diff --git a/Stroke Prediction/StrokePrediction-EDA.ipynb b/Stroke Prediction/StrokePrediction-EDA.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1c72284d-9f26-46dc-9f82-6d8d4e1d70a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "580a1a19-6d2b-4ca5-b953-df5e375b40cd",
+   "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>id</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "      <th>hypertension</th>\n",
+       "      <th>heart_disease</th>\n",
+       "      <th>ever_married</th>\n",
+       "      <th>work_type</th>\n",
+       "      <th>Residence_type</th>\n",
+       "      <th>avg_glucose_level</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>smoking_status</th>\n",
+       "      <th>stroke</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9046</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>228.69</td>\n",
+       "      <td>36.6</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>51676</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>202.21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31112</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>105.92</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>60182</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>171.23</td>\n",
+       "      <td>34.4</td>\n",
+       "      <td>smokes</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
+       "0   9046    Male  67.0             0              1          Yes   \n",
+       "1  51676  Female  61.0             0              0          Yes   \n",
+       "2  31112    Male  80.0             0              1          Yes   \n",
+       "3  60182  Female  49.0             0              0          Yes   \n",
+       "\n",
+       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
+       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
+       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
+       "2        Private          Rural             105.92  32.5     never smoked   \n",
+       "3        Private          Urban             171.23  34.4           smokes   \n",
+       "\n",
+       "   stroke  \n",
+       "0       1  \n",
+       "1       1  \n",
+       "2       1  \n",
+       "3       1  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
+    "df.head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "161d9eeb-9f1e-4376-99da-8cf4aa63a7ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id                     int64\n",
+      "gender                object\n",
+      "age                  float64\n",
+      "hypertension           int64\n",
+      "heart_disease          int64\n",
+      "ever_married          object\n",
+      "work_type             object\n",
+      "Residence_type        object\n",
+      "avg_glucose_level    float64\n",
+      "bmi                  float64\n",
+      "smoking_status        object\n",
+      "stroke                 int64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2409329-d7ac-4cd8-8288-badac35a3a6b",
+   "metadata": {},
+   "source": [
+    "# Calculating memory usage diffrenece (in bytes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5e41906-36ee-43cf-95f6-f7931893ae3b",
+   "metadata": {},
+   "source": [
+    "## Inital memory "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7c7631c0-7277-483c-b17b-c05aed1075fd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial memory usage: 1901853 bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "initial_memory = df.memory_usage(deep=True).sum()\n",
+    "print(\"Initial memory usage:\", initial_memory, \"bytes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "952832dd-4e2d-45b9-85af-a2262e1b8f4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert categorical columns to category dtype\n",
+    "categorical_columns = ['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']\n",
+    "df[categorical_columns] = df[categorical_columns].astype('category')\n",
+    "\n",
+    "# Convert boolean columns to bool dtype\n",
+    "boolean_columns = ['hypertension', 'heart_disease', 'stroke']\n",
+    "df[boolean_columns] = df[boolean_columns].astype('bool')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fdc9f2f-d967-44d3-b359-b6595f945079",
+   "metadata": {},
+   "source": [
+    "## Final Memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6390ce5b-9f65-490f-9374-a62d709786db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final memory usage: 206230 bytes\n",
+      "Memory saved: 1695623 bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "final_memory = df.memory_usage(deep=True).sum()\n",
+    "print(\"Final memory usage:\", final_memory, \"bytes\")\n",
+    "print(\"Memory saved:\", initial_memory - final_memory, \"bytes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21809a5c-ff09-4879-8ff0-4c1996f47953",
+   "metadata": {},
+   "source": [
+    "# Explore statistical facts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c026808c-f60e-49ce-8a29-d907d60bed49",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 id          age  avg_glucose_level          bmi\n",
+      "count   5110.000000  5110.000000        5110.000000  4909.000000\n",
+      "mean   36517.829354    43.226614         106.147677    28.893237\n",
+      "std    21161.721625    22.612647          45.283560     7.854067\n",
+      "min       67.000000     0.080000          55.120000    10.300000\n",
+      "25%    17741.250000    25.000000          77.245000    23.500000\n",
+      "50%    36932.000000    45.000000          91.885000    28.100000\n",
+      "75%    54682.000000    61.000000         114.090000    33.100000\n",
+      "max    72940.000000    82.000000         271.740000    97.600000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Explore statistical facts\n",
+    "print(df.describe())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04d769cb-81c7-47c8-a5c1-21faf3b2f3c7",
+   "metadata": {},
+   "source": [
+    "### percentile "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cf54b73a-26f5-471c-a3cd-43cbcef6dcf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percentiles for Age: [11. 20. 30. 38. 51. 57. 65. 75.]\n",
+      "Percentiles for Average Glucose Level: [ 65.789  73.76   80.038  85.6    98.914 108.516 124.16  192.181]\n",
+      "Percentiles for BMI: [nan nan nan nan nan nan nan nan]\n",
+      "Percentage of people with hypertension: 9.74559686888454\n",
+      "Percentage of people with heart disease: 5.401174168297456\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Calculate specific percentiles for a column using numpy\n",
+    "percentiles_custom_age = np.percentile(df['age'], [10, 20, 30, 40, 60, 70, 80, 90])\n",
+    "percentiles_custom_glucose = np.percentile(df['avg_glucose_level'], [10, 20, 30, 40, 60, 70, 80, 90])\n",
+    "percentiles_custom_bmi = np.percentile(df['bmi'], [10, 20, 30, 40, 60, 70, 80, 90])\n",
+    "percent_hypertension_true = (df['hypertension'].sum() / len(df['hypertension'])) * 100\n",
+    "percent_heart_disease_true = (df['heart_disease'].sum() / len(df['heart_disease'])) * 100\n",
+    "\n",
+    "# Print the calculated percentiles\n",
+    "print(\"Percentiles for Age:\", percentiles_custom_age)\n",
+    "print(\"Percentiles for Average Glucose Level:\", percentiles_custom_glucose)\n",
+    "print(\"Percentiles for BMI:\", percentiles_custom_bmi)\n",
+    "print(\"Percentage of people with hypertension:\", percent_hypertension_true)\n",
+    "print(\"Percentage of people with heart disease:\", percent_heart_disease_true)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53c7697d-a7a4-4e5a-8cc9-b468cbb366f9",
+   "metadata": {},
+   "source": [
+    "## median- central tendency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "91e00dfc-5eca-4a2a-9f54-4704b76657c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median Age: 45.0\n",
+      "Median Avg. Glucose Level: 91.88499999999999\n",
+      "Median BMI: 28.1\n",
+      "Median Hypertension: 0.0\n",
+      "Median Heart Disease: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate median for each column\n",
+    "median_age = df['age'].median()\n",
+    "median_glucose_level = df['avg_glucose_level'].median()\n",
+    "median_bmi = df['bmi'].median()\n",
+    "median_hypertension = df['hypertension'].median()\n",
+    "median_heart_disease = df['heart_disease'].median()\n",
+    "\n",
+    "# Print the calculated medians\n",
+    "print(\"Median Age:\", median_age)\n",
+    "print(\"Median Avg. Glucose Level:\", median_glucose_level)\n",
+    "print(\"Median BMI:\", median_bmi)\n",
+    "print(\"Median Hypertension:\", median_hypertension)\n",
+    "print(\"Median Heart Disease:\", median_heart_disease)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "905c348c-24c0-488e-a1ee-54880e7b17c1",
+   "metadata": {},
+   "source": [
+    "## Mean "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "87e60403-b555-4c7a-af7d-5bdb2c8bff7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Age: 43.226614481409\n",
+      "Mean Avg. Glucose Level: 106.1476771037182\n",
+      "Mean BMI: 28.893236911794666\n",
+      "Mean Hypertension: 0.0974559686888454\n",
+      "Mean Heart Disease: 0.05401174168297456\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate mean for each column\n",
+    "mean_age = df['age'].mean()\n",
+    "mean_avg_glucose_level = df['avg_glucose_level'].mean()\n",
+    "mean_bmi = df['bmi'].mean()\n",
+    "mean_hypertension = df['hypertension'].mean()\n",
+    "mean_heart_disease = df['heart_disease'].mean()\n",
+    "\n",
+    "# Print the calculated means\n",
+    "print(\"Mean Age:\", mean_age)\n",
+    "print(\"Mean Avg. Glucose Level:\", mean_avg_glucose_level)\n",
+    "print(\"Mean BMI:\", mean_bmi)\n",
+    "print(\"Mean Hypertension:\", mean_hypertension)\n",
+    "print(\"Mean Heart Disease:\", mean_heart_disease)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "962a27f0-460e-45f7-a38b-6b970fbea3e4",
+   "metadata": {},
+   "source": [
+    "# Missing value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "93a2aeb0-1aa8-48fa-b085-56effd68dfe2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                     0\n",
+       "gender                 0\n",
+       "age                    0\n",
+       "hypertension           0\n",
+       "heart_disease          0\n",
+       "ever_married           0\n",
+       "work_type              0\n",
+       "Residence_type         0\n",
+       "avg_glucose_level      0\n",
+       "bmi                  201\n",
+       "smoking_status         0\n",
+       "stroke                 0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2252f2c-209e-4c44-be35-4d825bc498d3",
+   "metadata": {},
+   "source": [
+    "### The Decision Tree model learns from known BMI values alongside age and gender features to predict missing BMI values, leveraging inherent patterns in the data for effective imputation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26b29149-704f-431d-9295-f9016a8a81ec",
+   "metadata": {},
+   "source": [
+    "### from https://www.kaggle.com/code/thomaskonstantin/analyzing-and-modeling-stroke-data  rather than imputing it naively with the mean or the median, we used a simple decision tree model which based on the age and gender of all other samples gave us a fair prediction for the missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b0a5febe-b1de-40cb-820f-804e37c9e4a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "\n",
+    "# Define the pipeline\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('gender', OneHotEncoder(), ['gender'])  # One-hot encode the 'gender' column\n",
+    "    ],\n",
+    "    remainder='passthrough'  # Keep other columns unchanged\n",
+    ")\n",
+    "\n",
+    "# Define the pipeline for predicting BMI\n",
+    "DT_bmi_pipe = Pipeline([\n",
+    "    ('preprocessor', preprocessor), \n",
+    "    ('imputer', SimpleImputer(strategy='mean')), \n",
+    "    ('scaler', StandardScaler()), \n",
+    "    ('regressor', DecisionTreeRegressor()) \n",
+    "])\n",
+    "\n",
+    "# Prepare data for training\n",
+    "X_train = df.dropna(subset=['bmi'])[['age', 'gender']].copy()\n",
+    "y_train = df.dropna(subset=['bmi'])['bmi']\n",
+    "\n",
+    "# Fit the pipeline\n",
+    "DT_bmi_pipe.fit(X_train, y_train)\n",
+    "\n",
+    "# Identify rows with missing BMI\n",
+    "missing_bmi = df[df['bmi'].isnull()][['age', 'gender']]\n",
+    "\n",
+    "# Predict missing BMI values\n",
+    "predicted_bmi = DT_bmi_pipe.predict(missing_bmi[['age', 'gender']])\n",
+    "\n",
+    "# Update the DataFrame with predicted BMI values\n",
+    "df.loc[missing_bmi.index, 'bmi'] = predicted_bmi\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e17df6f-a99a-4823-a47e-4d2ce64a95f8",
+   "metadata": {},
+   "source": [
+    "# Stroke Prediction | 2. EDA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28275fee-d437-450c-b79c-af4f4fd2e76a",
+   "metadata": {},
+   "source": [
+    "### Removing ID as it is nothing but a unique number assigned to every patient to keep track of them and making them unique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a1f10bce-01a5-4c7e-a052-6e2002d86733",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(\"id\",inplace=True,axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c077395d-eef5-4fb5-b2ac-6fb19a72ffb7",
+   "metadata": {},
+   "source": [
+    "## Univariate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46fb7595-7a36-4e75-888a-a7b909fda796",
+   "metadata": {},
+   "source": [
+    "#### Gender "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c5e9f126-2eb1-4cc2-b377-47d7ca16d7a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique values\n",
+      " ['Male', 'Female', 'Other']\n",
+      "Categories (3, object): ['Female', 'Male', 'Other']\n",
+      "Value Counts\n",
+      " gender\n",
+      "Female    2994\n",
+      "Male      2115\n",
+      "Other        1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Print unique values and counts\n",
+    "print('Unique values\\n', df['gender'].unique())\n",
+    "print('Value Counts\\n', df['gender'].value_counts())\n",
+    "\n",
+    "# Plot count plot\n",
+    "sns.countplot(data=df, x='gender')\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot count plot with hue for 'stroke'\n",
+    "sns.countplot(data=df, x='gender', hue='stroke')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4431fe6d-0244-46cc-bd0d-ea2e279f9a9c",
+   "metadata": {},
+   "source": [
+    "``` we can see there is not much difference between stroke rate concerning gender. ```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97a90989-809e-4b34-a132-ef0c23473a46",
+   "metadata": {},
+   "source": [
+    "### Histogram "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d61fe895-c6c5-40e2-a3cf-ccf2524174f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary Statistics for 'age':\n",
+      "Mean: 43.226614481409\n",
+      "Median: 45.0\n",
+      "Mode: 78.0\n",
+      "Standard Deviation: 22.61264672311349\n",
+      "Skewness: -0.1370190866396024\n",
+      "Kurtosis: -0.9912147700517671\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AAMBFphapt956S7169VJQUJCCgoKUkpKiBQsWONeff/75slgsDW5/+MMfGuwjPT1do0ePlp+fnyIjI/XAAw+orq7uTL8UAAAAAK2Ih5lPHhcXp2effVZJSUkyDEPvvfeeLrvsMm3atEndu3eXJN1yyy164oknnI/x8/Nz/rm+vl6jR49WdHS0Vq5cqaysLN10003y9PTUM888c8ZfDwAAAIDWwdQiNWbMmAb3n376ab311ltavXq1s0j5+fkpOjr6uI9fvHixdu7cqa+//lpRUVHq06ePnnzyST344IN67LHH5OXlddpfAwAAAIDWx23Okaqvr9e8efNUXl6ulJQU5/I5c+YoIiJCPXr00LRp01RRUeFct2rVKvXs2VNRUVHOZSNHjpTdbteOHTtO+FzV1dWy2+0NbgAAAABwskwdkZKkbdu2KSUlRVVVVQoICNAnn3yi5ORkSdINN9ygxMRExcbGauvWrXrwwQe1e/duffzxx5Kk7OzsBiVKkvN+dnb2CZ9z+vTpevzxx0/TKwIAAADQ0plepLp06aLNmzerpKRE//73vzVhwgQtX75cycnJuvXWW53b9ezZUzExMRo2bJjS0tLUsWPHRj/ntGnTNHXqVOd9u92u+Pj4U3odAAAAAFoP0w/t8/LyUqdOndSvXz9Nnz5dvXv31iuvvHLcbQcOHChJ2rdvnyQpOjpaOTk5Dbb56f6JzquSJG9vb+dMgT/dAAAAAOBkmV6kfsnhcKi6uvq46zZv3ixJiomJkSSlpKRo27Ztys3NdW6zZMkSBQUFOQ8PBAAAAICmZuqhfdOmTdOoUaOUkJCg0tJSzZ07V99++60WLVqktLQ0zZ07V5dcconCw8O1detW3XvvvRo6dKh69eolSRoxYoSSk5M1fvx4Pffcc8rOztbDDz+sKVOmyNvb28yXBgAAAKAFM7VI5ebm6qabblJWVpaCg4PVq1cvLVq0SBdddJEOHz6sr7/+Wi+//LLKy8sVHx+vsWPH6uGHH3Y+3maz6csvv9Ttt9+ulJQU+fv7a8KECQ2uOwUAAAAATc3UIvXOO++ccF18fLyWL1/+m/tITEzUV1991ZSxAADAL6Snpys/P9/sGJKkiIgIJSQkmB0DQCtn+qx9AADAvaWnp6trt26q/Nm1HM3k6+enXamplCkApqJIAQCAX5Wfn6/Kigrd+ODzikpo/OVHmkJOeprmzHhA33//vbp162ZqFonRMaA1o0gBAICTEpXQUXFJ3U3NYC/MkySNGzfO1Bw/YXQMaL0oUgAAoNmoLLNLkkbf9pC69OpnapafRsfy8/MpUkArRJECAADNTnhsoumjYwBaN7e7IC8AAAAAuDuKFAAAAAC4iCIFAAAAAC6iSAEAAACAiyhSAAAAAOAiihQAAAAAuIjpzwEAgFupqKlTXmm18sqqVVJRq/KaepVX16mipl7VSlLcXXO1oiJAa7/bL19Pm3y9jt5CfD0VEeCt8AAvhfp5yWa1mP1SALRgFCkAAGCq0qpapRdWKL2wQkeKK1VeXf8rW3vI5hukOkl1tfWqrK2XKo6zldWi2BBfJYT5KSHMTxEBXrJYKFYAmg5FCgAAnHEllbXanVOqPTmlKiirOWZ9iJ+n2gR4K8zfSwHeHvL39pCfl0271y3Xl/94Tpff8biSep2tqh/LVHl1nQoralRQdvRWU+9wljNJCvLxUJfoQHWNDlKYv9eZfrkAWiCKFAAAOCNq6hzanVOqHZklyrFXO5dbJEUGeTtHjyIDfeTlcfzTuDNUrbqCDPlZ69Um0Pu42xiGocLyGmeRyiiqlL2qTusOFmndwSJFBXmrT1yIkqICOfwPQKNRpAAAwGlVVF6jrRkl2pllV029Q9LR8hQX5qsuUYHq2CZAPp62Jns+i8Wi8ABvhQd4q29CqGrrHTqQX67ULLsOFVYox16tRTtztCKtQL3jg9UzNljeTfj8AFoHihQAADgtcuxVWnewUGl55c5lwb6e6tU2WF2iA+XvfWY+hnjarOocFajOUYGqqKnT9ky7thwuVll1nVbsK9D6g0U6u12YescFy8PGhMYATg5FCgAANKnM4kqtOVDoPD9JktpH+Kt3XLASwvxMnfTBz8tDA9qF6ayEEO3OLtXGQ8UqrKjRD/vytflwsQZ1CFO3mCBZmZgCwG+gSAEAgCaRV1qtlWn5OlhwtEBZLFLXqED1SwxVeMDxz2cyi4fVqu6xweoWE6TULLtW7y9UWXWdvk7N1fYjdl3YNfKE52ABgESRAgAAp8heWauVaQXanVMq6WiB6h4TpLPbhSnI19PkdL/OarGoe2ywukQFaktGidYeKFS2vUofrEtXn/gQDWoffsKJLwC0bhQpAADQKDV1Dq0/VKiN6cWqdxiSpM5RARrUIVyhfs1rinEPm1X9EkPVJSpQy/fmaV9umTalFystt0wjukerbYiv2REBuBmKFAAAcIlhGNqVXaof9uWrouboxXPjQn11blKEIgN9TE53agJ8PDS6Z4wO5Jdr2e5c2avq9J8NGerfLlQD24czXToAJ4oUAAA4aUXlNfpmV64yiislHZ2F79ykCHWI8Dd1Eomm1j7CX7EhCVq+J0+pWaVad7BIhwoqdHGP6GY32gbg9KBIAQCA32bz1M5im/YcTle9YcjDatHA9mHqmxDaYkdpvD1sGpEcrfbh/vpmV65yS6s1b+1hjegepY5tAsyOB8BkFCkAAPCrtuVWK3bSa0q12yQZSgz30wVdIhXs5hNJNJWkqEDFBPtqwfYsZZZU6cutWeqfGKoEw+xkAMxEkQIAAMdVUFatp79K1ccbC+UZHicfq6ELk2PUKTKgRR3GdzICfDx05VlxzutNrT9UpHQfD1m9/c2OBsAkzOcJAAAaMAxDH607rGEvLtfHG4/IIsm+4UuNiK1VUlRgqytRP7FZLTqvcxtd3D1anjaLcqusih7/V2WX1ZkdDYAJKFIAAMBpX26prn17tf74n60qrqhVt5ggTR8WrqKvZ8qTTw2SpC7Rgbq6X7x8bYY8w+P1p6UFWn+w0OxYAM4w/kkEAACqqq3XC4t3a9Qr32vtgUL5etr00CXd9MUdg9U5nFnqfqlNoLcuiK5VddZe2asduuHva/Tl1kyzYwE4gzhHCgCAVu6Hvfl6+NNtOlhQIUka1jVSj1/WXXGhfiYnc2++Ninngz/pqhe+1Noj1brzg00qqqjV+EGJZkcDcAZQpAAAaKXyy6r11Jc79enmoyMpUUHeevzS7hrZPbrVngflKqO2Wg+khOrTw16asyZdf/l0u4rLa3THhZ34GQItHEUKAIBWxuEw9NH6w5q+YJdKKmtlsUgTUtrpvhGdFejTOqY0b0o2q0VPXd5D4f5eevWbfXphyR4VVtTokd8lU6aAFowiBQBAK7I7u1QPf7pN6w4WSZK6xwbpmSt6qnd8iLnBmjmLxaKpI7ooxM9LT3y5U7NWHFRNnUNPXtZD1hZ6wWKgtaNIAQDQCpRV1+mVr/fo3RUHVe8w5Odl09SLOmviOe3kYWPuqaZy85D2CvTx0B//s1Vz1qTLYRh6+vKelCmgBaJIAQDQghmGoa+2ZevJL3cq214lSRrZPUqPjOmutiG+Jqdrma7uHy+b1aL752/RB2sPy+GQpl9JmQJaGooUAAAt1IH8cj3y2XZ9vzdfkpQQ5qfHL+2uC7pGmpys5bvyrDjZrBbd++Fmfbj+sCTKFNDSUKQAAGhhqmrr9eayfZq5fL9q6h3y8rDq9vM66vbzO8rH02Z2vFbjsj5tZbFYdM+8Tfpw/WH5etn06BgmoABaCooUAAAthMNh6PMtmXpu4S5llhw9jG9o5zZ64tLuahfhb3K61unS3rGqqXPo/vlbNHvlQQV4e+j+kV3MjgWgCVCkAABoAdYfLNST/03VlsPFkqS2Ib56aHQ3jerBNaHMdlW/OFXW1Okvn+3Q68v2yc/bpv87v5PZsQCcIooUAADN2OHCCj27YJf+uy1LkuTvZdP/XdBJk4e05zA+NzI+pZ3Ka+r17IJdem7hboX4eumGgQlmxwJwCihSAAA0Q/aqWr25LE3v/nBANfUOWS3StWfH696LOisy0MfseDiOP5zXUaVVtXpjWZoe/nSbIgK8NKJ7tNmxADQSRQoAgGakvLpOs1ce1N+Wp8leVSdJGtwpXA+PTla3mCCT0+G33D+ii/JLa/Th+sO684NNmvP7gerfLszsWAAagSIFAEAzUFlTr3+tPqS3lqepsLxGkpQUGaBpl3TVBV0iOQ+qmbBYLHr6ih7KL6vW0l25unn2Ov379nPUOSrQ7GgAXESRAgDAjVXX1euDNel649s05ZVWS5LaR/jrnuFJ+l2vWNm4LlGz42Gz6vUbztIN/1itTenFmjRrnT75v3MUGcQhmUBzQpECAMANVdXWa/76w3rr2zTnVOZxob66a1iSruzbVh42q8kJcSp8vWx6d8LZGvvWSu3PL9fv31+vebcOkp8XH82A5oK/rQAAuJGSylr9a/UhvfvDARX8eAhfdJCP7riwk67pHy8vDwpUSxHq76V3J56tK95coa0ZJbr3w81688Z+jDICzQRFCgAAN5Bjr9K7PxzQnDXpKqs+OolE2xBf3Tq0g649O56pzFuodhH+evum/rrx72u0aEeOnl2QqodGJ5sdC8BJoEgBAGCitLwy/f27/fp44xHV1DskSV2iAnX7+R01uleMPDmEr8U7u12Ynr+6l+6et1l///6AkiIDdc3Z8WbHAvAbTP3X+a233lKvXr0UFBSkoKAgpaSkaMGCBc71VVVVmjJlisLDwxUQEKCxY8cqJyenwT7S09M1evRo+fn5KTIyUg888IDq6urO9EsBAOCkGYah7/bkaeKstRr2wnLNW3dYNfUOnd0uVO9O7K+F95yry/u2pUS1Ipf1aat7h3eWJD386XZtTC8yORGA32LqiFRcXJyeffZZJSUlyTAMvffee7rsssu0adMmde/eXffee6/++9//av78+QoODtYdd9yhK6+8UitWrJAk1dfXa/To0YqOjtbKlSuVlZWlm266SZ6ennrmmWfMfGkAAByjsqZeH2/K0KwVB7Uvt0ySZLFIw7pG6rbzOupsrifUqt15YSftzCrRoh05+sM/N+iLO4coipn8ALdlapEaM2ZMg/tPP/203nrrLa1evVpxcXF65513NHfuXF144YWSpFmzZqlbt25avXq1Bg0apMWLF2vnzp36+uuvFRUVpT59+ujJJ5/Ugw8+qMcee0xeXl5mvCwAQDOWmpra5PvMr6jXgn3lWrK/QmU1hiTJ18OiC9v76pJO/ooJtEmFB7Wx8KDzMREREUpISGjyLHBfVqtFL1zTRwfeXKE9OWX6w782aN6tg+TtwflxgDtym3Ok6uvrNX/+fJWXlyslJUUbNmxQbW2thg8f7tyma9euSkhI0KpVqzRo0CCtWrVKPXv2VFRUlHObkSNH6vbbb9eOHTvUt2/f4z5XdXW1qqurnfftdvvpe2EAgGbBXpgnSRo3blyT7dM7vocC+42RX9IgWaxHPwzXFmWpdMMXKtu2RLtqKvXmCR7r6+enXamplKlWJsDbQ2+P769LX/9Bm9KL9ZdPt2vG2F5ccBlwQ6YXqW3btiklJUVVVVUKCAjQJ598ouTkZG3evFleXl4KCQlpsH1UVJSys7MlSdnZ2Q1K1E/rf1p3ItOnT9fjjz/etC8EANCsVZYd/VJt9G0PqUuvfo3eT51DOlxhVVqpVSW1/zvHqY23Q50C6xUTHy5L74mSJp5wHznpaZoz4wHl5+dTpFqhdhH+eu2GszRp1lp9tD5DPdoG66aUdmbHAvALphepLl26aPPmzSopKdG///1vTZgwQcuXLz+tzzlt2jRNnTrVed9utys+ntlxAABSeGyi4pK6u/w4e2WttmaUaHt2iarrjs6+52G1qGt0oHrHhygiwLupo6IFO69zGz14cVdNX7BLT3yxU52jAjWoQ7jZsQD8jOlFysvLS506dZIk9evXT+vWrdMrr7yia6+9VjU1NSouLm4wKpWTk6Po6GhJUnR0tNauXdtgfz/N6vfTNsfj7e0tb29+oQEATo1hGMooqtSWjGLtzyuX8ePyIB8P9Y4LUXJsENd/QqPdOrSDdmTa9fmWTE2Zs1Gf3zlEbUN8zY4F4EduN6+qw+FQdXW1+vXrJ09PTy1dutS5bvfu3UpPT1dKSookKSUlRdu2bVNubq5zmyVLligoKEjJyVzMDgBwetTWO7Q1o1hz1qTr401HlPZjiYoP89WYXjGacE47nZUYSonCKbFYLJoxtpe6xwapoLxGt76/XpU19WbHAvAjU0ekpk2bplGjRikhIUGlpaWaO3euvv32Wy1atEjBwcGaPHmypk6dqrCwMAUFBenOO+9USkqKBg0aJEkaMWKEkpOTNX78eD333HPKzs7Www8/rClTpjDiBABociWVtdqSUaydmXbn4XueNou6RQepd3yIwvyZLbY1Oh0zPf7cnX299ccCq3Zk2nXHrOWacnbIcbdjpkfgzDK1SOXm5uqmm25SVlaWgoOD1atXLy1atEgXXXSRJOmll16S1WrV2LFjVV1drZEjR+rNN/83v5HNZtOXX36p22+/XSkpKfL399eECRP0xBNPmPWSAAAtUHZJlTakFyktt8x5+F6wr6d6xwUrOTaI6albqdMx0+OJ+CT0UuS1T2rpgUp9+PrTKt/+zTHbMNMjcGaZWqTeeeedX13v4+OjN954Q2+88cYJt0lMTNRXX33V1NEAAK2cYRg6kF+uDelFyiyuci5PCPNTn/gQtQv3Y0rqVq6pZno8WaklhnaWSFG/u1cXTr5TQV6Gcx0zPQJnnumTTQAA4E4chrT9SIk2phepqKJWkmS1SF2iA3VWQiiz7+EYjZ3p0VWxhqGyzZlKL6zQBru/rj07Xl4ebne6O9BqUKQAAJBUL4sCz75CqyujVLvr6CRGXh5W9WwbrD5xIQrw4VcmzGW1WDSye5Tmrk1XYUWNlu3O1YjkKEZGAZPwWwEA0KrV1Dm0JaNY69RJYRd2Va2kAG8P9U0IUY/YYL7xh1vx8/LQqO4x+s/GDO3KLlXbEF/1aBtsdiygVaJIAQBapeq6em05XKJN6UWqqnNI8lBtUaZ6RPnqonPOls3Kt/xwT21DfZXSMVwr0wr07Z48RQX5mB0JaJX4mg0A0KpU19Vr9f4CvbvioFbtL1BVnUMhfp7qrCPK/PsfFO1RSYmC2+ufGKp24X6qdxj6aluWah1mJwJaH0akAACtQl29Q1uPlGjdwUJV/fipM8zfSwPahSkpKkCbvtkqGXwaRfNgsVg0Ijlac9emq7iyVhsLmYIfONMoUgCAFs3hMJSabdfq/YUqq66TJIX6eSqlQ7g6RQZwoj6aLV8vm0b1iNZ/NmYoo8KmgN4Xmx0JaFUoUgCAFskwDKXllWtlWr5zGvMAbw8N6hCmbtFBsnL4HlqA2BBfndMxQj/sy1fosN/rcEmtzjI7FNBKUKQAAC1OVkmllu/JU469WpLk42HV2e3D1KttsDxsnB6MluWshBDtychVrnz04upijRxSLx9PDvUDTjeKFACgxSivrtOKfflKzS6VJHnaLOobH6qzEkPk7cEHS7RMFotFZ4fX6fM9FTqkEM1YuEuPjjn9FwgGWju+lgMANHv1DkMbDhXp/VWHnCUqOSZIE1LaKaVjOCUKLZ6PTcr/6mVJ0qwVB7Xsx4tKAzh9KFIAgGbtYEG5/rXmkH7Yl6+aeoeigrx1bf94XZQcJX9vDrxA61G1f71GJ/lJku6fv0W5pVUmJwJaNn7DAACapeKKGn2/N1/788slSb6eNg3pFKFuMYHMxIdWa3yvIKWV2rQru1T3fbRF700awMQqwGlCkQIANCu19Q6tO1iojYeKVW8Yslqk3vEhGtg+rMUdwpeammp2BEnukwO/zctm0WvX99WY13/Q93vz9e6KA/r9uR3MjgW0SBQpAECzYBiG9uSU6Yd9+c7rQSWE+em8zm0U5u9lcrqmZS/MkySNGzfO5CQNlZWVmR0BJyEpKlB/+V2yHvpku2Ys3KVBHcLVo22w2bGAFociBQBwe3ml1Vq+J09HiislSUE+HhrauY06RPi3yMP4KsvskqTRtz2kLr36mZxGSl27XAvee0VVVZxz01zcMCBBy3fnafHOHN01b5O+vHOI/Lz42Ac0Jf5GAQDcVlVtvValFWjbkRIZkjysFvVvF6p+CaGt4npQ4bGJiksyfxrrnPQ0syPARRaLRTPG9tLWjO+1P69cT3yxU8+O7WV2LKBFafm/hQAAzY7DMLQ1o1jvrTyorT+WqKTIAI1PSdTA9uGtokQBpyrU30svXttbFos0b91hLdyebXYkoEVhRAoA4FaOFFdq+e485ZVVS5LC/b10Xuc2ig/zMzkZ0Pyc0zFCtw7toL8t369pH29V34QQRQX5mB0LaBH4Sg8A4BbKquq0cEe2/r0hQ3ll1fL2sOq8zm10w4AEShRwCu67qIu6xwapqKJW98/fIofDMDsS0CJQpAAA5rJ5aHeJVe+vPqjd2aWSpB6xQbopJVF94kO4Bg5wirw8rHrluj7y9rDq+735mrXyoNmRgBaBIgUAMM36zCrF3vymtpd4qLbeUEywj647O17DukUxwxjQhDpFBurh3yVLkmYs3KVd2XaTEwHNH0UKAHDGHcgv16RZa/XMD0XyDIuVj9XQiOQoXd0vjvM3gNNk3MAEDesaqZo6h+7+YLOqauvNjgQ0axQpAMAZU15dp2cX7NKIl5Zr2e48eVilkjX/0YjYWnWLCWqR14QC3IXFYtGMq3opIsBLu3NKNWPhLrMjAc0aRQoAcNoZhqFPNx3RhS98q5nL01Rbb+i8zm300og2Kv52ljz5bQScEREB3nr+qt6SpFkrDmr5njyTEwHNF7+6AACn1ebDxRr71krd8+Fm5dirlRDmp3/c1F+zJ52ttkGcBwWcaRd0jdRNKYmSpPvnb1HBj5caAOAafoMBAE6L7JIqPbdwlz7edESS5Otp0x0XdtLkIe3l42kzOR3Quv35km5amVagfbll+tPH2/T2+H4cWgu4iBEpAECTqqyp1ytf79UFf/3WWaLGnhWnbx84X1Mu6ESJAtyAj6dNr1zXR542i5bszNG8dYfNjgQ0O4xIAQCahGEY+nxLpp5dsEtZJVWSpP6JoXpkTLJ6xYWYGw7AMbrHBuuBkV30zFe79MQXOzWwfZg6tAkwOxbQbFCkAACnbFN6kZ74cqc2pRdLktqG+GraJV01umcMhwsBbuz3Qzro2915WplWoHs+3Kz/3H6OPG0csAScDP6mAAAaLaukUvd+uFlXvLlSm9KL5edl0/0jOmvpfefpd71iKVGAm7NaLXrhmt4K9vXU1owSvfz1HrMjAc0GI1IAAJeVVNZq5vI0zVpxQFW1DknSVf3i9MDILlxQF2hmYoJ9Nf3Knvq/ORv15rdpGprURgM7hJsdC3B7FCkAwEmrqq3XeysP6s1v01RSWStJOrtdqB75XXf1jAs2OR2AxrqkZ4yu7hen+RsyNPWjLfrq7nMV7OtpdizArVGkAAC/qa7eof9szNDLX+91TiTROSpAD4zsquHdIjmED2gBHr20u9YeLNShggo98tl2vXJdX7MjAW6NIgUAOCHDMLRoR47+uni39uWWSZJig31070WddeVZcbJZKVBASxHg7aGXru2jq2eu0mebM3VBl0hd3ret2bEAt0WRAgAc1+r9BZqxcJdzJr4QP0/dcUEnjRuUyLWgADeVmpp6yvu4upu/5u0o058/3iKfsiOK9G/cx8WIiAglJCScch7AXVGkAAAN7Mgs0fOLduvb3XmSJF9PmyYPaa9bz+ugIB/OmQDckb3w6N/XcePGnfrOLFZF3fCsFJesiTO/Vc4H0yTD4fJufP38tCs1lTKFFosiBQCQJO3KtuvlJXu1cEe2JMnDatF1A+J114VJimQmPsCtVZbZJUmjb3tIXXr1O+X9ldVJS7MM+cR31yVPf6yuwa4VqZz0NM2Z8YDy8/MpUmixKFIA0Mrtzi7VK0v36KttRwuUxSL9rles7ruos9pF+JucDoArwmMTFZfUvUn2ZYTYtXhnjlLtHuqRFK/oYL5QAX6OIgUArdS+3FK9/PVe/Xdblgzj6LLRvWJ097AkdY4KNDccANN1jQ7Ugfxy7c0t08Id2bphQIK8PKxmxwLcBkUKAFqZtLwyvbp0rz7fkuksUKN6ROvu4UnqGh1kbjgAbsNisejCrpHKKqlSSWWtvtubp+HdosyOBbgNihQAtBIH8sv16tK9+mzzETl+LFAju0fp7mGdlRxLgQJwLB9Pm0Z2j9J/Nh7Rjky72oX7q1NkgNmxALdAkQKAFu5gfrle/WavPt30vwI1vFuU7hmepB5tg80NB8DtxYX6qV9iqDYcKtLS1BxFB/sowJuPkAB/CwCghUovqNBr3+zVx5uOqP7HBjWsa6TuGd5ZPeMoUABOXkqHcB0urFBuabWW7MzR5X1iZbFwQW60bhQpADgD0tPTlZ+ff0aeK7e8TvN3lunbg5Wq/3EE6qxob13bPUBJ4TbV5qYp3cGFMgGcPJvVopHdo/XB2nSlF1Zo8+Fi9U0INTsWYCqKlBs6kx+4fgtXJQdOXXp6urp266bKiorT+jy2oDYKTrlGAT0vksV29J/3yv0bVLxirj7J3K1PfrYtF8oE4Kowfy+dmxShZbvztGJfgeJC/dQm0NvsWIBpKFJu5kx94DpZfNgCTl1+fr4qKyp044PPKyqhY5Pvv7xO2m236WCZVYaOHmoT6eNQcnC9whN6SudPb7A9F8oE0Fg92wbrYEGFDuSXa9GObF13drw8bEyJjtaJIuVmTvcHLlfwYQtoWlEJHZvsQpmSZK+s1bqDhdqZZXdOIhEX6qtBHcLVNsS3yZ4HAH5isVg0vFuk/rU6XQXlNVqRVqDzOrcxOxZgClOL1PTp0/Xxxx9r165d8vX11TnnnKMZM2aoS5cuzm3OP/98LV++vMHjbrvtNs2cOdN5Pz09XbfffruWLVumgIAATZgwQdOnT5eHR/PtiU39gQtAy3G8AhUf6quB7cPVNpQCBeD08vPy0IjkKH22JVObDxerXbifEsP9zY4FnHGmNo3ly5drypQpOvvss1VXV6c///nPGjFihHbu3Cl////9hbzlllv0xBNPOO/7+fk5/1xfX6/Ro0crOjpaK1euVFZWlm666SZ5enrqmWeeOaOvBwBOp5IfC1TqzwtU2I8FihEoAGdQuwh/9Y4L1paMEi3emaMbBybIz6v5foENNIap7/iFCxc2uD979mxFRkZqw4YNGjp0qHO5n5+foqOjj7uPxYsXa+fOnfr6668VFRWlPn366Mknn9SDDz6oxx57TF5eXqf1NQDA6Xa8ApUQ5qeB7cMUS4ECYJIhnSJ0uKhSheU1Wpqaq9/1imFKdLQqbnV2YElJiSQpLCyswfI5c+YoIiJCPXr00LRp01Txs4kYVq1apZ49eyoqKsq5bOTIkbLb7dqxY8dxn6e6ulp2u73BDQDcTUllrb5OzdH7qw5qR+bREpUQ5qer+8Xpir5tKVEATOVhs+ri7tGyWSzan1+uLRklZkcCzii3GYN1OBy65557NHjwYPXo0cO5/IYbblBiYqJiY2O1detWPfjgg9q9e7c+/vhjSVJ2dnaDEiXJeT87O/u4zzV9+nQ9/vjjp+mVAMCpKams1doDhUrNtstgBAqAG2sT6K3BncL13d58fb83T1FB3ooJ5t8ptA5uU6SmTJmi7du364cffmiw/NZbb3X+uWfPnoqJidGwYcOUlpamjh0bN6vdtGnTNHXqVOd9u92u+Pj4xgUHgCZyvAKVGOangR3C+GACwG31iQ9RVkmV9uaW6att2bp+AJ+p0Dq4RZG644479OWXX+q7775TXFzcr247cOBASdK+ffvUsWNHRUdHa+3atQ22ycnJkaQTnlfl7e0tb28uIAfAPRRX1GjdwSIKFIBmyWKxaFi3SOWXVauoolYLd2TrbCbxQytgapEyDEN33nmnPvnkE3377bdq3779bz5m8+bNkqSYmBhJUkpKip5++mnl5uYqMjJSkrRkyRIFBQUpOTn5tGUHgFNVXFGjtQcLtSu79H8FKvzoIXwUKADNibeHTaN7xmjeusM6XFgp/zqb2ZGA087UIjVlyhTNnTtXn332mQIDA53nNAUHB8vX11dpaWmaO3euLrnkEoWHh2vr1q269957NXToUPXq1UuSNGLECCUnJ2v8+PF67rnnlJ2drYcfflhTpkxh1AmAWzpRgRrUPlzRwT7mhgOARgoP8NawbpFatCNHu+w2+XTob3Yk4LQytUi99dZbko5edPfnZs2apYkTJ8rLy0tff/21Xn75ZZWXlys+Pl5jx47Vww8/7NzWZrPpyy+/1O23366UlBT5+/trwoQJDa47BQDuoKxWWrwzu0GBahfup4EUKAAtRNfoIGUVV2nrkRJF/O4+5ZbXmR0JOG1MP7Tv18THx2v58uW/uZ/ExER99dVXTRULAJrUoZJahf/uPi3K8pRUKokCBaDlOrdzhA7nFalIgXp+ZbEuSKmXtweH+qHlcavrSAFAS7LlcLFufX+97l2Ur4DuF0iyqF24n67tH6/L+rSlRAFokTysVg2KqFN9RYnSimr1xBc7zY4EnBYUKQBoQoZhaPX+Ao1/Z40ue2OFFu/MkUVS+a4fdGF0LQUKQKvg5yHlf/mCLJLmrEnXR+sPmx0JaHIUKQBoAoZhaNmuXF09c5Wue3u1vt+bL5vVoivPaqtXLo5Q/mfPKtTr1w9nBoCWpOrARl3bPUCS9PAn27XhUKHJiYCm5RbXkQKA5qreYWjRjmy9sWyfdmTaJUleHlZd0z9Otw3tqPgwP23cuNHklABgjquSA1RiCdCC7dm67Z8b9fkdgxUbwuUd0DJQpACgEapq6/WfjRl65/sD2p9fLkny87LpxoEJuuXcDooM4vA9ALBaLHrhml46WFCh1Cy7bnl/vf79h3Pk68XkE2j+KFIA4IL8smr9c9Uh/XP1IRWW10iSgnw8NHFwe006p51C/b1MTggA7sXPy0N/v6mfLnt9hXZk2nX//C167fq+slotZkcDTglFCgBOQlpemf7x/QH9Z2OGauockqS2Ib6aPKS9rjk7XgHe/HMKACcSF+qnmeP76Ya/r9Z/t2UpPsxPfxrV1exYwCnhNz8AnIDDYej7ffl6b+VBfbMr17m8d1ywbhnaQRd3j5aHjTl7AOBknN0uTDPG9tLUj7Zo5vI0xYf56saBiWbHAhqNIgUAv1BSWat/b8jQP1cd1MGCCkmSxSIN7xalW87toLPbhcpi4ZAUAHDVlWfF6XBhpV76eo8e+WyHYkN8dUGXSLNjAY1CkQKAH6Vm2fX+qkP6dNMRVdbWS5ICvT10Vf84jR+UqA5tAkxOCADN313DOim9sEL/2ZihO+Zs1Ie3pahH22CzYwEuo0gBaNUqaur01bZsfbguXesOFjmXd40O1PiURF3ep638Of8JAJqMxWLR9Ct7KqukUivTCjTh3bWa/4cUvqxCs8OnAwCtjmEY2ny4WB+tP6wvtmSprLpOkuRhtWhkj2jdNChRA9qHcfgeAJwmXh5WzRzfT9e/vVo7Mu0a/85a/fv2FMUEc40pNB8UKQCtRkFZtT7ZdEQfrT+sPTllzuWJ4X66pn+8ruoXpyiu/wQAZ0SQj6feu3mArpm5SvvzyzX+nbX66LYUhXEZCTQTFCkALVp5dZ2W7MzRZ5uP6Pu9+apzGJIkH0+rLukRo2vOjtdARp8AwBQRAd56f/IAXT1zlfbllmnirLX65+SBCvb1NDsa8JsoUgBanJo6h77bk6fPtmRqyc5sVdU6nOt6xQXrmv7xurRPrIJ8Wvcv6tTUVLMjuEUGAOaKC/XTPycP1DV/W6WtGSW66Z01ep8yhWaAIgWgRaipc2hlWr4W7cjWV9uyVVJZ61zXLtxPl/Zpq0t7x6pTJCcz2wvzJEnjxo0zOcn/lJWV/fZGAFqsTpEBmvP7gbrh76u1hTKFZoIiBaDZKq+u0/I9eVq4PVvLduWq9MdJIyQpMtBbY3rH6tLeseoVF8yhez9TWWaXJI2+7SF16dXP1Cypa5drwXuvqKqqytQcAMzXLSZIc28ZRJlCs0GRAlqA9PR05efnmx1DkhQREaGEhITTtv+80mot252rxTuy9d3efNXU/e+wvTaB3hqRHKVLesZoUIdw2ayUp18THpuouKTupmbISU8z9fkBuJdflqlr/7ZK7988QJFMBAQ3RJECmrn09HR17dZNlRUVZkeRJPn6+WlXamqTlal6h6HNh4v07e48Ldudq+1H7A3WJ4b7aWT3aI3sHqW+8aGy/qw8uUvB5DwgADh5P5Wp8e+s1a7sUl01c5X+OXmAEsP9zY4GNECRApq5/Px8VVZU6MYHn1dUQkdTs+Skp2nOjAeUn59/SkUqu6RKK/bl69s9efp+b56KK2obrO/RNkjDu0VpZPdodY0OPO5he+5WMCXOAwKAk9UtJkj/uT1F499Zq/TCCo19a6VmTxqgHm2DzY4GOFGkgBYiKqGj6YdpNVZeabVW7y/QyrQCrd5foAP55Q3WB/l46NzObXRBl0gN7RyhyMDfPsTDnQom5wEBgOsSw/3179tTNPHdddqZZde1f1ulV6/vq2HdosyOBkiiSAEwQVF5jVbvL9Cq/QValVagvbkNR2qsFqlH22CdmxShC7pEqk98iDxs1kY9lzsUTM4DAoDGiQz00bzbBum29zdo1f4C/f799frjyK76w3kdmEQIpqNIATjtSiprtfZAoVam5WtVWoF2ZZces023mCCldAhXSsdwDWgfxixNAABJUpCPp967eYAe+2KH5q5J14yFu7Q7265nx/aSj6fN7HhnhGEYyiiq1M4su1Kz7DpUUKHy6jpV1NSroqZOgT6eigv1VXyYn9qF+2lwpwgFtvJrJZ4JFCkATa6y1qFlu3KdI047MkvkMBpu0zkqwFmcBrYPV6i/lzlhAQBuz8vDqmeu6Klu0YF67Iud+nRzpnZll+r1G/qqU2Sg2fFOi4Kyan23N0/Ldh09X7joF+cL/xovm1VDO7fR6F7Ruig5WgHefOQ/HRr1U92/f786dOjQ1FkANFO19Q5lFlcqtdim6PF/1fhPc+Qwchps0yHCX4M6huucjuEa1CFcEQHeJqUFADRX41PaqWObAN35wSbtyi7V7177QY+N6a5rz45vEYf6VdbUa+GOLH20LkOrDxTI+NmXkJ42izpFBio5JkidIgMU5OshPy+bfD1tKqms1eHCSmUUVWjrkRLtzyvX16k5+jo1RyF+O3Xv8M66YWCCPBt5mDyOr1FFqlOnTjrvvPM0efJkXXXVVfLxYW5/oDVxOAzllFYpvbBChwsrlVVS+eOIk03esV3lMKSEMD/niNOgDuGKDubfCQDAqTunU4QW3H2upn60RT/sy9efPt6m7/bm6fFLe6hNYPP7ks4wDG3NKNFH6w/r882ZDS4unxwTpPO7tNEFXSPVOy5EXh6/XYQMw9CenDL9d1uWPt98RAcLKvTo5zv0z9WH9PDobjq/S+TpfDmtSqOK1MaNGzVr1ixNnTpVd9xxh6699lpNnjxZAwYMaOp8ANyAYRgqrqhVemGF0gsrlFFUqZp6R4NtArw9FOZRrY0fvap/v/mMLj6Xfw8AAKdHZJCP3r95gN7+fr/+umi3vtqWrRX7CjRtVFdd0z++wTUF3VVheY0+3XREH60/3ODc4bhQX13TP15XntVWcaF+Lu/XYrGoS3SgukQH6q4LO2neusN6ccke7cst08RZ63T9gAQ9cVnznOXX3TSqSPXp00evvPKKXnjhBX3++eeaPXu2hgwZos6dO+vmm2/W+PHj1aZNm6bOCuAMqncYOlJcqQN55dqfXyZ7VV2D9d4eVsWH+ikhzE/xYb4K9vXUkX079f32pYr051hsAMDpZbVa9IfzOmpwxwj96eOt2pFp158+3qb/bMzQ45f2UHJskNkRj1HvMPTDvnx9tO6wluzMcX4p6eVh1age0bqmf7xSOoQ3WRH0sFk1blCixvSO1atL9+rdFQf0wdp0Hcgv0+09mYziVJ3Spx0PDw9deeWVGj16tN58801NmzZN999/v/785z/rmmuu0YwZMxQTE9NUWQGcZlW19TqYX679+eU6VFDRYNTJZrEoJsRHCWFHy1ObQG9ZW8Dx6ACA5q1nXLA+mzJYs1ce1ItL9mjdwSJd8ur3GtM7VvcOT1KHNgFmR9ThwgrNX39Y/96QocyS/11TsEfbIF3TP16X9W6rYL/TV2yCfT31l98l65yO4brrg01avb9QB3Nt8giPO23P2RqcUpFav3693n33Xc2bN0/+/v66//77NXnyZGVkZOjxxx/XZZddprVr1zZVVgCnQXVdvfbllml3dqkyiisbnNjq52VT+wh/tY/wV0KYHyepAgDckofNqt+f20GjesZo+lep+nJrlr7YkqmvtmXpir5tdfPg9md8hKq0qlZLdubo3xsytDKtwLk82NdTl/eJ1TVnx6t7bPAZzTSsW5Q+/r/BmvzeOmUUVSr6xudlr+FL0cZqVJF68cUXNWvWLO3evVuXXHKJ3n//fV1yySWyWo9+yGrfvr1mz56tdu3aNWVWAE2kzuHQoYIK7cou1YH8ctX/bG7yiAAvtY/wV4eIAEUFebeIWZAAAK1D2xBfvX7DWfq/8+16cclufZ2aq39vyNC/N2SoX2Koxg1K0KgeMaft+lOlVbX6bk++vtiSqW9256qm7n9HdgzpFKFrzo7XiOQoU69/1SU6UJ9NGazr3lyuvYWB+iHPUHxVLdedaoRGFam33npLN998syZOnHjCQ/ciIyP1zjvvnFI4AE3HMAxllVQpNcuuvbllqv7ZP+5hfl7qEhOoLlGBXAgXANDsJccG6R8TztbG9CK988MBLdqerQ2HirThUJH+/PF2nZsUoeHJUbqgS+QpzfRXXVev1KxSrUzL1/LdedpwqEh1P/tyskOEv8b0jtVV/eIUH+b6xBGnS3iAtx4+N0w3vLteCo/XZ5szdXW/OHm3kgscN5VGFam9e/f+5jZeXl6aMGFCY3YPoAlV19VrV1apth0pUUF5jXO5v7dNXaKOzurTJoCRJwBAy3NWQqjOuiFUufYqfbjusOatO6wjxZVavDNHi3cevd5h2xBf9WgbpO6xwUoM91NEgLfCA7wU4uslh2Gort5QrcOhovIaHSmuVFZJlQ4VlGvbkRLtzi5VbX3DK863j/DXyO7RGtM7RskxQW77+zXQ26qcjx5RpzveVUF5jb7YmqXL+8TKg8P4T1qjitSsWbMUEBCgq6++usHy+fPnq6KiggIFuIGiihptTi9Warbd+Y+8h9WipKgAdY0OUlyoL5NFAABahcggH905LEl3XNhJOzLtzovVbj9i15HiSh0prtSiHTm/vaPjCPXzVL/EUJ3XuY2Gdm6jxHD/Jk5/+tTb8zSkTZ2+y/PWkeJKLdudp4uSo8yO1Ww0qkhNnz5df/vb345ZHhkZqVtvvZUiBZjEMAxlFldpQ3qRDuSXO5eH+XupZ9tgdYsOZNgeANBqWSwW9WgbrB5tg3XP8M4qqazVzky7dmSWaGemXZkllSooq1FBeY1KKmtls1jkYbPIZrUo2NdTbUN8FRviq7YhvkqODVKvuGC1DfF121GnkxHsZWh0rxh9sumIdmbZ1S7CT0mRgWbHahYaVaTS09PVvn37Y5YnJiYqPT39lEMBcF12pUUrN2Qo62fTqraP8Fff+BDFhTbvf+QBADgdgn09ldIxXCkdw82OYqqEMD/1TwzV+kNFWpqaq5ggXwX4cE3I39Kon1BkZKS2bt16zKx8W7ZsUXh4634jAmeSYRhal1ml6Akva0Wep6Qq2awWJccEqW98iEL9vcyOCAAAmoFBHcKVXlih3NJqLd6ZrSv6tuVL2N/QqCJ1/fXX66677lJgYKCGDh0qSVq+fLnuvvtuXXfddU0aEMCxDMPQin0F+uvi3dp8uFje0Z1ksxjqHR+qsxJC5e/Nt0gAAODk2awWjewerQ/WputwUaU2HS7WWQmhZsdya436tPXkk0/q4MGDGjZsmDw8ju7C4XDopptu0jPPPNOkAQE0tDPTrqf+u9N5cT8vm5S3Yr5uHHuZOia1MTndUampqWZHcIsMAAA0J2H+Xjo3KULLdudp5b4CtY/wV6gfR7ecSKOKlJeXlz788EM9+eST2rJli3x9fdWzZ08lJiY2dT4AP8ovq9YLi/do3rp0GYbkZbPqhoEJOje8QsOfeU/e11xmdkTZC/MkSePGjTM5yf+UlZWZHQEAWi13+VIrIiJCCQkJZsdoFnq2Ddb+vHIdKqzQd3vydFmftmZHclundPxP586d1blz56bKAuA4auocem/lQb26dK9Kq+skSaN7xehPF3dVfJifNm7caHLC/6kss0uSRt/2kLr06mdqltS1y7XgvVdUVVX12xsDAJqUu32x5uvnp12pqZSpk2CxWHRe5zb615pDOlhQoYP55WoX0XymdD+TGlWk6uvrNXv2bC1dulS5ublyOBwN1n/zzTdNEg5wZ+np6crPzz9t+zcMQ+uzqjV7s11ZZfWSpA6hHrq5T7CS20h5B3cp76D7fNv3c+GxiYpL6m5qhpz0NFOfHwBaM3f6Yi0nPU1zZjyg/Px8itRJCvX3Up/4EG1ML9byvXmKD/OTzcrEE7/UqCJ19913a/bs2Ro9erR69OjBjB5oddLT09W1WzdVVlSclv17hEQr7KLb5dvh6C+f+vIiFS1/X4e2L9Uyw3Hcx3AIGwDA3bjDF2tonAHtw5SaVariilptOVyssxKZeOKXGlWk5s2bp48++kiXXHJJU+cBmoX8/HxVVlToxgefV1RCxybbr8OQ9pZalVpiU71hkVWGOgU61DXOX57dbpd0+zGP4RA2AADQ1Lw9bDqnU7iWpuZqzYFCdYkOZFbgX2j0ZBOdOnVq6ixAsxOV0LHJvmnLsVdp6a5c5ZVWS5LiQn11YdfI35wth0PYAADA6dA9JkjbMkqUW1qt1fsLNKxblNmR3Iq1MQ+677779Morr8gwjKbOA7Q6tfUOfb83Tx+uO6y80mp5e1g1vFukruzblilHAQCAaSwWi4b+eGmVnVl22StrTU7kXho1IvXDDz9o2bJlWrBggbp37y5PT88G6z/++OMmCQe0dIcKyvXNrlzZq47Oxtc5KkBDk9owdA4AANxC21BfxYX6KqOoUusOFWpYV0alftKoT2shISG64oormjoL0GpU19Xr+7352pF5dFajAG8PXdg1Uu2ZXhQAALiZQe3D9e+iDO3MtGtAuzAF+nj+9oNagUYVqVmzZjXJk0+fPl0ff/yxdu3aJV9fX51zzjmaMWOGunTp4tymqqpK9913n+bNm6fq6mqNHDlSb775pqKi/teG09PTdfvtt2vZsmUKCAjQhAkTNH36dHl48K0+3M/hwgotSc1R6Y+jUL3jgnVOxwh5eTTqSFsAAIDTqm2or+JCfJVRXKn1B4t0QddIsyO5hUZ/cqurq9PXX3+tv/3tbyotLZUkZWZmujQF8/LlyzVlyhStXr1aS5YsUW1trUaMGKHy8nLnNvfee6+++OILzZ8/X8uXL1dmZqauvPJK5/r6+nqNHj1aNTU1Wrlypd577z3Nnj1bjzzySGNfGnBa1NY79O3uXH286YhKq+oU5OOhsWe11fldIilRAADArQ1oHyZJ2pFpV9mPXwa3do0asjl06JAuvvhipaenq7q6WhdddJECAwM1Y8YMVVdXa+bMmSe1n4ULFza4P3v2bEVGRmrDhg0aOnSoSkpK9M4772ju3Lm68MILJR0dDevWrZtWr16tQYMGafHixdq5c6e+/vprRUVFqU+fPnryySf14IMP6rHHHpOXFyfrw3yZxZVavDNHJT+epNmjbZDO7dSGAgUAAJqFuFBfxYb4KLO4ShsOFem8Lm3MjmS6Rn2Ku/vuu9W/f38VFRXJ19fXufyKK67Q0qVLGx2mpKREkhQWdrTxbtiwQbW1tRo+fLhzm65duyohIUGrVq2SJK1atUo9e/ZscKjfyJEjZbfbtWPHjuM+T3V1tex2e4MbcDrU1Tv0w958zd+QoZLKWgV4e+jyPrEa1jWKEgUAAJoNi8Wige3DJUnbMktUXs2oVKNGpL7//nutXLnymNGedu3a6ciRI40K4nA4dM8992jw4MHq0aOHJCk7O1teXl4KCQlpsG1UVJSys7Od2/y8RP20/qd1xzN9+nQ9/vjjjcoJnKwce5UW78xRYXmNJKlbTKDOS2ojb0+byckAAABcFx/qq+ggH2Xbq7TtSIkGdQg3O5KpGvWVuMPhUH19/THLMzIyFBgY2KggU6ZM0fbt2zVv3rxGPd4V06ZNU0lJifN2+PDh0/6caD3qHYZW7y/Qh+sPq7C8Rn5eNo3pFaMRydGUKAAA0GxZLBb1TQiRJG3NKFGdw2FuIJM1akRqxIgRevnll/X2229LOvpDLSsr06OPPqpLLrnE5f3dcccd+vLLL/Xdd98pLi7OuTw6Olo1NTUqLi5uMCqVk5Oj6Oho5zZr165tsL+cnBznuuPx9vaWt7e3yzmB31JQVq3FO3OUW1otSUqKDNAFXSLl60WBAgDATKmpqWZHkCRFREQoISHB7BiN1rFNgAK8PVRWXae9OWXqFhNkdiTTNKpIvfDCCxo5cqSSk5NVVVWlG264QXv37lVERIQ++OCDk96PYRi688479cknn+jbb79V+/btG6zv16+fPD09tXTpUo0dO1aStHv3bqWnpyslJUWSlJKSoqefflq5ubmKjDw6FeOSJUsUFBSk5OTkxrw8wGUOw9Cm9GKt2l+geochbw+rLugSqS7RjRuhBQAATcNemCdJGjdunMlJjvL189Ou1NRmW6ZsVot6xQVrZVqBNh8uVtfoQFksFrNjmaJRRSouLk5btmzRvHnztHXrVpWVlWny5Mm68cYbG0w+8VumTJmiuXPn6rPPPlNgYKDznKbg4GD5+voqODhYkydP1tSpUxUWFqagoCDdeeedSklJ0aBBgyQdHR1LTk7W+PHj9dxzzyk7O1sPP/ywpkyZwqgTzojiihot3pmjrJIqSVK7cD8N6xalAG+uYwYAgNkqy45OKjb6tofUpVc/U7PkpKdpzowHlJ+f32yLlCT1iA3WmgOFyi2tVlZJlWJDTv7zf0vS6E96Hh4ep9zs33rrLUnS+eef32D5rFmzNHHiREnSSy+9JKvVqrFjxza4IO9PbDabvvzyS91+++1KSUmRv7+/JkyYoCeeeOKUsgEnI63Uqu1r0lXnMORps2ho5zbqHhPUar+ZAQDAXYXHJiouqbvZMVoEXy+bukQFameWXZsPF1OkXPH+++//6vqbbrrppPZjGMZvbuPj46M33nhDb7zxxgm3SUxM1FdffXVSzwk0hfyKekVe84Q2F3lIMhQX4quLkqMU5OtpdjQAAIDTrk98iHZm2bUvr0ylVbUK9Gl9n4EaVaTuvvvuBvdra2tVUVEhLy8v+fn5nXSRApqbeoehOWsOafrCPPm2P0tWi6FzkyLVOy6YUSgAANBqtAn0VtsQXx0prtTWjBIN7hRhdqQzrlHTnxcVFTW4lZWVaffu3RoyZIhLk00AzcmubLuumrlSj3y2Q5V1hqqP7NLw6Fr1iQ+hRAEAgFanT3yIJGlHpl31jt8+0qylaVSROp6kpCQ9++yzx4xWAc1dVW29nl+0S7979QdtSi9WgLeHbukbpOw5f1Rg6xvFBgAAkCS1j/CXr6dNlbX1OlhQbnacM67JipR0dAKKzMzMptwlYKqV+/J18cvf6Y1laapzGBqRHKUlU4dqVJK/ZLTui9ABAIDWzWa1qFvM0Uu97Mi0m5zmzGvUOVKff/55g/uGYSgrK0uvv/66Bg8e3CTBADPl2qv07MJd+njjEUlSVJC3Hr+0hy7ucfQiz1lmhgMAAHAT3WODtTG9WAcLylVeXSf/VnT5l0a90ssvv7zBfYvFojZt2ujCCy/UCy+80BS5AFNU19Xr3R8O6vVv9qq8pl4WizRuYKIeuLiLglrhbDQAAAC/JszfSzHBPsoqqVJqll3924WZHemMaVSRcjg4pAkti8NhaMH2bD23aJcOFVRIknrHh+ixMcnqmxBqcjoAAAD3lRwbpKySKu3IsqtfYmirmYSr9Yy9AcdhGIa+35uv5xbt0vYjR4/tjQz01p9GddXlfdrKam0d/xAAAAA0VufIQH23J0/FFbXKLK5S29DWcYHeRhWpqVOnnvS2L774YmOeAjitDMPQyrQCvfbNXq3eXyhJ8vey6ffndtCtQzu0quN7AQAAToWXh1VJkYHamWXXjqwSitSv2bRpkzZt2qTa2lp16dJFkrRnzx7ZbDadddZZzu1ay7Aemg+Hw9DSXbl6fdk+bTlcLEnyslk1PiVR/3d+R4UHeJsbEAAAtHipqalmR2jyDN1jg7Qzy669OWU6r3O9vD1sTbp/d9SoIjVmzBgFBgbqvffeU2jo0fNHioqKNGnSJJ177rm67777mjQkcKpKq2r1nw0Zen/VIe3PP3qdA28Pq64fkKBbh3ZQbEjr+OYEAACYx16YJ0kaN26cyUn+p6ysrEn2ExPso1A/TxVV1Gpfbpm6xwY3yX7dWaOK1AsvvKDFixc7S5QkhYaG6qmnntKIESMoUnALhmFoS0aJ/r3hsD7ZeETlNfWSpEBvD41LSdTkIe0VwQgUAAA4QyrLjp6PPfq2h9SlVz9Ts6SuXa4F772iqqqqJtmfxWJR1+ggrdpfoN05pRSpE7Hb7crLyztmeV5enkpLS085FHAqDuaX68utmfp40xHtz/vfVbY7RQZoQkqirjgrTgGcAwUAAEwSHpuouKTupmbISU9r8n12iQ7Uqv0FyiisbBXXlGrUq7viiis0adIkvfDCCxowYIAkac2aNXrggQd05ZVXNmlA4LfUOwxtO1Kir3fmaPHObO3J+d8QtY+nVRd3j9bV/eN1TsdwztsDAAA4TYJ9PRUd5KNse5X25JS2+EvINKpIzZw5U/fff79uuOEG1dbWHt2Rh4cmT56s559/vkkDAr9U7zC0N7dUaw8UasW+fK1KK5C9qs653ma1aFCHMF3ep61G9Yxh9AkAAOAM6RIdqGx7lXZTpI7Pz89Pb775pp5//nmlpR0dFuzYsaP8/f2bNBxQW+/Qgfxy7couVWqWXVsOF2vL4WLn+U4/CfT20JCkCI3sHq0LukQq2M/TpMQAAACtV1JkgL7bk6cce7WKK2oU4udldqTT5pS+qs/KylJWVpaGDh0qX19fGYbBoVNoFMMwlGOv1q5su3Zll2p3dql2ZZcqLbdMNfWOY7b397KpT0KIzukYoXM6hqtn22B52KwmJAcAAMBP/L09FB/mp/TCCu3JKdOA9mFmRzptGlWkCgoKdM0112jZsmWyWCzau3evOnTooMmTJys0NFQvvPBCU+eEiZr6OgOVtQ6l2+t0qLhOh0pqlV5y9L9lNcZxt/f1sCgh2EOdowI1uGtb9UkIUVJkoGxWSjsAAIC76RIVqPTCCu3KtuvsdqEtdqClUUXq3nvvlaenp9LT09WtWzfn8muvvVZTp06lSLUQTXWtA4/gKHnHd5d3227ybttNnhEJsliOHT0yHPWqLTyi2ryDqsk7ePS/uQdVb8/VLknf+/npgdRUJUQHnVIeAAAAnD4dI/31zW6LiipqlV9WozaBLfNyM40qUosXL9aiRYsUFxfXYHlSUpIOHTrUJMFgvsZe68BhSPnVFmVXWpVdaVVp3bHfQvjYDAV7GgryPPrfYC9DgZ6GbO2iJUVLGtRg+5z0NM2Z8YDy8/OVkJBwKi8LAAAAp5G3h03tw/21L69Mu7NLKVI/V15eLj8/v2OWFxYWytu7Zf6gWrOTvdZBYXmNdmbZlZptV8XPJoOwWKToIB/FBvsqJsRHMcE+8vNiJj0AAICWqkt04NEilVOqwZ1a5iVoGvVp9txzz9X777+vJ598UtLRKxk7HA4999xzuuCCC5o0INybYRhKL6zQ2oOFyiz+35WxfTytah/hr/bh/koI85O3p83ElAAAADiT2oX7ydNmUVl1nXLs1YoO9jE7UpNrVJF67rnnNGzYMK1fv141NTX64x//qB07dqiwsFArVqxo6oxwQ4Zh6GBBhdYcKFCOvVrS0ZGnduH+So4JUvsIfyaDAAAAaKU8bFa1C/fX3twy7csro0j9pEePHtqzZ49ef/11BQYGqqysTFdeeaWmTJmimJiYps4IN1NUUaNvUnOVUVwpSfKwWtQzLlhnxYcqwIdD9gAAACB1igw4WqRyyzS4Y8s7vM/lT721tbW6+OKLNXPmTD300EOnIxPcVL3D0PpDhVp3sEj1DkMeVot6x4XorMQQznkCAABAA+3Cjx6hVFLZMmfvc/nTr6enp7Zu3Xo6ssCNFZRVa8H2bBWU10iSEsP9dEGXSAX7epqcDAAAAO7Iy8OqduF+Sssr177cshZXpI69mM9JGDdunN55552mzgI3lVfnow/XH1ZBeY18PW26uHu0LusdS4kCAADAr+rUJkCStC+3zOQkTa9Rx2PV1dXp3Xff1ddff61+/frJ39+/wfoXX3yxScLBXIakkPMmaGdNmCRDcSG+GtUzmsP4AAAAcFLat/GX1SIVVtSooKxa4QEtZ1TKpU/E+/fvV7t27bR9+3adddZZkqQ9e/Y02KalnUTWWtXVO5SqOAUP6iZJOishRIM7RsjKTHwAAAA4Sd4eNiWE+elgQYX25ZW13iKVlJSkrKwsLVu2TJJ07bXX6tVXX1VUVNRpCQdz1NY79OXWLBUqUI7aanX3L9e5SUlmxwIAAEAz1Cky4GiRyi3TwPbhZsdpMi6dI2UYRoP7CxYsUHl5eZMGgrlq6hz6bHOm0gsrZJVDufMfU6RH1W8/EAAAADiODm0CZLFI+WU1Kq6oMTtOk2nUZBM/+WWxQvNWU+fQp5uP6EhxpbxsVvVQuqoPbzM7FgAAAJoxX0+b4kJ9JUlpeS1nEMalImWxWI45B4pzoloGh8PQgu1ZyiqpkreHVVf0basgVZodCwAAAC1Ax4ijs/ftz2s5s/e5dI6UYRiaOHGivL2PniRWVVWlP/zhD8fM2vfxxx83XUKcdoZhaPmePB0sqJDNatHlfdoqOthHR8wOBgAAgBahQxt/fbsnT1klVaqoqWsRs0C79AomTJjQ4P64ceOaNAzMsflwsbYeKZEkXdw9WtHBPiYnAgAAQEsS6OOpNoHeyiut1oH8cnWPDTY70ilzqUjNmjXrdOWASdLyyvTd3nxJ0rmdItQpMsDkRAAAAGiJOkT4t6gidUqTTaB5K66o0eIdOZKknm2D1TchxNxAAAAAaLE6tjn6hf2hggrV1TtMTnPqKFKtVL3D0ILt2aqpdyg22Efnd27DxCEAAAA4bSICvBTo46E6h6H0wgqz45wyilQrtTItX7ml1fL2sOriHtGyWilRAAAAOH0sFos6RBydpG5/fvOfBp0i1QodzC/XxvRiSdJFyVEK9PE0NxAAAABahfY/FqkD+eXN/pq0FKlWpry6Tot3Hj0vqndcsPNYVQAAAOB0iwv1k5fNqoqaemXbq8yOc0ooUq3Mst25qqytV0SAl4Z0ijA7DgAAAFoRm9WidhF+kqT9ec378D6KVCuSllemtLxyWSzSiORoedj43w8AAIAzq0PE0SOiKFJoFqrr6vXt7jxJUr+EULUJ9DY5EQAAAFqjduF+slikwooalVTWmh2n0ShSrcTKfQUqq65TsK+nBrYPMzsOAAAAWilvT5tig30lHZ10ormiSLUCmcWV2nqkRJI0rGskh/QBAADAVD/N3newGRcpD7MD4PRyOAx9sytXkpQcE6T4MD+TE52a1NRUsyNIcp8cAAAAzVH7CH/9sC9fGUWV6tNMP55SpFq47ZklKiivkY+HVUOSmu8sffbCo+d3jRs3zuQkDZWVlZkdAQAAoNkJ9fNUkI+H7FV1yq2ymB2nUUwtUt99952ef/55bdiwQVlZWfrkk090+eWXO9dPnDhR7733XoPHjBw5UgsXLnTeLyws1J133qkvvvhCVqtVY8eO1SuvvKKAAK6PVF1Xr9X7CyVJgzqEy9fTZnKixqsss0uSRt/2kLr06mdyGil17XIteO8VVVU17+sfAAAAmMFisah9hL+2ZJQoq7J5nnZiapEqLy9X7969dfPNN+vKK6887jYXX3yxZs2a5bzv7d1wtrkbb7xRWVlZWrJkiWprazVp0iTdeuutmjt37mnN3hysP1ikytp6hfp5qkfbYLPjNInw2ETFJXU3O4Zy0tPMjgAAANCs/VSksqsoUi4bNWqURo0a9avbeHt7Kzo6+rjrUlNTtXDhQq1bt079+/eXJL322mu65JJL9Ne//lWxsbFNnrm5sFfWatPhYknSkE4Rslmb55ApAAAAWqa2Ib7ysFpUVS95RnYwO47L3L7+ffvtt4qMjFSXLl10++23q6CgwLlu1apVCgkJcZYoSRo+fLisVqvWrFlzwn1WV1fLbrc3uLU0K9LyVe8wFBfq65wVBQAAAHAXHjarEn6cCM2v49kmp3GdWxepiy++WO+//76WLl2qGTNmaPny5Ro1apTq6+slSdnZ2YqMjGzwGA8PD4WFhSk7O/uE+50+fbqCg4Odt/j4+NP6Os607JIq7ck5OgnC0KQ2slgYjQIAAID7+ekLf99Oza9IufWsfdddd53zzz179lSvXr3UsWNHffvttxo2bFij9ztt2jRNnTrVed9ut7eoMrVyf74kqVtMoNoEev/G1gAAAIA52oUfLVJeMZ1VUlVvchrXuPWI1C916NBBERER2rdvnyQpOjpaubm5Dbapq6tTYWHhCc+rko6edxUUFNTg1lIcKarU4cJKWS3SoPbhZscBAAAATijAx0PBng5ZLFZtzK42O45LmlWRysjIUEFBgWJiYiRJKSkpKi4u1oYNG5zbfPPNN3I4HBo4cKBZMU21+sDRc8iSY4MU5OtpchoAAADg18X4GjIc9copY0TqpJWVlWnz5s3avHmzJOnAgQPavHmz0tPTVVZWpgceeECrV6/WwYMHtXTpUl122WXq1KmTRo4cKUnq1q2bLr74Yt1yyy1au3atVqxYoTvuuEPXXXddq5yxL6OoQhlFR0ejzm4XZnYcAAAA4Dd1CqxXxqs36LoegWZHcYmpRWr9+vXq27ev+vbtK0maOnWq+vbtq0ceeUQ2m01bt27VpZdeqs6dO2vy5Mnq16+fvv/++wbXkpozZ466du2qYcOG6ZJLLtGQIUP09ttvm/WSTGMYhvPiuz1igxXkw2gUAAAA3J+3TXJUl5sdw2WmTjZx/vnnyzCME65ftGjRb+4jLCyMi+9Kyiiq1JHiStksFvVvF2p2HAAAAKBFa1bnSOH4jo5GHT03qkfbIAUyGgUAAACcVhSpFiCzpEqZJVWyWS3qn8i5UQAAAMDpRpFqATYcKpIkdYsOVICPW18aDAAAAGgRKFLNXEFZtQ7kHz0576xEzo0CAAAAzgSKVDO3If3oaFTHNv4K9fMyOQ0AAADQOlCkmrHSqlrtzi6VJM6NAgAAAM4gilQztvlwsRyG1DbEV9HBPmbHAQAAAFoNilQzVV1br+1H7JKkfpwbBQAAAJxRFKlmaltmiWrqHQr391K7cD+z4wAAAACtCkWqGXI4DG05XCJJOishVBaLxeREAAAAQOtCkWqG0vLLVFZdJ19PmzpHBZgdBwAAAGh1KFLN0E+jUT3aBsnDxv9CAAAA4EzjU3gzk1darSPFlbJYpF5tQ8yOAwAAALRKFKlmZktGsSQpqU2AAnw8zA0DAAAAtFIUqWaksrZeu368AG/v+BBzwwAAAACtGEWqGdlxpET1DkORgd6K4QK8AAAAgGkoUs2Ew2FoS8bRSSZ6x4cw5TkAAABgIopUM3GwoPx/U55HMuU5AAAAYCaKVDOx9cjR0ajkGKY8BwAAAMzGJ/JmwF5Zq0MFFZKOXjsKAAAAgLkoUs3A9syjo1HxYb4K8fMyOQ0AAAAAipSbq3cY2pFplyT1jA02OQ0AAAAAiSLl9vbnl6mipl5+XjZ1aMMkEwAAAIA7oEi5ue1Hjo5GdY8Nks3KlOcAAACAO6BIubHiihqlF/44yQSH9QEAAABugyLlxrb/eG5UYrifgnw9TU4DAAAA4CceZgfA8TkMKTX7aJFiNAoAAABwL4xIuansSotzkon2Ef5mxwEAAADwMxQpN3Ww3CZJ6hbNJBMAAACAu6FIuSGbf6iyK4+Wp+TYIJPTAAAAAPglipQb8u8xTIYsign2UZi/l9lxAAAAAPwCRcrNGIahgF4XSTp67SgAAAAA7oci5WZ25tfIM6ytPCyGkiIDzY4DAAAA4DgoUm5m6f5KSVKcn0NeHvzvAQAAANwRn9TdiL2qViszjhapdgEOk9MAAAAAOBGKlBvZnF4shyHV5KcrzMswOw4AAACAE6BIuZGhndvo77+LVMGXL8jCpaMAAAAAt0WRcjPBPjbV5KSZHQMAAADAr6BIAQAAAICLKFIAAAAA4CKKFAAAAAC4iCIFAAAAAC6iSAEAAACAiyhSAAAAAOAiihQAAAAAuIgiBQAAAAAuokgBAAAAgIsoUgAAAADgIlOL1HfffacxY8YoNjZWFotFn376aYP1hmHokUceUUxMjHx9fTV8+HDt3bu3wTaFhYW68cYbFRQUpJCQEE2ePFllZWVn8FUAAAAAaG1MLVLl5eXq3bu33njjjeOuf+655/Tqq69q5syZWrNmjfz9/TVy5EhVVVU5t7nxxhu1Y8cOLVmyRF9++aW+++473XrrrWfqJQAAAABohTzMfPJRo0Zp1KhRx11nGIZefvllPfzww7rsssskSe+//76ioqL06aef6rrrrlNqaqoWLlyodevWqX///pKk1157TZdccon++te/KjY29oy9FgAAAACth9ueI3XgwAFlZ2dr+PDhzmXBwcEaOHCgVq1aJUlatWqVQkJCnCVKkoYPHy6r1ao1a9accN/V1dWy2+0NbgAAAABwsty2SGVnZ0uSoqKiGiyPiopyrsvOzlZkZGSD9R4eHgoLC3NuczzTp09XcHCw8xYfH9/E6QEAAAC0ZG5bpE6nadOmqaSkxHk7fPiw2ZEAAAAANCNuW6Sio6MlSTk5OQ2W5+TkONdFR0crNze3wfq6ujoVFhY6tzkeb29vBQUFNbgBAAAAwMly2yLVvn17RUdHa+nSpc5ldrtda9asUUpKiiQpJSVFxcXF2rBhg3Obb775Rg6HQwMHDjzjmQEAAAC0DqbO2ldWVqZ9+/Y57x84cECbN29WWFiYEhISdM899+ipp55SUlKS2rdvr7/85S+KjY3V5ZdfLknq1q2bLr74Yt1yyy2aOXOmamtrdccdd+i6665jxj4AAAAAp42pRWr9+vW64IILnPenTp0qSZowYYJmz56tP/7xjyovL9ett96q4uJiDRkyRAsXLpSPj4/zMXPmzNEdd9yhYcOGyWq1auzYsXr11VfP+GsBAAAA0HqYWqTOP/98GYZxwvUWi0VPPPGEnnjiiRNuExYWprlz556OeAAAAABwXG57jhQAAAAAuCuKFAAAAAC4iCIFAAAAAC6iSAEAAACAiyhSAAAAAOAiihQAAAAAuIgiBQAAAAAuokgBAAAAgIsoUgAAAADgIooUAAAAALiIIgUAAAAALqJIAQAAAICLKFIAAAAA4CKKFAAAAAC4iCIFAAAAAC6iSAEAAACAiyhSAAAAAOAiihQAAAAAuIgiBQAAAAAuokgBAAAAgIsoUgAAAADgIooUAAAAALiIIgUAAAAALqJIAQAAAICLKFIAAAAA4CKKFAAAAAC4iCIFAAAAAC6iSAEAAACAiyhSAAAAAOAiihQAAAAAuIgiBQAAAAAuokgBAAAAgIsoUgAAAADgIooUAAAAALiIIgUAAAAALqJIAQAAAICLKFIAAAAA4CKKFAAAAAC4iCIFAAAAAC6iSAEAAACAiyhSAAAAAOAiihQAAAAAuIgiBQAAAAAuokgBAAAAgIsoUgAAAADgIooUAAAAALiIIgUAAAAALqJIAQAAAICL3LpIPfbYY7JYLA1uXbt2da6vqqrSlClTFB4eroCAAI0dO1Y5OTkmJgYAAADQGrh1kZKk7t27Kysry3n74YcfnOvuvfdeffHFF5o/f76WL1+uzMxMXXnllSamBQAAANAaeJgd4Ld4eHgoOjr6mOUlJSV65513NHfuXF144YWSpFmzZqlbt25avXq1Bg0adKajAgAAAGgl3H5Eau/evYqNjVWHDh104403Kj09XZK0YcMG1dbWavjw4c5tu3btqoSEBK1atepX91ldXS273d7gBgAAAAAny62L1MCBAzV79mwtXLhQb731lg4cOKBzzz1XpaWlys7OlpeXl0JCQho8JioqStnZ2b+63+nTpys4ONh5i4+PP42vAgAAAEBL49aH9o0aNcr55169emngwIFKTEzURx99JF9f30bvd9q0aZo6darzvt1up0wBAAAAOGluPSL1SyEhIercubP27dun6Oho1dTUqLi4uME2OTk5xz2n6ue8vb0VFBTU4AYAAAAAJ6tZFamysjKlpaUpJiZG/fr1k6enp5YuXepcv3v3bqWnpyslJcXElAAAAABaOrc+tO/+++/XmDFjlJiYqMzMTD366KOy2Wy6/vrrFRwcrMmTJ2vq1KkKCwtTUFCQ7rzzTqWkpDBjHwAAAIDTyq2LVEZGhq6//noVFBSoTZs2GjJkiFavXq02bdpIkl566SVZrVaNHTtW1dXVGjlypN58802TUwMAAABo6dy6SM2bN+9X1/v4+OiNN97QG2+8cYYSAQAAAEAzO0cKAAAAANwBRQoAAAAAXESRAgAAAAAXUaQAAAAAwEUUKQAAAABwEUUKAAAAAFxEkQIAAAAAF1GkAAAAAMBFFCkAAAAAcBFFCgAAAABcRJECAAAAABdRpAAAAADARRQpAAAAAHARRQoAAAAAXESRAgAAAAAXUaQAAAAAwEUUKQAAAABwEUUKAAAAAFxEkQIAAAAAF1GkAAAAAMBFFCkAAAAAcBFFCgAAAABcRJECAAAAABdRpAAAAADARRQpAAAAAHARRQoAAAAAXESRAgAAAAAXUaQAAAAAwEUUKQAAAABwEUUKAAAAAFxEkQIAAAAAF1GkAAAAAMBFFCkAAAAAcBFFCgAAAABcRJECAAAAABdRpAAAAADARRQpAAAAAHARRQoAAAAAXESRAgAAAAAXUaQAAAAAwEUUKQAAAABwEUUKAAAAAFxEkQIAAAAAF1GkAAAAAMBFFCkAAAAAcBFFCgAAAABcRJECAAAAABdRpAAAAADARS2mSL3xxhtq166dfHx8NHDgQK1du9bsSAAAAABaqBZRpD788ENNnTpVjz76qDZu3KjevXtr5MiRys3NNTsaAAAAgBaoRRSpF198UbfccosmTZqk5ORkzZw5U35+fnr33XfNjgYAAACgBfIwO8Cpqqmp0YYNGzRt2jTnMqvVquHDh2vVqlXHfUx1dbWqq6ud90tKSiRJdrv99IY9CWVlZZKkjL07VF1ZYWqWnPQ0SVL2wT1K8/cjy8+4Ux6ykMVV7pSHLO6fRXKvPGRx/yySe+Uhi/tnycs4IOno52B3+Dz+UwbDMH51O4vxW1u4uczMTLVt21YrV65USkqKc/kf//hHLV++XGvWrDnmMY899pgef/zxMxkTAAAAQDNy+PBhxcXFnXB9sx+Raoxp06Zp6tSpzvsOh0OFhYUKDw+XxWIxLZfdbld8fLwOHz6soKAg03IAJ4P3K5oT3q9oTni/ojlpie9XwzBUWlqq2NjYX92u2RepiIgI2Ww25eTkNFiek5Oj6Ojo4z7G29tb3t7eDZaFhIScroguCwoKajFvRLR8vF/RnPB+RXPC+xXNSUt7vwYHB//mNs1+sgkvLy/169dPS5cudS5zOBxaunRpg0P9AAAAAKCpNPsRKUmaOnWqJkyYoP79+2vAgAF6+eWXVV5erkmTJpkdDQAAAEAL1CKK1LXXXqu8vDw98sgjys7OVp8+fbRw4UJFRUWZHc0l3t7eevTRR4857BBwR7xf0ZzwfkVzwvsVzUlrfr82+1n7AAAAAOBMa/bnSAEAAADAmUaRAgAAAAAXUaQAAAAAwEUUKQAAAABwEUXKjbzxxhtq166dfHx8NHDgQK1du9bsSGjlpk+frrPPPluBgYGKjIzU5Zdfrt27dzfYpqqqSlOmTFF4eLgCAgI0duzYYy6QDZjh2WeflcVi0T333ONcxvsV7uTIkSMaN26cwsPD5evrq549e2r9+vXO9YZh6JFHHlFMTIx8fX01fPhw7d2718TEaK3q6+v1l7/8Re3bt5evr686duyoJ598Uj+fs641vl8pUm7iww8/1NSpU/Xoo49q48aN6t27t0aOHKnc3Fyzo6EVW758uaZMmaLVq1dryZIlqq2t1YgRI1ReXu7c5t5779UXX3yh+fPna/ny5crMzNSVV15pYmpAWrdunf72t7+pV69eDZbzfoW7KCoq0uDBg+Xp6akFCxZo586deuGFFxQaGurc5rnnntOrr76qmTNnas2aNfL399fIkSNVVVVlYnK0RjNmzNBbb72l119/XampqZoxY4aee+45vfbaa85tWuX71YBbGDBggDFlyhTn/fr6eiM2NtaYPn26iamAhnJzcw1JxvLlyw3DMIzi4mLD09PTmD9/vnOb1NRUQ5KxatUqs2KilSstLTWSkpKMJUuWGOedd55x9913G4bB+xXu5cEHHzSGDBlywvUOh8OIjo42nn/+eeey4uJiw9vb2/jggw/ORETAafTo0cbNN9/cYNmVV15p3HjjjYZhtN73KyNSbqCmpkYbNmzQ8OHDncusVquGDx+uVatWmZgMaKikpESSFBYWJknasGGDamtrG7x3u3btqoSEBN67MM2UKVM0evToBu9Lifcr3Mvnn3+u/v376+qrr1ZkZKT69u2rv//97871Bw4cUHZ2doP3a3BwsAYOHMj7FWfcOeeco6VLl2rPnj2SpC1btuiHH37QqFGjJLXe96uH2QEg5efnq76+XlFRUQ2WR0VFadeuXSalAhpyOBy65557NHjwYPXo0UOSlJ2dLS8vL4WEhDTYNioqStnZ2SakRGs3b948bdy4UevWrTtmHe9XuJP9+/frrbfe0tSpU/XnP/9Z69at01133SUvLy9NmDDB+Z483mcD3q840/70pz/Jbrera9eustlsqq+v19NPP60bb7xRklrt+5UiBeCkTJkyRdu3b9cPP/xgdhTguA4fPqy7775bS5YskY+Pj9lxgF/lcDjUv39/PfPMM5Kkvn37avv27Zo5c6YmTJhgcjqgoY8++khz5szR3Llz1b17d23evFn33HOPYmNjW/X7lUP73EBERIRsNtsxM0fl5OQoOjrapFTA/9xxxx368ssvtWzZMsXFxTmXR0dHq6amRsXFxQ22570LM2zYsEG5ubk666yz5OHhIQ8PDy1fvlyvvvqqPDw8FBUVxfsVbiMmJkbJyckNlnXr1k3p6emS5HxP8tkA7uCBBx7Qn/70J1133XXq2bOnxo8fr3vvvVfTp0+X1HrfrxQpN+Dl5aV+/fpp6dKlzmUOh0NLly5VSkqKicnQ2hmGoTvuuEOffPKJvvnmG7Vv377B+n79+snT07PBe3f37t1KT0/nvYszbtiwYdq2bZs2b97svPXv31833nij88+8X+EuBg8efMzlJPbs2aPExERJUvv27RUdHd3g/Wq327VmzRrerzjjKioqZLU2rA02m00Oh0NS632/cmifm5g6daomTJig/v37a8CAAXr55ZdVXl6uSZMmmR0NrdiUKVM0d+5cffbZZwoMDHQe5xwcHCxfX18FBwdr8uTJmjp1qsLCwhQUFKQ777xTKSkpGjRokMnp0doEBgY6z9/7ib+/v8LDw53Leb/CXdx7770655xz9Mwzz+iaa67R2rVr9fbbb+vtt9+WJOc10J566iklJSWpffv2+stf/qLY2Fhdfvnl5oZHqzNmzBg9/fTTSkhIUPfu3bVp0ya9+OKLuvnmmyW14ver2dMG4n9ee+01IyEhwfDy8jIGDBhgrF692uxIaOUkHfc2a9Ys5zaVlZXG//3f/xmhoaGGn5+fccUVVxhZWVnmhQZ+5ufTnxsG71e4ly+++MLo0aOH4e3tbXTt2tV4++23G6x3OBzGX/7yFyMqKsrw9vY2hg0bZuzevduktGjN7Ha7cffddxsJCQmGj4+P0aFDB+Ohhx4yqqurndu0xverxTB+dkliAAAAAMBv4hwpAAAAAHARRQoAAAAAXESRAgAAAAAXUaQAAAAAwEUUKQAAAABwEUUKAAAAAFxEkQIAAAAAF1GkAAAAAMBFFCkAAAAAcBFFCgAAAABcRJECAAAAABdRpAAALdrChQs1ZMgQhYSEKDw8XL/73e+UlpbmXL9y5Ur16dNHPj4+6t+/vz799FNZLBZt3rzZuc327ds1atQoBQQEKCoqSuPHj1d+fr4JrwYA4C4oUgCAFq28vFxTp07V+vXrtXTpUlmtVl1xxRVyOByy2+0aM2aMevbsqY0bN+rJJ5/Ugw8+2ODxxcXFuvDCC9W3b1+tX79eCxcuVE5Ojq655hqTXhEAwB1YDMMwzA4BAMCZkp+frzZt2mjbtm364Ycf9PDDDysjI0M+Pj6SpH/84x+65ZZbtGnTJvXp00dPPfWUvv/+ey1atMi5j4yMDMXHx2v37t3q3LmzWS8FAGAiRqQAAC3a3r17df3116tDhw4KCgpSu3btJEnp6enavXu3evXq5SxRkjRgwIAGj9+yZYuWLVumgIAA561r166S1OAQQQBA6+JhdgAAAE6nMWPGKDExUX//+98VGxsrh8OhHj16qKam5qQeX1ZWpjFjxmjGjBnHrIuJiWnquACAZoIiBQBosQoKCrR79279/e9/17nnnitJ+uGHH5zru3Tpon/961+qrq6Wt7e3JGndunUN9nHWWWfpP//5j9q1aycPD35tAgCO4tA+AECLFRoaqvDwcL399tvat2+fvvnmG02dOtW5/oYbbpDD4dCtt96q1NRULVq0SH/9618lSRaLRZI0ZcoUFRYW6vrrr9e6deuUlpamRYsWadKkSaqvrzfldQEAzEeRAgC0WFarVfPmzdOGDRvUo0cP3XvvvXr++eed64OCgvTFF19o8+bN6tOnjx566CE98sgjkuQ8byo2NlYrVqxQfX29RowYoZ49e+qee+5RSEiIrFZ+jQJAa8WsfQAA/MycOXM0adIklZSUyNfX1+w4AAA3xcHeAIBW7f3331eHDh3Utm1bbdmyRQ8++KCuueYaShQA4FdRpAAArVp2drYeeeQRZWdnKyYmRldffbWefvpps2MBANwch/YBAAAAgIs4SxYAAAAAXESRAgAAAAAXUaQAAAAAwEUUKQAAAABwEUUKAAAAAFxEkQIAAAAAF1GkAAAAAMBFFCkAAAAAcNH/A9WlFE9922+xAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary Statistics for 'avg_glucose_level':\n",
+      "Mean: 106.1476771037182\n",
+      "Median: 91.88499999999999\n",
+      "Mode: 93.88\n",
+      "Standard Deviation: 45.28356015058198\n",
+      "Skewness: 1.571822297397199\n",
+      "Kurtosis: 1.6776607484156187\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary Statistics for 'bmi':\n",
+      "Mean: 28.91841033735874\n",
+      "Median: 28.3\n",
+      "Mode: 28.7\n",
+      "Standard Deviation: 7.73184131003912\n",
+      "Skewness: 1.0471150922521133\n",
+      "Kurtosis: 3.5000214661741333\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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e3/fb7XZ99NFHuvnmm5WZmanw8HBNmzatyX2nAAAAAMDfAu4+UlbgPlIAAAAApA54HykAAAAA6CgoUgAAAADQQhQpAAAAAGghihQAAAAAtBBFCgAAAABayNLtzwEc2ajRmXK58o56Pjk5SStXLGvHRAAAADgYRQoIQC5Xnma9sOCo55+4fkI7pgEAAMChmNoHAAAAAC1EkQIAAACAFqJIAQAAAEALUaQAAAAAoIUoUgAAAADQQhQpAAAAAGghihQAAAAAtBBFCgAAAABaiCIFAAAAAC3ksDoAgLYxanSmXK68Y16TnJyklSuWtVMiAACAzoMiBXRSLleeZr2w4JjXPHH9hHZKAwAA0LkwtQ8AAAAAWogiBQAAAAAtRJECAAAAgBaiSAEAAABAC1GkAAAAAKCFKFIAAAAA0EIUKQAAAABoIYoUAAAAALQQRQoAAAAAWogiBQAAAAAtRJECAAAAgBaiSAEAAABACzmsDgDgxGzPL9PbK/cqNMiu7rGhSosJ1Ump0VbHAgAA6NQoUkAHNn9Dru56Z50qajxNjkeGOKSEXhalAgAA6PwoUkBHZBh6bMH3+tvnOyRJp/aMVd/ECO0tqtL2/HLlllTLPnaG9hZVKi02zOKwAAAAnQ9FCuhgvKYp25k3+ErUDWf20j0TBsphr1/yWFXj0Y3/Wqn/bduv99fm6MJhKcqID7cyMgAAQKfDZhNAB7Nhb4ls3YcqJMimP18xQr+bPNhXoiQpNNiuf0wdJe++DfJ4TX24Lld7DlRYmBgAAKDzoUgBHUiFu05f7zwgSfrd5MG6aET3I14XEmSX98sX1bdbhDymqU8356umztueUQEAADo1ihTQgXy5fb9q6rwyD+zRVaf1OPbFXo/Gn5SkqBCHyt11+mZ3YfuEBAAA6AIoUkAHsbeoUt+7yiRJnpVvy24zjvs9DrtNZw/oJklak1WkA+XuNs0IAADQVVCkgA7A4zX1+ZYCSdLQ7tFSYVazv7d3QoR6J4TLa0qfbymQaZptFRMAAKDLoEgBHcD6vcU6UFGj0CC7ftQnvsXff1b/brLbDO0trtKWvLI2SAgAANC1UKSAAOfxmlqdVSxJyuwTr5Age4ufIzo0SKf1jJMk/W/bfjaeAAAAOEEUKSDA7SgoV7m7TqFBdg1KiWz185zSI0bRoUGqrPFoY06JHxMCAAB0PdyQFwhwa7OLJUlD06LlsLX+3z4cdptG9ojVki35WpNdrGFpMSooyFdaRu+jfk9ycpJWrljW6tcEAADorChSQADLK61Wbkm1bIY0rHu07/jxClD9NQWHHRuUEqllOw+orLpO2/LL5PF6NeuFBUd9jieun9D68AAAAJ0YRQoIYOsaRqP6JUUq3PnD/1yPV4Ak6e4LRxx2zGG3aUR6jJbtPKBVe4r8GRUAAKBLYY0UEKAq3HXamlcuSRqRFuO35x2WFq0gu6H95TVy9hjmt+cFAADoSihSQIDasK9EHtNUclSIkqND/Pa8IUF2nZRaP00wctRFfnteAACAroQiBQQim10b9tXvrDciPcbvT39yeowMQwrpMUz5pdV+f34AAIDOjiIFBCAj9SRV1ngUFmxX38QIvz9/VGiQ+ifVb6XeeI8qAAAANB9FCghARs/TJEmDkqNktxlt8honN4x0bS8oV3Wtp01eAwAAoLOiSAEBpqiiRkbqSZKkgSdwA97jSYx0qqZgtzxeU9+7ytrsdQAAADojihQQYD5anyPD7lC3CKcSIpxt9jqGYajiu8WSpI05JTJNs81eCwAAoLOhSAEB5v+t3iepbUejGlV+/z/ZbfVboeeXudv89QAAADoLihQQQHYWlGttdrFMr0cDktq+SJnuCvXpFi5J2phT2uavBwAA0FlYWqSeffZZDRs2TFFRUYqKilJmZqbmz5/vO19dXa0ZM2YoPj5eERERuuyyy5SXl9fkObKysjR58mSFhYUpMTFRd999t+rq6tr7rQB+8d6a+tEo0/W9wp2OdnnNIQ33lNriKlOtx9surwkAANDRWVqk0tLS9Mgjj2jVqlVauXKlzj33XF100UXauHGjJOnOO+/Uhx9+qHfeeUdLly5VTk6OLr30Ut/3ezweTZ48WTU1Nfr666/18ssva968ebrvvvusektAq3m9pt5tmNZn7vqm3V43LTZUUSEO1Xi82p5f3m6vCwAA0JFZWqQuvPBCTZo0Sf369VP//v31xz/+UREREVq+fLlKSkr04osv6sknn9S5556rkSNH6qWXXtLXX3+t5cuXS5IWLlyoTZs26dVXX9WIESM0ceJEPfTQQ5o7d65qamqsfGtAi327u1D7iqsU6XTI3Leh3V7XMAyd1DAq9V1OSbu9LgAAQEcWMGukPB6P3nzzTVVUVCgzM1OrVq1SbW2txo0b57tm4MCB6tGjh5YtWyZJWrZsmYYOHaqkpCTfNePHj1dpaalvVOtI3G63SktLmzwAqzVO65s4NFny1Lbraw9OiZIhKae4WkWV/CMEAADA8VhepDZs2KCIiAg5nU7ddNNNeu+99zR48GC5XC4FBwcrJiamyfVJSUlyuVySJJfL1aRENZ5vPHc0c+bMUXR0tO+Rnp7u3zcFtFBNnVcfb8iVJF18cvd2f/2IEId6xIVJkrbmcU8pAACA47G8SA0YMEBr167VihUrdPPNN2vatGnatGlTm77m7NmzVVJS4ntkZ2e36esBx/O/bQUqra5Tt0inRveKtyRD/4ZdArflsU4KAADgeNpnW7BjCA4OVt++fSVJI0eO1Lfffqs///nPuvzyy1VTU6Pi4uImo1J5eXlKTk6WJCUnJ+ubb5ouym/c1a/xmiNxOp1yOtvuRqdAS320vn40avLQFNlthiUZencLl+176UBFjQ6UuxXfhjcDBgAA6OgsH5E6lNfrldvt1siRIxUUFKTFixf7zm3ZskVZWVnKzMyUJGVmZmrDhg3Kz8/3XbNo0SJFRUVp8ODB7Z4daI3qWo8Wbqyfinrh8BTLcoQE2ZURX39Pqa2MSgEAAByTpSNSs2fP1sSJE9WjRw+VlZXp9ddf1+eff65PPvlE0dHRmj59umbOnKm4uDhFRUXp1ltvVWZmpk4//XRJ0gUXXKDBgwfrmmuu0WOPPSaXy6V7771XM2bMYMQJHcbnW/JVUeNR95hQnZwea2mW/okR2rW/Qlvzy3R67zhLswAAAAQyS4tUfn6+pk6dqtzcXEVHR2vYsGH65JNPdP7550uSnnrqKdlsNl122WVyu90aP368/va3v/m+326366OPPtLNN9+szMxMhYeHa9q0aXrwwQetektAi324rn5a34+Hpchm0bS+Rr26hctuM1RcWav95ezeBwAAcDSWFqkXX3zxmOdDQkI0d+5czZ0796jXZGRk6OOPP/Z3NKBdVLjrtPj7+nV9Px6WanEayemwq2d8mHYUVLB7HwAAwDEE3BopoCv5dHOeqmu96hkfpiHdo6yOI+mH3fsoUgAAAEdHkQIs1Lhb34+HpcowrJ3W16hXQrgcNkOl1XVSXA+r4wAAAAQkihRgkZKqWi3dUiBJunC49dP6GgXZbeqVUL97n63HyRanAQAACEwUKcAiizblqcbjVb/ECA1IjrQ6ThP9kiIkSUb6CJmmaXEaAACAwEORAizy8YYfpvUFmoy4cNkNQ0ZEgnYUcE8pAACAQ1GkAAuUVNXqf9vqp/VNHpZscZrDBTtsSosNlSQt3px/nKsBAAC6HooUYIFFm/JU6zE1IClSfRMDa1pfo8Z1Uou/p0gBAAAciiIFWOC/63MkSZOGplic5Oh6NhSpVXuKVFzJzXkBAAAORpEC2llJZa2+3L5fUmBO62sUHRokszhHHq+ppVsLrI4DAAAQUBxWBwA6m1GjM+Vy5R31fMyI81U76CcBPa2vkZnznYyYVC3enK+LRnS3Og4AAEDAoEgBfuZy5WnWCwuOev6pf70vm6TJwwJ3Wl8j777vZBt8gT7fkq86j1cOO4PYAAAAElP7gHZVXeuRkTxQUmCvj/I5sFsxYUEqra7Tqj1FVqcBAAAIGBQpoB3tKCiXYbNrYHKk+iZGWB3n+ExT5wxIlCQtYfc+AAAAH4oU0I625dff3HZyRxiNanDuwPoixTboAAAAP6BIAe2kutaj7MJKSdKkDrA+qtFZ/bvJbjO0Pb9cew5UWB0HAAAgIFCkgHayo6BcXlMyi/apT7cOMK2vQXRokEZlxEqSvmAbdAAAAEkUKaDdNE7r82attjhJ8xUU5Csto7e+/s8rkqTfPfOq0jJ6N3mMGp1pcUoAAID2x/bnQDuoOmhan5m9xuI0zefxejXrhQXKLanS2yv3Kqz3SN147c9kGIbvmieun2BhQgAAAGswIgW0g8ZpfQkRwVJZx5selxgZoiC7oeo6r/aX11gdBwAAwHIUKaAdbM+rn9bXLzHS4iStY7cZ6h4TKkm+kTUAAICujCIFtLGqWo+yiurLR7+kjrPJxKHS48IkSdlFFCkAAACKFNDGdhSUy2yY1hcbFmx1nFZLj60vUvuKq+TxmhanAQAAsBZFCmhj2xqn9SV1zGl9jRIighUSZFOtx1ReabXVcQAAACxFkQLaUFWNxzcVrl9ix53WJ0mGYSitYVRqb1GVxWkAAACsRZEC2tCO/fXT+rpFODv0tL5G6bENG06wTgoAAHRxFCmgDe1ouAlv3w4+GtWocZ1Ubkm16jxei9MAAABYhyIFtBF3nUfZhfVT4DpLkYoJC1KE0yGP11ROCeukAABA10WRAtrI7v2V8pimYsOCFBfe8af1SY3rpOqn9+1leh8AAOjCKFJAG9le0Lmm9TVqnN7XONoGAADQFVGkgDZQ5/Fq9/4KSVKfbp2rSDWOSOWXsU4KAAB0XRQpoA3sKaxUnddUZIhDiZFOq+P4VWSIQ+FOu7ymlFfqtjoOAACAJShSQBto3K2vT7cIGYZhcRr/MgxDKdH1o1I5JUzvAwAAXRNFCvA3w6adDdP6+nayaX2NUqJDJNVvgw4AANAVOawOAHQko0ZnyuXKO+Y1JSHJctR5FRpkV0pMSDsla1+pDSNSuYxIAQCALooiBbSAy5WnWS8sOOY1f3xqriSpT7dw2TrZtL5G3SKdstsMVdd6pchEq+MAAAC0O6b2AX5kmqZCe4+SJPXpZNueH8xuM5QUVb+JhpHQy+I0AAAA7Y8iBfhRXplb9og4BdkN3/2WOqvGDSeMhN4WJwEAAGh/FCnAj3YV1G8ykREXLrutc07ra5TasOGE0Y0RKQAA0PVQpAA/2tWwW1+vbuEWJ2l7vhGp6BQVV9ZYnAYAAKB9UaQAPymrrlVBuVum6VXP+M49rU+SQoPtigkLkiStySq2NgwAAEA7Y9c+wE8aR6NqcrcqLHjAUa8rKMhXWsax1xUVFBT4NVtbSYkOUXFlrVbtKdI5A9m9DwAAdB0UKcBPGm/CW71zlaQLj3qdx+s97hbqd184wo/J2k5qdKg255Zp1Z4iq6MAAAC0K6b2AX5Q6/Fqb1H9zWmrdq60OE37SWnYcGJtdrFqPV6L0wAAALQfRqQAP8gqrJTHayoqxKG9hXutjtNu4sKD5a0uV5Ui1GvkOVJR9mHXJCcnaeWKZRakAwAAaDsUKcAPdjZse947IUKbLM7SngzDkDt3q0J7naJxtz2u4ekxh13zxPUT2j8YAABAG2NqH3CCTNPU7gNdZ9vzQ9XmbZck5ZVVW5wEAACg/VCkgBOUV+pWZY1HwXabuseEWh2n3dXk7ZAk5Ze6LU4CAADQfihSwAna1TAa1SM+THabYXGa9leTt1OSVFhRo5o6NpwAAABdA0UKOEFZByolSRld4Ca8R+KtLFaE0yFTUkEZo1IAAKBroEgBJ6C61qO80vq1QRlxXbNISVJSlFMS66QAAEDXQZECTkBWYaVMSfHhwYoMCbI6jmUSI+vvJ8U6KQAA0FVQpIATsKdhWl+PLjqtrxEjUgAAoKuhSAGtZJqm9hTWbzTRlaf1ST+MSBVX1spd67E4DQAAQNuztEjNmTNHp556qiIjI5WYmKiLL75YW7ZsaXLN2LFjZRhGk8dNN93U5JqsrCxNnjxZYWFhSkxM1N133626urr2fCvogg5U1KjC7ZHdZnTJbc8PFhpsV1RI/f2989lwAgAAdAGWFqmlS5dqxowZWr58uRYtWqTa2lpdcMEFqqioaHLdDTfcoNzcXN/jscce853zeDyaPHmyampq9PXXX+vll1/WvHnzdN9997X320EX07hbX1psqBx2BneToupHpZjeBwAAugKHlS++YMGCJl/PmzdPiYmJWrVqlc466yzf8bCwMCUnJx/xORYuXKhNmzbp008/VVJSkkaMGKGHHnpI99xzj+6//34FBwe36XtA17WnsGHb8y4+ra9RYpRT2/LL2XACAAB0CQH1z+glJSWSpLi4uCbHX3vtNSUkJGjIkCGaPXu2KisrfeeWLVumoUOHKikpyXds/PjxKi0t1caNG4/4Om63W6WlpU0eQEvUerzaV1wlScqID7c4TWBIalgn1bgdPAAAQGdm6YjUwbxer+644w6dccYZGjJkiO/4VVddpYyMDKWmpmr9+vW65557tGXLFr377ruSJJfL1aRESfJ97XK5jvhac+bM0QMPPNBG7wRdwb6iKnm8piKcDsWGdd1tzw+WGFm/c19pdZ2qajwKDbZbnAgAAKDtBEyRmjFjhr777jt9+eWXTY7feOONvt8PHTpUKSkpOu+887Rjxw716dOnVa81e/ZszZw50/d1aWmp0tPTWxccXZJvWl98mAzDsDhNYHAG2RUTFqTiylrll1UzUgcAADq1gJjad8stt+ijjz7SZ599prS0tGNeO3r0aEnS9u3bJUnJycnKy8trck3j10dbV+V0OhUVFdXkAbTEngMN25538ftHHeqH6X2skwIAAJ2bpUXKNE3dcssteu+997RkyRL16tXruN+zdu1aSVJKSookKTMzUxs2bFB+fr7vmkWLFikqKkqDBw9uk9zo2srddSqqrJUkpcdSpA6W2HBj3nx27gMAAJ2cpVP7ZsyYoddff13/+c9/FBkZ6VvTFB0drdDQUO3YsUOvv/66Jk2apPj4eK1fv1533nmnzjrrLA0bNkySdMEFF2jw4MG65ppr9Nhjj8nlcunee+/VjBkz5HQ6rXx76KT2FtVP60uMdCokiHVAB2NECgAAdBWWjkg9++yzKikp0dixY5WSkuJ7vPXWW5Kk4OBgffrpp7rgggs0cOBAzZo1S5dddpk+/PBD33PY7XZ99NFHstvtyszM1NVXX62pU6fqwQcftOptoZPbW1S/W19abNe+Ce+RdGvYcKLcXafKGm6KDQAAOi9LR6RM0zzm+fT0dC1duvS4z5ORkaGPP/7YX7GAY2osUt0pUocJdtgUExqk4qpaFZS5lREfMPvZAAAA+FVAbDYBdBihMSqpqpUhqXsMRepIGkelCsqZ3gcAADovihTQAkZSX0n1ZcHpYH3UkfiKVBlFCgAAdF4UKaAFjMR+klgfdSwUKQAA0BVQpIAW+KFIse350XSLqC9SRZW1qvV4LU4DAADQNihSQDPtK66SEdlNhiGlxoRYHSdghTsdCguun/a4n3VSAACgk6JIAc20fMcBSfX3j2J91LExvQ8AAHR2FCmgmZbvrC9STOs7vsbpfRQpAADQWVGkgGZa5itSbDRxPGyBDgAAOjuKFNAM2YWV2ltUJdPrUWo0Rep4GovU/vIayeA/MwAAoPPhJxygGRqn9akwS8EO/mdzPDGhQQqyG/J4TSky0eo4AAAAfsdPhEAzLN9ZKEky87dZnKRjMAxDCQ3rpIzYNIvTAAAA+B9FCjgO0zR9I1JmHkWquRqn9xmx3S1OAgAA4H8UKeA49hZVaV9xlRw2Q+b+nVbH6TAai5Ri060NAgAA0AYoUsBxLGu4f9Tw9BiprsbaMB1It4gfRqRM07Q4DQAAgH9RpIDjaJzWd3rvOIuTdCzx4cGyGZLhjFBuSbXVcQAAAPyKIgUcg2mavvtHZfZOsDhNx+Kw2xQbHixJ2pRTanEaAAAA/6JIAceQVVip3JJqBdkNjcyItTpOh5PYML1vI0UKAAB0MhQp4Bgap/WNSI9RaLDd4jQdT0LDhhObcylSAACgc6FIAcfQuNHE6b3jLU7SMTXeS2qziyIFAAA6F4oUcBT194+qvxFvJkWqVRp37ttzoFLl7jqL0wAAAPgPRQo4it0HKuUqrVaw3aaTe7A+qjVCg+0yK4slSVsYlQIAAJ0IRQo4CtZH+YdZvE+StCm3zOIkAAAA/kORAo7Ctz6qD9P6TkhDkWLDCQAA0JlQpIAjqF8fxY14/cEsokgBAIDOhyIFHMHO/RXKL3Mr2GHTKayPOiGNRer73DJ5vKbFaQAAAPyDIgUcQeNo1MnpMQoJYn3UCSnPV0iQTVW1Hu05UGF1GgAAAL+gSAFH0Lg+KpP1USfONDUgKVKStJkNJwAAQCdBkQIOcfD9o7gRr38MSomSxDopAADQeVCkgEPsKCjX/nK3nA6bRqTHWB2nU6BIAQCAzoYiBRxiWcNo1Ck9Ylkf5ScUKQAA0NlQpIBDLGd9lN8NTKlfI5VTUq3iyhqL0wAAAJw4ihRwkKb3j6JI+UtUSJDSYkMlseEEAADoHChSwEG25ZfrQEWNQoJsGp4ebXWcToXpfQAAoDOhSAEHaRyNGpkRK6eD9VH+NLihSG2iSAEAgE7AYXUAIJA0FqlMpvX5TUFBvtIyestIGyb7mTfonUXL9OZdlzS5Jjk5SStXLLMoIQAAQMtRpIAGXi/3j2oLHq9Xs15YoJKqWs37ercc8T106/PzZbcZvmueuH6ChQkBAABajql9QINt+eUqrKhRaJBdw9JirI7T6USFOBRst8ljmipi5z4AANDBMSIFNLj0V7+V+k9QRdZ36t3nV0e8pqCgoJ1TdR6GYSg+Ili5JdXaX+5WQoTT6kgAAACtRpECGpSFJssmacyYMTr16p8c8Zq7LxzRrpk6m24RzvoiVVYjJVudBgAAoPWY2geofn2UkdhPknz3O4L/JUTWj0IVlLstTgIAAHBiKFKApC15ZTKc4QqyG0qMDLE6TqfVrWE6336KFAAA6OAoUoCkZTvqtz1PjQ5tspsc/Cs+IliGpMoajyrcdVbHAQAAaDWKFKAf7h/VnWl9bSrIblNMWJAkRqUAAEDHRpFCl+f1mlqxq/7+UemxYRan6fwad+tjnRQAAOjIKFLo8ja7SlVSVSuztlrdItmSu601bjixv4x7SQEAgI6LIoUur3F9lFmwg/VR7YANJwAAQGdAkUKXt3xn/bQ+M3+bxUm6hoSIYElSYWWN6jxei9MAAAC0DkUKXZrHa2rFroYRqTyKVHuIcDoU4rDJNKXCCqb3AQCAjokihS5tc26pyqrrFOF0SEV7rY7TJRiGwY15AQBAh0eRQpfWuD7qtF5xksk0s/bSuHMfG04AAICOiiKFLq3x/lGn946zOEnX0o0RKQAA0MFRpNBlebymvmm4f1Rm7wSL03QtB+/cZ5qmxWkAAABarlVFaufOnf7OAbS77/aVqMxdp8gQhwanRlkdp0uJDQ+SzZDcdV6VueusjgMAANBirSpSffv21TnnnKNXX31V1dXV/s4EtIuvduyXJJ3eO577R7Uzh82m2PD6bdD3lzG9DwAAdDytKlKrV6/WsGHDNHPmTCUnJ+uXv/ylvvnmmxY/z5w5c3TqqacqMjJSiYmJuvjii7Vly5Ym11RXV2vGjBmKj49XRESELrvsMuXl5TW5JisrS5MnT1ZYWJgSExN19913q66Of+XGsTVuNPGjPvEWJ+mafpjex4YTAACg42lVkRoxYoT+/Oc/KycnR//85z+Vm5urMWPGaMiQIXryySdVUFDQrOdZunSpZsyYoeXLl2vRokWqra3VBRdcoIqKCt81d955pz788EO98847Wrp0qXJycnTppZf6zns8Hk2ePFk1NTX6+uuv9fLLL2vevHm67777WvPW0EW46zz6dnf9+qgz+rI+ygqNRSq/jFFtAADQ8ZzQZhMOh0OXXnqp3nnnHT366KPavn277rrrLqWnp2vq1KnKzc095vcvWLBA1157rU466SQNHz5c8+bNU1ZWllatWiVJKikp0Ysvvqgnn3xS5557rkaOHKmXXnpJX3/9tZYvXy5JWrhwoTZt2qRXX31VI0aM0MSJE/XQQw9p7ty5qqnhX7pxZGuyilVd61VChFP9EiOsjtMl+XbuY2ofAADogE6oSK1cuVK/+tWvlJKSoieffFJ33XWXduzYoUWLFiknJ0cXXXRRi56vpKREkhQXV78V9apVq1RbW6tx48b5rhk4cKB69OihZcuWSZKWLVumoUOHKikpyXfN+PHjVVpaqo0bNx7xddxut0pLS5s80LV8vb1+fdSP+sTLMFgfZYXGIlVaXScFhVqcBgAAoGUcrfmmJ598Ui+99JK2bNmiSZMm6ZVXXtGkSZNks9X3sl69emnevHnq2bNns5/T6/Xqjjvu0BlnnKEhQ4ZIklwul4KDgxUTE9Pk2qSkJLlcLt81B5eoxvON545kzpw5euCBB5qdDZ3P16yPslxIkF1RIQ6VVtfJiE2zOg4AAECLtGpE6tlnn9VVV12lPXv26P3339ePf/xjX4lqlJiYqBdffLHZzzljxgx99913evPNN1sTqUVmz56tkpIS3yM7O7vNXxOBo8Jdp7XZxZJYH2W1xlEpxaVbGwQAAKCFWjUitW3btuNeExwcrGnTpjXr+W655RZ99NFH+uKLL5SW9sO/TCcnJ6umpkbFxcVNRqXy8vKUnJzsu+bQHQMbd/VrvOZQTqdTTqezWdnQeYwanSmXK09GymDZx94ss3y/Mk8e4jvf3E1S4D+JkSHaUVDBiBQAAOhwWjUi9dJLL+mdd9457Pg777yjl19+udnPY5qmbrnlFr333ntasmSJevXq1eT8yJEjFRQUpMWLF/uObdmyRVlZWcrMzJQkZWZmasOGDcrPz/dds2jRIkVFRWnw4MEtfWvoxFyuPM16YYFOnTpbkjSkf2/NemGB7+HxeixO2PU0jkhRpAAAQEfTqiI1Z84cJSQcPiUqMTFRf/rTn5r9PDNmzNCrr76q119/XZGRkXK5XHK5XKqqqpIkRUdHa/r06Zo5c6Y+++wzrVq1Stddd50yMzN1+umnS5IuuOACDR48WNdcc43WrVunTz75RPfee69mzJjBqBOOKLuo/vOVHhtmcRIkNk7ti0xSVQ1FFgAAdBytKlJZWVmHjR5JUkZGhrKyspr9PM8++6xKSko0duxYpaSk+B5vvfWW75qnnnpKP/7xj3XZZZfprLPOUnJyst59913febvdro8++kh2u12ZmZm6+uqrNXXqVD344IOteWvo5KpqPb7tttNi2SnOauFOh8KC7TJsNm12sXsmAADoOFq1RioxMVHr168/bFe+devWKT6++bugmaZ53GtCQkI0d+5czZ0796jXZGRk6OOPP27266Lr2ltYKUmKDw9WuLNVH3/4WbdIp/YcqNTGfSU6pUes1XEAAACapVUjUldeeaVuu+02ffbZZ/J4PPJ4PFqyZIluv/12XXHFFf7OCPhN47Q+RqMCR+P0vo05jEgBAICOo1X/JP/QQw9p9+7dOu+88+Rw1D+F1+vV1KlTW7RGCmhv2UX1I1LpcayPChSNG058l1NicRIAAIDma1WRCg4O1ltvvaWHHnpI69atU2hoqIYOHaqMjAx/5wP8JzRGxZW1MiSlxTAiFSgSI0MkSVtd5aqp8yrY0aqBcgAAgHZ1QotE+vfvr/79+/srC9CmjKT6z2pilFPOILvFadAoKsQhs6ZSNcFh2pZfppNSo62OBAAAcFytKlIej0fz5s3T4sWLlZ+fL6/X2+T8kiVL/BIO8KfGIsW254HFMAyZRXtlJPXXxpxSihQAAOgQWlWkbr/9ds2bN0+TJ0/WkCFDZBiGv3MBfmWapq9IsdFEACraKyX118Z9JdKodKvTAAAAHFeritSbb76pt99+W5MmTfJ3HqBN7D5QKSM8VnbDUCrrowKOWZgtiZ37AABAx9GqVd3BwcHq27evv7MAbear7fslScnRIQqys5lBoDGL9kqSNuWWyuM9/v3lAAAArNaqnyhnzZqlP//5z826oS4QCJbtOCBJSo9jNCogleUpNMiuyhqPdu0vtzoNAADAcbVqat+XX36pzz77TPPnz9dJJ52koKCgJuffffddv4QD/MHrNfX1jvoRKTaaCFCmqZNSo7RyT5HWZZeob2Kk1YkAAACOqVVFKiYmRpdccom/swBt4ntXmYoqa2XWVispKsTqODiKYWkxWrmnSOv3FuuykWlWxwEAADimVhWpl156yd85gDbTOBpl5u+Q3TbU4jQ4muHp9duer9tbYnESAACA42v1qvu6ujp9+umn+vvf/66ysjJJUk5OjsrLWd+AwNK40YSZv9XiJDiWYWkxkuo3nKip8x77YgAAAIu1akRqz549mjBhgrKysuR2u3X++ecrMjJSjz76qNxut5577jl/5wSOadToTLlceYefMGyyX/aojKAQFW5e3v7B0Gw948MUFeJQaXWdtuaVaUh3bswLAAACV6tvyDtq1CitW7dO8fHxvuOXXHKJbrjhBr+FA5rL5crTrBcWHHY8p7hK76zaq5Agm9z5uyxIhuYyDEPD0mL05fb9Wre3mCIFAAACWqum9v3vf//Tvffeq+Dg4CbHe/bsqX379vklGOAPeworJUk9YsMksV1/oGtcJ7U+m3VSAAAgsLWqSHm9Xnk8nsOO7927V5GRbFuMwJF1oKFIxbPteUfQuE5q3d5iS3MAAAAcT6uK1AUXXKCnn37a97VhGCovL9cf/vAHTZo0yV/ZgBNSXetRXmm1JKlHHEWqIxjeUKS25Zerqubwf6wBAAAIFK0qUk888YS++uorDR48WNXV1brqqqt80/oeffRRf2cEWiW7sFKmpLjwYEWGBB33elgvOTpEiZFOebymNuYwvQ8AAASuVm02kZaWpnXr1unNN9/U+vXrVV5erunTp2vKlCkKDQ31d0agVRrXR2UwGtWhDEuL0aeb87Rub4lG9YyzOg4AAMARtapISZLD4dDVV1/tzyyA35imqT2sj+qQhqdF69PNeVrPOikAABDAWlWkXnnllWOenzp1aqvCAP5SVFmrcned7DZD3WMYJe1IhqXHSJLW72VqHwAACFytvo/UwWpra1VZWang4GCFhYVRpGC5rIZpfakxIQqyt2opICwyrOH+Ubv2V6ikqlbRoaxvAwAAgadVP2EWFRU1eZSXl2vLli0aM2aM3njjDX9nBFpsz4EKSVJGXLjFSdBSseHBvl0WNzAqBQAAApTf/qm+X79+euSRRw4brQLaW53Xq71FVZLY9ryjGpZWPyrF/aQAAECg8uucJ4fDoZycHH8+JdBiucXVqvOaCgu2KyEi2Oo4aIURDeuk1mYXW5oDAADgaFq1RuqDDz5o8rVpmsrNzdVf//pXnXHGGX4JBrTWwdueG4ZhcRq0xsk9YiRJa7KKZJomf48AACDgtKpIXXzxxU2+NgxD3bp107nnnqsnnnjCH7mAVsti2/MO76TUaAXbbdpfXqOswkplxLPWDQAABJZWFSmv1+vvHIBfVLjrVFDulsT6qI4sJMiuId2jtDqrWKv2FFGkAABAwGFfaHQq2Q3T+hIjnQoLbvX9phEARmbESpJWZxVZnAQAAOBwrfpJc+bMmc2+9sknn2zNSwCt0rg+itGoju+UHrGSdmnVnmKrowAAABymVUVqzZo1WrNmjWprazVgwABJ0tatW2W323XKKaf4rmOBONqTaZq+G/FmsD6qwzulYURqi6tU5e46RTgZYQQAAIGjVT+ZXHjhhYqMjNTLL7+s2Nj6H3aKiop03XXX6cwzz9SsWbP8GhJojv3lNaqs8SjIbiglOtTqODhBSVEhSosN1d6iKq3LLtYZfROsjgQAAODTqjVSTzzxhObMmeMrUZIUGxurhx9+mF37YJk9hRWSpLTYMNltjIZ2BvXT+6RVe1gnBQAAAkurRqRKS0tVUFBw2PGCggKVlZWdcCigNXzbnrM+qsMpKMhXWkbvw44b/c6SfdTP9MybH+u28263IBkAAMCRtapIXXLJJbruuuv0xBNP6LTTTpMkrVixQnfffbcuvfRSvwYEmsUerJziakmsj+qIPF6vZr2w4LDj+aXVeuPbbNVEpsrrNWVjpBEAAASIVhWp5557TnfddZeuuuoq1dbW1j+Rw6Hp06fr8ccf92tAoDmMxL7ymKYiQxyKCQ2yOg78JCHCKYfNUF1wmHYUlKtfUqTVkQAAACS1skiFhYXpb3/7mx5//HHt2LFDktSnTx+Fh3PTTFjDSBkoScqIC2O3yE7EZjOUHBWivcVVWrWniCIFAAACxgndkDc3N1e5ubnq16+fwsPDZZqmv3IBLWIk1xepHkzr63RSYkIkseEEAAAILK0qUgcOHNB5552n/v37a9KkScrNzZUkTZ8+na3P0e5yiqtkRKfIkNQjliLV2SRH1xep1VkUKQAAEDhaVaTuvPNOBQUFKSsrS2FhP/zgevnll2vBgsMXjANt6fMt9TtIJkeHyBlktzgN/C0lqv6eYDsKKlRUUWNxGgAAgHqtKlILFy7Uo48+qrS0tCbH+/Xrpz179vglGNBcS77PlyT1TGCNXmcUGmyXWeKSJK1keh8AAAgQrSpSFRUVTUaiGhUWFsrpdJ5wKKC5qms9+mr7fklSr3iKVGdlFmyXJK3YecDiJAAAAPVaVaTOPPNMvfLKK76vDcOQ1+vVY489pnPOOcdv4YDjWb7zgKpqPTIripQQEWx1HLQRM7+hSO0qtDgJAABAvVZtf/7YY4/pvPPO08qVK1VTU6Nf//rX2rhxowoLC/XVV1/5OyNwVI3T+sycjTKM0yxOg7bSWKQ25pSotLpWUSHcKwwAAFirVSNSQ4YM0datWzVmzBhddNFFqqio0KWXXqo1a9aoT58+/s4IHJFpmk2KFDqxqhJlxIfJa0qrdrNOCgAAWK/FI1K1tbWaMGGCnnvuOf3ud79ri0xAs2zPL9feoio5HTZV5G2xOg7a2OhecdpzoFLLdx3QOQMTrY4DAAC6uBaPSAUFBWn9+vVtkQVokcUNo1GZfeIlT63FadDWRveKlySt2Mk6KQAAYL1WTe27+uqr9eKLL/o7C9AijdP6zmV0oks4rVecJGnDvhJVuOssTgMAALq6Vm02UVdXp3/+85/69NNPNXLkSIWHN912+sknn/RLOOBoSiprtarhnkLnDKBIdQXpcWHqHhOqfcVVWp1VpDP7dbM6EgAA6MJaVKR27typnj176rvvvtMpp5wiSdq6dWuTawzD8F864CiWbiuQx2uqf1KE0uMOv6cZOqfRveL07pp9WrGzkCIFAAAs1aIi1a9fP+Xm5uqzzz6TJF1++eX6y1/+oqSkpDYJBxzNZw3T+th0oGsZ3buhSO3ixrwAAMBaLVojZZpmk6/nz5+viooKvwYCjsfjNfX5lvoidd5ASnxX0rjhxLrsElXXeixOAwAAurJWbTbR6NBi1VJffPGFLrzwQqWmpsowDL3//vtNzl977bUyDKPJY8KECU2uKSws1JQpUxQVFaWYmBhNnz5d5eXlJ5QLgW1tdpGKKmsVHRqkU3rEWB0H7SgjPkxJUU7VeLxancX9pAAAgHVaVKQay8yhx1qroqJCw4cP19y5c496zYQJE5Sbm+t7vPHGG03OT5kyRRs3btSiRYv00Ucf6YsvvtCNN97Y6kwIfIs3149Gnd2/mxz2E/q3AHQwhmGwDToAAAgILVojZZqmrr32WjmdTklSdXW1brrppsN27Xv33Xeb9XwTJ07UxIkTj3mN0+lUcnLyEc9t3rxZCxYs0LfffqtRo0ZJkp555hlNmjRJ//d//6fU1NRm5UDHwrbnXdvo3nH6YF2Olu08oDutDgMAALqsFhWpadOmNfn66quv9muYI/n888+VmJio2NhYnXvuuXr44YcVH1//L9LLli1TTEyMr0RJ0rhx42Sz2bRixQpdcsklR3xOt9stt9vt+7q0tLRt3wT8Jqe4St+7ymQz6kek0PWc0SdBkrQmq0iVNXUKC27VXRwAAABOSIt+AnnppZfaKscRTZgwQZdeeql69eqlHTt26Le//a0mTpyoZcuWyW63y+VyKTGx6aiEw+FQXFycXC7XUZ93zpw5euCBB9o6PtpA42jUKT1iFRsebHEatJeCgnylZfT2fW2/8H7VRsRrwFkXyczdLElKTk7SyhXLrIoIAAC6mID+p9wrrrjC9/uhQ4dq2LBh6tOnjz7//HOdd955rX7e2bNna+bMmb6vS0tLlZ6efkJZ0T7Y9rxr8ni9mvXCAt/Xn27O08acUp069be++0k9cf2Eo307AACA33Wolfq9e/dWQkKCtm/fLklKTk5Wfn5+k2vq6upUWFh41HVVUv26q6ioqCYPBL7qWo++2rFfEuujurr02PqbMGcVVlqcBAAAdFUdqkjt3btXBw4cUEpKiiQpMzNTxcXFWrVqle+aJUuWyOv1avTo0VbFRBtZtuOAqmu9So0O0cDkSKvjwELpcaGSpP3lNapw11mcBgAAdEWWFqny8nKtXbtWa9eulSTt2rVLa9euVVZWlsrLy3X33Xdr+fLl2r17txYvXqyLLrpIffv21fjx4yVJgwYN0oQJE3TDDTfom2++0VdffaVbbrlFV1xxBTv2dUJLDprWdyLb7qPjCwt2qFtE/e6he4uqLE4DAAC6IkuL1MqVK3XyySfr5JNPliTNnDlTJ598su677z7Z7XatX79eP/nJT9S/f39Nnz5dI0eO1P/+9z/f9uuS9Nprr2ngwIE677zzNGnSJI0ZM0bPP/+8VW8JbcQ0TV+ROm8Q0/rww6gU0/sAAIAVLN1sYuzYsTJN86jnP/nkk+M+R1xcnF5//XV/xkIA2ppXrn3FVXI6bMrsnWB1HASAHnFhWp1VrKzCymP+dwQAAKAtdKg1Uui6Fn+fJ0n6UZ94hQbbLU6DQJAaEyq7YajcXafiqlqr4wAAgC6GIoUOoXHb83MHJVmcBIEiyG5TSkyIJKb3AQCA9keRQsArrqzRqj1Fktj2HE2lx9Vvg55NkQIAAO2MIoWAt3RrgbymNCApUt1jQq2OgwDSo+F+UtlFVRI7OQIAgHZEkULAW+Kb1sdoFJpKjHLK6bCpps4rxWVYHQcAAHQhlu7aB0jSqNGZcrnyjnzSsMl+yZ9kOMOZ1ofD2AxD6XFh2p5fLlvKIKvjAACALoQiBcu5XHma9cKCI57bV1ylf6/aK9NdoZPTY9o3GDqEnvH1RcpIGWx1FAAA0IUwtQ8Bbff+CkmSmbtJDjsfVxwuIz68/jfxPbS/3G1tGAAA0GXwkykC2q7GIpWz0eIkCFQRToe6RTplGDZ9sbXA6jgAAKCLoEghYJVW1epARY0MSWbuZqvjIID1jK/fve/zLRQpAADQPihSCFi7DtSPRqVEh0g13CcIR9ezYXrf0q0F8nhNi9MAAICugCKFgNW4PqpnQrjFSRDokqNCZLorVFJVq7XZRVbHAQAAXQBFCgGp1uOtv8mqpF4UKRyHzWbIdH0viel9AACgfVCkEJCyiyrl8ZqKDHEoPjzY6jjoAMycTZKkz7bkW5wEAAB0BRQpBKTd++vXRPWKD5dhGBanQUdg5tYXqe/2lSq/tNriNAAAoLOjSCHgmKbp2/ac9VFoNne5hqVFS5I+Zxt0AADQxihSCDj7y2tU7q6Tw2YoPTbU6jjoQMYOSJQkfc70PgAA0MYoUgg4uxu2PU+PC5PDzkcUzXfOgG6SpC+27pe7zmNxGgAA0JnxUyoCjm9aX8NNVoHmGp4Wo8RIp8rddVq244DVcQAAQCdGkUJAqarxyFVSv1EA66PQUjabofMHJ0mSFm7KszgNAADozChSCCh7DlTIlJQQEayokCCr46ADuuCkZEnSok158npNi9MAAIDOiiKFgLLrQOO0Pkaj0DqZveMV6XSooMytdXuLrY4DAAA6KYoUAobXa2rPgYb7RzGtD60U7LBp7MD63fuY3gcAANoKRQoBI7ekWu46r0IcNiVHh1gdBx3YBY3rpDa6LE4CAAA6K4oUAkbjtucZCeGyGYbFadCRjR3QTUF2QzsKKrQ9v9zqOAAAoBNyWB0AaNQ4rY9tz9EaBQX5Ssvo7fvaNvZm2VIG65ypM2Vu/lSSlJycpJUrllkVEQAAdCIUKQSECnedCsrdkqQecRQptJzH69WsFxb4vl6/t1ifbSlQ97Mu1+Wz7pIkPXH9BKviAQCAToapfQgIewrrR6MSI50KC6bf48T17hYhSXKVVqvCXWdxGgAA0NlQpBAQ9rDtOfwswulQclT9piXbC1gnBQAA/IsiBesZhrIa1kdlsD4KftQvsX5UalseRQoAAPgXRQrWi+uh6jqvgh023wgC4A/9kuqL1L7iKpUzvQ8AAPgRRQqWM1IGS6rfZMJmY9tz+E9kSJBSGu5Jti2vzOI0AACgM2FVPyxnSxkk6djT+g7d2vrw8wV+z4XOoX9SpHJLqrWN+0kBAAA/okjBUkUVNVJchiQp4xjbnh+6tfWh7r5whL+joZPomxihpVsLlFtSLYXFWh0HAAB0Ekztg6W+3L5fhs2m+PBgRYYEWR0HnVCE06G0mFBJktHjFIvTAACAzoIiBUst3Vo/JY/d+tCWGjedsFGkAACAnzC1D21q1OhMuVx5Rz1vv/hhGaHRyuD+UWhDfRMj9PnWAim+h3bvr1DPBD5vAADgxFCk0KZcrryjrm3aX+7Wayuy5K11KzWabc/RdsKCHUqPDVNWYaX+uyFXM87pa3UkAADQwTG1D5bJLqy/CW9NzmY57HwU0bYap/d9sDZHpmlanAYAAHR0/PQKy2QXVUmSqrM2WJwEXUHfbhEyPbXaklemjTmlVscBAAAdHEUKlvB4Te1rKFJuihTaQUiQXebe+s/av1fttTgNAADo6ChSsEReabVqPF6FOGyqLdhtdRx0EeauFZKk/6zdp5o6r8VpAABAR0aRgiWyi+rXR6XFhUlivQrah+n6XomRThVV1mrJ9/lWxwEAAB0YRQqWyC6sn9bXI5b7R6EdmV5dckp3SUzvAwAAJ4YihXZX6/Eqt6S+SKXHhVqcBl3NT09JkyR9tiVfBWVui9MAAICOiiKFdrevuEpeU4oMcSg6NMjqOOhi+iVFanh6jDxeU/9Zu8/qOAAAoIOiSKHdNd4/qkdcmAzDsDgNuqKfjqwflfr3qr3cUwoAALQKRQrtrnF9VDrro2CRnwxLVbDdpu9d3FMKAAC0DkUK7aqypk4F5fXrUtJiWR8Fa0SHBemCk5IkSW98k2VxGgAA0BFRpNCu9jbchDchIljhTofFadCVTRmdIUl6b80+lVbXWpwGAAB0NBQptCvf/aOY1geLnd47Tv0SI1RZ49F7q9l0AgAAtAxFCu1qX1Hj+iim9cFahmHomsz6Ual/Ld/DphMAAKBFKFJoNxXuOhVV1k+hSo2hSMF6l5zcXeHBdm3PL9eynQesjgMAADoQihTazb7iH9ZHhQTZLU4DSJEhQbrklO6SpH8t22NxGgAA0JFYWqS++OILXXjhhUpNTZVhGHr//febnDdNU/fdd59SUlIUGhqqcePGadu2bU2uKSws1JQpUxQVFaWYmBhNnz5d5eXl7fgu0FyNG02wPgqB5JrTe0qSFm7KU25JlbVhAABAh2FpkaqoqNDw4cM1d+7cI55/7LHH9Je//EXPPfecVqxYofDwcI0fP17V1dW+a6ZMmaKNGzdq0aJF+uijj/TFF1/oxhtvbK+3gBZoHJHqzrQ+BJAByZE6rVecPF5Tb3yTbXUcAADQQVhapCZOnKiHH35Yl1xyyWHnTNPU008/rXvvvVcXXXSRhg0bpldeeUU5OTm+kavNmzdrwYIFeuGFFzR69GiNGTNGzzzzjN58803l5OS087vBsVTW1KmwokYSRQqB55rT6zedeH1Fltx1HovTAACAjiBg10jt2rVLLpdL48aN8x2Ljo7W6NGjtWzZMknSsmXLFBMTo1GjRvmuGTdunGw2m1asWHHU53a73SotLW3yQNtq3K0vPiJYocGsj0JgGX9SspKjQrS/3K3/rOEfYQAAwPEFbJFyuVySpKSkpCbHk5KSfOdcLpcSExObnHc4HIqLi/NdcyRz5sxRdHS075Genu7n9DhU47S+NEajEICCHTb9YkxPSdLfv9ghr5et0AEAwLEFbJFqS7Nnz1ZJSYnvkZ3Nuoi21rjRBNP6EKiuPK2HIp0O7Sio0JLv862OAwAAAlzAFqnk5GRJUl5eXpPjeXl5vnPJycnKz2/6A09dXZ0KCwt91xyJ0+lUVFRUkwfaTlWNRwca10dxI14EqMiQIF11eg9J9aNSAAAAxxKwRapXr15KTk7W4sWLfcdKS0u1YsUKZWZmSpIyMzNVXFysVatW+a5ZsmSJvF6vRo8e3e6ZcWSN0/riwoMVFuywOA1wdL84o5eC7Ia+3V2kVXuKrI4DAAACmKVFqry8XGvXrtXatWsl1W8wsXbtWmVlZckwDN1xxx16+OGH9cEHH2jDhg2aOnWqUlNTdfHFF0uSBg0apAkTJuiGG27QN998o6+++kq33HKLrrjiCqWmplr3xtDEPqb1oYNIigrRJSfX36D3eUalAADAMVg6PLBy5Uqdc845vq9nzpwpSZo2bZrmzZunX//616qoqNCNN96o4uJijRkzRgsWLFBISIjve1577TXdcsstOu+882Sz2XTZZZfpL3/5S7u/Fxzd3uJKSVIa0/pgsYKCfKVl9D7q+eTkJL354UK9vXKvFm7K086CcvXuFtGOCQEAQEdhaZEaO3asTPPou2MZhqEHH3xQDz744FGviYuL0+uvv94W8eAPwWHaX879oxAYPF6vZr2w4Kjnn7h+gvomRmrcoER9ujlfz36+Q4//bHg7JgQAAB1FwK6RQudgJPSSJMWEBSncyfoodAy/OqevJOndNfu050CFxWkAAEAgokihTRnd+kiSUqMZjULHcUqPWJ3dv5s8XlN/XbLd6jgAACAAUaTQphqLFNP60NHcPq6fJEalAADAkTHXCm2mutYjxdXflyc1JuQ4VwPWO3QzCtvZN0upg3XmzXPkXVG/FjM5OUkrVyyzKiIAAAgQFCm0mXXZxTLsDoUF2xUdGmR1HOC4Dt2MwlVSrbdWZsveJ1PXTblCMWHBeuL6CRYmBAAAgYKpfWgz3+4ulFQ/rc8wDIvTAC2XHB2ijPgwmab07W5u0AsAAH5AkUKbafzBM5X1UejATu8VL0na7CpVUUWNxWkAAECgoEihTXi8plbvaSxSrI9Cx5UcHaJeCeEyTWnZzgNWxwEAAAGCIoU28b2rVGXuOpm1VUqIcFodBzghP+pTPyq1Lb9ciku3OA0AAAgEFCm0iW931a+PMvfvko31UejgEiKcGpgcKUmyDf+JxWkAAEAgoEihTTSujzLzd1icBPCPzN7xshmSLXmgvty23+o4AADAYhQp+J1pmr4d+8yCnRanAfwjKjRIw7rHSJIeXfC9TNO0NhAAALAU95GC32UVViq/zK0gu6G6wj1WxwH85tResVq9M1cb9kk9zrxMZvbaw67hhr0AAHQNFCn4XeO0vmFpMVrhqbU4DeA/YcEOla/+UFGn/1xx59+ka07PkN3WdA0gN+wFAKBrYGof/K5xo4lRPWMtTgL4X9mqDxUWbFdJVa3W7S22Og4AALAIRQp+9+2e+iJ1Ws84i5MA/mfWViuzd/126N/sKlR1rcfiRAAAwAoUKfjV/nK3dhZUSJJGZjAihc5pcGqU4iOC5a7zakXDCCwAAOhaKFLwq5UN66MGJEUqJizY4jRA27AZhs7smyBJWr+3WMWVNRYnAgAA7Y0iBb9q3Pac9VHo7DLiw5URHyavKX25nftKAQDQ1VCk4FcrG4rUab1YH4XO78y+CTIk7Sio0N6iSqvjAACAdkSRgt9UuOv0XU6pJGkUG02gC4iPcGpI92hJ0tKtBfJyk14AALoMihT8Zm12sTxeU91jQtU9JtTqOEC7yOwTL6fDpv3lNdq4r9TqOAAAoJ1QpOA33zTsXnYq66PQhYQG2XV6w3boX+/cLwXxjwgAAHQFFCn4zco9jRtNMK0PXcuw7tGKDw9Wda1XtqETrY4DAADaAUUKflHr8Wr1nmJJbDSBrsdmM3RW/26SJKPfWdqWV2ZxIgAA0NYoUvCLTTmlqqr1KDo0SH27RVgdB2h3PeLC1KdbuAybXQ98uEkmG08AANCpUaTgF433jzq1Z6xsNsPiNIA1zuzXTWZdjb7cvl//3ZBrdRwAANCGKFLwix9uxMu0PnRd0aFB8m5aJEl66KNNKnfXWZwIAAC0FYoUTphpmlq5u0iSdCpFCl2cuflTZcSHKa/UracXbbU6DgAAaCMUKZyQUaMzlT40UwcqamTW1eiisacpLaO371FQUGB1RKB9eev0wE9OkiS99PVufe/i3lIAAHRGDqsDoGNzufI0/oG3tPj7fKUlROunz/+3yfm7LxxhTTDAQmMHJGrCSclasNGl37//nd7+ZaYMg7WDAAB0JoxI4YTllFRJklJjQixOAgSO+y4crNAgu77dXaS3V2ZbHQcAAPgZRQonLKe4WpLUPSbU4iRA4EiNCdXM8/tLkv74383KL622OBEAAPAnihROTGiUSqpqZUhKjmZECjjYdWf01NDu0SqtrtP9H260Og4AAPAjihROiJHQR5KUEOmU02G3OA0QWBx2mx69bJjsNkMfb3Dpk40uqyMBAAA/YbMJnBCjW29JUiqjUcARDU6N0i/P6q2/fb5Dv3//O53eO17njT1LLlfeMb8vOTlJK1csa6eUAACgpShSOCFGYv2IFOujgKO77bx+mv+dS7v2V+iR+ZvlcuVp1gsLjvk9T1w/oZ3SAQCA1mBqH1qttLpWiu4uqX5hPYAjCwmy65FLh0qS3vgmW0bKYIsTAQCAE0WRQqut3lMkw2ZTdGiQwp0MbgLHMrp3vKaP6SVJso2+SlW1HosTAQCAE0GRQqut3F0kiftHAc119/gB6psYISM0Wp99ny/TNK2OBAAAWokihVb7ZnehJKb1Ac0VEmTXUz8fIdPr0bb8cm3JK7M6EgAAaCWKFFrFXefRuuxiSVL3aIoU0FxD06Ll/a5+o4nPtxSorLrW4kQAAKA1KFJole/2lchd55VZXaaYsCCr4wAdirlpoZKinHLXeTX/O5c8Xqb4AQDQ0VCk0CrfNqyPMgt2yjAMi9MAHYzp1cQhKQq225RbUq3lOw9YnQgAALQQRQqt8s2u+vVRZsEOi5MAHVN0aJDGDUqUJK3cU6TdByosTgQAAFqCIoUWq/N4fyhS+dssTgN0XP2SIjW0e7QkaeHGPJW76yxOBAAAmoub/6DFvsspVbm7TtGhQTpQtM/qOEBAKSjIV1pG7+NcU+D7/Vn9EpRbUqX95TWa/12uLj05TXYb02UBAAh0FCm02LId9es5RveK08dikTxwMI/Xq1kvLDjmNXdfOML3e4fdpklDU/TmN9nKKa7W/7YVaOyAxDZOCQAAThRT+9BiX+/YL0nK7BNvcRKgc4gNC9b4k5IkSev2lmhTTqnFiQAAwPFQpNAiNXVerWzYse9HfRIsTgN0Hr27RWh0rzhJ0pIt+VJcD4sTAQCAY6FIoUXW7y1WVa1H8eHB6p8UYXUcoFMZ3StOvRPC5fGaso+5XgVlbqsjAQCAo6BIoUUa10ed3jue+0cBfmYYhi44KUmxYUEywmN1479WqrrWY3UsAABwBBQptMjXjUWK9VFAm3A67LpweKpMd4XWZBXr1/9eL9NkUxcAAAJNQBep+++/X4ZhNHkMHDjQd766ulozZsxQfHy8IiIidNlllykvL8/CxJ1bda1Hq7Lq10dl9qZIAW0lNixY3q/+KYfN0AfrcvTMku1WRwIAAIcI6CIlSSeddJJyc3N9jy+//NJ37s4779SHH36od955R0uXLlVOTo4uvfRSC9N2bmuyilVT51VipFN9uoVbHQfo1My8rXro4iGSpCcXbdVH63MsTgQAAA4W8PeRcjgcSk5OPux4SUmJXnzxRb3++us699xzJUkvvfSSBg0apOXLl+v0009v76id3rKDtj1nfRTQ9q48rYe255frxS93adbb65QWG6YR6TFWxwIAAOoAI1Lbtm1TamqqevfurSlTpigrK0uStGrVKtXW1mrcuHG+awcOHKgePXpo2bJlx3xOt9ut0tLSJg8c37Kd9eujmNYHtJ/fThqkcwcmyl3n1Q2vrFROcZXVkQAAgAK8SI0ePVrz5s3TggUL9Oyzz2rXrl0688wzVVZWJpfLpeDgYMXExDT5nqSkJLlcrmM+75w5cxQdHe17pKent+G76Byqajxam10siRvxAu3JbjP0lytP1sDkSBWUuTX95ZWqcNdZHQsAgC4voIvUxIkT9bOf/UzDhg3T+PHj9fHHH6u4uFhvv/32CT3v7NmzVVJS4ntkZ2f7KXHn9c3uQtV6TKVGh6hHXJjVcYAuJcLp0AvTRikhIlibc0t1x1tr5fWykx8AAFYK6CJ1qJiYGPXv31/bt29XcnKyampqVFxc3OSavLy8I66pOpjT6VRUVFSTB47t8y35kqSz+ndjfRRggbTYMD0/dZSCHTYt2pSnRz/53upIAAB0aR2qSJWXl2vHjh1KSUnRyJEjFRQUpMWLF/vOb9myRVlZWcrMzLQwZee0dGuBJGnsgG4WJwG6rlN6xOrxnw6TJP196U69vZLRdAAArBLQu/bddddduvDCC5WRkaGcnBz94Q9/kN1u15VXXqno6GhNnz5dM2fOVFxcnKKionTrrbcqMzOTHfv8LLuwUjsLKmS3GfpR3wSr4wBdQkFBvtIyeh/xnG3IJNmGTtTv3tugHnFhOp0NYAAAaHcBXaT27t2rK6+8UgcOHFC3bt00ZswYLV++XN261Y+KPPXUU7LZbLrsssvkdrs1fvx4/e1vf7M4defTOBo1skesokKCLE4DdA0er1ezXlhwxHOmaerR516R+v9Ilz+zWJ6F/yeV7z/suuTkJK1ccexdTAEAQOsEdJF68803j3k+JCREc+fO1dy5c9spUdf0+Zb6InU20/qAgGAYhg58MlcjR52jvFKp2+UP6/JT0+V02Jtc98T1EyxKCABA59eh1kih/dXUefV1w414z+5PkQIChqdGFw5LVYTToaLKWn26KV+myU5+AAC0F4oUjmnlnkJV1niUEOHU4BR2NwQCSbjToclDU2QzpO0F5VqdVWx1JAAAugyKFI6pcX3UWf0TZLOx7TkQaJKjQ3yjxV9t36/swkqLEwEA0DVQpHBMSxvXRzGtDwhYQ7tHa1BKpExJ879zqay61upIAAB0ehQpHJWrpFrfu8pkGNKZ/ShSQKAyDEPnDkhUtwinqmo9WvCdS14v66UAAGhLFCkc1RcN0/qGpcUoLjzY4jQAjsVht2nS0GQF223KKanWit2FVkcCAKBTo0jhqD7fmi+JaX1ARxETFqxzByZKkr7dVSgjsa/FiQAA6LwoUjii6lqPb31U4w9mAALfgORIDU6JkinJljlNhRU1VkcCAKBTokjhiL7ctl8VNR6lRIdoWPdoq+MAaIGxA7opNixIRliMfv3vddxfCgCANuCwOgAC04KNLknSvm8WqEevG456XUFBQXtFAtBMQXabJg5J0WvLdujTzfl6dUWWrjk9w+pYAAB0KhQpHKbW49WiTXmSpJ9dOUVpvzp6kbr7whHtlApAS3SLdKr0y9cUffa1uvffqzX7+iuksrwm1yQnJ2nlimUWJQQAoGOjSOEwK3YWqqSqVmZ1mVJjQq2OA6CVytZ8rKGX/EpZhVLqlQ/p56PSZT/oxtpPXD/BwnQAAHRsrJHCYRZszJUkmXvXy2YYx7kaQOAydf7gJIU4bMovc2v5zgNWBwIAoNOgSKEJr9fUJxvrp/+Ye9dZnAbAiYpwOnTeoCRJ0so9RdpXVGVxIgAAOgeKFJpYnVWkgjK3IkMcMvO2Wh0HgB/0TYzQ4JQoSdInm1xy13ksTgQAQMdHkUITC76r361v3KAkycsPW0BncXb/booODVJZdZ0+28JumwAAnCiKFHxM09T8hiI1/qRki9MA8Kdgh03jT0qSYUhbXGXa4iqzOhIAAB0aRQo+3+0r1b7iKoUG2XV2/25WxwHgZynRoTqtZ5wkacmWfCks1uJEAAB0XBQp+Py/1XslSecOSlRosN3iNADawmk945QcFaKaOq/sp18jj9e0OhIAAB0SRQqSpJo6r/6zdp8k6acj0yxOA6Ct2GyGxp+UpCC7ISOpn/7xv51WRwIAoEOiSEGStHhznooqa5UU5dRZ/ZjWB3RmMWHBvum7Tyzcou/2lVicCACAjociBUnSO6vqp/Vdekqa7DZuwgt0doNTouTNXqdaj6k73lqrqhp26QQAoCUoUlB+abU+35IvSfoZ0/qALsEwDHm/eUOJkU5tzy/XI/M3Wx0JAIAOhSIFvbtmn7ymNDIjVr27RVgdB0B7qanQ//1suCTp5WV79Nn3+RYHAgCg46BIdXGmaeqdldmSGI0CuqKz+nfTdWf0lCTd8dZa7TlQYW0gAAA6CIpUF7cmu1g7CioUEmTT5GEpVscBYIF7JgzUiPQYlVTV6oZXVqrcXWd1JAAAAh5Fqot7Z2X9JhOThqQoMiTI4jQArBASZNffrxmpxEintuaVa+Zba+Xl/lIAABwTRaoLO+WMc/T619skSe/836+VltG7yaOgoMDihADaS1JUiP5+zUgF221auClPf168zepIAAAENIfVAWCdgqgBsjuc6hbh1JV/fEaG0XTb87svHGFNMACWOLlHrP506VDd9c46/XnxNiVHh+jK03pYHQsAgIDEiFQXVV3rkW3A2ZKkUzJiDitRALqmn45M0y/P7i1J+u17G3yb0QAAgKYoUl3U/1u9V0ZIlCJDHOqXGGl1HAAB5DcTBuraH/WUaUq//n/r9d6avVZHAgAg4FCkuiCP19QL/9slSTo5PUZ2G6NRAH5gGIb+cOFgTRndQ6YpzXp7nd5dTZkCAOBgFKkuaNEml3btr5DprtBJqdFWxwEQgAzD0EMXDdHlo9LlNaWZb6/TQx9tUq3Ha3U0AAACAptNdDGmaeq5pTvrf7/tfwqeNMLaQAAsU1CQr7SM3se8Jik5STc/8JKe/XyHXvxyl77bV6K/XnWKukU62yklAACBiSLVxSzfWai12cUKdthUue0LSbdaHQmARTxer2a9sOCY1zxx/QTdM2GghqfF6K531mnFrkL9+Jn/6d7Jg/XjYSlsVAMA6LKY2teFeL2m/vjxJknSz0elSdVlFicC0FFMGJKs92ecob6JEcordevWN9bo0me/1qo9RVZHAwDAEhSpLuTfq/fqu32linQ6dMe4/lbHAdDB9E2M0Ie3jNHM8/srLNiuNVnFuuzZr3XtS9/o4w25ctd5rI4IAEC7YWpfF1HurtPjn2yRJN16Xl8lRLC+AUDLhQbbddt5/XTFqel6YuFWvb0qW59vKdDnWwoUExaknwxP1dgB3XRqzzhFhgQ1+3lHjc6Uy5V31PPJyUlauWKZP94CAAB+QZHqIuZ+tl0FZW71jA/TtT/qZXUcAB1cYlSIHv3pMP3y7N56Z9Vevbt6r/JK3Xpl2R69smyP7DZDw9KidWrPOA3tHq2h3aOVER921DVVLlfeMddrPXH9hLZ6KwAAtApFqgvIOlCpFxvuG/W7yYMV7GBGJwD/6N0tQvdMGKi7Lhig/20r0ILvXFq284D2HKjUmqxirckq9l0bFeLQkO7RGppWX6xGpMeoe0woG1YAADokilQnZ5qmHvrvJtV4vBrTN0HjBiVaHQlAJ3PEaXlhsTKS+suIz1BwUm/Z4tJVWl2nr3cc0Nc7DvguS4kO0ak942T0PVMlVbWKDm3+dEAAAKxEkerk3vw2W4s25clhM/T7Hw/mX34BtEhz7jVVUFCgR/6z6qjnf3PRKeqWmCxFp8iI6yEjLl1GXIYU2125JdX6YF2O7Kf+XPO+3q24sGD1SghXr4RwpUSHyGbjv1kAgMBEkerEvneV6v4PNkqS7h4/QAOSIy1OBKCjac69pu6+cMTxn+MfHx92vNbjlaukWjnFVfr8iy8U2mOICitrVJhVo1VZRXI6bMqID1OvhHApOOxE3kazHW/TC4mNLwAA9ShSnVSFu04zXlstd51XYwd00w1nHvtflAGgvQXZbUqPC1N6XJj+ffsDevjdVdpTWKld+yu0+0CFqmu92ppXrq155bJf8idd/vdlOn9wks4ZmKjeCeFtMsJ+vE0vJDa+AADUo0h1Uvf9Z6N2FFQoKcqpJ38+gukxAAKeM8iu/kmR6p8UKa9pylVSrV37K7Rrf4UOVNRoxa5CrdhVqIf/u1nJUSHK7BOvzN7xGpYerd4JEWykAwBoVxSpTujFL3fp/63eK5sh/eWKkxUXHmx1JABoEZthKDUmVKkxoTqjb4KeuH2KHvrH2/p0c56+3VUkV2m13luzT++t2SdJctgM9ekWoT6J4UqIcCouPFjx4cFyOuwyDMkwDHlNU1U1HlXWeFRZU9fk1wq3R1W1dbKfP1OvLt9Tn8FmyGZIDptNocF2hQXbFR7skNHzVH23r0R9EyMUEmS38o8JAGAhilQn888vd+mhjzZJku4aP0Cje8dbnAgA/KDigK47o5euO6OXqmo8Wp1VpGU7DmjFrgP6PrdMZe46bckr05a8shN6GSOhlw5U1BzzGnvmVP34mS9lml6pfL/MA3tkFuyUWbBDKnEpOTmRNVQA0AVQpDqRf365Sw82lKhbzumrm8/uY3EiAPC/0GC7zuiboDP6Jkiqv81DTkm1Tp/4c517w+8bRp3qVFXrkcdr1l/T8L271nypn1/yE4UF2xUW7Gj49Yff3zrjV/rp7Q/IkOQ1TXm8puq89SNZFTV1qnB7tObbZYruNVTVtZIiE2VEJko9T5UkhThsyt++Qv9Zu09n9eumWGYEAECnRZHqBEzT1D/+t1N/+vh7SfUlatYF/dnqHECXYBiGuseEyszZqBHpMce89om/vqT/e/33Rz1/y74N6hF37B0CF95zv+7+YI0qazzaX+5Wbkm1ckqq5CqpVnWdV7aep+r2N9fKZkgn94jVuQMTdc6ARA1KieS/ywDQiVCkOriiihr95t31+mRj/Xa9lCgAOLrj3ReroKCgWc9jGIbCnQ6FOx3KiA+XJHm9pnJLqvX2q//U4HMv0/euMq3aU6RVe4r0+CdblBwVonMHJer8QUm667pLlZeTc8zXYJt1AAhsFKkO7Kvt+zXz7bXKK3UryG7ongkDNX1ML0oUgE6nuTcGPp7j3RfrePfEOhabzVD32FB5132oBR/8WTnFVfpsS74++z5fX20/IFdptV5fkaXXV2TJPGOm+ifHqVe3cPWKD1e48/D/O2abdQAIbBSpDmhHQbn+umS73l+7T6Yp9e4Wrr9ccbKGdI/2XdOcm0o2919eAcBq/rgxcHtLjQnVlNEZmjI6Q9W1Hi3feUCfbs7T4s35yi2Rdu6v0M79FZKkpCinesSFqUdcmJKjQ+SwsZU7AAQ6ilQHsjWvTH/7bLs+WJejhvXTuvK0Hrrvx4MVGtx0C97m3FQy0H7oAIDOKiTIrrEDEjV2QKIeushU+slna8zNj2rn/grll7mVV1r/+HZ3kRw2Q8lRIbIN/4k+2ejSiPQYJUY6mW0AAAGGIhXgsgsr9dH6XH24Lkebckt9x8cNStLt5/XT0LToY3w3ACDQGIYhFe3V6N7xGt07XuXVddpTWKHsoiplF1aqssajvcVVsg0+X7/81ypJUlSIQ30SI9S3W4R6xIUpKTpESVEh6hbhVLjT3nCfK4dCg+yycwN2AGgXnaZIzZ07V48//rhcLpeGDx+uZ555RqeddprVsVrENE3tLarSN7sK9e3uQn2zu1A7Cyp85x02Q+cNStSKeX/UgjfWacF9R38upu0BgDVaup4rIsShk1KjdVJqtEzTVGFFjXJLq7Xow/c1eMx4bc0rU2l1ndZkFWtNVvFxXz/YYavf0j3ILlfuPnlqqiVPneSpk+mtlTy19V/XuRUWbNP0a65UuNOhCKdD4cGOH37vtDf8+sMxShoA/KBTFKm33npLM2fO1HPPPafRo0fr6aef1vjx47VlyxYlJiZaHa/Zvt5xQFNeWNHkmGl6ZeZtk5m1WnXZ6/TfmgoVFBTokf+sOuZzMW0PAKxxIuu5DMNQfIRT8RFOffLtG1rw7z+qutajXfsrtD2/XDsKypVTXKW8Ure+XP2d6uyhksMpOYJkGPXrqmrqvKqp86pYtVJYvIyDdnM/tAZVS5r72Y5mv7eQINsP5Sq4vmwFO2wKttvqf3XYfb93OmyHnLMpqPHcQccOPt/4dUiQTU6HXU5Hw69B9cdtFDkAAaRTFKknn3xSN9xwg6677jpJ0nPPPaf//ve/+uc//6nf/OY3FqdrvmFp0XI6bKp27dCoU05RakyIUmNCFRI0QNKPfddRkgCg6wgJsmtQSpQGpUQ1OZ72wBW+wmY23Dy41mOq1uNVrcerOq+pv9x9rX45558NNxb2+m4w7PGYqvF4tfDNfygsKk4KCqkvZEEhMhp+1UG/Gvb6Hxeqa72qrq3R/vKadv9zkCSzcTTN2/Cr56BfvbUKdth09hk/kjPI9kMJc9jkDLLJ3rDGrHGpmaEfvvbVs4aTXq8pj2nW/3rw701THq/07vv/UVW1WzJsDU9w8K82OUNCdOaYMbIZhmyGZLcZ9b+3NXxtGDIMQ3abZDvk940P39eHfU/917aG56w/Xn+t12z4+/XW563zmr4bS3sOeS+N5959/0NVud2+7PXvw/7D+5GpkOBgnTP2LNltDRmMHzLYjUPem8046H3oh+85wvtr8n58x4/8/o70Zyj9cLNts+E3ZsORH75uPN/4uyNf3+TYUb7Xd+VxX+vY53XI8/3wuqY8pg76rB39787T8Pf39r/fVWWVW7Id8vfW8AgJDdVZZ4754e/N1vTP2mj4szzW39PRPocHf/+h2RofB3/9zv97T5XVbslma5JRtvrczpBQPX3rzzV5WIo6ig5fpGpqarRq1SrNnj3bd8xms2ncuHFatuzI999wu91yu92+r0tKSiRJpaWlR7y+PX0583QNH36zRv343foDNVWqPuT/r0zTVHVF+TGfxx/XBMpzkJWsXe39kpWskuT1eo/5/0ter/ew5zAkBUsKNiTZJfe+zUp0eg8623RE560vX9est746Zo57Lz9D97/xP19Jq6mrL2o1Hq/qPF69+ecHdOktv2/4YUmH/+DuNbV80fsaed5PjvqDocdran/uXqVlZKimtv653Q0ja17zCKEMh+RwSI6QJofdkhau3XXM9+MX8QOOedot6dN1u9s+hz/E9z/uJdWS5q9uhz9XtFzCoGOerpa0cO3udolyXAkDj3naLen7bJfO7BnePnmOofG/vYcW8EMZ5vGuCHA5OTnq3r27vv76a2VmZvqO//rXv9bSpUu1YsWKw77n/vvv1wMPPNCeMQEAAAB0INnZ2UpLSzvq+Q4/ItUas2fP1syZM31fe71eFRYWKj4+nu1lO6jS0lKlp6crOztbUVFRx/8GoA3wOYTV+AzCanwGEQhO9HNomqbKysqUmpp6zOs6fJFKSEiQ3W5XXl7Tm8/m5eUpOTn5iN/jdDrldDqbHIuJiWmriGhHUVFR/IcbluNzCKvxGYTV+AwiEJzI5zA6+vi3GOrwt04PDg7WyJEjtXjxYt8xr9erxYsXN5nqBwAAAAD+0uFHpCRp5syZmjZtmkaNGqXTTjtNTz/9tCoqKny7+AEAAACAP3WKInX55ZeroKBA9913n1wul0aMGKEFCxYoKSnJ6mhoJ06nU3/4wx8Om7IJtCc+h7Aan0FYjc8gAkF7fQ47/K59AAAAANDeOvwaKQAAAABobxQpAAAAAGghihQAAAAAtBBFCgAAAABaiCKFDmPOnDk69dRTFRkZqcTERF188cXasmVLk2uqq6s1Y8YMxcfHKyIiQpdddtlhN2sG/OmRRx6RYRi64447fMf4HKKt7du3T1dffbXi4+MVGhqqoUOHauXKlb7zpmnqvvvuU0pKikJDQzVu3Dht27bNwsTobDwej37/+9+rV69eCg0NVZ8+ffTQQw/p4D3M+BzCn7744gtdeOGFSk1NlWEYev/995ucb87nrbCwUFOmTFFUVJRiYmI0ffp0lZeXtzoTRQodxtKlSzVjxgwtX75cixYtUm1trS644AJVVFT4rrnzzjv14Ycf6p133tHSpUuVk5OjSy+91MLU6My+/fZb/f3vf9ewYcOaHOdziLZUVFSkM844Q0FBQZo/f742bdqkJ554QrGxsb5rHnvsMf3lL3/Rc889pxUrVig8PFzjx49XdXW1hcnRmTz66KN69tln9de//lWbN2/Wo48+qscee0zPPPOM7xo+h/CniooKDR8+XHPnzj3i+eZ83qZMmaKNGzdq0aJF+uijj/TFF1/oxhtvbH0oE+ig8vPzTUnm0qVLTdM0zeLiYjMoKMh85513fNds3rzZlGQuW7bMqpjopMrKysx+/fqZixYtMs8++2zz9ttvN02TzyHa3j333GOOGTPmqOe9Xq+ZnJxsPv74475jxcXFptPpNN944432iIguYPLkyeYvfvGLJscuvfRSc8qUKaZp8jlE25Jkvvfee76vm/N527RpkynJ/Pbbb33XzJ8/3zQMw9y3b1+rcjAihQ6rpKREkhQXFydJWrVqlWprazVu3DjfNQMHDlSPHj20bNkySzKi85oxY4YmT57c5PMm8TlE2/vggw80atQo/exnP1NiYqJOPvlk/eMf//Cd37Vrl1wuV5PPYHR0tEaPHs1nEH7zox/9SIsXL9bWrVslSevWrdOXX36piRMnSuJziPbVnM/bsmXLFBMTo1GjRvmuGTdunGw2m1asWNGq13WcWGzAGl6vV3fccYfOOOMMDRkyRJLkcrkUHBysmJiYJtcmJSXJ5XJZkBKd1ZtvvqnVq1fr22+/Pewcn0O0tZ07d+rZZ5/VzJkz9dvf/lbffvutbrvtNgUHB2vatGm+z1lSUlKT7+MzCH/6zW9+o9LSUg0cOFB2u10ej0d//OMfNWXKFEnic4h21ZzPm8vlUmJiYpPzDodDcXFxrf5MUqTQIc2YMUPfffedvvzyS6ujoIvJzs7W7bffrkWLFikkJMTqOOiCvF6vRo0apT/96U+SpJNPPlnfffednnvuOU2bNs3idOgq3n77bb322mt6/fXXddJJJ2nt2rW64447lJqayucQXQZT+9Dh3HLLLfroo4/02WefKS0tzXc8OTlZNTU1Ki4ubnJ9Xl6ekpOT2zklOqtVq1YpPz9fp5xyihwOhxwOh5YuXaq//OUvcjgcSkpK4nOINpWSkqLBgwc3OTZo0CBlZWVJku9zduhOkXwG4U933323fvOb3+iKK67Q0KFDdc011+jOO+/UnDlzJPE5RPtqzuctOTlZ+fn5Tc7X1dWpsLCw1Z9JihQ6DNM0dcstt+i9997TkiVL1KtXrybnR44cqaCgIC1evNh3bMuWLcrKylJmZmZ7x0Undd5552nDhg1au3at7zFq1ChNmTLF93s+h2hLZ5xxxmG3fti6dasyMjIkSb169VJycnKTz2BpaalWrFjBZxB+U1lZKZut6Y+RdrtdXq9XEp9DtK/mfN4yMzNVXFysVatW+a5ZsmSJvF6vRo8e3boXbtUWFYAFbr75ZjM6Otr8/PPPzdzcXN+jsrLSd81NN91k9ujRw1yyZIm5cuVKMzMz08zMzLQwNbqCg3ftM00+h2hb33zzjelwOMw//vGP5rZt28zXXnvNDAsLM1999VXfNY888ogZExNj/uc//zHXr19vXnTRRWavXr3MqqoqC5OjM5k2bZrZvXt386OPPjJ37dplvvvuu2ZCQoL561//2ncNn0P4U1lZmblmzRpzzZo1piTzySefNNesWWPu2bPHNM3mfd4mTJhgnnzyyeaKFSvML7/80uzXr5955ZVXtjoTRQodhqQjPl566SXfNVVVVeavfvUrMzY21gwLCzMvueQSMzc317rQ6BIOLVJ8DtHWPvzwQ3PIkCGm0+k0Bw4caD7//PNNznu9XvP3v/+9mZSUZDqdTvO8884zt2zZYlFadEalpaXm7bffbvbo0cMMCQkxe/fubf7ud78z3W637xo+h/Cnzz777Ig/B06bNs00zeZ93g4cOGBeeeWVZkREhBkVFWVed911ZllZWaszGaZ50C2oAQAAAADHxRopAAAAAGghihQAAAAAtBBFCgAAAABaiCIFAAAAAC1EkQIAAACAFqJIAQAAAEALUaQAAAAAoIUoUgAAAADQQhQpAECnNXbsWN1xxx1+fc558+YpJibGr88JAOh4KFIAALTA5Zdfrq1bt1odAwBgMYfVAQAA6EhCQ0MVGhpqdQwAgMUYkQIAdGp1dXW65ZZbFB0drYSEBP3+97+XaZqSpJ49e+rhhx/W1KlTFRERoYyMDH3wwQcqKCjQRRddpIiICA0bNkwrV670PR9T+wAAEkUKANDJvfzyy3I4HPrmm2/05z//WU8++aReeOEF3/mnnnpKZ5xxhtasWaPJkyfrmmuu0dSpU3X11Vdr9erV6tOnj6ZOneorXwAASBQpAEAnl56erqeeekoDBgzQlClTdOutt+qpp57ynZ80aZJ++ctfql+/frrvvvtUWlqqU089VT/72c/Uv39/3XPPPdq8ebPy8vIsfBcAgEBDkQIAdGqnn366DMPwfZ2Zmalt27bJ4/FIkoYNG+Y7l5SUJEkaOnToYcfy8/PbIy4AoIOgSAEAurSgoCDf7xsL15GOeb3e9g0GAAhoFCkAQKe2YsWKJl8vX75c/fr1k91utygRAKAzoEgBADq1rKwszZw5U1u2bNEbb7yhZ555RrfffrvVsQAAHRz3kQIAdGpTp05VVVWVTjvtNNntdt1+++268cYbrY4FAOjgDJP9XAEAAACgRZjaBwAAAAAtRJECAAAAgBaiSAEAAABAC1GkAAAAAKCFKFIAAAAA0EIUKQAAAABoIYoUAAAAALQQRQoAAAAAWogiBQAAAAAtRJECAAAAgBaiSAEAAABAC/1/5qph+Wn4sxcAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import skew, kurtosis\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "\n",
+    "label_encoder = LabelEncoder()\n",
+    "categorical_cols = df.select_dtypes(include=['object']).columns\n",
+    "for col in categorical_cols:\n",
+    "    df[col] = label_encoder.fit_transform(df[col])\n",
+    "\n",
+    "# Define numerical columns\n",
+    "numerical_cols = df.select_dtypes(include=['int64', 'float64']).columns\n",
+    "\n",
+    "# Loop through each numerical column for analysis\n",
+    "for col in numerical_cols:\n",
+    "    # Summary statistics\n",
+    "    mean = df[col].mean()\n",
+    "    median = df[col].median()\n",
+    "    mode = df[col].mode()[0]\n",
+    "    std_dev = df[col].std()\n",
+    "    skewness = skew(df[col])\n",
+    "    kurt = kurtosis(df[col])\n",
+    "    \n",
+    "    print(f\"Summary Statistics for '{col}':\")\n",
+    "    print(f\"Mean: {mean}\")\n",
+    "    print(f\"Median: {median}\")\n",
+    "    print(f\"Mode: {mode}\")\n",
+    "    print(f\"Standard Deviation: {std_dev}\")\n",
+    "    print(f\"Skewness: {skewness}\")\n",
+    "    print(f\"Kurtosis: {kurt}\")\n",
+    "    \n",
+    "    # Plot histogram\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    sns.histplot(df[col], kde=True)\n",
+    "    plt.title(f'Histogram of {col}')\n",
+    "    plt.xlabel(col)\n",
+    "    plt.ylabel('Frequency')\n",
+    "    plt.show()\n",
+    "    \n",
+    "\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d7b58e6-3ecc-4b40-b26d-4c2d78122396",
+   "metadata": {},
+   "source": [
+    "```` ````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd380149-9e1b-4a6e-afaf-8f28b79e5a34",
+   "metadata": {},
+   "source": [
+    "### Age with Stroke & No Stroke"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47669d92-5f1a-4a58-bc73-1d8ee8728871",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# This will plot a distribution plot of the variable 'age'\n",
+    "plt.figure(figsize=(15, 7))\n",
+    "sns.boxplot(data=df, x='stroke', y='age')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f47b24ab-92e2-40e9-ba54-329392bdacba",
+   "metadata": {},
+   "source": [
+    "People aged <b>more than 60 years</b> tend to have a stroke. Some outliers can be seen as people below age 20 are having a stroke it might be possible that it’s valid data as stroke also depends on our eating and living habits. Another observation is people not having strokes also consist of people age > 60 years."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59c91a33-ffd5-42c4-abe7-a71b19eeb941",
+   "metadata": {},
+   "source": [
+    "### Hypertension with Stroke & No Stroke"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9622e923-a2f0-4f59-acce-9bf80a01e410",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 7))\n",
+    "sns.countplot(data=df, x='hypertension', hue='stroke')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09da2a70-cd1b-49ff-a4dd-b7381d688cdc",
+   "metadata": {},
+   "source": [
+    " Hypertension is rare in young people and common in aged people.It has quite little data on patients having hypertension."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c4e16e4-cba2-479a-8c05-216f301910c0",
+   "metadata": {},
+   "source": [
+    "### Heart Disease with Stroke & No Stroke"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7fc6fcb1-ae76-4cb1-9581-9512eb0046ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique Values\n",
+      " [ True False]\n",
+      "Value Counts\n",
+      " heart_disease\n",
+      "False    4834\n",
+      "True      276\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('Unique Values\\n', df['heart_disease'].unique())\n",
+    "print('Value Counts\\n', df['heart_disease'].value_counts())\n",
+    "\n",
+    "# Create a count plot with 'heart_disease' on the y-axis and 'stroke' on the x-axis\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.countplot(data=df, x='heart_disease', hue='stroke')\n",
+    "plt.xlabel('Count')\n",
+    "plt.ylabel('Heart Disease')\n",
+    "plt.title('Count of Stroke by Heart Disease')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f320c868-1729-404d-b77c-6facbb50facd",
+   "metadata": {},
+   "source": [
+    "Because of the imbalanced dataset, it’s a little bit difficult to get an idea. But as per this plot, we can say that heart disease is not affecting Stroke."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a703ac9-52e6-4862-947d-ea3181abf2ad",
+   "metadata": {},
+   "source": [
+    "### "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "5f5c4d92-9c2c-4786-b907-3f470870760b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique Values\n",
+      " ['Yes', 'No']\n",
+      "Categories (2, object): ['No', 'Yes']\n",
+      "Value Counts\n",
+      " ever_married\n",
+      "Yes    3353\n",
+      "No     1757\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Count of Stroke by Ever Married')"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('Unique Values\\n',df['ever_married'].unique())\n",
+    "print('Value Counts\\n',df['ever_married'].value_counts())\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.countplot(data=df,x='ever_married',hue='stroke')\n",
+    "plt.xlabel('Count')\n",
+    "plt.ylabel('Ever Married')\n",
+    "plt.title('Count of Stroke by Ever Married')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99ebeca7-a427-4189-a040-c0cf7782f4db",
+   "metadata": {},
+   "source": [
+    "People who are married have a higher stroke rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "be900801-ca17-4740-a480-cbde54d3d239",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique Values\n",
+      " ['Private', 'Self-employed', 'Govt_job', 'children', 'Never_worked']\n",
+      "Categories (5, object): ['Govt_job', 'Never_worked', 'Private', 'Self-employed', 'children']\n",
+      "Value Counts\n",
+      " work_type\n",
+      "Private          2925\n",
+      "Self-employed     819\n",
+      "children          687\n",
+      "Govt_job          657\n",
+      "Never_worked       22\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Count of Stroke by Work Type')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('Unique Values\\n',df['work_type'].unique())\n",
+    "print('Value Counts\\n',df['work_type'].value_counts())\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.countplot(data=df,x='work_type',hue='stroke')\n",
+    "plt.xlabel('Count')\n",
+    "plt.ylabel('Work Type')\n",
+    "plt.title('Count of Stroke by Work Type')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41f284c1-d56b-4ba6-b1f8-9e064c6c40d0",
+   "metadata": {},
+   "source": [
+    "People working in the Private sector have a higher risk of getting a stroke. And people who have never worked have a very less stroke rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0476da52-2cba-442c-a5ea-abab73d6ed89",
+   "metadata": {},
+   "source": [
+    "### Residence_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "3646a8c4-2c9a-46a6-94a3-8d0ce0944876",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique Values\n",
+      " ['Urban', 'Rural']\n",
+      "Categories (2, object): ['Rural', 'Urban']\n",
+      "Value Counts\n",
+      " Residence_type\n",
+      "Urban    2596\n",
+      "Rural    2514\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Count of Stroke by Residence_type')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('Unique Values\\n',df['Residence_type'].unique())\n",
+    "print('Value Counts\\n',df['Residence_type'].value_counts())\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.countplot(data=df,x='Residence_type',hue='stroke')\n",
+    "plt.xlabel('Count')\n",
+    "plt.ylabel('Residence_type')\n",
+    "plt.title('Count of Stroke by Residence_type')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49e2cc82-c046-4ce5-97e9-3600e476d777",
+   "metadata": {},
+   "source": [
+    "This attribute is of no use. As we can see there not much difference in both attribute values. Maybe we have to discard it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc8fe52d-33c8-4599-a93f-f7d54f74828c",
+   "metadata": {},
+   "source": [
+    "### BMI\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "3b11df6e-c343-49b0-9d76-71f4b1c3eb3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='bmi', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Filling null values in 'bmi' with the mean value\n",
+    "df['bmi'] = df['bmi'].fillna(df['bmi'].mean())\n",
+    "\n",
+    "# Plotting distribution of 'bmi'\n",
+    "sns.displot(df['bmi'])\n",
+    "\n",
+    "# Plotting boxplot of 'bmi' with respect to 'stroke'\n",
+    "sns.boxplot(data=df, x='stroke', y='bmi')\n",
+    "\n",
+    "# BMI with respect to Stroke"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0813c40-8139-41c6-a856-2dc6fac896c3",
+   "metadata": {},
+   "source": [
+    "There is as such no prominent observation of how does BMI affects the chances of having a stroke."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "624c5cb8-c61e-454c-bae5-6dec5e7fa7e2",
+   "metadata": {},
+   "source": [
+    "### Smoking Status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "edfe90c3-413d-4fb8-ab89-0b480937961f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique Values\n",
+      " ['formerly smoked', 'never smoked', 'smokes', 'Unknown']\n",
+      "Categories (4, object): ['Unknown', 'formerly smoked', 'never smoked', 'smokes']\n",
+      "Value Counts\n",
+      " smoking_status\n",
+      "never smoked       1892\n",
+      "Unknown            1544\n",
+      "formerly smoked     885\n",
+      "smokes              789\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Count of Stroke by smoking_status')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('Unique Values\\n',df['smoking_status'].unique())\n",
+    "print('Value Counts\\n',df['smoking_status'].value_counts())\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.countplot(data=df,x='smoking_status',hue='stroke')\n",
+    "plt.xlabel('Count')\n",
+    "plt.ylabel('smoking_status')\n",
+    "plt.title('Count of Stroke by smoking_status')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "801afbe9-e69f-498a-b185-7075062c624f",
+   "metadata": {},
+   "source": [
+    "As per these plots, we can see there is not much difference in the chances of stroke irrespective of smoking status."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cd330b7-c9c0-486f-9835-39708bd558f6",
+   "metadata": {},
+   "source": [
+    "### Outliners"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9df40a23-d2a3-4b2b-9b2d-8db4ed20685b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outliers detected in 'bmi':\n",
+      "      gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
+      "21    Female  52.0          True          False          Yes  Self-employed   \n",
+      "66    Female  70.0         False          False          Yes        Private   \n",
+      "113   Female  45.0         False          False          Yes        Private   \n",
+      "254   Female  47.0         False          False          Yes        Private   \n",
+      "258   Female  74.0          True          False          Yes  Self-employed   \n",
+      "...      ...   ...           ...            ...          ...            ...   \n",
+      "4906  Female  53.0         False          False          Yes        Private   \n",
+      "4952    Male  51.0          True          False          Yes  Self-employed   \n",
+      "5009  Female  50.0         False          False          Yes  Self-employed   \n",
+      "5057  Female  49.0         False          False          Yes       Govt_job   \n",
+      "5103  Female  18.0         False          False           No        Private   \n",
+      "\n",
+      "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
+      "21            Urban             233.29  48.9     never smoked    True  \n",
+      "66            Urban             221.58  47.5     never smoked    True  \n",
+      "113           Rural             224.10  56.6     never smoked    True  \n",
+      "254           Urban             210.95  50.1          Unknown   False  \n",
+      "258           Urban             205.84  54.6     never smoked   False  \n",
+      "...             ...                ...   ...              ...     ...  \n",
+      "4906          Urban              70.51  54.1     never smoked   False  \n",
+      "4952          Rural             211.83  56.6     never smoked   False  \n",
+      "5009          Rural             126.85  49.5  formerly smoked   False  \n",
+      "5057          Urban              69.92  47.6     never smoked   False  \n",
+      "5103          Urban              82.85  46.9          Unknown   False  \n",
+      "\n",
+      "[120 rows x 11 columns]\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    " Q1 = df[col].quantile(0.25)\n",
+    " Q3 = df[col].quantile(0.75)\n",
+    " IQR = Q3 - Q1\n",
+    " outliers = df[(df[col] < (Q1 - 1.5 * IQR)) | (df[col] > (Q3 + 1.5 * IQR))]\n",
+    " if len(outliers) > 0:\n",
+    "        print(f\"Outliers detected in '{col}':\")\n",
+    "        print(outliers)\n",
+    " else:\n",
+    "        print(f\"No outliers detected in '{col}'\")\n",
+    " print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fba28bbd-863e-48b4-86f5-82bb60cd6f77",
+   "metadata": {},
+   "source": [
+    "### Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3801b30-a3b8-43a1-beab-4de3bfa50711",
+   "metadata": {},
+   "source": [
+    "#### Label Encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "96473d7e-1baa-4456-a0d6-899b82b645a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index([], dtype='object')\n",
+      "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
+      "0    Male  67.0         False           True          Yes        Private   \n",
+      "1  Female  61.0         False          False          Yes  Self-employed   \n",
+      "2    Male  80.0         False           True          Yes        Private   \n",
+      "3  Female  49.0         False          False          Yes        Private   \n",
+      "4  Female  79.0          True          False          Yes  Self-employed   \n",
+      "5    Male  81.0         False          False          Yes        Private   \n",
+      "6    Male  74.0          True           True          Yes        Private   \n",
+      "7  Female  69.0         False          False           No        Private   \n",
+      "8  Female  59.0         False          False          Yes        Private   \n",
+      "9  Female  78.0         False          False          Yes        Private   \n",
+      "\n",
+      "  Residence_type  avg_glucose_level        bmi   smoking_status  stroke  \n",
+      "0          Urban             228.69  36.600000  formerly smoked    True  \n",
+      "1          Rural             202.21  29.879487     never smoked    True  \n",
+      "2          Rural             105.92  32.500000     never smoked    True  \n",
+      "3          Urban             171.23  34.400000           smokes    True  \n",
+      "4          Rural             174.12  24.000000     never smoked    True  \n",
+      "5          Urban             186.21  29.000000  formerly smoked    True  \n",
+      "6          Rural              70.09  27.400000     never smoked    True  \n",
+      "7          Urban              94.39  22.800000     never smoked    True  \n",
+      "8          Rural              76.15  30.556098          Unknown    True  \n",
+      "9          Urban              58.57  24.200000          Unknown    True  \n"
+     ]
+    }
+   ],
+   "source": [
+    "cols=df.select_dtypes(include=['object']).columns\n",
+    "print(cols)\n",
+    "# This code will fetech columns whose data type is object.\n",
+    "le=LabelEncoder()\n",
+    "# Initializing our Label Encoder object\n",
+    "df[cols]=df[cols].apply(le.fit_transform)\n",
+    "# Transfering categorical data into numeric\n",
+    "print(df.head(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73fe848b-84f5-4783-975a-e8357555d33e",
+   "metadata": {},
+   "source": [
+    "#### Correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ee15ea06-8124-40ae-9def-9ce11255a2d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Convert boolean columns to numerical values (0 for False, 1 for True)\n",
+    "boolean_df = df.select_dtypes(include=['bool']).astype(int)\n",
+    "\n",
+    "# Concatenate numerical and boolean columns\n",
+    "numerical_boolean_df = pd.concat([numerical_df, boolean_df], axis=1)\n",
+    "\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# Plot correlation matrix heatmap\n",
+    "sns.heatmap(numerical_boolean_df.corr(), annot=True, fmt='.2f', cmap='coolwarm')\n",
+    "plt.title('Correlation Matrix Heatmap with Boolean Values')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63f72b56-a1c0-4b78-a8d7-328c71ef525a",
+   "metadata": {},
+   "source": [
+    "## correlation matrices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "4f715585-4efe-46c2-8c3e-b3189d2f58e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select numerical columns for correlation matrix\n",
+    "numerical_df = df.select_dtypes(include=['int64', 'float64'])\n",
+    "\n",
+    "# Calculate correlation matrices using different methods\n",
+    "pearson_corr = numerical_df.corr(method='pearson')\n",
+    "spearman_corr = numerical_df.corr(method='spearman')\n",
+    "kendall_corr = numerical_df.corr(method='kendall')\n",
+    "\n",
+    "# Plot correlation matrices\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "# Pearson correlation\n",
+    "plt.subplot(1, 3, 1)\n",
+    "sns.heatmap(pearson_corr, annot=True, cmap='coolwarm', fmt=\".2f\", annot_kws={\"size\": 10})\n",
+    "plt.title('Pearson Correlation')\n",
+    "\n",
+    "# Spearman correlation\n",
+    "plt.subplot(1, 3, 2)\n",
+    "sns.heatmap(spearman_corr, annot=True, cmap='coolwarm', fmt=\".2f\", annot_kws={\"size\": 10})\n",
+    "plt.title('Spearman Correlation')\n",
+    "\n",
+    "# Kendall correlation\n",
+    "plt.subplot(1, 3, 3)\n",
+    "sns.heatmap(kendall_corr, annot=True, cmap='coolwarm', fmt=\".2f\", annot_kws={\"size\": 10})\n",
+    "plt.title('Kendall Correlation')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f9e9854-136a-4665-a315-ab1dceba673d",
+   "metadata": {},
+   "source": [
+    "#### This suggest that the relationships between variables in the dataset are consistent across different types of correlations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d33d45d3-fb54-45a8-a46c-6bbeb6399802",
+   "metadata": {},
+   "source": [
+    "The value in the first row and first column (1.0) represents the correlation between Pearson Correlation and itself, which is obviously 1.0. Similarly, the value in the fourth row and first column (0.24) represents the correlation between Pearson Correlation and age, and the value in the fourth row and second column (1.00) represents the correlation between age and itself, which is also 1.0,Suggesting that the values are the same in the table because they represent the correlation between the same two variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "7a00e0b7-7a12-46ae-ac1b-1e58db43e84f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skewness of Columns:\n",
+      "age                 -0.137059\n",
+      "avg_glucose_level    1.572284\n",
+      "bmi                  1.047423\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate skewness for each numerical column\n",
+    "skewness_values = df.select_dtypes(include=['int64', 'float64']).apply(lambda x: x.skew())\n",
+    "\n",
+    "# Display skewness values\n",
+    "print(\"Skewness of Columns:\")\n",
+    "print(skewness_values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f218313-9368-4c35-911b-7749dbde585c",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}