTUVIWEhHg7nC5Jtr7X5XWYexh6alijntjdQ7ZG0/mf4Af2Wcd1+rmB9PnoqqZRMoGiurpaN9xwg0aPHq13331Xffv21f/+7//qwgsvdLTJycnR0qVLlZubqwEDBmjx4sVKTU3VgQMHFBER4b3gAQAA0CFuK8YPHTqkLVu2aMOGDW22i4uLU79+/XTw4MFW25jN5haPmoeEhAR0MRII+dkaXFc82xpNLl2fN7nifQ2Ez0dXBVr+S5YsUUJCgtMJLvv37+/4v2EYWrZsmRYsWODYeZmXl6fY2Fjl5+drxowZzdbpjmk+gTRVoiu5uGIajisF0pSetnLxt8+dr35ffC0eAOiO3FaMr127Vn379tX48ePbbHf06FEdPnxYcXFx7goFAPzCW2+9pXHjxumuu+5ScXGxLr74Ys2cOVM/+9nPJEnl5eWqrKxUWlqa4zlms1mjRo1SSUlJi8W4O6f5BNJUic7k4sppOK4USFN6WsrFFdN8vMHXvi+BOM0HAPyNW4rxxsZGrV27VlOnTlVw8L9f4uTJk7JarfrRj36kuLg4ffHFF3r88cfVp08f3Xnnne4IBQD8xueff65Vq1YpMzNTjz/+uP7+97/rF7/4hcxms37yk5+osrJSkhQbG+v0vNjYWB06dKjFdbpjmk8gTZXoSi6umIbjSoE0paetXLoyzccbfPX7EmjTfADAH7mlGN+yZYu+/PJL/fSnP3VaHhQUpL179+rll1/WsWPHFBcXp9GjR+u1115jriOAbq+xsVHDhg1TVlaWJGnIkCHav3+/Vq1apZ/85CeOdmdfnUI6M3z93GVN3DnNJ5CmSnQmF1+dNhNIU3paysVfP3O+9n3xpVgAoLtySzGelpYmw2g+z6tXr1567z3fOpIAAL4iLi5OV155pdOy73//+3r99dclSRaLRZJUWVnpNLWnqqqq2dFyAAAA+LYe3g4AAHDGDTfcoAMHDjgt++c//6l+/fpJkhITE2WxWJzmntbX16u4uFgjR470aKwAAADoGrde2gxAy/o/9k6nn2sOMpRz3Zn5qq0NRf3imbZPnAjf9J//+Z8aOXKksrKyNHnyZP3973/X6tWrtXr1aklnhqdnZGQoKytLSUlJSkpKUlZWlsLCwjRlyhQvRw8AAICOoBgHAB9x7bXXauPGjZo/f76efPJJJSYmatmyZbrvvvscbebNm6e6ujrNnDlT1dXVGj58uAoLCznvBgAAgJ+hGAcAHzJhwgRNmDCh1cdNJpOsVqusVqvnggIAAIDLMWccAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAw5oyjS2f2BgBfdr7+rT1XJwAAAHAHjowDAAAAAOBhFOMAAAAAAHgYxTgAAAAAAB5GMQ4AAACftX37dk2cOFHx8fEymUx64403nB43DENWq1Xx8fHq1auXUlJStH//fqc2NptNs2fPVp8+fRQeHq7bbrtNR44c8WAWANAcxTgAAAB81qlTp3T11VdrxYoVLT6ek5OjpUuXasWKFSotLZXFYlFqaqpOnDjhaJORkaGNGzeqoKBAO3bs0MmTJzVhwgQ1NDR4Kg0AaIazqQMAAMBnpaenKz09vcXHDMPQsmXLtGDBAk2aNEmSlJeXp9jYWOXn52vGjBk6fvy41qxZo3Xr1mns2LGSpPXr1yshIUFbtmzRuHHjPJYLAJyNYhwAAAB+qby8XJWVlUpLS3MsM5vNGjVqlEpKSjRjxgyVlZXJbrc7tYmPj1dycrJKSkpaLcZtNptsNpvjfk1NjSTJbrfLbre7KaOWNb3e2a+bbH3Pra9pDnLRenoYTv9K8vj264yWtrm/IHb3cXVcFOMAAADwS5WVlZKk2NhYp+WxsbE6dOiQo01oaKh69+7drE3T81uSnZ2tRYsWNVteWFiosLCwrobeKUVFRY7/51znlRA67alhjY7/b9q0yYuRdMzZ29zfELvr1dbWunR9FOMAAADwayaTyem+YRjNlp3rfG3mz5+vzMxMx/2amholJCQoLS1NkZGRXQu4g+x2u4qKipSamqqQkBBJ7j8y7irmHoaeGtaoJ3b3kK3xzPbeZ/X9qQEtbXN/Qezu0zRCxlUoxgEAAOCXLBaLpDNHv+Pi4hzLq6qqHEfLLRaL6uvrVV1d7XR0vKqqSiNHjmx13WazWWazudnykJAQrxUJZ7+2raHtnQ2+xtZocsTsi0VWa7z5fncVsbueq2PibOoAAADwS4mJibJYLE5DWuvr61VcXOwotIcOHaqQkBCnNhUVFdq3b1+bxTgAuBtHxgEAAOCzTp48qc8++8xxv7y8XHv27FF0dLQuvfRSZWRkKCsrS0lJSUpKSlJWVpbCwsI0ZcoUSVJUVJSmT5+uOXPmKCYmRtHR0Zo7d64GDx7sOLs6AHgDxTgAAAB81u7duzV69GjH/aZ53FOnTlVubq7mzZunuro6zZw5U9XV1Ro+fLgKCwsVERHheM5zzz2n4OBgTZ48WXV1dRozZoxyc3MVFOSiU4YDQCdQjAMAAMBnpaSkyDCMVh83mUyyWq2yWq2ttunZs6eWL1+u5cuXuyFCAOgcinEAAAAAHtP/sXfcuv4vnhnv1vUDrsIJ3AAAAAAA8DCXF+NWq1Umk8np1nTZCenMNR2tVqvi4+PVq1cvpaSkaP/+/a4OAwAAAAAAn+WWI+ODBg1SRUWF47Z3717HYzk5OVq6dKlWrFih0tJSWSwWpaam6sSJE+4IBQAAAAAAn+OWYjw4OFgWi8Vxu+iiiySdOSq+bNkyLViwQJMmTVJycrLy8vJUW1ur/Px8d4QCAAAAAIDPccsJ3A4ePKj4+HiZzWYNHz5cWVlZuuyyy1ReXq7KykqlpaU52prNZo0aNUolJSWaMWNGi+uz2Wyy2WyO+zU1NZIku90uu93ujhS8qiknT+VmDmr9DKW+wNzDcPq3u2vP9gjE70VLAjnP7OxsPf7443rkkUe0bNkySWd2aC5atEirV692XL7nhRde0KBBg7wbLAAAADrM5cX48OHD9fLLL2vAgAH6+uuvtXjxYo0cOVL79+9XZWWlJCk2NtbpObGxsTp06FCr68zOztaiRYuaLS8sLFRYWJhrE/AhRUVFHnmdnOs88jJd9tSwRm+H4FPa2h6bNm3yYCTeU1tb6+0Q3KK0tFSrV6/WVVdd5bS8aZpPbm6uBgwYoMWLFys1NVUHDhxwup4uAAAAfJ/Li/H09HTH/wcPHqwRI0bo8ssvV15enq6//npJZ64HeTbDMJotO9v8+fOVmZnpuF9TU6OEhASlpaUpMjLSxRl4n91uV1FRkVJTUxUSEuL210u2vuf21+gKcw9DTw1r1BO7e8jW2PrnpLtoz/bYZx3n4ai8o2mUTCA5efKk7rvvPr300ktavHixY/m503wkKS8vT7GxscrPz/foyCJPj97pivON/AmkkTfdJRd/+NydzVe/L74WDwB0R26/znh4eLgGDx6sgwcP6o477pAkVVZWKi4uztGmqqqq2dHys5nNZpnN5mbLQ0JCPFKseoun8rM1+EeBa2s0+U2sntDW9gjk78XZAjHPWbNmafz48Ro7dqxTMd7ZaT7uHFnkqdE7XdHekT+BNPIm0HPx15E/vvZ9CdSRRQDgT9xejNtsNn3yySf64Q9/qMTERFksFhUVFWnIkCGSpPr6ehUXF2vJkiXuDgUAfFpBQYE++OADlZaWNnuss9N83DGyyNOjd7rifCN/AmnkDbm4hqtHFvnq9yUQRxYBgL9xeTE+d+5cTZw4UZdeeqmqqqq0ePFi1dTUaOrUqTKZTMrIyFBWVpaSkpKUlJSkrKwshYWFacqUKa4OBQD8xuHDh/XII4+osLBQPXv2bLVdR6f5uHNkkT+MTmrvaJpAGnlDLl3jrs+0r31ffCkWAOiuXF6MHzlyRPfee6+++eYbXXTRRbr++uu1a9cu9evXT5I0b9481dXVaebMmY6zARcWFnLyIQDdWllZmaqqqjR06FDHsoaGBm3fvl0rVqzQgQMHJHV8mg8AAAB8k8uL8YKCgjYfN5lMslqtslqtrn5pAPBbY8aM0d69e52WPfjgg7riiiv06KOP6rLLLmOaDwAAQABx+5xxAMD5RUREKDk52WlZeHi4YmJiHMuZ5gMAABA4KMYBwE8wzQcAACBwUIwDgI/atm2b032m+QAAAASOHt4OAAAAAACA7oZiHAAAAAAAD6MYBwAAAADAwyjGAQAAAADwMIpxAAAAAAA8jGIcAAAAAAAPoxgHAAAAAMDDKMYBAAAAAPAwinEAAAAAADyMYhwAAAAAAA8L9nYAAAAAAOAq/R97p8vrMAcZyrlOSra+J1uDybH8i2fGd3ndQBOKcQCA17jiBxMAAIA/ohgHAAAA3MBVOxxbO0oLwL8xZxwAAAAAAA+jGAcAAAAAwMMoxgEAAODXrFarTCaT081isTgeNwxDVqtV8fHx6tWrl1JSUrR//34vRgwAFOMAAAAIAIMGDVJFRYXjtnfvXsdjOTk5Wrp0qVasWKHS0lJZLBalpqbqxIkTXowYQHdHMQ4AAAC/FxwcLIvF4rhddNFFks4cFV+2bJkWLFigSZMmKTk5WXl5eaqtrVV+fr6XowbQnXE2dQAAAPi9gwcPKj4+XmazWcOHD1dWVpYuu+wylZeXq7KyUmlpaY62ZrNZo0aNUklJiWbMmNHi+mw2m2w2m+N+TU2NJMlut8tut7crJnOQ0YWMzlpPD8PpX3/ir7G3Fnd733tvaorRH2I9l6/H7uq4KMYBAADg14YPH66XX35ZAwYM0Ndff63Fixdr5MiR2r9/vyorKyVJsbGxTs+JjY3VoUOHWl1ndna2Fi1a1Gx5YWGhwsLC2hVXznUdSKIdnhrW6NoVepC/xn5u3Js2bfJSJB1XVFTk7RA6zVdjr62tden6KMYBAADg19LT0x3/Hzx4sEaMGKHLL79ceXl5uv766yVJJpPz9bkNw2i27Gzz589XZmam435NTY0SEhKUlpamyMjIdsWVbH2vI2m0ytzD0FPDGvXE7h6yNfrXdcb9NfbW4t5nHefFqNrHbrerqKhIqampCgkJ8XY4HeLrsTeNkHEVlxfj2dnZ2rBhgz799FP16tVLI0eO1JIlSzRw4EBHm2nTpikvL8/pecOHD9euXbtcHQ4AAAC6mfDwcA0ePFgHDx7UHXfcIUmqrKxUXFyco01VVVWzo+VnM5vNMpvNzZaHhIS0u0iwNbi2+LQ1mly+Tk/x19jPjdsXC8TWdOSz6mt8NXZXx+TyE7gVFxdr1qxZ2rVrl4qKinT69GmlpaXp1KlTTu1uueUWpzNe+tOQDwAAAPgum82mTz75RHFxcUpMTJTFYnEa9lpfX6/i4mKNHDnSi1EC6O5cXoxv3rxZ06ZN06BBg3T11Vdr7dq1+vLLL1VWVubUzmw2O53xMjo62tWhAIBfyc7O1rXXXquIiAj17dtXd9xxhw4cOODUhmvlAkBzc+fOVXFxscrLy/W3v/1NP/7xj1VTU6OpU6fKZDIpIyNDWVlZ2rhxo/bt26dp06YpLCxMU6ZM8XboALoxt88ZP378uCQ1K7a3bdumvn376sILL9SoUaP09NNPq2/fvi2uwxVns/Qnnj6LoKvO9Oku/noWTndpz/YIxO9FSwItz6aRRddee61Onz6tBQsWKC0tTR9//LHCw8Ml/ftaubm5uRowYIAWL16s1NRUHThwQBEREV7OAAC848iRI7r33nv1zTff6KKLLtL111+vXbt2qV+/fpKkefPmqa6uTjNnzlR1dbWGDx+uwsJC+k0AXuXWYtwwDGVmZurGG29UcnKyY3l6erruuusu9evXT+Xl5XriiSd08803q6ysrMW5Oa44H5NBygAAENRJREFUm6U/8tRZBF19pk938dezcLpLW9uju0z7cPUZLb1t8+bNTvfXrl2rvn37qqysTDfddFOza+VKUl5enmJjY5Wfn9/i5XncsTPTlTsMvb0zMJB29pGLa7h6J5+vXqbH1+LpqoKCgjYfN5lMslqtslqtngkIANrBrcX4ww8/rI8++kg7duxwWn733Xc7/p+cnKxhw4apX79+eueddxw/MM/mirNZ+hNPn0XQVWf6dBd/PQunu7Rne/jDmT5dwdVntPQ1544s6sy1ct25M9MVOwx9ZWdgIO3sI5eucdfOTF+7TE+g7cwEAH/ktmJ89uzZeuutt7R9+3ZdcsklbbaNi4tTv379dPDgwRYfd8XZLP2Rp/LzlzNb+utZON2lre0RyN+LswVyni2NLOrMtXLdsTPTlTsMvb0zMJB29pGLa7h6Z6avXqYn0HdmAoA/cHkxbhiGZs+erY0bN2rbtm1KTEw873OOHj2qw4cPO11uAgC6s9ZGFkkdu1auO3dmumIdvrKDLZB29pFL17irYPa1Awi+FAsAdFcuP5v6rFmztH79euXn5ysiIkKVlZWqrKxUXV2dJOnkyZOaO3eudu7cqS+++ELbtm3TxIkT1adPH915552uDgcA/E7TyKKtW7c6jSyyWCyS/n2EvMn5rpULAAAA3+PyYnzVqlU6fvy4UlJSFBcX57i99tprkqSgoCDt3btXt99+uwYMGKCpU6dqwIAB2rlzJ2e0BNCtGYahhx9+WBs2bND777/fbGQR18oFAAAIHG4Zpt6WXr166b33fPuEYQDgDbNmzVJ+fr7efPNNx8giSYqKilKvXr2crpWblJSkpKQkZWVlca1cAAAAP+T264wDANpn1apVkqSUlBSn5WvXrtW0adMkca1cAACAQEExDgA+4nwjiySulQsAABAoKMb9QP/H3vF2CAAAAAAAF3L5CdwAAAAAAEDbKMYBAAAAAPAwhqkDAAD8/1w9NcwcZCjnOinZ+p4OPD3BpesGAPg3inEgALnzPANfPDPebesGAAAAuguGqQMAAAAA4GEU4wAAAAAAeBjFOAAAAAAAHkYxDgAAAACAh1GMAwAAAADgYRTjAAAAAAB4GMU4AAAAAAAexnXGXcSV13U2BxnKuU5Ktr4nW4PJZesFAAAAAPgGjowDAAAAAOBhFOMAAAAAAHgYw9QBAAAAoB1cOTW1JV88M96t64dv4cg4AAAAAAAeRjEOAAAAAICHUYwDAAAAAOBhFOMAAAAAAHgYxTgAAAAAAB7G2dQBdAhnEe1+zn3PzUGGcq6Tkq3vydZg8lJUAAAEHlf8zmrt7zS/sXxPtynG3V1AAAAAAADQXl4txleuXKlf//rXqqio0KBBg7Rs2TL98Ic/9GZIAODz6DsB/8TIIu+j/wTgS7w2Z/y1115TRkaGFixYoA8//FA//OEPlZ6eri+//NJbIQGAz6PvBIDOof8E4Gu8dmR86dKlmj59uh566CFJ0rJly/Tee+9p1apVys7Odmprs9lks9kc948fPy5J+vbbb2W329v1esGnT7kocvcLbjRUW9uoYHsPNTQyH5Pt4SzQt8fRo0fb3fbEiROSJMMw3BWOz+lI3ym5p/8MpM8gufgmcukc+s+2+UL/2Vn+/J3w19j9NW6p9dg70kd0xvDsv3R5HeYehn41pFE/WLBBNg9u97/NH9Oudi7vOw0vsNlsRlBQkLFhwwan5b/4xS+Mm266qVn7hQsXGpK4cePGrcXb4cOHPdV9eVVH+07DoP/kxo1b2zf6T/pPbty4dfzmqr7TK0fGv/nmGzU0NCg2NtZpeWxsrCorK5u1nz9/vjIzMx33Gxsb9e233yomJkYmk3/tqWqPmpoaJSQk6PDhw4qMjPR2OF7H9nDG9vg3wzB04sQJxcfHezsUj+ho3ym5p/8MpM8gufgmcnE/+s8zPN1/dpavfo7aw19j99e4JWJ3J1f3nV49gdu5HZlhGC12bmazWWaz2WnZhRde6M7QfEJkZKRPfgi9he3hjO1xRlRUlLdD8Lj29p2Se/vPQPoMkotvIhf3ov/0Xv/ZWb74OWovf43dX+OWiN1dXNl3euUEbn369FFQUFCzPZFVVVXN9lgCAM6g7wSAzqH/BOCLvFKMh4aGaujQoSoqKnJaXlRUpJEjR3ojJADwefSdANA59J8AfJHXhqlnZmbqgQce0LBhwzRixAitXr1aX375pX7+8597KySfYTabtXDhwmZDo7ortocztkf35gt9ZyB9BsnFN5EL3MEX+s/O8ufPkb/G7q9xS8TuT0yG4b1rWqxcuVI5OTmqqKhQcnKynnvuOd10003eCgcA/AJ9JwB0Dv0nAF/i1WIcAAAAAIDuyCtzxgEAAAAA6M4oxgEAAAAA8DCKcQAAAAAAPIxiHAAAAAAAD6MY92HZ2dkymUzKyMjwdihe869//Uv333+/YmJiFBYWph/84AcqKyvzdlhecfr0af3qV79SYmKievXqpcsuu0xPPvmkGhsbvR0auont27dr4sSJio+Pl8lk0htvvOHtkDotOztb1157rSIiItS3b1/dcccdOnDggLfD6pRVq1bpqquuUmRkpCIjIzVixAi9++673g6ry/z9b6DVapXJZHK6WSwWb4cFH7Vy5UolJiaqZ8+eGjp0qP7617+22nbbtm3NPlsmk0mffvqpByM+ozN/F4qLizV06FD17NlTl112mV588UX3B9qCjsbuK9u9s3+/fGG7dyZ2X9nu7kIx7qNKS0u1evVqXXXVVd4OxWuqq6t1ww03KCQkRO+++64+/vhjPfvss7rwwgu9HZpXLFmyRC+++KJWrFihTz75RDk5Ofr1r3+t5cuXezs0dBOnTp3S1VdfrRUrVng7lC4rLi7WrFmztGvXLhUVFen06dNKS0vTqVOnvB1ah11yySV65plntHv3bu3evVs333yzbr/9du3fv9/boXVaoPwNHDRokCoqKhy3vXv3ejsk+KDXXntNGRkZWrBggT788EP98Ic/VHp6ur788ss2n3fgwAGnz1dSUpKHIv63jv5dKC8v16233qof/vCH+vDDD/X444/rF7/4hV5//XU3R9pcZ/+meXu7d+bvl69s96787fX2dncbAz7nxIkTRlJSklFUVGSMGjXKeOSRR7wdklc8+uijxo033ujtMHzG+PHjjZ/+9KdOyyZNmmTcf//9XooI3ZkkY+PGjd4Ow2WqqqoMSUZxcbG3Q3GJ3r17G7///e+9HUanBMrfwIULFxpXX321t8OAH7juuuuMn//8507LrrjiCuOxxx5rsf3WrVsNSUZ1dbUHomu/9vxdmDdvnnHFFVc4LZsxY4Zx/fXXuzGy82tP7L663dvz98tXt3t7YvfV7e4qHBn3QbNmzdL48eM1duxYb4fiVW+99ZaGDRumu+66S3379tWQIUP00ksveTssr7nxxhv1l7/8Rf/85z8lSf/4xz+0Y8cO3XrrrV6ODPB/x48flyRFR0d7OZKuaWhoUEFBgU6dOqURI0Z4O5xOCaS/gQcPHlR8fLwSExN1zz336PPPP/d2SPAx9fX1KisrU1pamtPytLQ0lZSUtPncIUOGKC4uTmPGjNHWrVvdGabL7Ny5s1mu48aN0+7du2W3270UVcf42nZvz98vX93uHfnb62vb3VWCvR0AnBUUFOiDDz5QaWmpt0Pxus8//1yrVq1SZmamHn/8cf3973/XL37xC5nNZv3kJz/xdnge9+ijj+r48eO64oorFBQUpIaGBj399NO69957vR0a4NcMw1BmZqZuvPFGJScnezucTtm7d69GjBih7777ThdccIE2btyoK6+80tthdVgg/Q0cPny4Xn75ZQ0YMEBff/21Fi9erJEjR2r//v2KiYnxdnjwEd98840aGhoUGxvrtDw2NlaVlZUtPicuLk6rV6/W0KFDZbPZtG7dOo0ZM0bbtm3TTTfd5ImwO62ysrLFXE+fPq1vvvlGcXFxXors/Hxxu7f375cvbvf2xu6L292VKMZ9yOHDh/XII4+osLBQPXv29HY4XtfY2Khhw4YpKytL0pk9Yvv379eqVau6ZTH+2muvaf369crPz9egQYO0Z88eZWRkKD4+XlOnTvV2eIDfevjhh/XRRx9px44d3g6l0wYOHKg9e/bo2LFjev311zV16lQVFxf7VUEeaH8D09PTHf8fPHiwRowYocsvv1x5eXnKzMz0YmTwRSaTyem+YRjNljUZOHCgBg4c6Lg/YsQIHT58WL/5zW/8ojhpKdeWlvsaX9zuHfn75Wvbvb2x++J2dyWGqfuQsrIyVVVVaejQoQoODlZwcLCKi4v129/+VsHBwWpoaPB2iB4VFxfX7Ifk97///fOe0CRQ/fKXv9Rjjz32/7V3PyFN/3Ecx1+/5uZEJPoHGjpneRiohLVDs8TDTh5E6lBehigIg5DYEKS6qsOLJCjBQAQR6xKiIsg8uJvQZV1EqCAQvOxoEqzL53f5JdTi12b2/XxXzwfs8P0exuv75jvevPf989HAwIA6OjoUi8WUSCSUSqVsRwMq1ujoqNbX17Wzs6PGxkbbcU7N5/OptbVV4XBYqVRKN27c0OzsrO1YZfnTe2Btba06Ojr0/v1721HgIpcvX5bH4ym6Cp7P54uuZP6f27dvV8S5VV9f/8Njraqqqsg7RmzWvZz+5ba6/2rvrZTzvRRcGXeRaDRa9KbVoaEhhUIhjY+Py+PxWEpmx507d4qWO3j37p2am5stJbLr8+fPOnfu2//PPB4PS5sBp2CM0ejoqFZXV5XNZtXS0mI70pkyxqhQKNiOUZY/vQcWCgXt7++ru7vbdhS4iM/n061bt7S9va179+6d7N/e3lZ/f3/J35PL5Vx9i/dXkUhEGxsb3+zLZDIKh8Pyer2WUp2ejbqfpn+5pe5n1Xsr5XwvBcO4i9TV1RU9M1FbW6tLly5V7HOMvyKRSKirq0tTU1N68OCB3rx5o3Q6rXQ6bTuaFX19fZqcnFQgEFBbW5tyuZxmZmY0PDxsOxr+EsfHx/rw4cPJ9sePH/X27VtdvHhRgUDAYrLyPXr0SCsrK1pbW1NdXd3JFYPz58+rpqbGcrryPH36VL29vWpqatKnT5/06tUrZbNZbW1t2Y5Wlj+tB46Njamvr0+BQED5fF4TExM6OjrisSIUSSaTisViCofDikQiSqfTOjg4UDwelyQ9efJEh4eHWlpakiQ9f/5cwWBQbW1t+vLli5aXl/X69Wsry4P9rC98nz0ej2tubk7JZFIjIyPa3d3VwsKCXr586frsbql7Kf3LrXU/TXa31P23sfUad5Smkpd1OQsbGxumvb3dVFdXm1AoZNLptO1I1hwdHZnHjx+bQCBg/H6/uXbtmnn27JkpFAq2o+Ev8XV5ke8/g4ODtqOV7UfHIcksLi7ajla24eFh09zcbHw+n7ly5YqJRqMmk8nYjnUmKrkHPnz40DQ0NBiv12uuXr1q7t+/b/b29mzHgkvNz8+f/I5v3rz5zVJPg4ODpqen52R7enraXL9+3fj9fnPhwgVz9+5ds7m5aSH1z/vC99mNMSabzZrOzk7j8/lMMBg0L168cD64KT+7W+peSv9ya91Pk90tdf9d/jHmv6f3AQAAAACAI3iBGwAAAAAADmMYBwAAAADAYQzjAAAAAAA4jGEcAAAAAACHMYwDAAAAAOAwhnEAAAAAABzGMA4AAAAAgMMYxgEAAAAAcBjDOAAAAAAADmMYBwAAAADAYQzjAAAAAAA47F/DCL21NIcTHAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1200x1000 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "numerical_vars = ['Age', 'Gender','Total_Bilirubin', 'Direct_Bilirubin', 'Alkaline_Phosphotase', \n",
-    "                  'Alamine_Aminotransferase', 'Aspartate_Aminotransferase', 'Total_Protiens', \n",
-    "                  'Albumin', 'Albumin_and_Globulin_Ratio']\n",
-    "df[numerical_vars].hist(figsize=(12, 10))\n",
-    "plt.suptitle('Histograms of Numerical Variables', fontsize=16)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#bar chart for gender distribution in the dataset and also for the liver patient and non liver patient\n",
-    "categorical_vars = ['Gender', 'Dataset']\n",
-    "plt.figure(figsize=(12, 5))\n",
-    "for i, var in enumerate(categorical_vars, 1):\n",
-    "    plt.subplot(1, 2, i)\n",
-    "    sns.countplot(x=var, data=df)\n",
-    "    plt.title(f'Bar Chart of {var}', fontsize=14)\n",
-    "    plt.xlabel(var)\n",
-    "    plt.ylabel('Count')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#to find the correlation we change the genders to numeric\n",
-    "df['Gender']=df['Gender'].replace({'Male':1,'Female':0})\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x2000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "coormat=df.corr()#correlation between features in the dataset\n",
-    "top_corr_features=coormat.index\n",
-    "plt.figure(figsize=(20,20))\n",
-    "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\",fmt=\".2f\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Age</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.056560</td>\n",
-       "      <td>0.011763</td>\n",
-       "      <td>0.007529</td>\n",
-       "      <td>0.080425</td>\n",
-       "      <td>-0.086883</td>\n",
-       "      <td>-0.019910</td>\n",
-       "      <td>-0.187461</td>\n",
-       "      <td>-0.265924</td>\n",
-       "      <td>-0.216408</td>\n",
-       "      <td>-0.137351</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <td>0.056560</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.089291</td>\n",
-       "      <td>0.100436</td>\n",
-       "      <td>-0.027496</td>\n",
-       "      <td>0.082332</td>\n",
-       "      <td>0.080336</td>\n",
-       "      <td>-0.089121</td>\n",
-       "      <td>-0.093799</td>\n",
-       "      <td>-0.003424</td>\n",
-       "      <td>-0.082416</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <td>0.011763</td>\n",
-       "      <td>0.089291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.874618</td>\n",
-       "      <td>0.206669</td>\n",
-       "      <td>0.214065</td>\n",
-       "      <td>0.237831</td>\n",
-       "      <td>-0.008099</td>\n",
-       "      <td>-0.222250</td>\n",
-       "      <td>-0.206267</td>\n",
-       "      <td>-0.220208</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <td>0.007529</td>\n",
-       "      <td>0.100436</td>\n",
-       "      <td>0.874618</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.234939</td>\n",
-       "      <td>0.233894</td>\n",
-       "      <td>0.257544</td>\n",
-       "      <td>-0.000139</td>\n",
-       "      <td>-0.228531</td>\n",
-       "      <td>-0.200125</td>\n",
-       "      <td>-0.246046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <td>0.080425</td>\n",
-       "      <td>-0.027496</td>\n",
-       "      <td>0.206669</td>\n",
-       "      <td>0.234939</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.125680</td>\n",
-       "      <td>0.167196</td>\n",
-       "      <td>-0.028514</td>\n",
-       "      <td>-0.165453</td>\n",
-       "      <td>-0.234166</td>\n",
-       "      <td>-0.184866</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <td>-0.086883</td>\n",
-       "      <td>0.082332</td>\n",
-       "      <td>0.214065</td>\n",
-       "      <td>0.233894</td>\n",
-       "      <td>0.125680</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.791966</td>\n",
-       "      <td>-0.042518</td>\n",
-       "      <td>-0.029742</td>\n",
-       "      <td>-0.002375</td>\n",
-       "      <td>-0.163416</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <td>-0.019910</td>\n",
-       "      <td>0.080336</td>\n",
-       "      <td>0.237831</td>\n",
-       "      <td>0.257544</td>\n",
-       "      <td>0.167196</td>\n",
-       "      <td>0.791966</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.025645</td>\n",
-       "      <td>-0.085290</td>\n",
-       "      <td>-0.070040</td>\n",
-       "      <td>-0.151934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <td>-0.187461</td>\n",
-       "      <td>-0.089121</td>\n",
-       "      <td>-0.008099</td>\n",
-       "      <td>-0.000139</td>\n",
-       "      <td>-0.028514</td>\n",
-       "      <td>-0.042518</td>\n",
-       "      <td>-0.025645</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.784053</td>\n",
-       "      <td>0.234887</td>\n",
-       "      <td>0.035008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albumin</th>\n",
-       "      <td>-0.265924</td>\n",
-       "      <td>-0.093799</td>\n",
-       "      <td>-0.222250</td>\n",
-       "      <td>-0.228531</td>\n",
-       "      <td>-0.165453</td>\n",
-       "      <td>-0.029742</td>\n",
-       "      <td>-0.085290</td>\n",
-       "      <td>0.784053</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.689632</td>\n",
-       "      <td>0.161388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <td>-0.216408</td>\n",
-       "      <td>-0.003424</td>\n",
-       "      <td>-0.206267</td>\n",
-       "      <td>-0.200125</td>\n",
-       "      <td>-0.234166</td>\n",
-       "      <td>-0.002375</td>\n",
-       "      <td>-0.070040</td>\n",
-       "      <td>0.234887</td>\n",
-       "      <td>0.689632</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.163131</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dataset</th>\n",
-       "      <td>-0.137351</td>\n",
-       "      <td>-0.082416</td>\n",
-       "      <td>-0.220208</td>\n",
-       "      <td>-0.246046</td>\n",
-       "      <td>-0.184866</td>\n",
-       "      <td>-0.163416</td>\n",
-       "      <td>-0.151934</td>\n",
-       "      <td>0.035008</td>\n",
-       "      <td>0.161388</td>\n",
-       "      <td>0.163131</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 Age    Gender  Total_Bilirubin  \\\n",
-       "Age                         1.000000  0.056560         0.011763   \n",
-       "Gender                      0.056560  1.000000         0.089291   \n",
-       "Total_Bilirubin             0.011763  0.089291         1.000000   \n",
-       "Direct_Bilirubin            0.007529  0.100436         0.874618   \n",
-       "Alkaline_Phosphotase        0.080425 -0.027496         0.206669   \n",
-       "Alamine_Aminotransferase   -0.086883  0.082332         0.214065   \n",
-       "Aspartate_Aminotransferase -0.019910  0.080336         0.237831   \n",
-       "Total_Protiens             -0.187461 -0.089121        -0.008099   \n",
-       "Albumin                    -0.265924 -0.093799        -0.222250   \n",
-       "Albumin_and_Globulin_Ratio -0.216408 -0.003424        -0.206267   \n",
-       "Dataset                    -0.137351 -0.082416        -0.220208   \n",
-       "\n",
-       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
-       "Age                                 0.007529              0.080425   \n",
-       "Gender                              0.100436             -0.027496   \n",
-       "Total_Bilirubin                     0.874618              0.206669   \n",
-       "Direct_Bilirubin                    1.000000              0.234939   \n",
-       "Alkaline_Phosphotase                0.234939              1.000000   \n",
-       "Alamine_Aminotransferase            0.233894              0.125680   \n",
-       "Aspartate_Aminotransferase          0.257544              0.167196   \n",
-       "Total_Protiens                     -0.000139             -0.028514   \n",
-       "Albumin                            -0.228531             -0.165453   \n",
-       "Albumin_and_Globulin_Ratio         -0.200125             -0.234166   \n",
-       "Dataset                            -0.246046             -0.184866   \n",
-       "\n",
-       "                            Alamine_Aminotransferase  \\\n",
-       "Age                                        -0.086883   \n",
-       "Gender                                      0.082332   \n",
-       "Total_Bilirubin                             0.214065   \n",
-       "Direct_Bilirubin                            0.233894   \n",
-       "Alkaline_Phosphotase                        0.125680   \n",
-       "Alamine_Aminotransferase                    1.000000   \n",
-       "Aspartate_Aminotransferase                  0.791966   \n",
-       "Total_Protiens                             -0.042518   \n",
-       "Albumin                                    -0.029742   \n",
-       "Albumin_and_Globulin_Ratio                 -0.002375   \n",
-       "Dataset                                    -0.163416   \n",
-       "\n",
-       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
-       "Age                                          -0.019910       -0.187461   \n",
-       "Gender                                        0.080336       -0.089121   \n",
-       "Total_Bilirubin                               0.237831       -0.008099   \n",
-       "Direct_Bilirubin                              0.257544       -0.000139   \n",
-       "Alkaline_Phosphotase                          0.167196       -0.028514   \n",
-       "Alamine_Aminotransferase                      0.791966       -0.042518   \n",
-       "Aspartate_Aminotransferase                    1.000000       -0.025645   \n",
-       "Total_Protiens                               -0.025645        1.000000   \n",
-       "Albumin                                      -0.085290        0.784053   \n",
-       "Albumin_and_Globulin_Ratio                   -0.070040        0.234887   \n",
-       "Dataset                                      -0.151934        0.035008   \n",
-       "\n",
-       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
-       "Age                        -0.265924                   -0.216408 -0.137351  \n",
-       "Gender                     -0.093799                   -0.003424 -0.082416  \n",
-       "Total_Bilirubin            -0.222250                   -0.206267 -0.220208  \n",
-       "Direct_Bilirubin           -0.228531                   -0.200125 -0.246046  \n",
-       "Alkaline_Phosphotase       -0.165453                   -0.234166 -0.184866  \n",
-       "Alamine_Aminotransferase   -0.029742                   -0.002375 -0.163416  \n",
-       "Aspartate_Aminotransferase -0.085290                   -0.070040 -0.151934  \n",
-       "Total_Protiens              0.784053                    0.234887  0.035008  \n",
-       "Albumin                     1.000000                    0.689632  0.161388  \n",
-       "Albumin_and_Globulin_Ratio  0.689632                    1.000000  0.163131  \n",
-       "Dataset                     0.161388                    0.163131  1.000000  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.corr()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Filling the null values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#we can also fill the nan values in the column Albumin_and_Globulin_Ratio\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "imputer=SimpleImputer(strategy='mean')\n",
-    "imputer.fit(df[['Albumin_and_Globulin_Ratio']])\n",
-    "df['Albumin_and_Globulin_Ratio']=imputer.transform(df[['Albumin_and_Globulin_Ratio']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                           0\n",
-       "Gender                        0\n",
-       "Total_Bilirubin               0\n",
-       "Direct_Bilirubin              0\n",
-       "Alkaline_Phosphotase          0\n",
-       "Alamine_Aminotransferase      0\n",
-       "Aspartate_Aminotransferase    0\n",
-       "Total_Protiens                0\n",
-       "Albumin                       0\n",
-       "Albumin_and_Globulin_Ratio    0\n",
-       "Dataset                       0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 282690fafd89971a12ae016c1f5b80e33d9ecf16 Mon Sep 17 00:00:00 2001
From: Rakesh Joshi <rakeshjoshi21021369@gmail.com>
Date: Sun, 19 May 2024 23:03:59 +0530
Subject: [PATCH 3/5] Data set exploration

---
 .../Liver_disease_EDA.ipynb                   | 1739 +++++++++++++++++
 1 file changed, 1739 insertions(+)
 create mode 100644 Liver DIsease prediction/Liver_disease_EDA.ipynb

diff --git a/Liver DIsease prediction/Liver_disease_EDA.ipynb b/Liver DIsease prediction/Liver_disease_EDA.ipynb
new file mode 100644
index 00000000..60ed328a
--- /dev/null
+++ b/Liver DIsease prediction/Liver_disease_EDA.ipynb	
@@ -0,0 +1,1739 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Liver Disease Prediction | Dataset exploration "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(r\"C:\\Users\\rakes\\health_proj\\Liver Disease Prediction\\Dataset\\indian_liver_patient.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Explore and get the labels of the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(583, 11)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>65</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>16</td>\n",
+       "      <td>18</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>62</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>699</td>\n",
+       "      <td>64</td>\n",
+       "      <td>100</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>62</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>490</td>\n",
+       "      <td>60</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>58</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>182</td>\n",
+       "      <td>14</td>\n",
+       "      <td>20</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>72</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>195</td>\n",
+       "      <td>27</td>\n",
+       "      <td>59</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "0   65  Female              0.7               0.1                   187   \n",
+       "1   62    Male             10.9               5.5                   699   \n",
+       "2   62    Male              7.3               4.1                   490   \n",
+       "3   58    Male              1.0               0.4                   182   \n",
+       "4   72    Male              3.9               2.0                   195   \n",
+       "\n",
+       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "0                        16                          18             6.8   \n",
+       "1                        64                         100             7.5   \n",
+       "2                        60                          68             7.0   \n",
+       "3                        14                          20             6.8   \n",
+       "4                        27                          59             7.3   \n",
+       "\n",
+       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
+       "0      3.3                        0.90        1  \n",
+       "1      3.2                        0.74        1  \n",
+       "2      3.3                        0.89        1  \n",
+       "3      3.4                        1.00        1  \n",
+       "4      2.4                        0.40        1  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>60</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>500</td>\n",
+       "      <td>20</td>\n",
+       "      <td>34</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>40</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>98</td>\n",
+       "      <td>35</td>\n",
+       "      <td>31</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>52</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>245</td>\n",
+       "      <td>48</td>\n",
+       "      <td>49</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>184</td>\n",
+       "      <td>29</td>\n",
+       "      <td>32</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>216</td>\n",
+       "      <td>21</td>\n",
+       "      <td>24</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Age Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "578   60   Male              0.5               0.1                   500   \n",
+       "579   40   Male              0.6               0.1                    98   \n",
+       "580   52   Male              0.8               0.2                   245   \n",
+       "581   31   Male              1.3               0.5                   184   \n",
+       "582   38   Male              1.0               0.3                   216   \n",
+       "\n",
+       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "578                        20                          34             5.9   \n",
+       "579                        35                          31             6.0   \n",
+       "580                        48                          49             6.4   \n",
+       "581                        29                          32             6.8   \n",
+       "582                        21                          24             7.3   \n",
+       "\n",
+       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
+       "578      1.6                        0.37        2  \n",
+       "579      3.2                        1.10        1  \n",
+       "580      3.2                        1.00        1  \n",
+       "581      3.4                        1.00        1  \n",
+       "582      4.4                        1.50        2  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',\n",
+       "       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',\n",
+       "       'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',\n",
+       "       'Albumin_and_Globulin_Ratio', 'Dataset'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Statistical details like mean, meadain and x percentiles of the labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>579.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>44.746141</td>\n",
+       "      <td>3.298799</td>\n",
+       "      <td>1.486106</td>\n",
+       "      <td>290.576329</td>\n",
+       "      <td>80.713551</td>\n",
+       "      <td>109.910806</td>\n",
+       "      <td>6.483190</td>\n",
+       "      <td>3.141852</td>\n",
+       "      <td>0.947064</td>\n",
+       "      <td>1.286449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>16.189833</td>\n",
+       "      <td>6.209522</td>\n",
+       "      <td>2.808498</td>\n",
+       "      <td>242.937989</td>\n",
+       "      <td>182.620356</td>\n",
+       "      <td>288.918529</td>\n",
+       "      <td>1.085451</td>\n",
+       "      <td>0.795519</td>\n",
+       "      <td>0.319592</td>\n",
+       "      <td>0.452490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>63.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>2.700000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>175.500000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>208.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>3.100000</td>\n",
+       "      <td>0.930000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>58.000000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>1.300000</td>\n",
+       "      <td>298.000000</td>\n",
+       "      <td>60.500000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>7.200000</td>\n",
+       "      <td>3.800000</td>\n",
+       "      <td>1.100000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>19.700000</td>\n",
+       "      <td>2110.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>4929.000000</td>\n",
+       "      <td>9.600000</td>\n",
+       "      <td>5.500000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "count  583.000000       583.000000        583.000000            583.000000   \n",
+       "mean    44.746141         3.298799          1.486106            290.576329   \n",
+       "std     16.189833         6.209522          2.808498            242.937989   \n",
+       "min      4.000000         0.400000          0.100000             63.000000   \n",
+       "25%     33.000000         0.800000          0.200000            175.500000   \n",
+       "50%     45.000000         1.000000          0.300000            208.000000   \n",
+       "75%     58.000000         2.600000          1.300000            298.000000   \n",
+       "max     90.000000        75.000000         19.700000           2110.000000   \n",
+       "\n",
+       "       Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "count                583.000000                  583.000000      583.000000   \n",
+       "mean                  80.713551                  109.910806        6.483190   \n",
+       "std                  182.620356                  288.918529        1.085451   \n",
+       "min                   10.000000                   10.000000        2.700000   \n",
+       "25%                   23.000000                   25.000000        5.800000   \n",
+       "50%                   35.000000                   42.000000        6.600000   \n",
+       "75%                   60.500000                   87.000000        7.200000   \n",
+       "max                 2000.000000                 4929.000000        9.600000   \n",
+       "\n",
+       "          Albumin  Albumin_and_Globulin_Ratio     Dataset  \n",
+       "count  583.000000                  579.000000  583.000000  \n",
+       "mean     3.141852                    0.947064    1.286449  \n",
+       "std      0.795519                    0.319592    0.452490  \n",
+       "min      0.900000                    0.300000    1.000000  \n",
+       "25%      2.600000                    0.700000    1.000000  \n",
+       "50%      3.100000                    0.930000    1.000000  \n",
+       "75%      3.800000                    1.100000    2.000000  \n",
+       "max      5.500000                    2.800000    2.000000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.Finiding NULL valur and performing imputation and changes as required"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                           0\n",
+       "Gender                        0\n",
+       "Total_Bilirubin               0\n",
+       "Direct_Bilirubin              0\n",
+       "Alkaline_Phosphotase          0\n",
+       "Alamine_Aminotransferase      0\n",
+       "Aspartate_Aminotransferase    0\n",
+       "Total_Protiens                0\n",
+       "Albumin                       0\n",
+       "Albumin_and_Globulin_Ratio    4\n",
+       "Dataset                       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "feature Albumin_and_Globulin_Ratio have 4 null values which should to filled for furthing finding for correlations and other insights. <br />\n",
+    "->so here i am using mean method to impute the values in place of null values as only few values are null so default function will work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.impute import SimpleImputer\n",
+    "imputer=SimpleImputer(strategy='mean')\n",
+    "imputer.fit(df[['Albumin_and_Globulin_Ratio']])\n",
+    "df['Albumin_and_Globulin_Ratio']=imputer.transform(df[['Albumin_and_Globulin_Ratio']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 583 entries, 0 to 582\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column                      Non-Null Count  Dtype  \n",
+      "---  ------                      --------------  -----  \n",
+      " 0   Age                         583 non-null    int64  \n",
+      " 1   Gender                      583 non-null    object \n",
+      " 2   Total_Bilirubin             583 non-null    float64\n",
+      " 3   Direct_Bilirubin            583 non-null    float64\n",
+      " 4   Alkaline_Phosphotase        583 non-null    int64  \n",
+      " 5   Alamine_Aminotransferase    583 non-null    int64  \n",
+      " 6   Aspartate_Aminotransferase  583 non-null    int64  \n",
+      " 7   Total_Protiens              583 non-null    float64\n",
+      " 8   Albumin                     583 non-null    float64\n",
+      " 9   Albumin_and_Globulin_Ratio  583 non-null    float64\n",
+      " 10  Dataset                     583 non-null    int64  \n",
+      "dtypes: float64(5), int64(5), object(1)\n",
+      "memory usage: 50.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#to get the data type of each feature\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In above we can see that Gender is not a numeric type so we convert it numeric for further <br />\n",
+    "-> Gender: 1-male and 0-female"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Gender']=df['Gender'].replace({'Male':1,'Female':0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. EDA\n",
+    "-> Check the distribution of the columns like gender and target class(dataset)<br />\n",
+    "-> Perfoming multivariant analysis on all parameters and drawing conclusions from them<br />\n",
+    "-> explore the correlation matrix<br />\n",
+    "-> Overall Insights<br />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ->Check for Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "416 167\n"
+     ]
+    }
+   ],
+   "source": [
+    "# target class distribution in data set, 1 represent its a liver patient and 2 represent it is not a liver patient\n",
+    "true_count=len(df.loc[df['Dataset']==1])\n",
+    "false_count=len(df.loc[df['Dataset']==2])\n",
+    "print(true_count,false_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#bar chart for the target class (which is dataset here) and gender to visualize its distriubtion\n",
+    "categorical_vars = ['Gender', 'Dataset']\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "for i, var in enumerate(categorical_vars, 1):\n",
+    "    plt.subplot(1, 2, i)\n",
+    "    sns.countplot(x=var, data=df)\n",
+    "    plt.title(f'Bar Chart of {var}', fontsize=14)\n",
+    "    plt.xlabel(var)\n",
+    "    plt.ylabel('Count')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ->Mulitvariant analysis on Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "numerical_vars = ['Age', 'Gender','Total_Bilirubin', 'Direct_Bilirubin', 'Alkaline_Phosphotase', \n",
+    "                  'Alamine_Aminotransferase', 'Aspartate_Aminotransferase', 'Total_Protiens', \n",
+    "                  'Albumin', 'Albumin_and_Globulin_Ratio']\n",
+    "df[numerical_vars].hist(figsize=(12, 10))\n",
+    "plt.suptitle('Histograms of Numerical Variables', fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analysis of the histogram:<br />\n",
+    "-> Age: The graph shows that most of the people age is between 30 to 70 in the dataset<br />\n",
+    "-> Gender: Gender is only 0 (for female) and 1 (for male) also indicates that male dominant data is present in the dataset<br />\n",
+    "-> Total Billirubin: around 500 people in the dataset have total billirubin between 0 to 10 in their blood<br />\n",
+    "-> Direct Billirubin: Direct billirubin which is processed by liver, in the dataset around 450 people have direct billirubin between 0 to 2 in their blood.\n",
+    "-> Alkaline phosphotase(ALP):Few people in dataset have high elevated ALP enzyme which indicates liver disorders<br />\n",
+    "-> Alanine Aminotransferase(ALT): This enzyme which convert alanine and amino acid into pyruvate but high level of this causes damages to liver and result int fatty liver disease. In dataset very few have high level ALT.<br />\n",
+    "-> Aspartate Aminotransferase(AST): High AST causes hepatitis, few have high level AST in the dataset.<br />\n",
+    "-> Total Protien: This indicates total amount of albumin and globulin in the blood, its low levels (less than 6g) indicates liver disease. Around 60 people in the dataset have it bewtween 5-6 gm and some have even less than this.<br />\n",
+    "-> Albumin: Main protien made by the liver, low levels(less than 3.5) indicate chronic liver disease. Around 100 people have low level of albumin the dataset.<br />\n",
+    "-> A/G ratio: low ratio signifise liver disease, in dataset around 50 have this ratio less than 0.5."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### -> Explore the Correaltion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr=df.corr(method='kendall')\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.heatmap(corr,annot=True,cmap='coolwarm',fmt=\".2f\",linewidth=.5)\n",
+    "plt.title(\"Correlation\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the exact correaltion value is not visible in the heatmap so going to print the whole matrix and derive something fromt there.<br />\n",
+    "Correlation with respect to different methods can be derived like pearson, spearman, kendall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.056560</td>\n",
+       "      <td>0.011763</td>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.080425</td>\n",
+       "      <td>-0.086883</td>\n",
+       "      <td>-0.019910</td>\n",
+       "      <td>-0.187461</td>\n",
+       "      <td>-0.265924</td>\n",
+       "      <td>-0.216089</td>\n",
+       "      <td>-0.137351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <td>0.056560</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.089291</td>\n",
+       "      <td>0.100436</td>\n",
+       "      <td>-0.027496</td>\n",
+       "      <td>0.082332</td>\n",
+       "      <td>0.080336</td>\n",
+       "      <td>-0.089121</td>\n",
+       "      <td>-0.093799</td>\n",
+       "      <td>-0.003404</td>\n",
+       "      <td>-0.082416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <td>0.011763</td>\n",
+       "      <td>0.089291</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.874618</td>\n",
+       "      <td>0.206669</td>\n",
+       "      <td>0.214065</td>\n",
+       "      <td>0.237831</td>\n",
+       "      <td>-0.008099</td>\n",
+       "      <td>-0.222250</td>\n",
+       "      <td>-0.206159</td>\n",
+       "      <td>-0.220208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.100436</td>\n",
+       "      <td>0.874618</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.234939</td>\n",
+       "      <td>0.233894</td>\n",
+       "      <td>0.257544</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.228531</td>\n",
+       "      <td>-0.200004</td>\n",
+       "      <td>-0.246046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <td>0.080425</td>\n",
+       "      <td>-0.027496</td>\n",
+       "      <td>0.206669</td>\n",
+       "      <td>0.234939</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.125680</td>\n",
+       "      <td>0.167196</td>\n",
+       "      <td>-0.028514</td>\n",
+       "      <td>-0.165453</td>\n",
+       "      <td>-0.233960</td>\n",
+       "      <td>-0.184866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <td>-0.086883</td>\n",
+       "      <td>0.082332</td>\n",
+       "      <td>0.214065</td>\n",
+       "      <td>0.233894</td>\n",
+       "      <td>0.125680</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.791966</td>\n",
+       "      <td>-0.042518</td>\n",
+       "      <td>-0.029742</td>\n",
+       "      <td>-0.002374</td>\n",
+       "      <td>-0.163416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <td>-0.019910</td>\n",
+       "      <td>0.080336</td>\n",
+       "      <td>0.237831</td>\n",
+       "      <td>0.257544</td>\n",
+       "      <td>0.167196</td>\n",
+       "      <td>0.791966</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.025645</td>\n",
+       "      <td>-0.085290</td>\n",
+       "      <td>-0.070024</td>\n",
+       "      <td>-0.151934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <td>-0.187461</td>\n",
+       "      <td>-0.089121</td>\n",
+       "      <td>-0.008099</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.028514</td>\n",
+       "      <td>-0.042518</td>\n",
+       "      <td>-0.025645</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.784053</td>\n",
+       "      <td>0.233904</td>\n",
+       "      <td>0.035008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin</th>\n",
+       "      <td>-0.265924</td>\n",
+       "      <td>-0.093799</td>\n",
+       "      <td>-0.222250</td>\n",
+       "      <td>-0.228531</td>\n",
+       "      <td>-0.165453</td>\n",
+       "      <td>-0.029742</td>\n",
+       "      <td>-0.085290</td>\n",
+       "      <td>0.784053</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.686322</td>\n",
+       "      <td>0.161388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <td>-0.216089</td>\n",
+       "      <td>-0.003404</td>\n",
+       "      <td>-0.206159</td>\n",
+       "      <td>-0.200004</td>\n",
+       "      <td>-0.233960</td>\n",
+       "      <td>-0.002374</td>\n",
+       "      <td>-0.070024</td>\n",
+       "      <td>0.233904</td>\n",
+       "      <td>0.686322</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.162319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dataset</th>\n",
+       "      <td>-0.137351</td>\n",
+       "      <td>-0.082416</td>\n",
+       "      <td>-0.220208</td>\n",
+       "      <td>-0.246046</td>\n",
+       "      <td>-0.184866</td>\n",
+       "      <td>-0.163416</td>\n",
+       "      <td>-0.151934</td>\n",
+       "      <td>0.035008</td>\n",
+       "      <td>0.161388</td>\n",
+       "      <td>0.162319</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Age    Gender  Total_Bilirubin  \\\n",
+       "Age                         1.000000  0.056560         0.011763   \n",
+       "Gender                      0.056560  1.000000         0.089291   \n",
+       "Total_Bilirubin             0.011763  0.089291         1.000000   \n",
+       "Direct_Bilirubin            0.007529  0.100436         0.874618   \n",
+       "Alkaline_Phosphotase        0.080425 -0.027496         0.206669   \n",
+       "Alamine_Aminotransferase   -0.086883  0.082332         0.214065   \n",
+       "Aspartate_Aminotransferase -0.019910  0.080336         0.237831   \n",
+       "Total_Protiens             -0.187461 -0.089121        -0.008099   \n",
+       "Albumin                    -0.265924 -0.093799        -0.222250   \n",
+       "Albumin_and_Globulin_Ratio -0.216089 -0.003404        -0.206159   \n",
+       "Dataset                    -0.137351 -0.082416        -0.220208   \n",
+       "\n",
+       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "Age                                 0.007529              0.080425   \n",
+       "Gender                              0.100436             -0.027496   \n",
+       "Total_Bilirubin                     0.874618              0.206669   \n",
+       "Direct_Bilirubin                    1.000000              0.234939   \n",
+       "Alkaline_Phosphotase                0.234939              1.000000   \n",
+       "Alamine_Aminotransferase            0.233894              0.125680   \n",
+       "Aspartate_Aminotransferase          0.257544              0.167196   \n",
+       "Total_Protiens                     -0.000139             -0.028514   \n",
+       "Albumin                            -0.228531             -0.165453   \n",
+       "Albumin_and_Globulin_Ratio         -0.200004             -0.233960   \n",
+       "Dataset                            -0.246046             -0.184866   \n",
+       "\n",
+       "                            Alamine_Aminotransferase  \\\n",
+       "Age                                        -0.086883   \n",
+       "Gender                                      0.082332   \n",
+       "Total_Bilirubin                             0.214065   \n",
+       "Direct_Bilirubin                            0.233894   \n",
+       "Alkaline_Phosphotase                        0.125680   \n",
+       "Alamine_Aminotransferase                    1.000000   \n",
+       "Aspartate_Aminotransferase                  0.791966   \n",
+       "Total_Protiens                             -0.042518   \n",
+       "Albumin                                    -0.029742   \n",
+       "Albumin_and_Globulin_Ratio                 -0.002374   \n",
+       "Dataset                                    -0.163416   \n",
+       "\n",
+       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "Age                                          -0.019910       -0.187461   \n",
+       "Gender                                        0.080336       -0.089121   \n",
+       "Total_Bilirubin                               0.237831       -0.008099   \n",
+       "Direct_Bilirubin                              0.257544       -0.000139   \n",
+       "Alkaline_Phosphotase                          0.167196       -0.028514   \n",
+       "Alamine_Aminotransferase                      0.791966       -0.042518   \n",
+       "Aspartate_Aminotransferase                    1.000000       -0.025645   \n",
+       "Total_Protiens                               -0.025645        1.000000   \n",
+       "Albumin                                      -0.085290        0.784053   \n",
+       "Albumin_and_Globulin_Ratio                   -0.070024        0.233904   \n",
+       "Dataset                                      -0.151934        0.035008   \n",
+       "\n",
+       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
+       "Age                        -0.265924                   -0.216089 -0.137351  \n",
+       "Gender                     -0.093799                   -0.003404 -0.082416  \n",
+       "Total_Bilirubin            -0.222250                   -0.206159 -0.220208  \n",
+       "Direct_Bilirubin           -0.228531                   -0.200004 -0.246046  \n",
+       "Alkaline_Phosphotase       -0.165453                   -0.233960 -0.184866  \n",
+       "Alamine_Aminotransferase   -0.029742                   -0.002374 -0.163416  \n",
+       "Aspartate_Aminotransferase -0.085290                   -0.070024 -0.151934  \n",
+       "Total_Protiens              0.784053                    0.233904  0.035008  \n",
+       "Albumin                     1.000000                    0.686322  0.161388  \n",
+       "Albumin_and_Globulin_Ratio  0.686322                    1.000000  0.162319  \n",
+       "Dataset                     0.161388                    0.162319  1.000000  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='pearson')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.062364</td>\n",
+       "      <td>0.113827</td>\n",
+       "      <td>0.106473</td>\n",
+       "      <td>0.059205</td>\n",
+       "      <td>-0.067737</td>\n",
+       "      <td>-0.018285</td>\n",
+       "      <td>-0.174271</td>\n",
+       "      <td>-0.260791</td>\n",
+       "      <td>-0.249505</td>\n",
+       "      <td>-0.129572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <td>0.062364</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.200503</td>\n",
+       "      <td>0.209210</td>\n",
+       "      <td>0.079131</td>\n",
+       "      <td>0.201107</td>\n",
+       "      <td>0.209434</td>\n",
+       "      <td>-0.090905</td>\n",
+       "      <td>-0.095440</td>\n",
+       "      <td>-0.008342</td>\n",
+       "      <td>-0.082416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <td>0.113827</td>\n",
+       "      <td>0.200503</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.959216</td>\n",
+       "      <td>0.383794</td>\n",
+       "      <td>0.436586</td>\n",
+       "      <td>0.508869</td>\n",
+       "      <td>-0.019252</td>\n",
+       "      <td>-0.222184</td>\n",
+       "      <td>-0.284200</td>\n",
+       "      <td>-0.303879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <td>0.106473</td>\n",
+       "      <td>0.209210</td>\n",
+       "      <td>0.959216</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.367818</td>\n",
+       "      <td>0.412322</td>\n",
+       "      <td>0.504138</td>\n",
+       "      <td>-0.019987</td>\n",
+       "      <td>-0.232664</td>\n",
+       "      <td>-0.297338</td>\n",
+       "      <td>-0.297270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <td>0.059205</td>\n",
+       "      <td>0.079131</td>\n",
+       "      <td>0.383794</td>\n",
+       "      <td>0.367818</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.410752</td>\n",
+       "      <td>0.395732</td>\n",
+       "      <td>0.014028</td>\n",
+       "      <td>-0.170809</td>\n",
+       "      <td>-0.321095</td>\n",
+       "      <td>-0.273247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <td>-0.067737</td>\n",
+       "      <td>0.201107</td>\n",
+       "      <td>0.436586</td>\n",
+       "      <td>0.412322</td>\n",
+       "      <td>0.410752</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.773611</td>\n",
+       "      <td>-0.018811</td>\n",
+       "      <td>-0.052673</td>\n",
+       "      <td>-0.082942</td>\n",
+       "      <td>-0.290709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <td>-0.018285</td>\n",
+       "      <td>0.209434</td>\n",
+       "      <td>0.508869</td>\n",
+       "      <td>0.504138</td>\n",
+       "      <td>0.395732</td>\n",
+       "      <td>0.773611</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.084779</td>\n",
+       "      <td>-0.204867</td>\n",
+       "      <td>-0.208809</td>\n",
+       "      <td>-0.308897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <td>-0.174271</td>\n",
+       "      <td>-0.090905</td>\n",
+       "      <td>-0.019252</td>\n",
+       "      <td>-0.019987</td>\n",
+       "      <td>0.014028</td>\n",
+       "      <td>-0.018811</td>\n",
+       "      <td>-0.084779</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.779077</td>\n",
+       "      <td>0.272490</td>\n",
+       "      <td>0.032220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin</th>\n",
+       "      <td>-0.260791</td>\n",
+       "      <td>-0.095440</td>\n",
+       "      <td>-0.222184</td>\n",
+       "      <td>-0.232664</td>\n",
+       "      <td>-0.170809</td>\n",
+       "      <td>-0.052673</td>\n",
+       "      <td>-0.204867</td>\n",
+       "      <td>0.779077</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.751223</td>\n",
+       "      <td>0.167079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <td>-0.249505</td>\n",
+       "      <td>-0.008342</td>\n",
+       "      <td>-0.284200</td>\n",
+       "      <td>-0.297338</td>\n",
+       "      <td>-0.321095</td>\n",
+       "      <td>-0.082942</td>\n",
+       "      <td>-0.208809</td>\n",
+       "      <td>0.272490</td>\n",
+       "      <td>0.751223</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.187377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dataset</th>\n",
+       "      <td>-0.129572</td>\n",
+       "      <td>-0.082416</td>\n",
+       "      <td>-0.303879</td>\n",
+       "      <td>-0.297270</td>\n",
+       "      <td>-0.273247</td>\n",
+       "      <td>-0.290709</td>\n",
+       "      <td>-0.308897</td>\n",
+       "      <td>0.032220</td>\n",
+       "      <td>0.167079</td>\n",
+       "      <td>0.187377</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Age    Gender  Total_Bilirubin  \\\n",
+       "Age                         1.000000  0.062364         0.113827   \n",
+       "Gender                      0.062364  1.000000         0.200503   \n",
+       "Total_Bilirubin             0.113827  0.200503         1.000000   \n",
+       "Direct_Bilirubin            0.106473  0.209210         0.959216   \n",
+       "Alkaline_Phosphotase        0.059205  0.079131         0.383794   \n",
+       "Alamine_Aminotransferase   -0.067737  0.201107         0.436586   \n",
+       "Aspartate_Aminotransferase -0.018285  0.209434         0.508869   \n",
+       "Total_Protiens             -0.174271 -0.090905        -0.019252   \n",
+       "Albumin                    -0.260791 -0.095440        -0.222184   \n",
+       "Albumin_and_Globulin_Ratio -0.249505 -0.008342        -0.284200   \n",
+       "Dataset                    -0.129572 -0.082416        -0.303879   \n",
+       "\n",
+       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "Age                                 0.106473              0.059205   \n",
+       "Gender                              0.209210              0.079131   \n",
+       "Total_Bilirubin                     0.959216              0.383794   \n",
+       "Direct_Bilirubin                    1.000000              0.367818   \n",
+       "Alkaline_Phosphotase                0.367818              1.000000   \n",
+       "Alamine_Aminotransferase            0.412322              0.410752   \n",
+       "Aspartate_Aminotransferase          0.504138              0.395732   \n",
+       "Total_Protiens                     -0.019987              0.014028   \n",
+       "Albumin                            -0.232664             -0.170809   \n",
+       "Albumin_and_Globulin_Ratio         -0.297338             -0.321095   \n",
+       "Dataset                            -0.297270             -0.273247   \n",
+       "\n",
+       "                            Alamine_Aminotransferase  \\\n",
+       "Age                                        -0.067737   \n",
+       "Gender                                      0.201107   \n",
+       "Total_Bilirubin                             0.436586   \n",
+       "Direct_Bilirubin                            0.412322   \n",
+       "Alkaline_Phosphotase                        0.410752   \n",
+       "Alamine_Aminotransferase                    1.000000   \n",
+       "Aspartate_Aminotransferase                  0.773611   \n",
+       "Total_Protiens                             -0.018811   \n",
+       "Albumin                                    -0.052673   \n",
+       "Albumin_and_Globulin_Ratio                 -0.082942   \n",
+       "Dataset                                    -0.290709   \n",
+       "\n",
+       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "Age                                          -0.018285       -0.174271   \n",
+       "Gender                                        0.209434       -0.090905   \n",
+       "Total_Bilirubin                               0.508869       -0.019252   \n",
+       "Direct_Bilirubin                              0.504138       -0.019987   \n",
+       "Alkaline_Phosphotase                          0.395732        0.014028   \n",
+       "Alamine_Aminotransferase                      0.773611       -0.018811   \n",
+       "Aspartate_Aminotransferase                    1.000000       -0.084779   \n",
+       "Total_Protiens                               -0.084779        1.000000   \n",
+       "Albumin                                      -0.204867        0.779077   \n",
+       "Albumin_and_Globulin_Ratio                   -0.208809        0.272490   \n",
+       "Dataset                                      -0.308897        0.032220   \n",
+       "\n",
+       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
+       "Age                        -0.260791                   -0.249505 -0.129572  \n",
+       "Gender                     -0.095440                   -0.008342 -0.082416  \n",
+       "Total_Bilirubin            -0.222184                   -0.284200 -0.303879  \n",
+       "Direct_Bilirubin           -0.232664                   -0.297338 -0.297270  \n",
+       "Alkaline_Phosphotase       -0.170809                   -0.321095 -0.273247  \n",
+       "Alamine_Aminotransferase   -0.052673                   -0.082942 -0.290709  \n",
+       "Aspartate_Aminotransferase -0.204867                   -0.208809 -0.308897  \n",
+       "Total_Protiens              0.779077                    0.272490  0.032220  \n",
+       "Albumin                     1.000000                    0.751223  0.167079  \n",
+       "Albumin_and_Globulin_Ratio  0.751223                    1.000000  0.187377  \n",
+       "Dataset                     0.167079                    0.187377  1.000000  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='spearman')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.051520</td>\n",
+       "      <td>0.078099</td>\n",
+       "      <td>0.074733</td>\n",
+       "      <td>0.038868</td>\n",
+       "      <td>-0.046261</td>\n",
+       "      <td>-0.013206</td>\n",
+       "      <td>-0.120690</td>\n",
+       "      <td>-0.180176</td>\n",
+       "      <td>-0.177241</td>\n",
+       "      <td>-0.107040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <td>0.051520</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.168671</td>\n",
+       "      <td>0.180362</td>\n",
+       "      <td>0.064846</td>\n",
+       "      <td>0.165521</td>\n",
+       "      <td>0.172013</td>\n",
+       "      <td>-0.075289</td>\n",
+       "      <td>-0.079378</td>\n",
+       "      <td>-0.007077</td>\n",
+       "      <td>-0.082416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <td>0.078099</td>\n",
+       "      <td>0.168671</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.898136</td>\n",
+       "      <td>0.270267</td>\n",
+       "      <td>0.306483</td>\n",
+       "      <td>0.361626</td>\n",
+       "      <td>-0.014417</td>\n",
+       "      <td>-0.153281</td>\n",
+       "      <td>-0.203865</td>\n",
+       "      <td>-0.255635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <td>0.074733</td>\n",
+       "      <td>0.180362</td>\n",
+       "      <td>0.898136</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.265809</td>\n",
+       "      <td>0.292416</td>\n",
+       "      <td>0.364823</td>\n",
+       "      <td>-0.015559</td>\n",
+       "      <td>-0.164638</td>\n",
+       "      <td>-0.219255</td>\n",
+       "      <td>-0.256279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <td>0.038868</td>\n",
+       "      <td>0.064846</td>\n",
+       "      <td>0.270267</td>\n",
+       "      <td>0.265809</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.277939</td>\n",
+       "      <td>0.264168</td>\n",
+       "      <td>0.010076</td>\n",
+       "      <td>-0.115934</td>\n",
+       "      <td>-0.227519</td>\n",
+       "      <td>-0.223921</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <td>-0.046261</td>\n",
+       "      <td>0.165521</td>\n",
+       "      <td>0.306483</td>\n",
+       "      <td>0.292416</td>\n",
+       "      <td>0.277939</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.596488</td>\n",
+       "      <td>-0.012909</td>\n",
+       "      <td>-0.033134</td>\n",
+       "      <td>-0.055798</td>\n",
+       "      <td>-0.239269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <td>-0.013206</td>\n",
+       "      <td>0.172013</td>\n",
+       "      <td>0.361626</td>\n",
+       "      <td>0.364823</td>\n",
+       "      <td>0.264168</td>\n",
+       "      <td>0.596488</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.057158</td>\n",
+       "      <td>-0.137458</td>\n",
+       "      <td>-0.145771</td>\n",
+       "      <td>-0.253705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <td>-0.120690</td>\n",
+       "      <td>-0.075289</td>\n",
+       "      <td>-0.014417</td>\n",
+       "      <td>-0.015559</td>\n",
+       "      <td>0.010076</td>\n",
+       "      <td>-0.012909</td>\n",
+       "      <td>-0.057158</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.613098</td>\n",
+       "      <td>0.192220</td>\n",
+       "      <td>0.026685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin</th>\n",
+       "      <td>-0.180176</td>\n",
+       "      <td>-0.079378</td>\n",
+       "      <td>-0.153281</td>\n",
+       "      <td>-0.164638</td>\n",
+       "      <td>-0.115934</td>\n",
+       "      <td>-0.033134</td>\n",
+       "      <td>-0.137458</td>\n",
+       "      <td>0.613098</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.592980</td>\n",
+       "      <td>0.138960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <td>-0.177241</td>\n",
+       "      <td>-0.007077</td>\n",
+       "      <td>-0.203865</td>\n",
+       "      <td>-0.219255</td>\n",
+       "      <td>-0.227519</td>\n",
+       "      <td>-0.055798</td>\n",
+       "      <td>-0.145771</td>\n",
+       "      <td>0.192220</td>\n",
+       "      <td>0.592980</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.158967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dataset</th>\n",
+       "      <td>-0.107040</td>\n",
+       "      <td>-0.082416</td>\n",
+       "      <td>-0.255635</td>\n",
+       "      <td>-0.256279</td>\n",
+       "      <td>-0.223921</td>\n",
+       "      <td>-0.239269</td>\n",
+       "      <td>-0.253705</td>\n",
+       "      <td>0.026685</td>\n",
+       "      <td>0.138960</td>\n",
+       "      <td>0.158967</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Age    Gender  Total_Bilirubin  \\\n",
+       "Age                         1.000000  0.051520         0.078099   \n",
+       "Gender                      0.051520  1.000000         0.168671   \n",
+       "Total_Bilirubin             0.078099  0.168671         1.000000   \n",
+       "Direct_Bilirubin            0.074733  0.180362         0.898136   \n",
+       "Alkaline_Phosphotase        0.038868  0.064846         0.270267   \n",
+       "Alamine_Aminotransferase   -0.046261  0.165521         0.306483   \n",
+       "Aspartate_Aminotransferase -0.013206  0.172013         0.361626   \n",
+       "Total_Protiens             -0.120690 -0.075289        -0.014417   \n",
+       "Albumin                    -0.180176 -0.079378        -0.153281   \n",
+       "Albumin_and_Globulin_Ratio -0.177241 -0.007077        -0.203865   \n",
+       "Dataset                    -0.107040 -0.082416        -0.255635   \n",
+       "\n",
+       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "Age                                 0.074733              0.038868   \n",
+       "Gender                              0.180362              0.064846   \n",
+       "Total_Bilirubin                     0.898136              0.270267   \n",
+       "Direct_Bilirubin                    1.000000              0.265809   \n",
+       "Alkaline_Phosphotase                0.265809              1.000000   \n",
+       "Alamine_Aminotransferase            0.292416              0.277939   \n",
+       "Aspartate_Aminotransferase          0.364823              0.264168   \n",
+       "Total_Protiens                     -0.015559              0.010076   \n",
+       "Albumin                            -0.164638             -0.115934   \n",
+       "Albumin_and_Globulin_Ratio         -0.219255             -0.227519   \n",
+       "Dataset                            -0.256279             -0.223921   \n",
+       "\n",
+       "                            Alamine_Aminotransferase  \\\n",
+       "Age                                        -0.046261   \n",
+       "Gender                                      0.165521   \n",
+       "Total_Bilirubin                             0.306483   \n",
+       "Direct_Bilirubin                            0.292416   \n",
+       "Alkaline_Phosphotase                        0.277939   \n",
+       "Alamine_Aminotransferase                    1.000000   \n",
+       "Aspartate_Aminotransferase                  0.596488   \n",
+       "Total_Protiens                             -0.012909   \n",
+       "Albumin                                    -0.033134   \n",
+       "Albumin_and_Globulin_Ratio                 -0.055798   \n",
+       "Dataset                                    -0.239269   \n",
+       "\n",
+       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "Age                                          -0.013206       -0.120690   \n",
+       "Gender                                        0.172013       -0.075289   \n",
+       "Total_Bilirubin                               0.361626       -0.014417   \n",
+       "Direct_Bilirubin                              0.364823       -0.015559   \n",
+       "Alkaline_Phosphotase                          0.264168        0.010076   \n",
+       "Alamine_Aminotransferase                      0.596488       -0.012909   \n",
+       "Aspartate_Aminotransferase                    1.000000       -0.057158   \n",
+       "Total_Protiens                               -0.057158        1.000000   \n",
+       "Albumin                                      -0.137458        0.613098   \n",
+       "Albumin_and_Globulin_Ratio                   -0.145771        0.192220   \n",
+       "Dataset                                      -0.253705        0.026685   \n",
+       "\n",
+       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
+       "Age                        -0.180176                   -0.177241 -0.107040  \n",
+       "Gender                     -0.079378                   -0.007077 -0.082416  \n",
+       "Total_Bilirubin            -0.153281                   -0.203865 -0.255635  \n",
+       "Direct_Bilirubin           -0.164638                   -0.219255 -0.256279  \n",
+       "Alkaline_Phosphotase       -0.115934                   -0.227519 -0.223921  \n",
+       "Alamine_Aminotransferase   -0.033134                   -0.055798 -0.239269  \n",
+       "Aspartate_Aminotransferase -0.137458                   -0.145771 -0.253705  \n",
+       "Total_Protiens              0.613098                    0.192220  0.026685  \n",
+       "Albumin                     1.000000                    0.592980  0.138960  \n",
+       "Albumin_and_Globulin_Ratio  0.592980                    1.000000  0.158967  \n",
+       "Dataset                     0.138960                    0.158967  1.000000  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='kendall')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-> Pearson correlation: Feature in which target class (dataset) depends the most<br />\n",
+    "* Albumin\n",
+    "* A/G ratio\n",
+    "* Total protiens<br />\n",
+    "<br />\n",
+    "\n",
+    "-> Spearman correlation: Feature in which target class depends the most <br />\n",
+    "* A/G ratio\n",
+    "* Albumin\n",
+    "* Total protiens<br />\n",
+    "<br />\n",
+    "\n",
+    "-> Kendall correlation : Feature in which target class depends the most <br />\n",
+    "* A/G ratio\n",
+    "* Albumin\n",
+    "* Total protiens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ->Overall Insights\n",
+    "From the above correlation and visualisation we can conclude that target class(dataset) mostly depends on these features (descending order):<br />\n",
+    "* A/G ratio\n",
+    "* Albumin\n",
+    "* Total protiens\n",
+    "<br />\n",
+    "\n",
+    "So from the original features<br /> \n",
+    "**10 independent variable - 1 dependent class**<br />\n",
+    "to<br />\n",
+    "**3 independent variable - 1 dependent class**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From cf9cc7f89ed50fe8e6075922826ad4a330c965a3 Mon Sep 17 00:00:00 2001
From: Rakesh Joshi <rakeshjoshi21021369@gmail.com>
Date: Sun, 19 May 2024 23:08:11 +0530
Subject: [PATCH 4/5] Delete Liver DIsease prediction/Liver_disease_EDA.ipynb

---
 .../Liver_disease_EDA.ipynb                   | 1739 -----------------
 1 file changed, 1739 deletions(-)
 delete mode 100644 Liver DIsease prediction/Liver_disease_EDA.ipynb

diff --git a/Liver DIsease prediction/Liver_disease_EDA.ipynb b/Liver DIsease prediction/Liver_disease_EDA.ipynb
deleted file mode 100644
index 60ed328a..00000000
--- a/Liver DIsease prediction/Liver_disease_EDA.ipynb	
+++ /dev/null
@@ -1,1739 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Liver Disease Prediction | Dataset exploration "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Load the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df=pd.read_csv(r\"C:\\Users\\rakes\\health_proj\\Liver Disease Prediction\\Dataset\\indian_liver_patient.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Explore and get the labels of the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(583, 11)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>65</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>187</td>\n",
-       "      <td>16</td>\n",
-       "      <td>18</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>0.90</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>62</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>10.9</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>699</td>\n",
-       "      <td>64</td>\n",
-       "      <td>100</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>0.74</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>62</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>7.3</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>490</td>\n",
-       "      <td>60</td>\n",
-       "      <td>68</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>0.89</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>58</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>182</td>\n",
-       "      <td>14</td>\n",
-       "      <td>20</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>72</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>3.9</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>195</td>\n",
-       "      <td>27</td>\n",
-       "      <td>59</td>\n",
-       "      <td>7.3</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
-       "0   65  Female              0.7               0.1                   187   \n",
-       "1   62    Male             10.9               5.5                   699   \n",
-       "2   62    Male              7.3               4.1                   490   \n",
-       "3   58    Male              1.0               0.4                   182   \n",
-       "4   72    Male              3.9               2.0                   195   \n",
-       "\n",
-       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
-       "0                        16                          18             6.8   \n",
-       "1                        64                         100             7.5   \n",
-       "2                        60                          68             7.0   \n",
-       "3                        14                          20             6.8   \n",
-       "4                        27                          59             7.3   \n",
-       "\n",
-       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
-       "0      3.3                        0.90        1  \n",
-       "1      3.2                        0.74        1  \n",
-       "2      3.3                        0.89        1  \n",
-       "3      3.4                        1.00        1  \n",
-       "4      2.4                        0.40        1  "
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>578</th>\n",
-       "      <td>60</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>500</td>\n",
-       "      <td>20</td>\n",
-       "      <td>34</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.37</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>579</th>\n",
-       "      <td>40</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>98</td>\n",
-       "      <td>35</td>\n",
-       "      <td>31</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.10</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>580</th>\n",
-       "      <td>52</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>245</td>\n",
-       "      <td>48</td>\n",
-       "      <td>49</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>581</th>\n",
-       "      <td>31</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>184</td>\n",
-       "      <td>29</td>\n",
-       "      <td>32</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>582</th>\n",
-       "      <td>38</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>216</td>\n",
-       "      <td>21</td>\n",
-       "      <td>24</td>\n",
-       "      <td>7.3</td>\n",
-       "      <td>4.4</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Age Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
-       "578   60   Male              0.5               0.1                   500   \n",
-       "579   40   Male              0.6               0.1                    98   \n",
-       "580   52   Male              0.8               0.2                   245   \n",
-       "581   31   Male              1.3               0.5                   184   \n",
-       "582   38   Male              1.0               0.3                   216   \n",
-       "\n",
-       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
-       "578                        20                          34             5.9   \n",
-       "579                        35                          31             6.0   \n",
-       "580                        48                          49             6.4   \n",
-       "581                        29                          32             6.8   \n",
-       "582                        21                          24             7.3   \n",
-       "\n",
-       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
-       "578      1.6                        0.37        2  \n",
-       "579      3.2                        1.10        1  \n",
-       "580      3.2                        1.00        1  \n",
-       "581      3.4                        1.00        1  \n",
-       "582      4.4                        1.50        2  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',\n",
-       "       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',\n",
-       "       'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',\n",
-       "       'Albumin_and_Globulin_Ratio', 'Dataset'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Statistical details like mean, meadain and x percentiles of the labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "      <td>579.000000</td>\n",
-       "      <td>583.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>44.746141</td>\n",
-       "      <td>3.298799</td>\n",
-       "      <td>1.486106</td>\n",
-       "      <td>290.576329</td>\n",
-       "      <td>80.713551</td>\n",
-       "      <td>109.910806</td>\n",
-       "      <td>6.483190</td>\n",
-       "      <td>3.141852</td>\n",
-       "      <td>0.947064</td>\n",
-       "      <td>1.286449</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>16.189833</td>\n",
-       "      <td>6.209522</td>\n",
-       "      <td>2.808498</td>\n",
-       "      <td>242.937989</td>\n",
-       "      <td>182.620356</td>\n",
-       "      <td>288.918529</td>\n",
-       "      <td>1.085451</td>\n",
-       "      <td>0.795519</td>\n",
-       "      <td>0.319592</td>\n",
-       "      <td>0.452490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>63.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>2.700000</td>\n",
-       "      <td>0.900000</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>33.000000</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>175.500000</td>\n",
-       "      <td>23.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "      <td>5.800000</td>\n",
-       "      <td>2.600000</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>45.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>208.000000</td>\n",
-       "      <td>35.000000</td>\n",
-       "      <td>42.000000</td>\n",
-       "      <td>6.600000</td>\n",
-       "      <td>3.100000</td>\n",
-       "      <td>0.930000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>58.000000</td>\n",
-       "      <td>2.600000</td>\n",
-       "      <td>1.300000</td>\n",
-       "      <td>298.000000</td>\n",
-       "      <td>60.500000</td>\n",
-       "      <td>87.000000</td>\n",
-       "      <td>7.200000</td>\n",
-       "      <td>3.800000</td>\n",
-       "      <td>1.100000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>90.000000</td>\n",
-       "      <td>75.000000</td>\n",
-       "      <td>19.700000</td>\n",
-       "      <td>2110.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>4929.000000</td>\n",
-       "      <td>9.600000</td>\n",
-       "      <td>5.500000</td>\n",
-       "      <td>2.800000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
-       "count  583.000000       583.000000        583.000000            583.000000   \n",
-       "mean    44.746141         3.298799          1.486106            290.576329   \n",
-       "std     16.189833         6.209522          2.808498            242.937989   \n",
-       "min      4.000000         0.400000          0.100000             63.000000   \n",
-       "25%     33.000000         0.800000          0.200000            175.500000   \n",
-       "50%     45.000000         1.000000          0.300000            208.000000   \n",
-       "75%     58.000000         2.600000          1.300000            298.000000   \n",
-       "max     90.000000        75.000000         19.700000           2110.000000   \n",
-       "\n",
-       "       Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
-       "count                583.000000                  583.000000      583.000000   \n",
-       "mean                  80.713551                  109.910806        6.483190   \n",
-       "std                  182.620356                  288.918529        1.085451   \n",
-       "min                   10.000000                   10.000000        2.700000   \n",
-       "25%                   23.000000                   25.000000        5.800000   \n",
-       "50%                   35.000000                   42.000000        6.600000   \n",
-       "75%                   60.500000                   87.000000        7.200000   \n",
-       "max                 2000.000000                 4929.000000        9.600000   \n",
-       "\n",
-       "          Albumin  Albumin_and_Globulin_Ratio     Dataset  \n",
-       "count  583.000000                  579.000000  583.000000  \n",
-       "mean     3.141852                    0.947064    1.286449  \n",
-       "std      0.795519                    0.319592    0.452490  \n",
-       "min      0.900000                    0.300000    1.000000  \n",
-       "25%      2.600000                    0.700000    1.000000  \n",
-       "50%      3.100000                    0.930000    1.000000  \n",
-       "75%      3.800000                    1.100000    2.000000  \n",
-       "max      5.500000                    2.800000    2.000000  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(df.describe())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3.Finiding NULL valur and performing imputation and changes as required"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                           0\n",
-       "Gender                        0\n",
-       "Total_Bilirubin               0\n",
-       "Direct_Bilirubin              0\n",
-       "Alkaline_Phosphotase          0\n",
-       "Alamine_Aminotransferase      0\n",
-       "Aspartate_Aminotransferase    0\n",
-       "Total_Protiens                0\n",
-       "Albumin                       0\n",
-       "Albumin_and_Globulin_Ratio    4\n",
-       "Dataset                       0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "feature Albumin_and_Globulin_Ratio have 4 null values which should to filled for furthing finding for correlations and other insights. <br />\n",
-    "->so here i am using mean method to impute the values in place of null values as only few values are null so default function will work"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.impute import SimpleImputer\n",
-    "imputer=SimpleImputer(strategy='mean')\n",
-    "imputer.fit(df[['Albumin_and_Globulin_Ratio']])\n",
-    "df['Albumin_and_Globulin_Ratio']=imputer.transform(df[['Albumin_and_Globulin_Ratio']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 583 entries, 0 to 582\n",
-      "Data columns (total 11 columns):\n",
-      " #   Column                      Non-Null Count  Dtype  \n",
-      "---  ------                      --------------  -----  \n",
-      " 0   Age                         583 non-null    int64  \n",
-      " 1   Gender                      583 non-null    object \n",
-      " 2   Total_Bilirubin             583 non-null    float64\n",
-      " 3   Direct_Bilirubin            583 non-null    float64\n",
-      " 4   Alkaline_Phosphotase        583 non-null    int64  \n",
-      " 5   Alamine_Aminotransferase    583 non-null    int64  \n",
-      " 6   Aspartate_Aminotransferase  583 non-null    int64  \n",
-      " 7   Total_Protiens              583 non-null    float64\n",
-      " 8   Albumin                     583 non-null    float64\n",
-      " 9   Albumin_and_Globulin_Ratio  583 non-null    float64\n",
-      " 10  Dataset                     583 non-null    int64  \n",
-      "dtypes: float64(5), int64(5), object(1)\n",
-      "memory usage: 50.2+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "#to get the data type of each feature\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In above we can see that Gender is not a numeric type so we convert it numeric for further <br />\n",
-    "-> Gender: 1-male and 0-female"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Gender']=df['Gender'].replace({'Male':1,'Female':0})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 4. EDA\n",
-    "-> Check the distribution of the columns like gender and target class(dataset)<br />\n",
-    "-> Perfoming multivariant analysis on all parameters and drawing conclusions from them<br />\n",
-    "-> explore the correlation matrix<br />\n",
-    "-> Overall Insights<br />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ->Check for Distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "416 167\n"
-     ]
-    }
-   ],
-   "source": [
-    "# target class distribution in data set, 1 represent its a liver patient and 2 represent it is not a liver patient\n",
-    "true_count=len(df.loc[df['Dataset']==1])\n",
-    "false_count=len(df.loc[df['Dataset']==2])\n",
-    "print(true_count,false_count)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#bar chart for the target class (which is dataset here) and gender to visualize its distriubtion\n",
-    "categorical_vars = ['Gender', 'Dataset']\n",
-    "plt.figure(figsize=(12, 5))\n",
-    "for i, var in enumerate(categorical_vars, 1):\n",
-    "    plt.subplot(1, 2, i)\n",
-    "    sns.countplot(x=var, data=df)\n",
-    "    plt.title(f'Bar Chart of {var}', fontsize=14)\n",
-    "    plt.xlabel(var)\n",
-    "    plt.ylabel('Count')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ->Mulitvariant analysis on Features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "numerical_vars = ['Age', 'Gender','Total_Bilirubin', 'Direct_Bilirubin', 'Alkaline_Phosphotase', \n",
-    "                  'Alamine_Aminotransferase', 'Aspartate_Aminotransferase', 'Total_Protiens', \n",
-    "                  'Albumin', 'Albumin_and_Globulin_Ratio']\n",
-    "df[numerical_vars].hist(figsize=(12, 10))\n",
-    "plt.suptitle('Histograms of Numerical Variables', fontsize=16)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Analysis of the histogram:<br />\n",
-    "-> Age: The graph shows that most of the people age is between 30 to 70 in the dataset<br />\n",
-    "-> Gender: Gender is only 0 (for female) and 1 (for male) also indicates that male dominant data is present in the dataset<br />\n",
-    "-> Total Billirubin: around 500 people in the dataset have total billirubin between 0 to 10 in their blood<br />\n",
-    "-> Direct Billirubin: Direct billirubin which is processed by liver, in the dataset around 450 people have direct billirubin between 0 to 2 in their blood.\n",
-    "-> Alkaline phosphotase(ALP):Few people in dataset have high elevated ALP enzyme which indicates liver disorders<br />\n",
-    "-> Alanine Aminotransferase(ALT): This enzyme which convert alanine and amino acid into pyruvate but high level of this causes damages to liver and result int fatty liver disease. In dataset very few have high level ALT.<br />\n",
-    "-> Aspartate Aminotransferase(AST): High AST causes hepatitis, few have high level AST in the dataset.<br />\n",
-    "-> Total Protien: This indicates total amount of albumin and globulin in the blood, its low levels (less than 6g) indicates liver disease. Around 60 people in the dataset have it bewtween 5-6 gm and some have even less than this.<br />\n",
-    "-> Albumin: Main protien made by the liver, low levels(less than 3.5) indicate chronic liver disease. Around 100 people have low level of albumin the dataset.<br />\n",
-    "-> A/G ratio: low ratio signifise liver disease, in dataset around 50 have this ratio less than 0.5."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### -> Explore the Correaltion matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "corr=df.corr(method='kendall')\n",
-    "plt.figure(figsize=(10,10))\n",
-    "sns.heatmap(corr,annot=True,cmap='coolwarm',fmt=\".2f\",linewidth=.5)\n",
-    "plt.title(\"Correlation\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "the exact correaltion value is not visible in the heatmap so going to print the whole matrix and derive something fromt there.<br />\n",
-    "Correlation with respect to different methods can be derived like pearson, spearman, kendall"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Age</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.056560</td>\n",
-       "      <td>0.011763</td>\n",
-       "      <td>0.007529</td>\n",
-       "      <td>0.080425</td>\n",
-       "      <td>-0.086883</td>\n",
-       "      <td>-0.019910</td>\n",
-       "      <td>-0.187461</td>\n",
-       "      <td>-0.265924</td>\n",
-       "      <td>-0.216089</td>\n",
-       "      <td>-0.137351</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <td>0.056560</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.089291</td>\n",
-       "      <td>0.100436</td>\n",
-       "      <td>-0.027496</td>\n",
-       "      <td>0.082332</td>\n",
-       "      <td>0.080336</td>\n",
-       "      <td>-0.089121</td>\n",
-       "      <td>-0.093799</td>\n",
-       "      <td>-0.003404</td>\n",
-       "      <td>-0.082416</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <td>0.011763</td>\n",
-       "      <td>0.089291</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.874618</td>\n",
-       "      <td>0.206669</td>\n",
-       "      <td>0.214065</td>\n",
-       "      <td>0.237831</td>\n",
-       "      <td>-0.008099</td>\n",
-       "      <td>-0.222250</td>\n",
-       "      <td>-0.206159</td>\n",
-       "      <td>-0.220208</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <td>0.007529</td>\n",
-       "      <td>0.100436</td>\n",
-       "      <td>0.874618</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.234939</td>\n",
-       "      <td>0.233894</td>\n",
-       "      <td>0.257544</td>\n",
-       "      <td>-0.000139</td>\n",
-       "      <td>-0.228531</td>\n",
-       "      <td>-0.200004</td>\n",
-       "      <td>-0.246046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <td>0.080425</td>\n",
-       "      <td>-0.027496</td>\n",
-       "      <td>0.206669</td>\n",
-       "      <td>0.234939</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.125680</td>\n",
-       "      <td>0.167196</td>\n",
-       "      <td>-0.028514</td>\n",
-       "      <td>-0.165453</td>\n",
-       "      <td>-0.233960</td>\n",
-       "      <td>-0.184866</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <td>-0.086883</td>\n",
-       "      <td>0.082332</td>\n",
-       "      <td>0.214065</td>\n",
-       "      <td>0.233894</td>\n",
-       "      <td>0.125680</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.791966</td>\n",
-       "      <td>-0.042518</td>\n",
-       "      <td>-0.029742</td>\n",
-       "      <td>-0.002374</td>\n",
-       "      <td>-0.163416</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <td>-0.019910</td>\n",
-       "      <td>0.080336</td>\n",
-       "      <td>0.237831</td>\n",
-       "      <td>0.257544</td>\n",
-       "      <td>0.167196</td>\n",
-       "      <td>0.791966</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.025645</td>\n",
-       "      <td>-0.085290</td>\n",
-       "      <td>-0.070024</td>\n",
-       "      <td>-0.151934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <td>-0.187461</td>\n",
-       "      <td>-0.089121</td>\n",
-       "      <td>-0.008099</td>\n",
-       "      <td>-0.000139</td>\n",
-       "      <td>-0.028514</td>\n",
-       "      <td>-0.042518</td>\n",
-       "      <td>-0.025645</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.784053</td>\n",
-       "      <td>0.233904</td>\n",
-       "      <td>0.035008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albumin</th>\n",
-       "      <td>-0.265924</td>\n",
-       "      <td>-0.093799</td>\n",
-       "      <td>-0.222250</td>\n",
-       "      <td>-0.228531</td>\n",
-       "      <td>-0.165453</td>\n",
-       "      <td>-0.029742</td>\n",
-       "      <td>-0.085290</td>\n",
-       "      <td>0.784053</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.686322</td>\n",
-       "      <td>0.161388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <td>-0.216089</td>\n",
-       "      <td>-0.003404</td>\n",
-       "      <td>-0.206159</td>\n",
-       "      <td>-0.200004</td>\n",
-       "      <td>-0.233960</td>\n",
-       "      <td>-0.002374</td>\n",
-       "      <td>-0.070024</td>\n",
-       "      <td>0.233904</td>\n",
-       "      <td>0.686322</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.162319</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dataset</th>\n",
-       "      <td>-0.137351</td>\n",
-       "      <td>-0.082416</td>\n",
-       "      <td>-0.220208</td>\n",
-       "      <td>-0.246046</td>\n",
-       "      <td>-0.184866</td>\n",
-       "      <td>-0.163416</td>\n",
-       "      <td>-0.151934</td>\n",
-       "      <td>0.035008</td>\n",
-       "      <td>0.161388</td>\n",
-       "      <td>0.162319</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 Age    Gender  Total_Bilirubin  \\\n",
-       "Age                         1.000000  0.056560         0.011763   \n",
-       "Gender                      0.056560  1.000000         0.089291   \n",
-       "Total_Bilirubin             0.011763  0.089291         1.000000   \n",
-       "Direct_Bilirubin            0.007529  0.100436         0.874618   \n",
-       "Alkaline_Phosphotase        0.080425 -0.027496         0.206669   \n",
-       "Alamine_Aminotransferase   -0.086883  0.082332         0.214065   \n",
-       "Aspartate_Aminotransferase -0.019910  0.080336         0.237831   \n",
-       "Total_Protiens             -0.187461 -0.089121        -0.008099   \n",
-       "Albumin                    -0.265924 -0.093799        -0.222250   \n",
-       "Albumin_and_Globulin_Ratio -0.216089 -0.003404        -0.206159   \n",
-       "Dataset                    -0.137351 -0.082416        -0.220208   \n",
-       "\n",
-       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
-       "Age                                 0.007529              0.080425   \n",
-       "Gender                              0.100436             -0.027496   \n",
-       "Total_Bilirubin                     0.874618              0.206669   \n",
-       "Direct_Bilirubin                    1.000000              0.234939   \n",
-       "Alkaline_Phosphotase                0.234939              1.000000   \n",
-       "Alamine_Aminotransferase            0.233894              0.125680   \n",
-       "Aspartate_Aminotransferase          0.257544              0.167196   \n",
-       "Total_Protiens                     -0.000139             -0.028514   \n",
-       "Albumin                            -0.228531             -0.165453   \n",
-       "Albumin_and_Globulin_Ratio         -0.200004             -0.233960   \n",
-       "Dataset                            -0.246046             -0.184866   \n",
-       "\n",
-       "                            Alamine_Aminotransferase  \\\n",
-       "Age                                        -0.086883   \n",
-       "Gender                                      0.082332   \n",
-       "Total_Bilirubin                             0.214065   \n",
-       "Direct_Bilirubin                            0.233894   \n",
-       "Alkaline_Phosphotase                        0.125680   \n",
-       "Alamine_Aminotransferase                    1.000000   \n",
-       "Aspartate_Aminotransferase                  0.791966   \n",
-       "Total_Protiens                             -0.042518   \n",
-       "Albumin                                    -0.029742   \n",
-       "Albumin_and_Globulin_Ratio                 -0.002374   \n",
-       "Dataset                                    -0.163416   \n",
-       "\n",
-       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
-       "Age                                          -0.019910       -0.187461   \n",
-       "Gender                                        0.080336       -0.089121   \n",
-       "Total_Bilirubin                               0.237831       -0.008099   \n",
-       "Direct_Bilirubin                              0.257544       -0.000139   \n",
-       "Alkaline_Phosphotase                          0.167196       -0.028514   \n",
-       "Alamine_Aminotransferase                      0.791966       -0.042518   \n",
-       "Aspartate_Aminotransferase                    1.000000       -0.025645   \n",
-       "Total_Protiens                               -0.025645        1.000000   \n",
-       "Albumin                                      -0.085290        0.784053   \n",
-       "Albumin_and_Globulin_Ratio                   -0.070024        0.233904   \n",
-       "Dataset                                      -0.151934        0.035008   \n",
-       "\n",
-       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
-       "Age                        -0.265924                   -0.216089 -0.137351  \n",
-       "Gender                     -0.093799                   -0.003404 -0.082416  \n",
-       "Total_Bilirubin            -0.222250                   -0.206159 -0.220208  \n",
-       "Direct_Bilirubin           -0.228531                   -0.200004 -0.246046  \n",
-       "Alkaline_Phosphotase       -0.165453                   -0.233960 -0.184866  \n",
-       "Alamine_Aminotransferase   -0.029742                   -0.002374 -0.163416  \n",
-       "Aspartate_Aminotransferase -0.085290                   -0.070024 -0.151934  \n",
-       "Total_Protiens              0.784053                    0.233904  0.035008  \n",
-       "Albumin                     1.000000                    0.686322  0.161388  \n",
-       "Albumin_and_Globulin_Ratio  0.686322                    1.000000  0.162319  \n",
-       "Dataset                     0.161388                    0.162319  1.000000  "
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.corr(method='pearson')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Age</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.062364</td>\n",
-       "      <td>0.113827</td>\n",
-       "      <td>0.106473</td>\n",
-       "      <td>0.059205</td>\n",
-       "      <td>-0.067737</td>\n",
-       "      <td>-0.018285</td>\n",
-       "      <td>-0.174271</td>\n",
-       "      <td>-0.260791</td>\n",
-       "      <td>-0.249505</td>\n",
-       "      <td>-0.129572</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <td>0.062364</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.200503</td>\n",
-       "      <td>0.209210</td>\n",
-       "      <td>0.079131</td>\n",
-       "      <td>0.201107</td>\n",
-       "      <td>0.209434</td>\n",
-       "      <td>-0.090905</td>\n",
-       "      <td>-0.095440</td>\n",
-       "      <td>-0.008342</td>\n",
-       "      <td>-0.082416</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <td>0.113827</td>\n",
-       "      <td>0.200503</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.959216</td>\n",
-       "      <td>0.383794</td>\n",
-       "      <td>0.436586</td>\n",
-       "      <td>0.508869</td>\n",
-       "      <td>-0.019252</td>\n",
-       "      <td>-0.222184</td>\n",
-       "      <td>-0.284200</td>\n",
-       "      <td>-0.303879</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <td>0.106473</td>\n",
-       "      <td>0.209210</td>\n",
-       "      <td>0.959216</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.367818</td>\n",
-       "      <td>0.412322</td>\n",
-       "      <td>0.504138</td>\n",
-       "      <td>-0.019987</td>\n",
-       "      <td>-0.232664</td>\n",
-       "      <td>-0.297338</td>\n",
-       "      <td>-0.297270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <td>0.059205</td>\n",
-       "      <td>0.079131</td>\n",
-       "      <td>0.383794</td>\n",
-       "      <td>0.367818</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.410752</td>\n",
-       "      <td>0.395732</td>\n",
-       "      <td>0.014028</td>\n",
-       "      <td>-0.170809</td>\n",
-       "      <td>-0.321095</td>\n",
-       "      <td>-0.273247</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <td>-0.067737</td>\n",
-       "      <td>0.201107</td>\n",
-       "      <td>0.436586</td>\n",
-       "      <td>0.412322</td>\n",
-       "      <td>0.410752</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.773611</td>\n",
-       "      <td>-0.018811</td>\n",
-       "      <td>-0.052673</td>\n",
-       "      <td>-0.082942</td>\n",
-       "      <td>-0.290709</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <td>-0.018285</td>\n",
-       "      <td>0.209434</td>\n",
-       "      <td>0.508869</td>\n",
-       "      <td>0.504138</td>\n",
-       "      <td>0.395732</td>\n",
-       "      <td>0.773611</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.084779</td>\n",
-       "      <td>-0.204867</td>\n",
-       "      <td>-0.208809</td>\n",
-       "      <td>-0.308897</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <td>-0.174271</td>\n",
-       "      <td>-0.090905</td>\n",
-       "      <td>-0.019252</td>\n",
-       "      <td>-0.019987</td>\n",
-       "      <td>0.014028</td>\n",
-       "      <td>-0.018811</td>\n",
-       "      <td>-0.084779</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.779077</td>\n",
-       "      <td>0.272490</td>\n",
-       "      <td>0.032220</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albumin</th>\n",
-       "      <td>-0.260791</td>\n",
-       "      <td>-0.095440</td>\n",
-       "      <td>-0.222184</td>\n",
-       "      <td>-0.232664</td>\n",
-       "      <td>-0.170809</td>\n",
-       "      <td>-0.052673</td>\n",
-       "      <td>-0.204867</td>\n",
-       "      <td>0.779077</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.751223</td>\n",
-       "      <td>0.167079</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <td>-0.249505</td>\n",
-       "      <td>-0.008342</td>\n",
-       "      <td>-0.284200</td>\n",
-       "      <td>-0.297338</td>\n",
-       "      <td>-0.321095</td>\n",
-       "      <td>-0.082942</td>\n",
-       "      <td>-0.208809</td>\n",
-       "      <td>0.272490</td>\n",
-       "      <td>0.751223</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.187377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dataset</th>\n",
-       "      <td>-0.129572</td>\n",
-       "      <td>-0.082416</td>\n",
-       "      <td>-0.303879</td>\n",
-       "      <td>-0.297270</td>\n",
-       "      <td>-0.273247</td>\n",
-       "      <td>-0.290709</td>\n",
-       "      <td>-0.308897</td>\n",
-       "      <td>0.032220</td>\n",
-       "      <td>0.167079</td>\n",
-       "      <td>0.187377</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 Age    Gender  Total_Bilirubin  \\\n",
-       "Age                         1.000000  0.062364         0.113827   \n",
-       "Gender                      0.062364  1.000000         0.200503   \n",
-       "Total_Bilirubin             0.113827  0.200503         1.000000   \n",
-       "Direct_Bilirubin            0.106473  0.209210         0.959216   \n",
-       "Alkaline_Phosphotase        0.059205  0.079131         0.383794   \n",
-       "Alamine_Aminotransferase   -0.067737  0.201107         0.436586   \n",
-       "Aspartate_Aminotransferase -0.018285  0.209434         0.508869   \n",
-       "Total_Protiens             -0.174271 -0.090905        -0.019252   \n",
-       "Albumin                    -0.260791 -0.095440        -0.222184   \n",
-       "Albumin_and_Globulin_Ratio -0.249505 -0.008342        -0.284200   \n",
-       "Dataset                    -0.129572 -0.082416        -0.303879   \n",
-       "\n",
-       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
-       "Age                                 0.106473              0.059205   \n",
-       "Gender                              0.209210              0.079131   \n",
-       "Total_Bilirubin                     0.959216              0.383794   \n",
-       "Direct_Bilirubin                    1.000000              0.367818   \n",
-       "Alkaline_Phosphotase                0.367818              1.000000   \n",
-       "Alamine_Aminotransferase            0.412322              0.410752   \n",
-       "Aspartate_Aminotransferase          0.504138              0.395732   \n",
-       "Total_Protiens                     -0.019987              0.014028   \n",
-       "Albumin                            -0.232664             -0.170809   \n",
-       "Albumin_and_Globulin_Ratio         -0.297338             -0.321095   \n",
-       "Dataset                            -0.297270             -0.273247   \n",
-       "\n",
-       "                            Alamine_Aminotransferase  \\\n",
-       "Age                                        -0.067737   \n",
-       "Gender                                      0.201107   \n",
-       "Total_Bilirubin                             0.436586   \n",
-       "Direct_Bilirubin                            0.412322   \n",
-       "Alkaline_Phosphotase                        0.410752   \n",
-       "Alamine_Aminotransferase                    1.000000   \n",
-       "Aspartate_Aminotransferase                  0.773611   \n",
-       "Total_Protiens                             -0.018811   \n",
-       "Albumin                                    -0.052673   \n",
-       "Albumin_and_Globulin_Ratio                 -0.082942   \n",
-       "Dataset                                    -0.290709   \n",
-       "\n",
-       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
-       "Age                                          -0.018285       -0.174271   \n",
-       "Gender                                        0.209434       -0.090905   \n",
-       "Total_Bilirubin                               0.508869       -0.019252   \n",
-       "Direct_Bilirubin                              0.504138       -0.019987   \n",
-       "Alkaline_Phosphotase                          0.395732        0.014028   \n",
-       "Alamine_Aminotransferase                      0.773611       -0.018811   \n",
-       "Aspartate_Aminotransferase                    1.000000       -0.084779   \n",
-       "Total_Protiens                               -0.084779        1.000000   \n",
-       "Albumin                                      -0.204867        0.779077   \n",
-       "Albumin_and_Globulin_Ratio                   -0.208809        0.272490   \n",
-       "Dataset                                      -0.308897        0.032220   \n",
-       "\n",
-       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
-       "Age                        -0.260791                   -0.249505 -0.129572  \n",
-       "Gender                     -0.095440                   -0.008342 -0.082416  \n",
-       "Total_Bilirubin            -0.222184                   -0.284200 -0.303879  \n",
-       "Direct_Bilirubin           -0.232664                   -0.297338 -0.297270  \n",
-       "Alkaline_Phosphotase       -0.170809                   -0.321095 -0.273247  \n",
-       "Alamine_Aminotransferase   -0.052673                   -0.082942 -0.290709  \n",
-       "Aspartate_Aminotransferase -0.204867                   -0.208809 -0.308897  \n",
-       "Total_Protiens              0.779077                    0.272490  0.032220  \n",
-       "Albumin                     1.000000                    0.751223  0.167079  \n",
-       "Albumin_and_Globulin_Ratio  0.751223                    1.000000  0.187377  \n",
-       "Dataset                     0.167079                    0.187377  1.000000  "
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.corr(method='spearman')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <th>Albumin</th>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <th>Dataset</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Age</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.051520</td>\n",
-       "      <td>0.078099</td>\n",
-       "      <td>0.074733</td>\n",
-       "      <td>0.038868</td>\n",
-       "      <td>-0.046261</td>\n",
-       "      <td>-0.013206</td>\n",
-       "      <td>-0.120690</td>\n",
-       "      <td>-0.180176</td>\n",
-       "      <td>-0.177241</td>\n",
-       "      <td>-0.107040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <td>0.051520</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.168671</td>\n",
-       "      <td>0.180362</td>\n",
-       "      <td>0.064846</td>\n",
-       "      <td>0.165521</td>\n",
-       "      <td>0.172013</td>\n",
-       "      <td>-0.075289</td>\n",
-       "      <td>-0.079378</td>\n",
-       "      <td>-0.007077</td>\n",
-       "      <td>-0.082416</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Total_Bilirubin</th>\n",
-       "      <td>0.078099</td>\n",
-       "      <td>0.168671</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.898136</td>\n",
-       "      <td>0.270267</td>\n",
-       "      <td>0.306483</td>\n",
-       "      <td>0.361626</td>\n",
-       "      <td>-0.014417</td>\n",
-       "      <td>-0.153281</td>\n",
-       "      <td>-0.203865</td>\n",
-       "      <td>-0.255635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Direct_Bilirubin</th>\n",
-       "      <td>0.074733</td>\n",
-       "      <td>0.180362</td>\n",
-       "      <td>0.898136</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.265809</td>\n",
-       "      <td>0.292416</td>\n",
-       "      <td>0.364823</td>\n",
-       "      <td>-0.015559</td>\n",
-       "      <td>-0.164638</td>\n",
-       "      <td>-0.219255</td>\n",
-       "      <td>-0.256279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alkaline_Phosphotase</th>\n",
-       "      <td>0.038868</td>\n",
-       "      <td>0.064846</td>\n",
-       "      <td>0.270267</td>\n",
-       "      <td>0.265809</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.277939</td>\n",
-       "      <td>0.264168</td>\n",
-       "      <td>0.010076</td>\n",
-       "      <td>-0.115934</td>\n",
-       "      <td>-0.227519</td>\n",
-       "      <td>-0.223921</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alamine_Aminotransferase</th>\n",
-       "      <td>-0.046261</td>\n",
-       "      <td>0.165521</td>\n",
-       "      <td>0.306483</td>\n",
-       "      <td>0.292416</td>\n",
-       "      <td>0.277939</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.596488</td>\n",
-       "      <td>-0.012909</td>\n",
-       "      <td>-0.033134</td>\n",
-       "      <td>-0.055798</td>\n",
-       "      <td>-0.239269</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Aspartate_Aminotransferase</th>\n",
-       "      <td>-0.013206</td>\n",
-       "      <td>0.172013</td>\n",
-       "      <td>0.361626</td>\n",
-       "      <td>0.364823</td>\n",
-       "      <td>0.264168</td>\n",
-       "      <td>0.596488</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.057158</td>\n",
-       "      <td>-0.137458</td>\n",
-       "      <td>-0.145771</td>\n",
-       "      <td>-0.253705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Total_Protiens</th>\n",
-       "      <td>-0.120690</td>\n",
-       "      <td>-0.075289</td>\n",
-       "      <td>-0.014417</td>\n",
-       "      <td>-0.015559</td>\n",
-       "      <td>0.010076</td>\n",
-       "      <td>-0.012909</td>\n",
-       "      <td>-0.057158</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.613098</td>\n",
-       "      <td>0.192220</td>\n",
-       "      <td>0.026685</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albumin</th>\n",
-       "      <td>-0.180176</td>\n",
-       "      <td>-0.079378</td>\n",
-       "      <td>-0.153281</td>\n",
-       "      <td>-0.164638</td>\n",
-       "      <td>-0.115934</td>\n",
-       "      <td>-0.033134</td>\n",
-       "      <td>-0.137458</td>\n",
-       "      <td>0.613098</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.592980</td>\n",
-       "      <td>0.138960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albumin_and_Globulin_Ratio</th>\n",
-       "      <td>-0.177241</td>\n",
-       "      <td>-0.007077</td>\n",
-       "      <td>-0.203865</td>\n",
-       "      <td>-0.219255</td>\n",
-       "      <td>-0.227519</td>\n",
-       "      <td>-0.055798</td>\n",
-       "      <td>-0.145771</td>\n",
-       "      <td>0.192220</td>\n",
-       "      <td>0.592980</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.158967</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Dataset</th>\n",
-       "      <td>-0.107040</td>\n",
-       "      <td>-0.082416</td>\n",
-       "      <td>-0.255635</td>\n",
-       "      <td>-0.256279</td>\n",
-       "      <td>-0.223921</td>\n",
-       "      <td>-0.239269</td>\n",
-       "      <td>-0.253705</td>\n",
-       "      <td>0.026685</td>\n",
-       "      <td>0.138960</td>\n",
-       "      <td>0.158967</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 Age    Gender  Total_Bilirubin  \\\n",
-       "Age                         1.000000  0.051520         0.078099   \n",
-       "Gender                      0.051520  1.000000         0.168671   \n",
-       "Total_Bilirubin             0.078099  0.168671         1.000000   \n",
-       "Direct_Bilirubin            0.074733  0.180362         0.898136   \n",
-       "Alkaline_Phosphotase        0.038868  0.064846         0.270267   \n",
-       "Alamine_Aminotransferase   -0.046261  0.165521         0.306483   \n",
-       "Aspartate_Aminotransferase -0.013206  0.172013         0.361626   \n",
-       "Total_Protiens             -0.120690 -0.075289        -0.014417   \n",
-       "Albumin                    -0.180176 -0.079378        -0.153281   \n",
-       "Albumin_and_Globulin_Ratio -0.177241 -0.007077        -0.203865   \n",
-       "Dataset                    -0.107040 -0.082416        -0.255635   \n",
-       "\n",
-       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
-       "Age                                 0.074733              0.038868   \n",
-       "Gender                              0.180362              0.064846   \n",
-       "Total_Bilirubin                     0.898136              0.270267   \n",
-       "Direct_Bilirubin                    1.000000              0.265809   \n",
-       "Alkaline_Phosphotase                0.265809              1.000000   \n",
-       "Alamine_Aminotransferase            0.292416              0.277939   \n",
-       "Aspartate_Aminotransferase          0.364823              0.264168   \n",
-       "Total_Protiens                     -0.015559              0.010076   \n",
-       "Albumin                            -0.164638             -0.115934   \n",
-       "Albumin_and_Globulin_Ratio         -0.219255             -0.227519   \n",
-       "Dataset                            -0.256279             -0.223921   \n",
-       "\n",
-       "                            Alamine_Aminotransferase  \\\n",
-       "Age                                        -0.046261   \n",
-       "Gender                                      0.165521   \n",
-       "Total_Bilirubin                             0.306483   \n",
-       "Direct_Bilirubin                            0.292416   \n",
-       "Alkaline_Phosphotase                        0.277939   \n",
-       "Alamine_Aminotransferase                    1.000000   \n",
-       "Aspartate_Aminotransferase                  0.596488   \n",
-       "Total_Protiens                             -0.012909   \n",
-       "Albumin                                    -0.033134   \n",
-       "Albumin_and_Globulin_Ratio                 -0.055798   \n",
-       "Dataset                                    -0.239269   \n",
-       "\n",
-       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
-       "Age                                          -0.013206       -0.120690   \n",
-       "Gender                                        0.172013       -0.075289   \n",
-       "Total_Bilirubin                               0.361626       -0.014417   \n",
-       "Direct_Bilirubin                              0.364823       -0.015559   \n",
-       "Alkaline_Phosphotase                          0.264168        0.010076   \n",
-       "Alamine_Aminotransferase                      0.596488       -0.012909   \n",
-       "Aspartate_Aminotransferase                    1.000000       -0.057158   \n",
-       "Total_Protiens                               -0.057158        1.000000   \n",
-       "Albumin                                      -0.137458        0.613098   \n",
-       "Albumin_and_Globulin_Ratio                   -0.145771        0.192220   \n",
-       "Dataset                                      -0.253705        0.026685   \n",
-       "\n",
-       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
-       "Age                        -0.180176                   -0.177241 -0.107040  \n",
-       "Gender                     -0.079378                   -0.007077 -0.082416  \n",
-       "Total_Bilirubin            -0.153281                   -0.203865 -0.255635  \n",
-       "Direct_Bilirubin           -0.164638                   -0.219255 -0.256279  \n",
-       "Alkaline_Phosphotase       -0.115934                   -0.227519 -0.223921  \n",
-       "Alamine_Aminotransferase   -0.033134                   -0.055798 -0.239269  \n",
-       "Aspartate_Aminotransferase -0.137458                   -0.145771 -0.253705  \n",
-       "Total_Protiens              0.613098                    0.192220  0.026685  \n",
-       "Albumin                     1.000000                    0.592980  0.138960  \n",
-       "Albumin_and_Globulin_Ratio  0.592980                    1.000000  0.158967  \n",
-       "Dataset                     0.138960                    0.158967  1.000000  "
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.corr(method='kendall')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-> Pearson correlation: Feature in which target class (dataset) depends the most<br />\n",
-    "* Albumin\n",
-    "* A/G ratio\n",
-    "* Total protiens<br />\n",
-    "<br />\n",
-    "\n",
-    "-> Spearman correlation: Feature in which target class depends the most <br />\n",
-    "* A/G ratio\n",
-    "* Albumin\n",
-    "* Total protiens<br />\n",
-    "<br />\n",
-    "\n",
-    "-> Kendall correlation : Feature in which target class depends the most <br />\n",
-    "* A/G ratio\n",
-    "* Albumin\n",
-    "* Total protiens"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ->Overall Insights\n",
-    "From the above correlation and visualisation we can conclude that target class(dataset) mostly depends on these features (descending order):<br />\n",
-    "* A/G ratio\n",
-    "* Albumin\n",
-    "* Total protiens\n",
-    "<br />\n",
-    "\n",
-    "So from the original features<br /> \n",
-    "**10 independent variable - 1 dependent class**<br />\n",
-    "to<br />\n",
-    "**3 independent variable - 1 dependent class**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 3741b473fc2ad2cd47cdbd29bf2e4485f44e27ac Mon Sep 17 00:00:00 2001
From: Rakesh Joshi <rakeshjoshi21021369@gmail.com>
Date: Sun, 19 May 2024 23:08:59 +0530
Subject: [PATCH 5/5] Data set exploration and analysis

---
 .../Liver_disease_EDA.ipynb                   | 1739 +++++++++++++++++
 1 file changed, 1739 insertions(+)
 create mode 100644 Liver DIsease prediction/Liver_disease_EDA.ipynb

diff --git a/Liver DIsease prediction/Liver_disease_EDA.ipynb b/Liver DIsease prediction/Liver_disease_EDA.ipynb
new file mode 100644
index 00000000..05239c14
--- /dev/null
+++ b/Liver DIsease prediction/Liver_disease_EDA.ipynb	
@@ -0,0 +1,1739 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Liver Disease Prediction | Dataset exploration "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(r\"C:\\Users\\rakes\\health_proj\\Liver Disease Prediction\\Dataset\\indian_liver_patient.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Explore and get the labels of the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(583, 11)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>65</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>16</td>\n",
+       "      <td>18</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>62</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>699</td>\n",
+       "      <td>64</td>\n",
+       "      <td>100</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>62</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>490</td>\n",
+       "      <td>60</td>\n",
+       "      <td>68</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>58</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>182</td>\n",
+       "      <td>14</td>\n",
+       "      <td>20</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>72</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>195</td>\n",
+       "      <td>27</td>\n",
+       "      <td>59</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "0   65  Female              0.7               0.1                   187   \n",
+       "1   62    Male             10.9               5.5                   699   \n",
+       "2   62    Male              7.3               4.1                   490   \n",
+       "3   58    Male              1.0               0.4                   182   \n",
+       "4   72    Male              3.9               2.0                   195   \n",
+       "\n",
+       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "0                        16                          18             6.8   \n",
+       "1                        64                         100             7.5   \n",
+       "2                        60                          68             7.0   \n",
+       "3                        14                          20             6.8   \n",
+       "4                        27                          59             7.3   \n",
+       "\n",
+       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
+       "0      3.3                        0.90        1  \n",
+       "1      3.2                        0.74        1  \n",
+       "2      3.3                        0.89        1  \n",
+       "3      3.4                        1.00        1  \n",
+       "4      2.4                        0.40        1  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>578</th>\n",
+       "      <td>60</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>500</td>\n",
+       "      <td>20</td>\n",
+       "      <td>34</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>40</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>98</td>\n",
+       "      <td>35</td>\n",
+       "      <td>31</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>52</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>245</td>\n",
+       "      <td>48</td>\n",
+       "      <td>49</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581</th>\n",
+       "      <td>31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>184</td>\n",
+       "      <td>29</td>\n",
+       "      <td>32</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>216</td>\n",
+       "      <td>21</td>\n",
+       "      <td>24</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Age Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "578   60   Male              0.5               0.1                   500   \n",
+       "579   40   Male              0.6               0.1                    98   \n",
+       "580   52   Male              0.8               0.2                   245   \n",
+       "581   31   Male              1.3               0.5                   184   \n",
+       "582   38   Male              1.0               0.3                   216   \n",
+       "\n",
+       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "578                        20                          34             5.9   \n",
+       "579                        35                          31             6.0   \n",
+       "580                        48                          49             6.4   \n",
+       "581                        29                          32             6.8   \n",
+       "582                        21                          24             7.3   \n",
+       "\n",
+       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
+       "578      1.6                        0.37        2  \n",
+       "579      3.2                        1.10        1  \n",
+       "580      3.2                        1.00        1  \n",
+       "581      3.4                        1.00        1  \n",
+       "582      4.4                        1.50        2  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',\n",
+       "       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',\n",
+       "       'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',\n",
+       "       'Albumin_and_Globulin_Ratio', 'Dataset'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Statistical details like mean, meadain and x percentiles of the labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "      <td>579.000000</td>\n",
+       "      <td>583.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>44.746141</td>\n",
+       "      <td>3.298799</td>\n",
+       "      <td>1.486106</td>\n",
+       "      <td>290.576329</td>\n",
+       "      <td>80.713551</td>\n",
+       "      <td>109.910806</td>\n",
+       "      <td>6.483190</td>\n",
+       "      <td>3.141852</td>\n",
+       "      <td>0.947064</td>\n",
+       "      <td>1.286449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>16.189833</td>\n",
+       "      <td>6.209522</td>\n",
+       "      <td>2.808498</td>\n",
+       "      <td>242.937989</td>\n",
+       "      <td>182.620356</td>\n",
+       "      <td>288.918529</td>\n",
+       "      <td>1.085451</td>\n",
+       "      <td>0.795519</td>\n",
+       "      <td>0.319592</td>\n",
+       "      <td>0.452490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>63.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>2.700000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>175.500000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>208.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>3.100000</td>\n",
+       "      <td>0.930000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>58.000000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>1.300000</td>\n",
+       "      <td>298.000000</td>\n",
+       "      <td>60.500000</td>\n",
+       "      <td>87.000000</td>\n",
+       "      <td>7.200000</td>\n",
+       "      <td>3.800000</td>\n",
+       "      <td>1.100000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>19.700000</td>\n",
+       "      <td>2110.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>4929.000000</td>\n",
+       "      <td>9.600000</td>\n",
+       "      <td>5.500000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "count  583.000000       583.000000        583.000000            583.000000   \n",
+       "mean    44.746141         3.298799          1.486106            290.576329   \n",
+       "std     16.189833         6.209522          2.808498            242.937989   \n",
+       "min      4.000000         0.400000          0.100000             63.000000   \n",
+       "25%     33.000000         0.800000          0.200000            175.500000   \n",
+       "50%     45.000000         1.000000          0.300000            208.000000   \n",
+       "75%     58.000000         2.600000          1.300000            298.000000   \n",
+       "max     90.000000        75.000000         19.700000           2110.000000   \n",
+       "\n",
+       "       Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "count                583.000000                  583.000000      583.000000   \n",
+       "mean                  80.713551                  109.910806        6.483190   \n",
+       "std                  182.620356                  288.918529        1.085451   \n",
+       "min                   10.000000                   10.000000        2.700000   \n",
+       "25%                   23.000000                   25.000000        5.800000   \n",
+       "50%                   35.000000                   42.000000        6.600000   \n",
+       "75%                   60.500000                   87.000000        7.200000   \n",
+       "max                 2000.000000                 4929.000000        9.600000   \n",
+       "\n",
+       "          Albumin  Albumin_and_Globulin_Ratio     Dataset  \n",
+       "count  583.000000                  579.000000  583.000000  \n",
+       "mean     3.141852                    0.947064    1.286449  \n",
+       "std      0.795519                    0.319592    0.452490  \n",
+       "min      0.900000                    0.300000    1.000000  \n",
+       "25%      2.600000                    0.700000    1.000000  \n",
+       "50%      3.100000                    0.930000    1.000000  \n",
+       "75%      3.800000                    1.100000    2.000000  \n",
+       "max      5.500000                    2.800000    2.000000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.Finiding NULL valur and performing imputation and changes as required"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                           0\n",
+       "Gender                        0\n",
+       "Total_Bilirubin               0\n",
+       "Direct_Bilirubin              0\n",
+       "Alkaline_Phosphotase          0\n",
+       "Alamine_Aminotransferase      0\n",
+       "Aspartate_Aminotransferase    0\n",
+       "Total_Protiens                0\n",
+       "Albumin                       0\n",
+       "Albumin_and_Globulin_Ratio    4\n",
+       "Dataset                       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "feature Albumin_and_Globulin_Ratio have 4 null values which should to filled for furthing finding for correlations and other insights. <br />\n",
+    "->so here i am using mean method to impute the values in place of null values as only few values are null so default function will work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.impute import SimpleImputer\n",
+    "imputer=SimpleImputer(strategy='mean')\n",
+    "imputer.fit(df[['Albumin_and_Globulin_Ratio']])\n",
+    "df['Albumin_and_Globulin_Ratio']=imputer.transform(df[['Albumin_and_Globulin_Ratio']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 583 entries, 0 to 582\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column                      Non-Null Count  Dtype  \n",
+      "---  ------                      --------------  -----  \n",
+      " 0   Age                         583 non-null    int64  \n",
+      " 1   Gender                      583 non-null    object \n",
+      " 2   Total_Bilirubin             583 non-null    float64\n",
+      " 3   Direct_Bilirubin            583 non-null    float64\n",
+      " 4   Alkaline_Phosphotase        583 non-null    int64  \n",
+      " 5   Alamine_Aminotransferase    583 non-null    int64  \n",
+      " 6   Aspartate_Aminotransferase  583 non-null    int64  \n",
+      " 7   Total_Protiens              583 non-null    float64\n",
+      " 8   Albumin                     583 non-null    float64\n",
+      " 9   Albumin_and_Globulin_Ratio  583 non-null    float64\n",
+      " 10  Dataset                     583 non-null    int64  \n",
+      "dtypes: float64(5), int64(5), object(1)\n",
+      "memory usage: 50.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#to get the data type of each feature\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In above we can see that Gender is not a numeric type so we convert it numeric for further analysis <br />\n",
+    "-> Gender: 1-male and 0-female"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Gender']=df['Gender'].replace({'Male':1,'Female':0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. EDA\n",
+    "-> Check the distribution of the columns like gender and target class(dataset)<br />\n",
+    "-> Perfoming multivariant analysis on all parameters and drawing conclusions from them<br />\n",
+    "-> explore the correlation matrix<br />\n",
+    "-> Overall Insights<br />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ->Check for Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "416 167\n"
+     ]
+    }
+   ],
+   "source": [
+    "# target class distribution in data set, 1 represent its a liver patient and 2 represent it is not a liver patient\n",
+    "true_count=len(df.loc[df['Dataset']==1])\n",
+    "false_count=len(df.loc[df['Dataset']==2])\n",
+    "print(true_count,false_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#bar chart for the target class (which is dataset here) and gender to visualize its distriubtion\n",
+    "categorical_vars = ['Gender', 'Dataset']\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "for i, var in enumerate(categorical_vars, 1):\n",
+    "    plt.subplot(1, 2, i)\n",
+    "    sns.countplot(x=var, data=df)\n",
+    "    plt.title(f'Bar Chart of {var}', fontsize=14)\n",
+    "    plt.xlabel(var)\n",
+    "    plt.ylabel('Count')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ->Mulitvariant analysis on Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "numerical_vars = ['Age', 'Gender','Total_Bilirubin', 'Direct_Bilirubin', 'Alkaline_Phosphotase', \n",
+    "                  'Alamine_Aminotransferase', 'Aspartate_Aminotransferase', 'Total_Protiens', \n",
+    "                  'Albumin', 'Albumin_and_Globulin_Ratio']\n",
+    "df[numerical_vars].hist(figsize=(12, 10))\n",
+    "plt.suptitle('Histograms of Numerical Variables', fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analysis of the histogram:<br />\n",
+    "-> Age: The graph shows that most of the people age is between 30 to 70 in the dataset<br />\n",
+    "-> Gender: Gender is only 0 (for female) and 1 (for male) also indicates that male dominant data is present in the dataset<br />\n",
+    "-> Total Billirubin: around 500 people in the dataset have total billirubin between 0 to 10 in their blood<br />\n",
+    "-> Direct Billirubin: Direct billirubin which is processed by liver, in the dataset around 450 people have direct billirubin between 0 to 2 in their blood.\n",
+    "-> Alkaline phosphotase(ALP):Few people in dataset have high elevated ALP enzyme which indicates liver disorders<br />\n",
+    "-> Alanine Aminotransferase(ALT): This enzyme which convert alanine and amino acid into pyruvate but high level of this causes damages to liver and result int fatty liver disease. In dataset very few have high level ALT.<br />\n",
+    "-> Aspartate Aminotransferase(AST): High AST causes hepatitis, few have high level AST in the dataset.<br />\n",
+    "-> Total Protien: This indicates total amount of albumin and globulin in the blood, its low levels (less than 6g) indicates liver disease. Around 60 people in the dataset have it bewtween 5-6 gm and some have even less than this.<br />\n",
+    "-> Albumin: Main protien made by the liver, low levels(less than 3.5) indicate chronic liver disease. Around 100 people have low level of albumin the dataset.<br />\n",
+    "-> A/G ratio: low ratio signifise liver disease, in dataset around 50 have this ratio less than 0.5."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### -> Explore the Correaltion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr=df.corr(method='kendall')\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.heatmap(corr,annot=True,cmap='coolwarm',fmt=\".2f\",linewidth=.5)\n",
+    "plt.title(\"Correlation\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the exact correaltion value is not visible in the heatmap so going to print the whole matrix and derive something from there.<br />\n",
+    "Correlation with respect to different methods can be derived, correlation methods like pearson, spearman, kendall will be used here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.056560</td>\n",
+       "      <td>0.011763</td>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.080425</td>\n",
+       "      <td>-0.086883</td>\n",
+       "      <td>-0.019910</td>\n",
+       "      <td>-0.187461</td>\n",
+       "      <td>-0.265924</td>\n",
+       "      <td>-0.216089</td>\n",
+       "      <td>-0.137351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <td>0.056560</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.089291</td>\n",
+       "      <td>0.100436</td>\n",
+       "      <td>-0.027496</td>\n",
+       "      <td>0.082332</td>\n",
+       "      <td>0.080336</td>\n",
+       "      <td>-0.089121</td>\n",
+       "      <td>-0.093799</td>\n",
+       "      <td>-0.003404</td>\n",
+       "      <td>-0.082416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <td>0.011763</td>\n",
+       "      <td>0.089291</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.874618</td>\n",
+       "      <td>0.206669</td>\n",
+       "      <td>0.214065</td>\n",
+       "      <td>0.237831</td>\n",
+       "      <td>-0.008099</td>\n",
+       "      <td>-0.222250</td>\n",
+       "      <td>-0.206159</td>\n",
+       "      <td>-0.220208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.100436</td>\n",
+       "      <td>0.874618</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.234939</td>\n",
+       "      <td>0.233894</td>\n",
+       "      <td>0.257544</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.228531</td>\n",
+       "      <td>-0.200004</td>\n",
+       "      <td>-0.246046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <td>0.080425</td>\n",
+       "      <td>-0.027496</td>\n",
+       "      <td>0.206669</td>\n",
+       "      <td>0.234939</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.125680</td>\n",
+       "      <td>0.167196</td>\n",
+       "      <td>-0.028514</td>\n",
+       "      <td>-0.165453</td>\n",
+       "      <td>-0.233960</td>\n",
+       "      <td>-0.184866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <td>-0.086883</td>\n",
+       "      <td>0.082332</td>\n",
+       "      <td>0.214065</td>\n",
+       "      <td>0.233894</td>\n",
+       "      <td>0.125680</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.791966</td>\n",
+       "      <td>-0.042518</td>\n",
+       "      <td>-0.029742</td>\n",
+       "      <td>-0.002374</td>\n",
+       "      <td>-0.163416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <td>-0.019910</td>\n",
+       "      <td>0.080336</td>\n",
+       "      <td>0.237831</td>\n",
+       "      <td>0.257544</td>\n",
+       "      <td>0.167196</td>\n",
+       "      <td>0.791966</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.025645</td>\n",
+       "      <td>-0.085290</td>\n",
+       "      <td>-0.070024</td>\n",
+       "      <td>-0.151934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <td>-0.187461</td>\n",
+       "      <td>-0.089121</td>\n",
+       "      <td>-0.008099</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.028514</td>\n",
+       "      <td>-0.042518</td>\n",
+       "      <td>-0.025645</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.784053</td>\n",
+       "      <td>0.233904</td>\n",
+       "      <td>0.035008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin</th>\n",
+       "      <td>-0.265924</td>\n",
+       "      <td>-0.093799</td>\n",
+       "      <td>-0.222250</td>\n",
+       "      <td>-0.228531</td>\n",
+       "      <td>-0.165453</td>\n",
+       "      <td>-0.029742</td>\n",
+       "      <td>-0.085290</td>\n",
+       "      <td>0.784053</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.686322</td>\n",
+       "      <td>0.161388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <td>-0.216089</td>\n",
+       "      <td>-0.003404</td>\n",
+       "      <td>-0.206159</td>\n",
+       "      <td>-0.200004</td>\n",
+       "      <td>-0.233960</td>\n",
+       "      <td>-0.002374</td>\n",
+       "      <td>-0.070024</td>\n",
+       "      <td>0.233904</td>\n",
+       "      <td>0.686322</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.162319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dataset</th>\n",
+       "      <td>-0.137351</td>\n",
+       "      <td>-0.082416</td>\n",
+       "      <td>-0.220208</td>\n",
+       "      <td>-0.246046</td>\n",
+       "      <td>-0.184866</td>\n",
+       "      <td>-0.163416</td>\n",
+       "      <td>-0.151934</td>\n",
+       "      <td>0.035008</td>\n",
+       "      <td>0.161388</td>\n",
+       "      <td>0.162319</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Age    Gender  Total_Bilirubin  \\\n",
+       "Age                         1.000000  0.056560         0.011763   \n",
+       "Gender                      0.056560  1.000000         0.089291   \n",
+       "Total_Bilirubin             0.011763  0.089291         1.000000   \n",
+       "Direct_Bilirubin            0.007529  0.100436         0.874618   \n",
+       "Alkaline_Phosphotase        0.080425 -0.027496         0.206669   \n",
+       "Alamine_Aminotransferase   -0.086883  0.082332         0.214065   \n",
+       "Aspartate_Aminotransferase -0.019910  0.080336         0.237831   \n",
+       "Total_Protiens             -0.187461 -0.089121        -0.008099   \n",
+       "Albumin                    -0.265924 -0.093799        -0.222250   \n",
+       "Albumin_and_Globulin_Ratio -0.216089 -0.003404        -0.206159   \n",
+       "Dataset                    -0.137351 -0.082416        -0.220208   \n",
+       "\n",
+       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "Age                                 0.007529              0.080425   \n",
+       "Gender                              0.100436             -0.027496   \n",
+       "Total_Bilirubin                     0.874618              0.206669   \n",
+       "Direct_Bilirubin                    1.000000              0.234939   \n",
+       "Alkaline_Phosphotase                0.234939              1.000000   \n",
+       "Alamine_Aminotransferase            0.233894              0.125680   \n",
+       "Aspartate_Aminotransferase          0.257544              0.167196   \n",
+       "Total_Protiens                     -0.000139             -0.028514   \n",
+       "Albumin                            -0.228531             -0.165453   \n",
+       "Albumin_and_Globulin_Ratio         -0.200004             -0.233960   \n",
+       "Dataset                            -0.246046             -0.184866   \n",
+       "\n",
+       "                            Alamine_Aminotransferase  \\\n",
+       "Age                                        -0.086883   \n",
+       "Gender                                      0.082332   \n",
+       "Total_Bilirubin                             0.214065   \n",
+       "Direct_Bilirubin                            0.233894   \n",
+       "Alkaline_Phosphotase                        0.125680   \n",
+       "Alamine_Aminotransferase                    1.000000   \n",
+       "Aspartate_Aminotransferase                  0.791966   \n",
+       "Total_Protiens                             -0.042518   \n",
+       "Albumin                                    -0.029742   \n",
+       "Albumin_and_Globulin_Ratio                 -0.002374   \n",
+       "Dataset                                    -0.163416   \n",
+       "\n",
+       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "Age                                          -0.019910       -0.187461   \n",
+       "Gender                                        0.080336       -0.089121   \n",
+       "Total_Bilirubin                               0.237831       -0.008099   \n",
+       "Direct_Bilirubin                              0.257544       -0.000139   \n",
+       "Alkaline_Phosphotase                          0.167196       -0.028514   \n",
+       "Alamine_Aminotransferase                      0.791966       -0.042518   \n",
+       "Aspartate_Aminotransferase                    1.000000       -0.025645   \n",
+       "Total_Protiens                               -0.025645        1.000000   \n",
+       "Albumin                                      -0.085290        0.784053   \n",
+       "Albumin_and_Globulin_Ratio                   -0.070024        0.233904   \n",
+       "Dataset                                      -0.151934        0.035008   \n",
+       "\n",
+       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
+       "Age                        -0.265924                   -0.216089 -0.137351  \n",
+       "Gender                     -0.093799                   -0.003404 -0.082416  \n",
+       "Total_Bilirubin            -0.222250                   -0.206159 -0.220208  \n",
+       "Direct_Bilirubin           -0.228531                   -0.200004 -0.246046  \n",
+       "Alkaline_Phosphotase       -0.165453                   -0.233960 -0.184866  \n",
+       "Alamine_Aminotransferase   -0.029742                   -0.002374 -0.163416  \n",
+       "Aspartate_Aminotransferase -0.085290                   -0.070024 -0.151934  \n",
+       "Total_Protiens              0.784053                    0.233904  0.035008  \n",
+       "Albumin                     1.000000                    0.686322  0.161388  \n",
+       "Albumin_and_Globulin_Ratio  0.686322                    1.000000  0.162319  \n",
+       "Dataset                     0.161388                    0.162319  1.000000  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='pearson')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.062364</td>\n",
+       "      <td>0.113827</td>\n",
+       "      <td>0.106473</td>\n",
+       "      <td>0.059205</td>\n",
+       "      <td>-0.067737</td>\n",
+       "      <td>-0.018285</td>\n",
+       "      <td>-0.174271</td>\n",
+       "      <td>-0.260791</td>\n",
+       "      <td>-0.249505</td>\n",
+       "      <td>-0.129572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <td>0.062364</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.200503</td>\n",
+       "      <td>0.209210</td>\n",
+       "      <td>0.079131</td>\n",
+       "      <td>0.201107</td>\n",
+       "      <td>0.209434</td>\n",
+       "      <td>-0.090905</td>\n",
+       "      <td>-0.095440</td>\n",
+       "      <td>-0.008342</td>\n",
+       "      <td>-0.082416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <td>0.113827</td>\n",
+       "      <td>0.200503</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.959216</td>\n",
+       "      <td>0.383794</td>\n",
+       "      <td>0.436586</td>\n",
+       "      <td>0.508869</td>\n",
+       "      <td>-0.019252</td>\n",
+       "      <td>-0.222184</td>\n",
+       "      <td>-0.284200</td>\n",
+       "      <td>-0.303879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <td>0.106473</td>\n",
+       "      <td>0.209210</td>\n",
+       "      <td>0.959216</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.367818</td>\n",
+       "      <td>0.412322</td>\n",
+       "      <td>0.504138</td>\n",
+       "      <td>-0.019987</td>\n",
+       "      <td>-0.232664</td>\n",
+       "      <td>-0.297338</td>\n",
+       "      <td>-0.297270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <td>0.059205</td>\n",
+       "      <td>0.079131</td>\n",
+       "      <td>0.383794</td>\n",
+       "      <td>0.367818</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.410752</td>\n",
+       "      <td>0.395732</td>\n",
+       "      <td>0.014028</td>\n",
+       "      <td>-0.170809</td>\n",
+       "      <td>-0.321095</td>\n",
+       "      <td>-0.273247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <td>-0.067737</td>\n",
+       "      <td>0.201107</td>\n",
+       "      <td>0.436586</td>\n",
+       "      <td>0.412322</td>\n",
+       "      <td>0.410752</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.773611</td>\n",
+       "      <td>-0.018811</td>\n",
+       "      <td>-0.052673</td>\n",
+       "      <td>-0.082942</td>\n",
+       "      <td>-0.290709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <td>-0.018285</td>\n",
+       "      <td>0.209434</td>\n",
+       "      <td>0.508869</td>\n",
+       "      <td>0.504138</td>\n",
+       "      <td>0.395732</td>\n",
+       "      <td>0.773611</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.084779</td>\n",
+       "      <td>-0.204867</td>\n",
+       "      <td>-0.208809</td>\n",
+       "      <td>-0.308897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <td>-0.174271</td>\n",
+       "      <td>-0.090905</td>\n",
+       "      <td>-0.019252</td>\n",
+       "      <td>-0.019987</td>\n",
+       "      <td>0.014028</td>\n",
+       "      <td>-0.018811</td>\n",
+       "      <td>-0.084779</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.779077</td>\n",
+       "      <td>0.272490</td>\n",
+       "      <td>0.032220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin</th>\n",
+       "      <td>-0.260791</td>\n",
+       "      <td>-0.095440</td>\n",
+       "      <td>-0.222184</td>\n",
+       "      <td>-0.232664</td>\n",
+       "      <td>-0.170809</td>\n",
+       "      <td>-0.052673</td>\n",
+       "      <td>-0.204867</td>\n",
+       "      <td>0.779077</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.751223</td>\n",
+       "      <td>0.167079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <td>-0.249505</td>\n",
+       "      <td>-0.008342</td>\n",
+       "      <td>-0.284200</td>\n",
+       "      <td>-0.297338</td>\n",
+       "      <td>-0.321095</td>\n",
+       "      <td>-0.082942</td>\n",
+       "      <td>-0.208809</td>\n",
+       "      <td>0.272490</td>\n",
+       "      <td>0.751223</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.187377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dataset</th>\n",
+       "      <td>-0.129572</td>\n",
+       "      <td>-0.082416</td>\n",
+       "      <td>-0.303879</td>\n",
+       "      <td>-0.297270</td>\n",
+       "      <td>-0.273247</td>\n",
+       "      <td>-0.290709</td>\n",
+       "      <td>-0.308897</td>\n",
+       "      <td>0.032220</td>\n",
+       "      <td>0.167079</td>\n",
+       "      <td>0.187377</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Age    Gender  Total_Bilirubin  \\\n",
+       "Age                         1.000000  0.062364         0.113827   \n",
+       "Gender                      0.062364  1.000000         0.200503   \n",
+       "Total_Bilirubin             0.113827  0.200503         1.000000   \n",
+       "Direct_Bilirubin            0.106473  0.209210         0.959216   \n",
+       "Alkaline_Phosphotase        0.059205  0.079131         0.383794   \n",
+       "Alamine_Aminotransferase   -0.067737  0.201107         0.436586   \n",
+       "Aspartate_Aminotransferase -0.018285  0.209434         0.508869   \n",
+       "Total_Protiens             -0.174271 -0.090905        -0.019252   \n",
+       "Albumin                    -0.260791 -0.095440        -0.222184   \n",
+       "Albumin_and_Globulin_Ratio -0.249505 -0.008342        -0.284200   \n",
+       "Dataset                    -0.129572 -0.082416        -0.303879   \n",
+       "\n",
+       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "Age                                 0.106473              0.059205   \n",
+       "Gender                              0.209210              0.079131   \n",
+       "Total_Bilirubin                     0.959216              0.383794   \n",
+       "Direct_Bilirubin                    1.000000              0.367818   \n",
+       "Alkaline_Phosphotase                0.367818              1.000000   \n",
+       "Alamine_Aminotransferase            0.412322              0.410752   \n",
+       "Aspartate_Aminotransferase          0.504138              0.395732   \n",
+       "Total_Protiens                     -0.019987              0.014028   \n",
+       "Albumin                            -0.232664             -0.170809   \n",
+       "Albumin_and_Globulin_Ratio         -0.297338             -0.321095   \n",
+       "Dataset                            -0.297270             -0.273247   \n",
+       "\n",
+       "                            Alamine_Aminotransferase  \\\n",
+       "Age                                        -0.067737   \n",
+       "Gender                                      0.201107   \n",
+       "Total_Bilirubin                             0.436586   \n",
+       "Direct_Bilirubin                            0.412322   \n",
+       "Alkaline_Phosphotase                        0.410752   \n",
+       "Alamine_Aminotransferase                    1.000000   \n",
+       "Aspartate_Aminotransferase                  0.773611   \n",
+       "Total_Protiens                             -0.018811   \n",
+       "Albumin                                    -0.052673   \n",
+       "Albumin_and_Globulin_Ratio                 -0.082942   \n",
+       "Dataset                                    -0.290709   \n",
+       "\n",
+       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "Age                                          -0.018285       -0.174271   \n",
+       "Gender                                        0.209434       -0.090905   \n",
+       "Total_Bilirubin                               0.508869       -0.019252   \n",
+       "Direct_Bilirubin                              0.504138       -0.019987   \n",
+       "Alkaline_Phosphotase                          0.395732        0.014028   \n",
+       "Alamine_Aminotransferase                      0.773611       -0.018811   \n",
+       "Aspartate_Aminotransferase                    1.000000       -0.084779   \n",
+       "Total_Protiens                               -0.084779        1.000000   \n",
+       "Albumin                                      -0.204867        0.779077   \n",
+       "Albumin_and_Globulin_Ratio                   -0.208809        0.272490   \n",
+       "Dataset                                      -0.308897        0.032220   \n",
+       "\n",
+       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
+       "Age                        -0.260791                   -0.249505 -0.129572  \n",
+       "Gender                     -0.095440                   -0.008342 -0.082416  \n",
+       "Total_Bilirubin            -0.222184                   -0.284200 -0.303879  \n",
+       "Direct_Bilirubin           -0.232664                   -0.297338 -0.297270  \n",
+       "Alkaline_Phosphotase       -0.170809                   -0.321095 -0.273247  \n",
+       "Alamine_Aminotransferase   -0.052673                   -0.082942 -0.290709  \n",
+       "Aspartate_Aminotransferase -0.204867                   -0.208809 -0.308897  \n",
+       "Total_Protiens              0.779077                    0.272490  0.032220  \n",
+       "Albumin                     1.000000                    0.751223  0.167079  \n",
+       "Albumin_and_Globulin_Ratio  0.751223                    1.000000  0.187377  \n",
+       "Dataset                     0.167079                    0.187377  1.000000  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='spearman')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <th>Albumin</th>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.051520</td>\n",
+       "      <td>0.078099</td>\n",
+       "      <td>0.074733</td>\n",
+       "      <td>0.038868</td>\n",
+       "      <td>-0.046261</td>\n",
+       "      <td>-0.013206</td>\n",
+       "      <td>-0.120690</td>\n",
+       "      <td>-0.180176</td>\n",
+       "      <td>-0.177241</td>\n",
+       "      <td>-0.107040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <td>0.051520</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.168671</td>\n",
+       "      <td>0.180362</td>\n",
+       "      <td>0.064846</td>\n",
+       "      <td>0.165521</td>\n",
+       "      <td>0.172013</td>\n",
+       "      <td>-0.075289</td>\n",
+       "      <td>-0.079378</td>\n",
+       "      <td>-0.007077</td>\n",
+       "      <td>-0.082416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Bilirubin</th>\n",
+       "      <td>0.078099</td>\n",
+       "      <td>0.168671</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.898136</td>\n",
+       "      <td>0.270267</td>\n",
+       "      <td>0.306483</td>\n",
+       "      <td>0.361626</td>\n",
+       "      <td>-0.014417</td>\n",
+       "      <td>-0.153281</td>\n",
+       "      <td>-0.203865</td>\n",
+       "      <td>-0.255635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Direct_Bilirubin</th>\n",
+       "      <td>0.074733</td>\n",
+       "      <td>0.180362</td>\n",
+       "      <td>0.898136</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.265809</td>\n",
+       "      <td>0.292416</td>\n",
+       "      <td>0.364823</td>\n",
+       "      <td>-0.015559</td>\n",
+       "      <td>-0.164638</td>\n",
+       "      <td>-0.219255</td>\n",
+       "      <td>-0.256279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alkaline_Phosphotase</th>\n",
+       "      <td>0.038868</td>\n",
+       "      <td>0.064846</td>\n",
+       "      <td>0.270267</td>\n",
+       "      <td>0.265809</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.277939</td>\n",
+       "      <td>0.264168</td>\n",
+       "      <td>0.010076</td>\n",
+       "      <td>-0.115934</td>\n",
+       "      <td>-0.227519</td>\n",
+       "      <td>-0.223921</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alamine_Aminotransferase</th>\n",
+       "      <td>-0.046261</td>\n",
+       "      <td>0.165521</td>\n",
+       "      <td>0.306483</td>\n",
+       "      <td>0.292416</td>\n",
+       "      <td>0.277939</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.596488</td>\n",
+       "      <td>-0.012909</td>\n",
+       "      <td>-0.033134</td>\n",
+       "      <td>-0.055798</td>\n",
+       "      <td>-0.239269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aspartate_Aminotransferase</th>\n",
+       "      <td>-0.013206</td>\n",
+       "      <td>0.172013</td>\n",
+       "      <td>0.361626</td>\n",
+       "      <td>0.364823</td>\n",
+       "      <td>0.264168</td>\n",
+       "      <td>0.596488</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.057158</td>\n",
+       "      <td>-0.137458</td>\n",
+       "      <td>-0.145771</td>\n",
+       "      <td>-0.253705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total_Protiens</th>\n",
+       "      <td>-0.120690</td>\n",
+       "      <td>-0.075289</td>\n",
+       "      <td>-0.014417</td>\n",
+       "      <td>-0.015559</td>\n",
+       "      <td>0.010076</td>\n",
+       "      <td>-0.012909</td>\n",
+       "      <td>-0.057158</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.613098</td>\n",
+       "      <td>0.192220</td>\n",
+       "      <td>0.026685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin</th>\n",
+       "      <td>-0.180176</td>\n",
+       "      <td>-0.079378</td>\n",
+       "      <td>-0.153281</td>\n",
+       "      <td>-0.164638</td>\n",
+       "      <td>-0.115934</td>\n",
+       "      <td>-0.033134</td>\n",
+       "      <td>-0.137458</td>\n",
+       "      <td>0.613098</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.592980</td>\n",
+       "      <td>0.138960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albumin_and_Globulin_Ratio</th>\n",
+       "      <td>-0.177241</td>\n",
+       "      <td>-0.007077</td>\n",
+       "      <td>-0.203865</td>\n",
+       "      <td>-0.219255</td>\n",
+       "      <td>-0.227519</td>\n",
+       "      <td>-0.055798</td>\n",
+       "      <td>-0.145771</td>\n",
+       "      <td>0.192220</td>\n",
+       "      <td>0.592980</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.158967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dataset</th>\n",
+       "      <td>-0.107040</td>\n",
+       "      <td>-0.082416</td>\n",
+       "      <td>-0.255635</td>\n",
+       "      <td>-0.256279</td>\n",
+       "      <td>-0.223921</td>\n",
+       "      <td>-0.239269</td>\n",
+       "      <td>-0.253705</td>\n",
+       "      <td>0.026685</td>\n",
+       "      <td>0.138960</td>\n",
+       "      <td>0.158967</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Age    Gender  Total_Bilirubin  \\\n",
+       "Age                         1.000000  0.051520         0.078099   \n",
+       "Gender                      0.051520  1.000000         0.168671   \n",
+       "Total_Bilirubin             0.078099  0.168671         1.000000   \n",
+       "Direct_Bilirubin            0.074733  0.180362         0.898136   \n",
+       "Alkaline_Phosphotase        0.038868  0.064846         0.270267   \n",
+       "Alamine_Aminotransferase   -0.046261  0.165521         0.306483   \n",
+       "Aspartate_Aminotransferase -0.013206  0.172013         0.361626   \n",
+       "Total_Protiens             -0.120690 -0.075289        -0.014417   \n",
+       "Albumin                    -0.180176 -0.079378        -0.153281   \n",
+       "Albumin_and_Globulin_Ratio -0.177241 -0.007077        -0.203865   \n",
+       "Dataset                    -0.107040 -0.082416        -0.255635   \n",
+       "\n",
+       "                            Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
+       "Age                                 0.074733              0.038868   \n",
+       "Gender                              0.180362              0.064846   \n",
+       "Total_Bilirubin                     0.898136              0.270267   \n",
+       "Direct_Bilirubin                    1.000000              0.265809   \n",
+       "Alkaline_Phosphotase                0.265809              1.000000   \n",
+       "Alamine_Aminotransferase            0.292416              0.277939   \n",
+       "Aspartate_Aminotransferase          0.364823              0.264168   \n",
+       "Total_Protiens                     -0.015559              0.010076   \n",
+       "Albumin                            -0.164638             -0.115934   \n",
+       "Albumin_and_Globulin_Ratio         -0.219255             -0.227519   \n",
+       "Dataset                            -0.256279             -0.223921   \n",
+       "\n",
+       "                            Alamine_Aminotransferase  \\\n",
+       "Age                                        -0.046261   \n",
+       "Gender                                      0.165521   \n",
+       "Total_Bilirubin                             0.306483   \n",
+       "Direct_Bilirubin                            0.292416   \n",
+       "Alkaline_Phosphotase                        0.277939   \n",
+       "Alamine_Aminotransferase                    1.000000   \n",
+       "Aspartate_Aminotransferase                  0.596488   \n",
+       "Total_Protiens                             -0.012909   \n",
+       "Albumin                                    -0.033134   \n",
+       "Albumin_and_Globulin_Ratio                 -0.055798   \n",
+       "Dataset                                    -0.239269   \n",
+       "\n",
+       "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
+       "Age                                          -0.013206       -0.120690   \n",
+       "Gender                                        0.172013       -0.075289   \n",
+       "Total_Bilirubin                               0.361626       -0.014417   \n",
+       "Direct_Bilirubin                              0.364823       -0.015559   \n",
+       "Alkaline_Phosphotase                          0.264168        0.010076   \n",
+       "Alamine_Aminotransferase                      0.596488       -0.012909   \n",
+       "Aspartate_Aminotransferase                    1.000000       -0.057158   \n",
+       "Total_Protiens                               -0.057158        1.000000   \n",
+       "Albumin                                      -0.137458        0.613098   \n",
+       "Albumin_and_Globulin_Ratio                   -0.145771        0.192220   \n",
+       "Dataset                                      -0.253705        0.026685   \n",
+       "\n",
+       "                             Albumin  Albumin_and_Globulin_Ratio   Dataset  \n",
+       "Age                        -0.180176                   -0.177241 -0.107040  \n",
+       "Gender                     -0.079378                   -0.007077 -0.082416  \n",
+       "Total_Bilirubin            -0.153281                   -0.203865 -0.255635  \n",
+       "Direct_Bilirubin           -0.164638                   -0.219255 -0.256279  \n",
+       "Alkaline_Phosphotase       -0.115934                   -0.227519 -0.223921  \n",
+       "Alamine_Aminotransferase   -0.033134                   -0.055798 -0.239269  \n",
+       "Aspartate_Aminotransferase -0.137458                   -0.145771 -0.253705  \n",
+       "Total_Protiens              0.613098                    0.192220  0.026685  \n",
+       "Albumin                     1.000000                    0.592980  0.138960  \n",
+       "Albumin_and_Globulin_Ratio  0.592980                    1.000000  0.158967  \n",
+       "Dataset                     0.138960                    0.158967  1.000000  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='kendall')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-> Pearson correlation: Feature in which target class (dataset) depends the most<br />\n",
+    "* Albumin\n",
+    "* A/G ratio\n",
+    "* Total protiens<br />\n",
+    "<br />\n",
+    "\n",
+    "-> Spearman correlation: Feature in which target class depends the most <br />\n",
+    "* A/G ratio\n",
+    "* Albumin\n",
+    "* Total protiens<br />\n",
+    "<br />\n",
+    "\n",
+    "-> Kendall correlation : Feature in which target class depends the most <br />\n",
+    "* A/G ratio\n",
+    "* Albumin\n",
+    "* Total protiens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ->Overall Insights\n",
+    "From the above correlation and visualisation we can conclude that target class(dataset) mostly depends on these features (descending order):<br />\n",
+    "* A/G ratio\n",
+    "* Albumin\n",
+    "* Total protiens\n",
+    "<br />\n",
+    "\n",
+    "So from the original features<br /> \n",
+    "**10 independent variable - 1 dependent class**<br />\n",
+    "to<br />\n",
+    "**3 independent variable - 1 dependent class**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}