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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Heart Attack Prediction | 1. Dataset Exploration\n",
+ "\n",
+ "+ Load the dataset\n",
+ "\n",
+ "+ Explore and confirm features and label(s) of this dataset\n",
+ "\n",
+ "+ Explore size/shape of dataset\n",
+ "\n",
+ "+ Explore data - Data Dictionary\n",
+ "\n",
+ "+ Calculate the memory usage differences\n",
+ "\n",
+ "+ Explore the statistical facts like mean, median, x percentiles of the columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Load the dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "df = pd.read_csv(\"heart.csv\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Explore and confirm features and label(s) of this dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " chol | \n",
+ " fbs | \n",
+ " restecg | \n",
+ " thalachh | \n",
+ " exng | \n",
+ " oldpeak | \n",
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+ "
303 rows × 14 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp \\\n",
+ "0 63 1 3 145 233 1 0 150 0 2.3 0 \n",
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+ "2 41 0 1 130 204 0 0 172 0 1.4 2 \n",
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+ ".. ... ... .. ... ... ... ... ... ... ... ... \n",
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+ "\n",
+ " caa thall output \n",
+ "0 0 1 1 \n",
+ "1 0 2 1 \n",
+ "2 0 2 1 \n",
+ "3 0 2 1 \n",
+ "4 0 2 1 \n",
+ ".. ... ... ... \n",
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+ "302 1 2 0 \n",
+ "\n",
+ "[303 rows x 14 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df # Displaying column names/features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp \\\n",
+ "0 63 1 3 145 233 1 0 150 0 2.3 0 \n",
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+ "2 41 0 1 130 204 0 0 172 0 1.4 2 \n",
+ "3 56 1 1 120 236 0 1 178 0 0.8 2 \n",
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+ " caa thall output \n",
+ "0 0 1 1 \n",
+ "1 0 2 1 \n",
+ "2 0 2 1 \n",
+ "3 0 2 1 \n",
+ "4 0 2 1 "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head() # Displaying first 5 rows"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " | \n",
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+ " sex | \n",
+ " cp | \n",
+ " trtbps | \n",
+ " chol | \n",
+ " fbs | \n",
+ " restecg | \n",
+ " thalachh | \n",
+ " exng | \n",
+ " oldpeak | \n",
+ " slp | \n",
+ " caa | \n",
+ " thall | \n",
+ " output | \n",
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+ " \n",
+ " \n",
+ " \n",
+ " 298 | \n",
+ " 57 | \n",
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+ " 1 | \n",
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+ " 0 | \n",
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+ " 3 | \n",
+ " 0 | \n",
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+ " \n",
+ " 301 | \n",
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+ " 1 | \n",
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+ " 131 | \n",
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+ " 1 | \n",
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+ " 1 | \n",
+ " 1.2 | \n",
+ " 1 | \n",
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+ " 3 | \n",
+ " 0 | \n",
+ "
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+ " \n",
+ " 302 | \n",
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+ " 174 | \n",
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+ " 0.0 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 0 | \n",
+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp \\\n",
+ "298 57 0 0 140 241 0 1 123 1 0.2 1 \n",
+ "299 45 1 3 110 264 0 1 132 0 1.2 1 \n",
+ "300 68 1 0 144 193 1 1 141 0 3.4 1 \n",
+ "301 57 1 0 130 131 0 1 115 1 1.2 1 \n",
+ "302 57 0 1 130 236 0 0 174 0 0.0 1 \n",
+ "\n",
+ " caa thall output \n",
+ "298 0 3 0 \n",
+ "299 0 3 0 \n",
+ "300 2 3 0 \n",
+ "301 1 3 0 \n",
+ "302 1 2 0 "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.tail() # Displaying last 5 rows"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Explore size/shape of dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The shape of the dataset is : (303, 14)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"The shape of the dataset is : \", df.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Explore data - Data Dictionary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 303 entries, 0 to 302\n",
+ "Data columns (total 14 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 age 303 non-null int64 \n",
+ " 1 sex 303 non-null int64 \n",
+ " 2 cp 303 non-null int64 \n",
+ " 3 trtbps 303 non-null int64 \n",
+ " 4 chol 303 non-null int64 \n",
+ " 5 fbs 303 non-null int64 \n",
+ " 6 restecg 303 non-null int64 \n",
+ " 7 thalachh 303 non-null int64 \n",
+ " 8 exng 303 non-null int64 \n",
+ " 9 oldpeak 303 non-null float64\n",
+ " 10 slp 303 non-null int64 \n",
+ " 11 caa 303 non-null int64 \n",
+ " 12 thall 303 non-null int64 \n",
+ " 13 output 303 non-null int64 \n",
+ "dtypes: float64(1), int64(13)\n",
+ "memory usage: 33.3 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.info() # Displays Meta Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Observations\n",
+ "**Data Types:** The dataset consists of integers (int64) and one float (float64). No categorical (object) data types are present.\n",
+ "\n",
+ "**No Missing Values:** All columns have non-null counts equal to the total number of entries (303), indicating there are no missing values in the dataset.\n",
+ "\n",
+ "**Columns to be Encoded:** While there are no object datatypes that need encoding, some columns, despite being integers, represent categorical data and need to be handled appropriately. These columns include:\n",
+ "- sex (binary: 0, 1)\n",
+ "- cp (chest pain type: 0-3)\n",
+ "- fbs (fasting blood sugar: binary 0, 1)\n",
+ "- restecg (resting electrocardiographic results: 0-2)\n",
+ "- exng (exercise-induced angina: binary 0, 1)\n",
+ "- slp (the slope of the peak exercise ST segment: 0-2)\n",
+ "- caa (number of major vessels colored by fluoroscopy: 0-4)\n",
+ "- thall (thalassemia: 0-3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " unique count | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " age | \n",
+ " 41 | \n",
+ "
\n",
+ " \n",
+ " sex | \n",
+ " 2 | \n",
+ "
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+ " \n",
+ " cp | \n",
+ " 4 | \n",
+ "
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+ " \n",
+ " trtbps | \n",
+ " 49 | \n",
+ "
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+ " \n",
+ " chol | \n",
+ " 152 | \n",
+ "
\n",
+ " \n",
+ " fbs | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " restecg | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " thalachh | \n",
+ " 91 | \n",
+ "
\n",
+ " \n",
+ " exng | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " oldpeak | \n",
+ " 40 | \n",
+ "
\n",
+ " \n",
+ " slp | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " caa | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " thall | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " output | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " unique count\n",
+ "age 41\n",
+ "sex 2\n",
+ "cp 4\n",
+ "trtbps 49\n",
+ "chol 152\n",
+ "fbs 2\n",
+ "restecg 3\n",
+ "thalachh 91\n",
+ "exng 2\n",
+ "oldpeak 40\n",
+ "slp 3\n",
+ "caa 5\n",
+ "thall 4\n",
+ "output 2"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Unique value counts\n",
+ "dict = {}\n",
+ "for i in list(df.columns):\n",
+ " dict[i] = df[i].value_counts().shape[0]\n",
+ "\n",
+ "pd.DataFrame(dict,index=[\"unique count\"]).transpose()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The categorial cols are : ['sex', 'exng', 'caa', 'cp', 'fbs', 'restecg', 'slp', 'thall']\n",
+ "The continuous cols are : ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']\n",
+ "The target variable is : ['output']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Classification of columns\n",
+ "cat_cols = ['sex','exng','caa','cp','fbs','restecg','slp','thall']\n",
+ "con_cols = [\"age\",\"trtbps\",\"chol\",\"thalachh\",\"oldpeak\"]\n",
+ "target_col = [\"output\"]\n",
+ "print(\"The categorial cols are : \", cat_cols)\n",
+ "print(\"The continuous cols are : \", con_cols)\n",
+ "print(\"The target variable is : \", target_col)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Summary of the statistics for each column in con_cols, with each column of the original DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " count | \n",
+ " mean | \n",
+ " std | \n",
+ " min | \n",
+ " 25% | \n",
+ " 50% | \n",
+ " 75% | \n",
+ " max | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " age | \n",
+ " 303.0 | \n",
+ " 54.366337 | \n",
+ " 9.082101 | \n",
+ " 29.0 | \n",
+ " 47.5 | \n",
+ " 55.0 | \n",
+ " 61.0 | \n",
+ " 77.0 | \n",
+ "
\n",
+ " \n",
+ " trtbps | \n",
+ " 303.0 | \n",
+ " 131.623762 | \n",
+ " 17.538143 | \n",
+ " 94.0 | \n",
+ " 120.0 | \n",
+ " 130.0 | \n",
+ " 140.0 | \n",
+ " 200.0 | \n",
+ "
\n",
+ " \n",
+ " chol | \n",
+ " 303.0 | \n",
+ " 246.264026 | \n",
+ " 51.830751 | \n",
+ " 126.0 | \n",
+ " 211.0 | \n",
+ " 240.0 | \n",
+ " 274.5 | \n",
+ " 564.0 | \n",
+ "
\n",
+ " \n",
+ " thalachh | \n",
+ " 303.0 | \n",
+ " 149.646865 | \n",
+ " 22.905161 | \n",
+ " 71.0 | \n",
+ " 133.5 | \n",
+ " 153.0 | \n",
+ " 166.0 | \n",
+ " 202.0 | \n",
+ "
\n",
+ " \n",
+ " oldpeak | \n",
+ " 303.0 | \n",
+ " 1.039604 | \n",
+ " 1.161075 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.8 | \n",
+ " 1.6 | \n",
+ " 6.2 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " count mean std min 25% 50% 75% max\n",
+ "age 303.0 54.366337 9.082101 29.0 47.5 55.0 61.0 77.0\n",
+ "trtbps 303.0 131.623762 17.538143 94.0 120.0 130.0 140.0 200.0\n",
+ "chol 303.0 246.264026 51.830751 126.0 211.0 240.0 274.5 564.0\n",
+ "thalachh 303.0 149.646865 22.905161 71.0 133.5 153.0 166.0 202.0\n",
+ "oldpeak 303.0 1.039604 1.161075 0.0 0.0 0.8 1.6 6.2"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[con_cols].describe().transpose()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Calculate the memory usage differences"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pandas Memory Usage: 34068 Bytes\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Pandas Memory Usage:\", df.memory_usage().sum(), 'Bytes')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pandas Deep Memory Usage: 34068 Bytes\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Pandas Deep Memory Usage:\", df.memory_usage(deep=True).sum(), 'Bytes')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Polars Memory Usage: 33.140625 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "import polars as pl\n",
+ "\n",
+ "df1 = pl.read_csv('heart.csv')\n",
+ "\n",
+ "print(\"Polars Memory Usage:\", df1.estimated_size('kb') , 'KB')\n",
+ "\n",
+ "del df1 # deleting polars implementation since pandas implementation is not memory-intensive"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Memory usage for dataset\n",
+ "\n",
+ "### Pandas\n",
+ "\n",
+ "memory usage: 34068 Bytes or 33.27 KB\n",
+ "\n",
+ "lazy read memory usage: 34068 Bytes or 33.27 KB\n",
+ "### Polars\n",
+ "\n",
+ "lazy read memory usage: 33.140625 KB"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 6. Explore the statistical facts like mean, median, x percentiles of the columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " age | \n",
+ " sex | \n",
+ " cp | \n",
+ " trtbps | \n",
+ " chol | \n",
+ " fbs | \n",
+ " restecg | \n",
+ " thalachh | \n",
+ " exng | \n",
+ " oldpeak | \n",
+ " slp | \n",
+ " caa | \n",
+ " thall | \n",
+ " output | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " count | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ " 303.000000 | \n",
+ "
\n",
+ " \n",
+ " mean | \n",
+ " 54.366337 | \n",
+ " 0.683168 | \n",
+ " 0.966997 | \n",
+ " 131.623762 | \n",
+ " 246.264026 | \n",
+ " 0.148515 | \n",
+ " 0.528053 | \n",
+ " 149.646865 | \n",
+ " 0.326733 | \n",
+ " 1.039604 | \n",
+ " 1.399340 | \n",
+ " 0.729373 | \n",
+ " 2.313531 | \n",
+ " 0.544554 | \n",
+ "
\n",
+ " \n",
+ " std | \n",
+ " 9.082101 | \n",
+ " 0.466011 | \n",
+ " 1.032052 | \n",
+ " 17.538143 | \n",
+ " 51.830751 | \n",
+ " 0.356198 | \n",
+ " 0.525860 | \n",
+ " 22.905161 | \n",
+ " 0.469794 | \n",
+ " 1.161075 | \n",
+ " 0.616226 | \n",
+ " 1.022606 | \n",
+ " 0.612277 | \n",
+ " 0.498835 | \n",
+ "
\n",
+ " \n",
+ " min | \n",
+ " 29.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 94.000000 | \n",
+ " 126.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 71.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 10% | \n",
+ " 42.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 110.000000 | \n",
+ " 188.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 116.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 2.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 25% | \n",
+ " 47.500000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 120.000000 | \n",
+ " 211.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 133.500000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 2.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 55.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 130.000000 | \n",
+ " 240.000000 | \n",
+ " 0.000000 | \n",
+ " 1.000000 | \n",
+ " 153.000000 | \n",
+ " 0.000000 | \n",
+ " 0.800000 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 2.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 61.000000 | \n",
+ " 1.000000 | \n",
+ " 2.000000 | \n",
+ " 140.000000 | \n",
+ " 274.500000 | \n",
+ " 0.000000 | \n",
+ " 1.000000 | \n",
+ " 166.000000 | \n",
+ " 1.000000 | \n",
+ " 1.600000 | \n",
+ " 2.000000 | \n",
+ " 1.000000 | \n",
+ " 3.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " 99% | \n",
+ " 71.000000 | \n",
+ " 1.000000 | \n",
+ " 3.000000 | \n",
+ " 180.000000 | \n",
+ " 406.740000 | \n",
+ " 1.000000 | \n",
+ " 1.980000 | \n",
+ " 191.960000 | \n",
+ " 1.000000 | \n",
+ " 4.200000 | \n",
+ " 2.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 77.000000 | \n",
+ " 1.000000 | \n",
+ " 3.000000 | \n",
+ " 200.000000 | \n",
+ " 564.000000 | \n",
+ " 1.000000 | \n",
+ " 2.000000 | \n",
+ " 202.000000 | \n",
+ " 1.000000 | \n",
+ " 6.200000 | \n",
+ " 2.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " age sex cp trtbps chol fbs \\\n",
+ "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n",
+ "mean 54.366337 0.683168 0.966997 131.623762 246.264026 0.148515 \n",
+ "std 9.082101 0.466011 1.032052 17.538143 51.830751 0.356198 \n",
+ "min 29.000000 0.000000 0.000000 94.000000 126.000000 0.000000 \n",
+ "10% 42.000000 0.000000 0.000000 110.000000 188.000000 0.000000 \n",
+ "25% 47.500000 0.000000 0.000000 120.000000 211.000000 0.000000 \n",
+ "50% 55.000000 1.000000 1.000000 130.000000 240.000000 0.000000 \n",
+ "75% 61.000000 1.000000 2.000000 140.000000 274.500000 0.000000 \n",
+ "99% 71.000000 1.000000 3.000000 180.000000 406.740000 1.000000 \n",
+ "max 77.000000 1.000000 3.000000 200.000000 564.000000 1.000000 \n",
+ "\n",
+ " restecg thalachh exng oldpeak slp caa \\\n",
+ "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n",
+ "mean 0.528053 149.646865 0.326733 1.039604 1.399340 0.729373 \n",
+ "std 0.525860 22.905161 0.469794 1.161075 0.616226 1.022606 \n",
+ "min 0.000000 71.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "10% 0.000000 116.000000 0.000000 0.000000 1.000000 0.000000 \n",
+ "25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n",
+ "50% 1.000000 153.000000 0.000000 0.800000 1.000000 0.000000 \n",
+ "75% 1.000000 166.000000 1.000000 1.600000 2.000000 1.000000 \n",
+ "99% 1.980000 191.960000 1.000000 4.200000 2.000000 4.000000 \n",
+ "max 2.000000 202.000000 1.000000 6.200000 2.000000 4.000000 \n",
+ "\n",
+ " thall output \n",
+ "count 303.000000 303.000000 \n",
+ "mean 2.313531 0.544554 \n",
+ "std 0.612277 0.498835 \n",
+ "min 0.000000 0.000000 \n",
+ "10% 2.000000 0.000000 \n",
+ "25% 2.000000 0.000000 \n",
+ "50% 2.000000 1.000000 \n",
+ "75% 3.000000 1.000000 \n",
+ "99% 3.000000 1.000000 \n",
+ "max 3.000000 1.000000 "
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Describes numerical interpretations of the dataset in terms of mean, max, min, quartiles, etc.\n",
+ "df.describe(percentiles=[0.1,0.25,0.5,0.75,0.99]) \n",
+ "\n",
+ "# Percentiles considered: 10, 25, 50, 75, 99"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 7. Finding NULL/NaN count"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "age 0\n",
+ "sex 0\n",
+ "cp 0\n",
+ "trtbps 0\n",
+ "chol 0\n",
+ "fbs 0\n",
+ "restecg 0\n",
+ "thalachh 0\n",
+ "exng 0\n",
+ "oldpeak 0\n",
+ "slp 0\n",
+ "caa 0\n",
+ "thall 0\n",
+ "output 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.isnull().sum() # Counting null values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### There are no null values which means the data is consistent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Heart Attack Prediction | 2. EDA\n",
+ "\n",
+ "+ Univariate Analysis \n",
+ " - Count plot of categorical features \n",
+ " - Boxen plot of continuous features\n",
+ " - Count plot of target\n",
+ "\n",
+ "+ Bivariate Analysis\n",
+ " - Correlation matrix of continuous features\n",
+ " - Scatterplot heatmap of dataframe\n",
+ " - Distribution of continuous features according to target variable\n",
+ " - Some other relations that seemed intuitive\n",
+ " - Pairplot according to target variable - one plot to rule them all\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Univariate analysis "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- ## Count plot of categorical features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(18,15))\n",
+ "gs = fig.add_gridspec(3,3)\n",
+ "gs.update(wspace=0.5, hspace=0.25)\n",
+ "ax0 = fig.add_subplot(gs[0,0])\n",
+ "ax1 = fig.add_subplot(gs[0,1])\n",
+ "ax2 = fig.add_subplot(gs[0,2])\n",
+ "ax3 = fig.add_subplot(gs[1,0])\n",
+ "ax4 = fig.add_subplot(gs[1,1])\n",
+ "ax5 = fig.add_subplot(gs[1,2])\n",
+ "ax6 = fig.add_subplot(gs[2,0])\n",
+ "ax7 = fig.add_subplot(gs[2,1])\n",
+ "ax8 = fig.add_subplot(gs[2,2])\n",
+ "\n",
+ "background_color = \"#ffe6e6\"\n",
+ "color_palette = [\"#800000\",\"#8000ff\",\"#6aac90\",\"#5833ff\",\"#da8829\"]\n",
+ "fig.patch.set_facecolor(background_color) \n",
+ "ax0.set_facecolor(background_color) \n",
+ "ax1.set_facecolor(background_color) \n",
+ "ax2.set_facecolor(background_color) \n",
+ "ax3.set_facecolor(background_color) \n",
+ "ax4.set_facecolor(background_color) \n",
+ "ax5.set_facecolor(background_color) \n",
+ "ax6.set_facecolor(background_color) \n",
+ "ax7.set_facecolor(background_color) \n",
+ "ax8.set_facecolor(background_color) \n",
+ "\n",
+ "# Title of the plot\n",
+ "ax0.spines[\"bottom\"].set_visible(False)\n",
+ "ax0.spines[\"left\"].set_visible(False)\n",
+ "ax0.spines[\"top\"].set_visible(False)\n",
+ "ax0.spines[\"right\"].set_visible(False)\n",
+ "ax0.tick_params(left=False, bottom=False)\n",
+ "ax0.set_xticklabels([])\n",
+ "ax0.set_yticklabels([])\n",
+ "ax0.text(0.5,0.5,\n",
+ " 'Count plot for various\\n categorical features\\n_________________',\n",
+ " horizontalalignment='center',\n",
+ " verticalalignment='center',\n",
+ " fontsize=18, fontweight='bold',\n",
+ " fontfamily='serif',\n",
+ " color=\"#000000\")\n",
+ "\n",
+ "# Sex count\n",
+ "ax1.text(0.3, 220, 'Sex', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.countplot(ax=ax1,data=df,x='sex',palette=color_palette)\n",
+ "ax1.set_xlabel(\"\")\n",
+ "ax1.set_ylabel(\"\")\n",
+ "\n",
+ "# Exng count\n",
+ "ax2.text(0.3, 220, 'Exng', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax2.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.countplot(ax=ax2,data=df,x='exng',palette=color_palette)\n",
+ "ax2.set_xlabel(\"\")\n",
+ "ax2.set_ylabel(\"\")\n",
+ "\n",
+ "# Caa count\n",
+ "ax3.text(1.5, 200, 'Caa', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.countplot(ax=ax3,data=df,x='caa',palette=color_palette)\n",
+ "ax3.set_xlabel(\"\")\n",
+ "ax3.set_ylabel(\"\")\n",
+ "\n",
+ "# Cp count\n",
+ "ax4.text(1.5, 162, 'Cp', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax4.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.countplot(ax=ax4,data=df,x='cp',palette=color_palette)\n",
+ "ax4.set_xlabel(\"\")\n",
+ "ax4.set_ylabel(\"\")\n",
+ "\n",
+ "# Fbs count\n",
+ "ax5.text(0.5, 290, 'Fbs', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.countplot(ax=ax5,data=df,x='fbs',palette=color_palette)\n",
+ "ax5.set_xlabel(\"\")\n",
+ "ax5.set_ylabel(\"\")\n",
+ "\n",
+ "# Restecg count\n",
+ "ax6.text(0.75, 165, 'Restecg', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax6.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.countplot(ax=ax6,data=df,x='restecg',palette=color_palette)\n",
+ "ax6.set_xlabel(\"\")\n",
+ "ax6.set_ylabel(\"\")\n",
+ "\n",
+ "# Slp count\n",
+ "ax7.text(0.85, 155, 'Slp', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax7.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.countplot(ax=ax7,data=df,x='slp',palette=color_palette)\n",
+ "ax7.set_xlabel(\"\")\n",
+ "ax7.set_ylabel(\"\")\n",
+ "\n",
+ "# Thall count\n",
+ "ax8.text(1.2, 180, 'Thall', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax8.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.countplot(ax=ax8,data=df,x='thall',palette=color_palette)\n",
+ "ax8.set_xlabel(\"\")\n",
+ "ax8.set_ylabel(\"\")\n",
+ "\n",
+ "for s in [\"top\",\"right\",\"left\"]:\n",
+ " ax1.spines[s].set_visible(False)\n",
+ " ax2.spines[s].set_visible(False)\n",
+ " ax3.spines[s].set_visible(False)\n",
+ " ax4.spines[s].set_visible(False)\n",
+ " ax5.spines[s].set_visible(False)\n",
+ " ax6.spines[s].set_visible(False)\n",
+ " ax7.spines[s].set_visible(False)\n",
+ " ax8.spines[s].set_visible(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- ## Boxen plot of continuous features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(18,16))\n",
+ "gs = fig.add_gridspec(2,3)\n",
+ "gs.update(wspace=0.3, hspace=0.15)\n",
+ "ax0 = fig.add_subplot(gs[0,0])\n",
+ "ax1 = fig.add_subplot(gs[0,1])\n",
+ "ax2 = fig.add_subplot(gs[0,2])\n",
+ "ax3 = fig.add_subplot(gs[1,0])\n",
+ "ax4 = fig.add_subplot(gs[1,1])\n",
+ "ax5 = fig.add_subplot(gs[1,2])\n",
+ "\n",
+ "background_color = \"#ffe6e6\"\n",
+ "color_palette = [\"#800000\",\"#8000ff\",\"#6aac90\",\"#5833ff\",\"#da8829\"]\n",
+ "fig.patch.set_facecolor(background_color) \n",
+ "ax0.set_facecolor(background_color) \n",
+ "ax1.set_facecolor(background_color) \n",
+ "ax2.set_facecolor(background_color) \n",
+ "ax3.set_facecolor(background_color) \n",
+ "ax4.set_facecolor(background_color) \n",
+ "ax5.set_facecolor(background_color) \n",
+ "\n",
+ "# Title of the plot\n",
+ "ax0.spines[\"bottom\"].set_visible(False)\n",
+ "ax0.spines[\"left\"].set_visible(False)\n",
+ "ax0.spines[\"top\"].set_visible(False)\n",
+ "ax0.spines[\"right\"].set_visible(False)\n",
+ "ax0.tick_params(left=False, bottom=False)\n",
+ "ax0.set_xticklabels([])\n",
+ "ax0.set_yticklabels([])\n",
+ "ax0.text(0.5,0.5,\n",
+ " 'Boxen plot for various\\n continuous features\\n_________________',\n",
+ " horizontalalignment='center',\n",
+ " verticalalignment='center',\n",
+ " fontsize=18, fontweight='bold',\n",
+ " fontfamily='serif',\n",
+ " color=\"#000000\")\n",
+ "\n",
+ "# Age \n",
+ "ax1.text(-0.05, 81, 'Age', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.boxenplot(ax=ax1,y=df['age'],palette=[\"#800000\"],width=0.6)\n",
+ "ax1.set_xlabel(\"\")\n",
+ "ax1.set_ylabel(\"\")\n",
+ "\n",
+ "# Trtbps \n",
+ "ax2.text(-0.05, 208, 'Trtbps', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax2.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.boxenplot(ax=ax2,y=df['trtbps'],palette=[\"#8000ff\"],width=0.6)\n",
+ "ax2.set_xlabel(\"\")\n",
+ "ax2.set_ylabel(\"\")\n",
+ "\n",
+ "# Chol \n",
+ "ax3.text(-0.05, 600, 'Chol', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.boxenplot(ax=ax3,y=df['chol'],palette=[\"#6aac90\"],width=0.6)\n",
+ "ax3.set_xlabel(\"\")\n",
+ "ax3.set_ylabel(\"\")\n",
+ "\n",
+ "# Thalachh \n",
+ "ax4.text(-0.09, 210, 'Thalachh', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax4.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.boxenplot(ax=ax4,y=df['thalachh'],palette=[\"#5833ff\"],width=0.6)\n",
+ "ax4.set_xlabel(\"\")\n",
+ "ax4.set_ylabel(\"\")\n",
+ "\n",
+ "# oldpeak \n",
+ "ax5.text(-0.1, 6.6, 'Oldpeak', fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.boxenplot(ax=ax5,y=df['oldpeak'],palette=[\"#da8829\"],width=0.6)\n",
+ "ax5.set_xlabel(\"\")\n",
+ "ax5.set_ylabel(\"\")\n",
+ "\n",
+ "for s in [\"top\",\"right\",\"left\"]:\n",
+ " ax1.spines[s].set_visible(False)\n",
+ " ax2.spines[s].set_visible(False)\n",
+ " ax3.spines[s].set_visible(False)\n",
+ " ax4.spines[s].set_visible(False)\n",
+ " ax5.spines[s].set_visible(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- ## Count plot of target"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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hMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAwC3vw0Uez2U47pXPPnqnp3Dk333HHFMf+7tBDU9O5c8648MKK9i+GD88Oe++d9ksumY7du2e3Aw/M6Lq6H3vqAAD8yITDAAAwC6sbMybL9+iRc0444VvH3XTHHXn0ySfTeb75mvTtsM8+efHVV3PPtdfmtssuy4NDhmTPgw/+saYMAMB00mJGTwAAAPjxbLzuutl43XW/dcwHH36Yffv3z11XX51Nf/3rir6XX389d953Xx6/446suPzySZKBxx2XTXbcMQOOOKJqmAwAwMzBymEAACiwxsbG/PoPf8jBe+2VHt26Nel/5Ikn0rFDh3IwnCR9+/RJs2bNMuTpp6vWrK+vz8hRoyq/Ro7M2LFjq44fXVeXiRMnNmlvaGjIyFGjpngN9dRTT71Zud64xur1JpbqM6FxXPVrNdalodS0XmNJPfXU+ynUm573l6klHAYAgAI76Zxz0qJ58/xht92q9n/06aeZZ665KtpatGiROTt2zEeffFL1nBMHDkyHbt0qvn6xyy7Zt3//quNX2XTT3DtoUJP2V4YOzYK9ek3xGuqpp556s3K9097rXfWch748N7d/cWTVvos+3DJvjXu4SftnE95QTz31fgL1puf9ZWrVlIYNK/1PFQAAgJlCTefOuenii7PFxhsnSZ587rls+utf56m77ipvD7Hoyitn/z32yP577JEkOeGss3LZ9dfn1ck+rMyz7LI5+qCDstfOOze5Tn19ferHj69sLJXSsmXLtGnTpsn40XV1aV1bmxYtKne9a2hoSN2YMWnfrl3VazQ2NqqnnnrqzbL1+q8+PK2bNa03sVSfUqmUls1aN71WY11a1NSmeU1lvcZSQ8aXxqinnnozoN6Rg2v/e850vL9MLeEwAAAUxOTh8BkXXpgDjzoqzZr99w8KGxoa0qxZsyzUuXPefuyxXHLNNfnjMcdk+Msvl8dMnDgxrbt0yd8vuCBbfl0LgGnr6N71M3oKwDQwaTj8U+SBdAAAUFC/3nrr9O3Tp6Jtw+23z6+33jq7brNNkmS1FVfMlyNG5Mnnnkuv5ZZLkvxn0KA0NjZmlZ49p/ucAQCYdoTDAAAwCxtdV5ehb71VPn7rvffyzAsvZM6OHbPwggtmrjnnrBjfskWLzDfPPOm2xBJJkqW6ds1G66yTPQ46KOeddFImTJiQffr3z7abb17eigIAgJmTcBgAAGZhTzz7bNb55S/LxwcedVSSZOd+/XLpGWdMVY2rzj47+xx+eNbr1y/NmjXL1ptskrOOO+5HmC0AANOTPYcBAIBZ2gFLLz2jpwD8j05/6aUZPYXpzp7DMGv4qe853Oy7hwAAAAAAMKsRDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAwC3vw0Uez2U47pXPPnqnp3Dk333FHuW/ChAk59Ljjsuy662a2xRdP5549s9Mf/pBhH31UUeOL4cOzw957p/2SS6Zj9+7Z7cADM7qubnq/FAAApjHhMAAAzMLqxozJ8j165JwTTmjSN2bs2Dz1/PP58/7756m77sqNF12UV994I7/YZZeKcTvss09efPXV3HPttbntssvy4JAh2fPgg6fTKwAA4MfSYkZPAAAA+PFsvO662Xjddav2dWjfPvdcd11F29nHH5+VN9kk777/fhZecMG8/PrrufO++/L4HXdkxeWXT5IMPO64bLLjjhlwxBHpPN98P/prAADgx2HlMAAAUDZi5MjU1NSkY4cOSZJHnngiHTt0KAfDSdK3T580a9YsQ55+umqN+vr6jBw1qvJr5MiMHTu26vjRdXWZOHFik/aGhoaMHDVqitf4vvUaS6XUl0pVz5lYKmXiFPrGl0pprNKnnnrqTb960/p+MDPUG9dYvd7EUn0mNI6rfq3GujSUqt3/1FNPvZ9Cvel5f5lawmEAACBJMm7cuBx6/PHZbost0r5duyTJR59+mnnmmqtiXIsWLTJnx4756JNPqtY5ceDAdOjWreLrF7vskn379686fpVNN829gwY1aX9l6NAs2KvXFK/xfesNb2zMxSNGVD3n8XHjct8UPlxdO2pU3q3yYU099dSbfvWm9f1gZqh32nu9q57z0Jfn5vYvjqzad9GHW+atcQ83af9swhvqqafeT6De9Ly/TK2a0rBh1X9dBwAAzFJqOnfOTRdfnC023rhJ34QJE7L17rvn/Q8/zP3/+Ec5HD7hrLNy2fXX59XJPqzMs+yyOfqgg7LXzjs3qVVfX5/68eMrG0ultGzZMm3atGkyfnRdXVrX1qZFi8pd7xoaGlI3Zkx5LpNfo7GxcarqHbD00km+Wsk4IUltTU2Tc75ZxdiiSt/4UiktkjSbrE899dSbfvUGPP/8NLkffGNa3V9+zHr9Vx+e1s2a1ptYqk+pVErLZq2bXquxLi1qatO8prJeY6kh40tj1FNPvRlQ78jBtf89ZzreX6aWPYcBAKDgJkyYkH6//W3e+eCD/Of66ys+fMzXqVM++fzzivETJ07MF19+mfnmmadqvdra2tTW1lbtq2b22War2t68efOqH4S+ucb3rdespiZTOqtaSPWNVlPoU0899aZfvWl9P5gZ6lULqpKkRU1tMoW3sLbZlO5/zdO6Rj311JvR9abn/WVq2VYCAAAK7Jtg+PW33sq/r7suc805Z0X/aiuumC9HjMiTzz1XbvvPoEFpbGzMKj17Tu/pAgAwDVk5DAAAs7DRdXUZ+tZb5eO33nsvz7zwQubs2DHzzztvfrnHHnnq+edz2+WXp6GhobyP8JwdO6ZVq1ZZqmvXbLTOOtnjoINy3kknZcKECdmnf/9su/nm6TzffDPqZQEAMA3YcxgAAGZh9w8enHV++csm7Tv365ej/vjHdFlllarn3XfDDVm791cPU/li+PDsc/jhufWee9KsWbNsvckmOeu446b4548/Nd/sOQzMvE5/6aUZPYXp7uje9TN6CsA0MOmewz9FVg4DAMAsbO3evVMaNmyK/d/W940555gjV//1r9NyWgAA/ATYcxgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAADMwh589NFsttNO6dyzZ2o6d87Nd9xR0V8qlXLEySdn/hVWSJvFFkvffv3y+ptvVoz5Yvjw7LD33mm/5JLp2L17djvwwIyuq5ueLwMAgB+BcBgAAGZhdWPGZPkePXLOCSdU7T/5nHNy1iWX5Ly//CVDbrsts7Vtmw233z7jxo0rj9lhn33y4quv5p5rr81tl12WB4cMyZ4HHzy9XgIAAD+SFjN6AgAAwI9n43XXzcbrrlu1r1Qq5YyLLkr//fbL5httlCS5/KyzMu/yy+fmO+/MtltskZdffz133ndfHr/jjqy4/PJJkoHHHZdNdtwxA444Ip3nm2+6vRYAAKYtK4cBAKCg3nr33Xz0ySfp26dPua1D+/ZZpWfPPPLkk0mSR554Ih07dCgHw0nSt0+fNGvWLEOefrpq3fr6+owcNarya+TIjB07tur40XV1mThxYpP2hoaGjBw1aorX+L71Gkul1JdKVc+ZWCpl4hT6xpdKaazSp5566k2/etP6fjAz1BvXWL3exFJ9JjSOq9pX31iXhlK1+5966qn3U6g3Pe8vU0s4DAAABfXRJ58kSebt1Kmifd5Oncp9H336aeaZa66K/hYtWmTOjh3LYyZ34sCB6dCtW8XXL3bZJfv27191/Cqbbpp7Bw1q0v7K0KFZsFevKV7j+9Yb3tiYi0eMqHrO4+PG5b4pfLi6dtSovFvlw5p66qk3/epN6/vBzFDvtPd6Vz3noS/Pze1fHFm176IPt8xb4x5u0v7ZhDfUU0+9n0C96Xl/mVo1pWHDqv+6DgAAmKXUdO6cmy6+OFtsvHGSZPDjj2f1zTfPsKefzvzzzlse1++3v01NkuvOPz8nnHVWLrv++rw62YeVeZZdNkcfdFD22nnnJtepr69P/fjxlY2lUlq2bJk2bdo0GT+6ri6ta2vTokXlrncNDQ2pGzMm7du1q3qNxsbGqap3wNJLJ/lqJeOEJLU1NU3O+WYVY4sqfeNLpbRI0myyPvXUU2/61Rvw/PPT5H7wjWl1f/kx6/VffXhaN2tab2KpPqVSKS2btW56rca6tKipTfOaynqNpYaML41RTz31ZkC9IwfX/vec6Xh/mVr2HAYAgIKab555kiQff/ppRTj88aefZoUePb4a06lTPvn884rzJk6cmC++/LJ8/uRqa2tTW1tbta+a2WebrWp78+bNq34Q+uYa37des5qaTOmsaiHVN1pNoU899dSbfvWm9f1gZqhXLahKkhY1tckU3sLaZlO6/zVP6xr11FNvRtebnveXqWVbCQAAKKguCy+c+eaZp+JPGEeOGpUhTz+d1b7+88XVVlwxX44YkSefe6485j+DBqWxsTGr9Ow53ecMAMC0Y+UwAADMwkbX1WXoW2+Vj996770888ILmbNjxyy84ILZf/fdc9yZZ6Zrly7psvDC+fPJJ6fzvPNmi402SpIs1bVrNlpnnexx0EE576STMmHChOzTv3+23XzzdJ5vvhn1sgAAmAaEwwAAMAt74tlns84vf1k+PvCoo5IkO/frl0vPOCOH7L136saMyZ6HHJIvR47MGiutlDuvuiqtW/93b72rzj47+xx+eNbr1y/NmjXL1ptskrOOO256vxQAAKYxD6QDAABmad88kA6YeZ3+0kszegrT3dG962f0FIBpYNIH0v0U2XMYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDwE/Kjbffnp/vtFPmX2GFtFpkkcy2+OJZ4Gc/S58ttsjRp546Ta814NxzU9O5c5OvRVdeeZpeBwAAAOCnqMWMngD82B4aMiS33HlnBj/xRN754IN88eWXKZVK6di+fRZfZJEsu9RS6bPKKunbp0/m7dRpRk+30H536KE5/4orysdrrbZadtxqqzzx3HM5/4orMmr06Bz5xz9Os+tttv76WXD++XPDv/6Vf/zrX9OsLgAAAMDMQDjMLOvJ557Lbw85JE8+91ySpKamJhuts0769umTTnPNlc+++CL3DhqUC668MudfcUWaNWuWNVddNbdfcUXatGkzg2f/49pl//1z2fXXl4/XWm213P+Pf8zAGSUPPPJIRTDcunXr3PK3v6VD+/bZfYcd8tqbb+aL4cOnqtb9gwdnnV/+sqLtrSFDsuhCC1W0dVtiiXRbYom8MnToTBMOr7311nngkUfKxzv365dLzzhjxk3oJ+qn+DMOAAAAPzXCYWZJl153XX576KEZP358kqRN69a5+ZJLssHaa1eMO2DPPXPfww/nF7vsktF1dbl/8ODUjx8/y4fDP0W333tvxXG3xRZLh/bty8eXnHZaRo0ePb2nBQAAADDLsucws5zb7703ux90UDkYTpIzjjmmSTD8jXVWXz3nn3TSdJodU/Lp559XHHfs0KHieNGFFsqySy01PacEAAAAMEsTDjNLGTduXH536KFpaGgot80/77zZbbvtvvW87bbcMkt06fJjT49v0VgqzegpAAAAABSKcJhZylU33pj3hg2raNt4nXXSvHnzbz2vpqYm++66a9ZbY420bNmySX+pVMpt99yT7X//+yzRu3fade2a2kUXzXzLL5++/fplwLnnZviXXzY5r8VCC6Wmc+cmX/cPHlwe07F796pjJnXUgAFVx6y99db55LPPcuBRR6XbGmtktsUXz2yLL56VN9kk1958c5P5fFNn0r1Yk6/2+53S/L6PZ154Ifv83/9l+b59M8dSS6Xlwgtnrh49svImm+TgY47J0Lfe+sFzmhpvv/deajp3brLfcJJ0WWWVcq1d9t9/qupdcs01WfXnP0/H7t3TZrHFstx66+XsSy751nNKpVL+eddd6ffb36bLKquk7WKLpe1ii2WxVVfNTn/4Qx5+7LGpuvbkdtl//9R07lyx33CSXHb99RXv09vvvVfue+LZZ3Ps6adn/W22Sbc11ih/T+ZYaqms0Ldv9jz44CnOZ9GVV676M3fUgAEZN25cjjzllCy5xhpptcgi5Z/FSf391luzwbbbZu4ePdJqkUXSuWfPbP/73+e1N96YYu3Jf+4mTJiQK264IZvvsksW7NUrrbt0yexLLJHuffrkt4cckhdeeaXJvH/sn3EAAACYldSUhg2zXI9Zxpa/+U1uvvPOirYzjzkmf9h99x9c88OPP862e+2VBx99NEky1xxz5OC99kqnuebKlTfemPsefjhJMuccc+TS00/PZhtsUD73+n/+Mx9/+mn+8Oc/V9S874Ybsnbv3kmSG2+/Pfc8+GDOu/zyijGlSULuF155JS+88krOvfzy8jySpOtii6Vt69bZbost0r5du5xx4YV57c03y/3n/uUv+d1OO31nnaW6ds0RBxxQPl53jTUyz9xzT/V7NGHChOx3xBE597LLkiTNmzfPH3bbLcsvvXQGPfZYLrr66nL7EQcckCMOPPB7z2nbLbb4znnUjRmTW+++Oy+99lqOnewhbWcde2w6zTVXkqTLwgtnlZ/9rNx31IABOfq008rHiyy4YDZZb72ce9llqampSWmyVc2H7r13/nL44U2u/9nnn2ebvfbKfwYNSpJ0nm++HLbPPhldV5fjzjwzdWPGJEn2/c1vcsYxx6RZs6n//dyQp57KW+++m2NOPz0vv/56uX3NVVfNXpN8jzfbYIPM1rZtkmSNzTfPw48/ngXnnz977LBDFl5ggXz86ae58sYbK4LVfXbdNWcdd1xqamrKbd3WWCPvfPBB6uvrK+bRf//989CQIV+FrZO8N9888K2hoSE7/eEPufqmm8rn9O3TJ9tvuWXeevfdXHzttRldV5eRo0aV+7fedNP8ctNNK37u3nznnWy122559qWXkiTdl1giB/72t3n7vfdy8l//mokTJ6ZZs2Y54U9/yqH77FOu9WP9jAMwcztg6aVn9BSA/9HpX/9/YZEc3bv+uwcBP3lHDq6d0VP4Vh5Ixyzl6RdeaNK28AIL/OB6dWPGZIPttqsI0q4655xs+PX+xTtuvXWWWmutvPnOO/li+PBsvcceufOqq7LuGmskSfr94hd5+733moTDk9pqk00yctSoJuHwpJbp3j3LdO+eO++/vyLwev3NN3P7lVdm43XXTZIsv/TSWX3zzcv9h590UnbdZpvU1tZ+a5155p57qsLXKfntIYfkb9ddVz4+eK+9cuL//V+SZOd+/TJ23LhcdeONaWhoyJEDBqSmpiZ//jqom5Zzmq1t22y7xRa5f/DgJuHwZhtskEUXWmiq6rz7wQcZ/PjjeemBB7LYwgvn1PPPz+F/+Uu5/9Tzz89+u++e+eedt9w2ceLE/HznnTPkqafKbRecfHI27ds3yVfB+CHHHZckGXjJJZm3U6ccvt9+U/3aVvnZz7LKz36W8664oiIc7rLwwt/6PrWbffY8cuutWXCS1dcH/va36bXRRnn+5ZeTJGf/7W9ZaYUVstOvflUe8+rXAfeiK6+cd95/v9x+6XXXZfbZZsuTd96ZFZZZJg8NGZKNdtih3H/SOedUBMOLL7pobr/yyvKK/DnnmCMHHHlkxRyX6dat4jV8OWJENthuu7zx9tvlthsuvDA9unUr9//1ssvS2NiYP51wQhbs3Dk7bLXVV7V+pJ9xAAAAmBXZVoJZyuQPNUtSXkX5Q5x+wQUVwXCH9u2zwVprlY9btWqVzdZfv3w8YcKE/P6ww9LY2PiDr/l9zNGxYzZaZ53y8XKTrYr5YvjwPPHssz/qHAYNGVIRDCfJr37+84rjX266acXxcWeembfeffdHndf/olQq5cIBA7JU166pra3NgXvuWbHdyMSJE/Ofr1eMf+Oy66+vCIZbtmyZ9ddcs3w86fcpSY4/88x8/sUXP9Ir+Mo+u+6ay848syIY/mZu2/ziFxVt36zu/i7vf/hhrj333PxsueXSrFmzrLXaatlo7bXTvHnzjBkzJqece27F+M3WX7/ivdtuKgLaU88/vyIYXniBBcrBcNL0vTz0+OMzYcKEqZo/AAAA8F/CYfgWV9xwQ8XxEosuWvGn99+0TerVN97IY08//WNPLUmy2MILV8xn9tlmazJm8j2Yp7Ur/vGPJm1LLr54xfHkD/sbP358rrvllh91Xv+L1q1bZ8Xll684nmuOOSrGvPfBBxXHV0+2x/P888yTVq1alY8XWXDBiv6x48blxjvumEYzrm7bLbbIlhtv/N9rjh2bz7/4Ip99/nnazz57xdjnvl5F/F1W+dnPsnyPHhVtN11ySe69/voMeuyxfDliREVf18m+9/N26pT27dp96zUmXXmcpMmK78nfyw8+/NAewgAAAPAD2FaCWUqnueaq+BP4JOV9Xr+vMWPGVOzfmyRzduzYZNwcVdqeev75rNqr1w+67vcxNauix9X/uPtUTb6VR8uWLZuE1HN06NDkvKeqbAHyUzH3HHM0+SVA7SRBb9L0fX32xRcrjlvX1uazSVayN1RZTf7Y009nj0m2ZJjWJkyYkEuuvTbX3Hxznn7hhYp9fic3YuTIqarZY8klp9j3ytChTdqqfe/bzz77FOcyuq4ub77zTkVbyxYtKt7L8VVWCT/2zDNZf5JV/QAAAMB3Ew4zS+m5zDJNwuF3J1vhObWGT7YCMknFn8eX21o0/c/oy6kM2v5XkweYM8Lk71O196Pa+zb5CtOfkubNm3/vc0ZMFna+9uab6bTsst96zseffvq9rzO16uvr03ebbTLoscfKbeuvuWZ23WabdJprrtz9wANNtoCYGpPuszy5kaNHN2lrUeXn4dve32oh9b2DBs3Q9xIAAABmVcJhZik/79s3N995Z0Xb1P65/IOPPprx48eny8ILZ/FFF6264rHavqYTJk5s0lbt3G9TKpW+1/ifkslfa7X3o9r79n3fo5+6Du3a5fPhw8vHCy+wQC4+9dRvPafaSvRp5dzLL68IhpdecsnccdVV5WD2/Q8//EF1W3xLsDv5VhXJV/szT66hoWGKNTq0b9+kbaUVVsgJf/rTt85rgfnn/9Z+AAAAoCnhMLOUHbbaKkefdlrFPrt33HdfGhoavnW14suvv561ttoqSXLb5Zdn8UUXTdu2bdNt8cXz6htvlMd98eWXTc4dXqWt13LLlf/dura2Sf/k4dioKisuZxY/W3bZPP7MM+XjCRMmZHRdXcXWEtVWYU/6Hs0KlunePQ888kj5eGJDQ/pO8kC66e2+yR6Y17dPnx+0Ivr7WKpr1yZt1b731VYYf2P22WbLIgsuWPEXAC1btJih7yUAAADMqjyQjllK69atc95JJ1WEYMM++ih/u/babz1vwNd/Xr9U167ZaJ11yu07/epXFeOGvv12k1W+r7/1VsXxUl27VjzMbO4550zr1q0rxkwejk2+t/GPrdVk2zxM+poGDRmSAeeem2EffTRVtXb65S+btL02SaCeNH2Pamtrs83mm0/tdL+3yV9f8t/XOPzLLzPg3HNz9/33T9Nr/urnP684HvbRR/nks8+qjt14hx3SukuX3DLZKvep8W3fu3/9+98ZcO65GV1X12Rf3sm3+/gxtj5ZfaWV0nGyFeGTf+8/+eyzb937OGn6Xr742mtVVyAnSY+1107rLl3yzGR7WE/Ln3EAAACYVQmHmeVsst56uWjAgIp9bvc74oj8+8EHq44fePHFueTaa9OyZctcNGBARbC8/+67Z7mlly4fjxg5Mnc/8ED5ePz48fnn3XeXj1u1apXzTjopzZr99z+tFi1aZL011qi45ouvvlr+d92YMbnpB4SE/4sFJ/sT/ElXRJ9/5ZU5+Nhjp3o1c++VVsru229f0fb3226rOL5hsuOj//jHLLLggt9jxt/P5K8v+e9rfOr553PwscfmvsGDp+k1d99++yy71FIVbSefc06TcXfdf3/ufuCBLLLAAtlkvfW+93WafO8m2criL2efnUOPPz7NamqybPfuFeOGPP10xfGt99zzva/9Xdq2bZtDfv/7JteZdFuRa26++TvrHLL33uk833zl4xEjR+a8yy9vMu7Cq67KS6+9lt4rrpgVllmmom9a/owDAADArMq2EsySdtlmmyzTvXt+d+ihefK55zJm7NhssN122XjdddO3T5/M2bFj3v3gg/zr3nsz5KmnMs/cc+eqs89O75VWqqjTtm3b3HX11dnu97/P/V+HiTvus08O3muvzD3nnLnyxhvz9nvvJflqhfAVAwdmzVVXbTKf4w45JPc9/HDGjB2bJDn5r39N8+bN02nOOXPJtdem2+KLN1nFeO3NN2eeuefOumuskRdeeSUvvPJK3nr33Yoxn3z2Wa69+easu8Ya+eSzz/LCK680ufaQp59O69rarLvGGpln7rmTJFtvummOPeOM8mrMl157LScOHJhSqZS/33ZblunePUsuvvhUv9/n/uUvqW3VKudcemmS5JRzz039+PFZoUePDHrssVx9001JvgrKjz344By6zz7lc996990MeeqpKb62JBVznxoLL7hgVu3VK48++WS57agBA7L5hhvmrEsuSbNmzbLlxhuXr/3CJGF98lVgf+3NN2eVn/0sSTLkqadSN2ZMxZgXXn21PKbLwguntrY2/7r88my1++554tlnkySnnn9+Xh46NBustVZa19bm0SefzFU33ZQF5psvN11ySdUH9X2XbTbfPH+77rry8X2DB+esiy7KBx99lIcffzybrLde2rZtm7133TUXXn11+cF/gx57LL854ID0XnHF/Ovee6uuap70Z+4/gwblk88+m+Lr/mbc5A7de++88Mor5e/5G2+/nU123DHbb7ll3n7vvZx7+eWZrW3bJnUn1WmuuXLX1Vdny912y9CvVx7v279/Bj/xRNZYeeWUSqXcN3hwbrz99izTvXuuOvvsJjWm9c84AAAAzIpqSsOGzbxPwoKp8OCjj+aWu+7Kw48/nnfefz/DR4xIqVTKXHPMkWW6dcvP+/bNLttsk/bt2k2xRqlUyr/+/e9cfdNNGfL00/nok08yYeLEzNGhQ5bt3j2brLdefrPttk3+pH5Sz7zwQo4cMCAPPfZYRtfVZc6OHbNKz5754+9+lzffeSe7HnBAk3PWWm213P+Pf+SoAQNy9GmnTbH2fTfckPsHD/7OMWv37l0+vuv++3Pa+efnmRdfzBdffpmamprM16lT1ll99Rx3yCFZaIEFpljr217jRVdfnQeHDMk777+fMWPHpt3ss2fxRRbJuquvnj133DGLL7poxTmXXndd1df+bXOfGh9+/HGOOvXU/Ofhh/P+hx9m/Pjx6dC+fZZbaqkcuOee+cWGG37ntf92+ulJ8p1jdtlmm/LxxIkTc/0//5nrb701Tz73XD794os0NjZmzo4ds1TXrvl5377ZY4cdvvXn7btcfeONOf/KK/PCq69mxMiRad68eRacf/5ssu66OebggzPH1w+6e+m119L/pJPywKOPZuSoUZlrjjmy6s9+ln123TXvf/jht/7Mrb311hV7KE9pXDWlUil/v/XWXHjVVXny+eczavTozDXHHFlz1VVzxAEH5Oc77VSxp/Bxhx6aw/fbr0mdsWPH5vIbbshNd9yRZ196qbz6d+4558yy3btny403zi79+qW2yr7eyY/zMw7AzOmASf4SDJg5nf7SSzN6CtPd0b3rZ/QUgGngyMHVP7P+VAiHAZiu5l1uuYqVy5ecdlp23XbbGTgjAGZ1wmGY+QmHgZnVTz0ctucwANPMkKeeyhqbb57nX365av+XI0bk088/Lx83b94866+55vSaHgAAADAJ4TAA08zwESPy8OOP58SBA6v2n3PppSmV/vsHKwfssUcW7Nx5ek0PAAAAmIQH0gEwzV1z8815+733stUmm2TeTp0yctSo3DtoUG66444kSbNmzXLAHnvkpP79Z/BMAQAAoLiEwwBMMyuvsEIuPOWUDHn66Tz1/PM586KL8vnw4ZnY0JAO7dpltV69suaqq2bnfv2yVNeuM3q6AAAAUGjCYQCmmTnnmCO777BDdt9hhxk9FQAAAOA72HMYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAUGANDQ3588knp8sqq6TNYotl8dVWy7Gnn55SqVQeUyqVcsTJJ2f+FVZIm8UWS99+/fL6m2/OwFkDADAtCIcBAKDATjrnnJx72WU5+/jj8/IDD+Skww/PyX/9awZefHF5zMnnnJOzLrkk5/3lLxly222ZrW3bbLj99hk3btwMnDkAAP+rFjN6AgAAwIwz+IknsvmGG2bTvn2TJIsutFCuufnmPPbMM0m+WjV8xkUXpf9++2XzjTZKklx+1lmZd/nlc/Odd2bbLbaYQTMHAOB/ZeUwAAAUWO8VV8y9gwbltTfeSJI8++KLGfTYY9l43XWTJG+9+24++uST9O3Tp3xOh/bts0rPnnnkySer1qyvr8/IUaMqv0aOzNixY6uOH11Xl4kTJzZpb2hoyMhRo6Z4je9br7FUSv0k22VMamKplIlT6BtfKqWxSp966qk3/epN6/vBzFBvXGP1ehNL9ZnQWP0vN+ob69JQqnb/U0899X4K9abn/WVqCYcBAKDA/rTPPtl2883Tfc0103LhhdNzgw2y/x57ZIettkqSfPTJJ0mSeTt1qjhv3k6dyn2TO3HgwHTo1q3i6xe77JJ9+/evOn6VTTfNvYMGNWl/ZejQLNir1xSv8X3rDW9szMUjRlQ95/Fx43LfFD5cXTtqVN6t8mFNPfXUm371pvX9YGaod9p7vaue89CX5+b2L46s2nfRh1vmrXEPN2n/bMIb6qmn3k+g3vS8v0ytmtKwYdV/XQcAAMzyrr355hx87LE55c9/To9u3fLMiy9m/yOPzGlHHpmd+/XL4Mcfz+qbb55hTz+d+eedt3xev9/+NjVJrjv//CY16+vrUz9+fGVjqZSWLVumTZs2TcaPrqtL69ratGhRuetdQ0ND6saMSft27apeo7GxcarqHbD00km+Wsk4IUltTU2Tc75ZxdiiSt/4UiktkjSbrE899dSbfvUGPP/8NLkffGNa3V9+zHr9Vx+e1s2a1ptYqk+pVErLZq2bXquxLi1qatO8prJeY6kh40tj1FNPvRlQ78jBtf89ZzreX6aWcBgAAApsoV698qd99sneu+5abjvujDNy5T/+kVceeihvvvNOFl9ttTx9991ZYZllymPW2mqrrNCjR8489tgZMe3v5ZtwGJh5nf7SSzN6CtPd0b3rZ/QUgGlg0nD4p8i2EgAAUGBjxo1Ls2aVHwuaN29e3vOzy8ILZ7555qn4M8eRo0ZlyNNPZ7Up/IkjAAAzhxbfPQSYmbzy+uvfa/zcc86Zz7744keajWv8VK8x91xz/ajXAGDmsdn66+f4s87KwgsskB7duuXpF17Iaeefn99su22SpKamJvvvvnuOO/PMdO3SJV0WXjh/PvnkdJ533myx0UYzePYAAPwvhMMwi1lqrbW+1/gjDzwwR5922o80G9f4qV7jqIMO+lGvAcDMY+Bxx+XPJ5+c3x92WD75/PN0nnfe/PbXv84RBxxQHnPI3nunbsyY7HnIIfly5MissdJKufOqq9K6ddP99wAAmHkIhwEAoMDazT57zjjmmJxxzDFTHFNTU5NjDjkkxxxyyHScGQAAPzYPpAMAAGZpHkgHMz8PpANmVh5IBwAAAADAT45wGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAABQcB98+GF23GefzNWjR9ostliWXXfdPPHss+X+UqmUI04+OfOvsELaLLZY+vbrl9fffHMGzhgAgGlBOAwAAAU2/Msvs/rmm6dlixa548or89L99+fUI47IHB06lMecfM45OeuSS3LeX/6SIbfdltnats2G22+fcePGzcCZAwDwv2oxoycAAADMOCedc04W6tw5fzvjjHJbl4UXLv+7VCrljIsuSv/99svmG22UJLn8rLMy7/LL5+Y778y2W2wxnWcMAMC0YuUwAAAU2D/vvjsrLr98frXnnpln2WXTc/31c+FVV5X733r33Xz0ySfp26dPua1D+/ZZpWfPPPLkkzNiygAATCPCYQAAKLA33303515+ebp26ZK7rr46e+28c/7w5z/nsuuvT5J89MknSZJ5O3WqOG/eTp3KfZOrr6/PyFGjKr9GjszYsWOrjh9dV5eJEyc2aW9oaMjIUaOmeI3vW6+xVEp9qVT1nImlUiZOoW98qZTGKn3qqafe9Ks3re8HM0O9cY3V600s1WdCY/Vtfeob69JQqnb/U0899X4K9abn/WVqCYcBAKDAGhsb87NllskJhx2Wnssumz133DF7bL99zrviih9c88SBA9OhW7eKr1/sskv27d+/6vhVNt009w4a1KT9laFDs2CvXlO8xvetN7yxMRePGFH1nMfHjct9U/hwde2oUXm3yoc19dRTb/rVm9b3g5mh3mnv9a56zkNfnpvbvziyat9FH26Zt8Y93KT9swlvqKeeej+BetPz/jK1akrDhlX/dR0AADDLW2SllbL+mmvmolNPLbede9llOe7MM/PBU0/lzXfeyeKrrZan7747KyyzTHnMWlttlRV69MiZxx7bpGZ9fX3qx4+vbCyV0rJly7Rp06bJ+NF1dWldW5sWLSofidLQ0JC6MWPSvl27qtdobGycqnoHLL10kq9WMk5IUltT0+Scb1YxtqjSN75USoskzSbrU0899aZfvQHPPz9N7gffmFb3lx+zXv/Vh6d1s6b1JpbqUyqV0rJZ66bXaqxLi5raNK+prNdYasj40hj11FNvBtQ7cnDtf8+ZjveXqeWBdAAAUGCrr7RSXn3jjYq21958M4sssECSrx5ON9888+TeQYPK4fDIUaMy5Omns9dOO1WtWVtbm9ra2qp91cw+22xV25s3b171g9A31/i+9ZrV1GRKZ1ULqb7Ragp96qmn3vSrN63vBzNDvWpBVZK0qKlNpvAW1jab0v2veVrXqKeeejO63vS8v0wt20oAAECBHbDnnnn0qadywllnZehbb+XqG2/MBVdemb133TVJUlNTk/133z3HnXlm/nnXXXn+5Zez0x/+kM7zzpstNtpoBs8eAID/hZXDAABQYCutsEJuuvjiHHbiiTnm9NPTZaGFcsYxx2SHrbYqjzlk771TN2ZM9jzkkHw5cmTWWGml3HnVVWnduumfWAIAMPOw5zAAADBL+2bPYWDmdfpLL83oKUx3R/eun9FTAKaBSfcc/imyrQQAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAABlfxk4MDWdO2f/I44ot40bNy57H3ZY5urRI7MvsUS23n33fPzppzNwlgAATAvCYQAAIEny+DPP5Pwrr8xySy9d0X7AUUfl1nvuyd/PPz8P3Hhjhn38cbbabbcZNEsAAKYV4TAAAJDRdXXZYZ99cuEpp2SODh3K7SNGjszF11yT0446KuuusUZ6Lbdc/nbaaRn8xBN59MknZ+CMAQD4XwmHAQCA7P1//5dN11svfddcs6L9yeeey4QJE9K3T59yW/euXbPwAgvkkSmEw/X19Rk5alTl18iRGTt2bNXxo+vqMnHixCbtDQ0NGTlq1BSv8X3rNZZKqS+Vqp4zsVTKxCn0jS+V0lilTz311Jt+9ab1/WBmqDeusXq9iaX6TGgcV/1ajXVpKFW7/6mnnno/hXrT8/4ytYTDAABQcNfefHOeev75nHjYYU36Pvrkk7Rq1SodJ1lNnCTzduqUjz75pGq9EwcOTIdu3Sq+frHLLtm3f/+q41fZdNPcO2hQk/ZXhg7Ngr16TfEa37fe8MbGXDxiRNVzHh83LvdN4cPVtaNG5d0qH9bUU0+96VdvWt8PZoZ6p73Xu+o5D315bm7/4siqfRd9uGXeGvdwk/bPJryhnnrq/QTqTc/7y9SqKQ0bVv3XdQAAwCzvvQ8+yIobb5x7rr22vNfw2ltvnRV69MgZxxyTq2+8MbseeGDq33674ryVN9kk6/TunZOqfCCpr69P/fjxlY2lUlq2bJk2bdo0GT+6ri6ta2vTokWLivaGhobUjRmT9u3aVb1GY2PjVNU74OvX1VgqZUKS2pqaJud8s4qxRZW+8aVSWiRpNlmfeuqpN/3qDXj++WlyP/jGtLq//Jj1+q8+PK2bNa03sVSfUqmUls1aN71WY11a1NSmeU1lvcZSQ8aXxqinnnozoN6Rg2v/e850vL9MLeEwAAAU2M133JEtd9stzZs3L7c1NDSkpqYmzZo1y11XX52+22yT4S+/XLF6eJGVVsr+e+yRA/bcc0ZM+3s5YLIH7AEzn9NfemlGT2G6O7p3/YyeAjANTBoO/xS1+O4hAADArGq9Pn3y/H/+U9G26wEHpPsSS+TQvffOQp07p2XLlrl30KBsvemmSZJXhw7Nux98kNWm8CeOAADMHITDAABQYO1mnz3LdO9e0TZb27aZa445yu27bbddDjzqqMzZsWPat2uXfQ8/PKv16pVVhcMAADM14TAAAPCtTj/qqDSrqcnWe+yR+vr6bLj22vnriSfO6GkBAPA/sucwAAAwS7PnMMz87DkMzKx+6nsON5vREwAAAAAAYPoTDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAABQYCcOHJiVNt447bp2zTzLLpstdt01rw4dWjFm3Lhx2fuwwzJXjx6ZfYklsvXuu+fjTz+dQTMGAGBaEQ4DAECBPfDII9l7l13y6G235Z5rr82EiROzwXbbpW7MmPKYA446Krfec0/+fv75eeDGGzPs44+z1W67zcBZAwAwLbSY0RMAAABmnDuvvrri+NIzzsg8yy6bJ597LmuuumpGjByZi6+5Jlefc07WXWONJMnfTjstS621Vh598sms2qvXjJg2AADTgJXDAABA2YiRI5Mkc3bsmCR58rnnMmHChPTt06c8pnvXrll4gQXyyJNPVq1RX1+fkaNGVX6NHJmxY8dWHT+6ri4TJ05s0t7Q0JCRo0ZN8Rrft15jqZT6UqnqORNLpUycQt/4UimNVfrUU0+96VdvWt8PZoZ64xqr15tYqs+ExnHVr9VYl4ZStfufeuqp91OoNz3vL1NLOAwAACRJGhsbs/+RR2b1lVbKMt27J0k++uSTtGrVKh07dKgYO2+nTvnok0+q1jlx4MB06Nat4usXu+ySffv3rzp+lU03zb2DBjVpf2Xo0Cw4hZXJJw4c+L3rDW9szMUjRlQ95/Fx43LfFD5cXTtqVN6t8mFNPfXUm371pvX9YGaod9p7vaue89CX5+b2L46s2nfRh1vmrXEPN2n/bMIb6qmn3k+g3vS8v0ytmtKwYdV/XQcAABTKXn/6U+74z38y6Oabs2DnzkmSq2+8MbseeGDq3367YuzKm2ySdXr3zklVPpDU19enfvz4ysZSKS1btkybNm2ajB9dV5fWtbVp0aJy17uGhobUjRmT9u3aVb1GY2PjVNU7YOmlk3y1knFCktqamibnfLOKsUWVvvGlUlokaTZZn3rqqTf96g14/vlpcj/4xrS6v/yY9fqvPjytmzWtN7FUn1KplJbNWje9VmNdWtTUpnlNZb3GUkPGl8aop556M6DekYNr/3vOdLy/TC17DgMAANnn//4vt91zTx686aZyMJwk880zT8aPH58vR4yoWD388aefZr555qlaq7a2NrW1tVX7qpl9ttmqtjdv3rzqB6FvrvF96zWrqcmUzqoWUn2j1RT61FNPvelXb1rfD2aGetWCqiRpUVObTOEtrG02pftf87SuUU899WZ0vel5f5latpUAAIACK5VK2ef//i833Xln/vP3v6fLwgtX9Pdabrm0bNmy4s8cXx06NO9+8EFW8zA6AICZmpXDAABQYHv/3//l6ptuyi1/+1vazT57eR/hDu3apU2bNunQvn122267HHjUUZmzY8e0b9cu+x5+eFbr1SurCocBAGZqwmEAACiwcy+7LEmy9tZbV7T/7fTTs8s22yRJTj/qqDSrqcnWe+yR+vr6bLj22vnriSdO97kCADBtCYcBAKDASsOGfeeY1q1b55wTT8w5AmEAgFmKPYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAAAAUkHAYAAAAAKCAhMMAAAAAAAUkHAYAAAAAKCDhMAAAAABAAQmHAQAAAAAKSDgMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAACggITDAAAAAAAFJBwGAAAAACgg4TAAAAAAQAEJhwEAAAAACkg4DAAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUEDCYQAAAACAAhIOAwAAU+Wcv/0ti668clp36ZJVNt00jz399IyeEgAA/wPhMAAA8J2uu+WWHHj00TnywAPz1F13Zfmll86G22+fTz77bEZPDQCAH0g4DAAAfKfTLrgge2y/fXbddtssveSSOe+kk9K2TZtccs01M3pqAAD8QC1m9AQAAICftvHjx+fJ557LYfvsU25r1qxZ+vbpk0eefLLJ+Pr6+tSPH18+LpVKGTV6dFq2aJE2bdo0GT+6ri6ta2vTokXlx5OGhobUjR2b9rPPXvUajY2NU1WvvlRKkjSWSpmYpFVNTZNzGr4e07xK34RSKc2TNJusTz311Jt+9YZ/+eU0uR+U5zCN7i8/Zr0RE79MbbOm9RpK41MqldKiWW2TvvGNY9KiplWa1VTWK5UaMr40Vj311JsB9UaO+u//E03P+0uStJt99tRUuddOSjgMAAB8q8+++CINDQ2Zt1OnivZ55547rwwd2mT8iQMH5ujTTpte0wMK4Nyll57RUwD4Qf7SbcZde8Srr6Z9u3bfOkY4DAAATFOH7btvDvztb8vHpVIp48ePT21t01U38L8aOXp0FurVK+89+WTVVVUAP0XuXUwP7abiZ0s4DAAAfKu555wzzZs3z8efflrR/vFnn2W+yVYTJ0ltba0gmOmu/eyzf+fqKICfGvcuZjQPpAMAAL5Vq1at0mu55XLvoEHltsbGxtw7aFBW69VrBs4MAID/hZXDAADAdzpwzz2z8/77Z8Xll8/KPXvmjAsvTN2YMdl1221n9NQAAPiBhMMAAMB32mbzzfPp55/niFNOyUeffpoVevTInVdd1eQhdTC91bZqlSMPPDC1rVrN6KkATDX3Ln4qakrDhpVm9CQAAAAAAJi+7DkMAAAAAFBAwmEAAAAAgAISDgMAAAAAFJBwGAAAAIAf7NLrrkvH7t2/1zm77L9/tth112k6j0VXXjlnXHjhNK05szhqwIDMu9xyqencOTffcceMns53mtqfmT+ffHL2PPjg71V71Z//PP/4179+6NQKRzgMAADADwp3ZhWvvP56Vv35z9O6S5es0LfvjJ7OVJmaEOzzL77IPMsum7ffe2+q6553+eXZbKed/tfpMYuYUoB7/+DBqencOV+OGJEk2eYXv8hrgwZN7+nxtZdffz1Hn3Zazj/ppHz4zDPZeN11f1Cdat/vt997LzWdO+eZF16YFlP9Xj765JOcedFFOXy//cptDz76aDbbaad07tlzikF4//32y59OOCGNjY3Tc7ozLeEwAADAdPJjrJTjf3fkgAGZrW3bvPrQQ7n3+ut/UI0pBSgz8nt+/FlnZfMNN8yiCy1Ubnv3/fez6a9/nbaLLZZ5ll02Bx9zTCZOnFju/8222+apF17IQ0OGzIgpM5Nq06ZN5pl77hk9jcJ64+23kySbb7RR5ptnntTW1s7YCU0jF119dXqvuGIWWXDBclvdmDFZvkePnHPCCVM8b+N1182o0aNzx3/+Mz2mOdMTDgMAAFBob7zzTtZYaaUssuCCmWvOOWf0dKaJMWPG5OJrrslu221XbmtoaMimO+2U8ePHZ/A//5nLzjwzl15/fY445ZTymFatWmX7LbbIWRdfPCOmzUyq2l8eHHfGGZln2WXTrmvX7P7HP+ZPxx9fdWX+gHPPzfwrrJC5evTI3ocdlgkTJnzrtW69++6stPHGad2lS+bu0SNb/uY3Ff1jxo7Nbw44IO26ds3CK66YC668sqL/0OOOy5JrrJG2iy2WxVZdNX8++eSKax41YEBW6Ns3V9xwQxZdeeV06NYt2/7udxk1enR5TGNjY04+55ws0bt3ahddNAuvuGKOP/PMcv97H3yQfr/9bTp27545l146m++yS8UK/vsHD87Km2yS2RZfPB27d8/qv/hF3nn//Sm+5udffjnr/upXabPYYpmrR4/sefDBGV1XV57vZjvvnCRptsACqencuWqNhoaG7Hbggemyyipps9hi6bbGGjnzoosqXvdl11+fW+66KzWdO6emc+fcP3hwuqyySpKk5wYbpKZz56y99dZJksefeSbrb7NN5u7RIx26dctaW22Vp557ruKaX44Ykd8eckjmXW65tO7SJcuss05uu+eeqvP79PPPs+JGG2XL3/wm9fX1SZJrb7klm62/fsW4jdddN8cdemi23HjjKb5fzZs3zybrrptrb7llimP4L+EwAADAT8QDjzySlTfZJLWLLpr5V1ghfzr++PKqztvuuScdu3dPQ0NDkuSZF15ITefO+dPxx5fP3/2Pf8yO++wzxfpT80H9rvvvz1JrrpnZl1giG22/fT78+ONy39SEATWdO+eiq67Klr/5Tdoutli6rr56/nnXXRVjXnz11fx8p53Sfskl065r1/TZYovyyrckueiqq7LUmmumdZcu6d6nT/566aXlvvHjx2ef//u/zL/CCmndpUsWWWmlnDhw4BRfc2NjY4457bQs2KtXahddNCv07Zs777uvYr5PPvdcjjn99NR07pyjBgyoWufO++7LGptvno7du2euHj3y8512qphztQBlSmFL8t0BVfLdIdikLrrqqnTs3j33PvRQkuT2//wnta1aZdVevcpj7n7ggbz02mu58uyzs8Iyy2TjddfNsYccknMuvTTjx48vj9tsgw3yz7vvztixY6d4Pfg2V914Y44/66ycdPjhefLOO7PwAgvk3MsvbzLuvsGD88Y77+S+v/89l51xRi69/vpc+i2r9//1739ny912yybrrpun7747915/fVbu2bNizKnnn58Vl18+T999d36/887Z609/yqtDh5b7280+ey49/fS89MADOfOYY3LhVVfl9AsuqKjxxjvv5OY778xtl1+e2y6/PA88+mj+cvbZ5f7DTjghfznnnPx5//3z0v335+pzzsm8X6+cnjBhQjbcfvu0m222PHTTTXn4llsy+2yzZaPtt8/48eMzceLEbPGb32StVVfNc/fem0duvTV77rhjaqbwmuvGjMmG22+fOTp0yOO3356/n39+/v3QQ9nn8MOTJAfttVf+dvrpSZIPn3kmHz7zTNU6jY2NWXD++fP3Cy7IS/ffnyMOPDD/d+KJuf6f/yzX6bfZZtlonXXKdXqvuGIeu/32JMm/r7suHz7zTG78OlAeNXp0du7XL4NuvjmP3nZbunbpkk1+/etyiN7Y2JiNd9wxDz/+eK4cODAv3X9//vJ//5fmzZs3mdt7H3yQPltskWW6d88NF16Y2trafDF8eF567bWsuPzyU3hnvt3KPXv6C4ip1GJGTwAAAIDkgw8/zCY77phd+vXL5WeemVeGDs0eBx+c1rW1Oeqgg9JnlVUyavToPP3CC1lx+eXzwKOPZu4558z9jzxSrvHAo4/m0N//vmr9bz6ojxo9OlcOHJjFF100L732WsUH9TFjx2bAuefmioED06ymJjvuu28OOuaYXHXOOUn+GwYMPO64lJKcet552eTXv87rDz+cdrPPXq5z9Gmn5eT+/XPKn/+cgZdckh322SfvPPZY5pxjjnzw4YdZc6utsvZqq+U/f/972s8+ex5+/PFyCH7VjTfmiAEDcvbxx6fnMsvk6RdeyB4HH5zZ2rbNzv365ayLL84/7747159/fhZeYIG898EHeW/YsCm+r2dedFFOPf/8nH/SSem5zDK55Npr84tddsmL992Xrostlg+feSZ9t9kmG629dg7aa6/MPttsVevUjRmTA3/72yy31FIZXVeXI045JVvutlueueeeNGvWLI/dfntW3mST/Pu669KjW7e0atkyrVq1ysuvv56Ro0eXw5s5O3ZM8t+AqvN88+X5l1/OHgcfnHazzZZD9t47yX9DsMP/8IdcftZZGT9+fG6fwp9In3zOOTn5r3/N3ddcUw7KHhoyJL2WW65i3CNPPJFlu3fPvJ06lds2XHvt7PWnP+XFV19Nz2WXTZKsuNxymThxYoY8/XTW7t17iu8txXDbv/+d2ZdYoqKt4Tv2ch14ySXZbdtts+u22yZJjjjwwNz9wAPl1a7fmKNDh5x9/PFp3rx5unftmk379s29Dz2UPXbYoWrd4888M9tuvnmOnuQBZcv36FExZpN1183vd9klSXLoPvvk9AsvzH2DB6fb16+h//77l8cuutBCOeiNN3LtLbeU/9tLvrpfXnrGGeX72q+33jr3DhqU4/PVffDMiy/O2ccdl5379UuSLL7oolnj618QXffPf6axsTEXnXpqamq+inz/dvrp6di9e+4fPDgrLr98RowcmZ+vv34WX3TRJMlSXbtO8b28+qabMq6+PpefdVZma9s2SXL28cdns513zkmHH555O3VKx/btkyTzzTPPFOu0bNmy4n3rsvDCeeSJJ3L9rbem3y9+kdlnmy1t2rRJ/fjxFXU6zTVXkmSuOeaoaF93jTUq6l9wyinp2L17Hnjkkfx8/fXz7wcfzGNPP52XH3ggSy6+eJJksUUWaTKvV4cOzfrbbpstN944ZxxzTPk9e/eDD1IqldJ53nmn+Jq+Ted55817w4alsbExzZpZG/tthMMAAAA/AX+97LIs1Llzzj7hhNTU1KR7164Z9vHHOfT443PEgQemQ/v2WaFHj3K4cP/gwTlgzz1z9GmnZXRdXUaMHJmhb72VtVZbrWr9qfmgPmHChJx30knlwGKfXXfNMV+Hmsl3hwHf2GWbbbLdllsmSU447LCcdfHFeeyZZ7LROuvknEsvTYd27XLtueemZcuWSVKeT/LV/r+nHnFEttpkkyRfBRgvvfZazr/iiuzcr1/e/eCDdF1ssayx8sqpqamp2IuymgHnnZdD9947226xRZLkpP79c9/gwTnjwgtzzoknZr555kmL5s0z+2yzfWuwsvWmm1YcX3Laaem07LJ56bXXskz37lMMUKqFLcl3B1RTE4IlX61AvuIf/8gDN96YHt26ldvfef/9JqHKR59+WhEMJymvdvzo00/LbW3btk2H9u2/9c/cKY51evfOuX/5S0XbkKef/ta/Unj1jTfy+6+3OvjGyj175j+TPbSuR7duFb+gmn+eefL8K69Mse4zL744xeD4G8sttVT53zU1NZlvnnnyyWeflduuu+WWnHXxxXnjnXcyuq4uExsa0n6SX24lX/03OekvvOafd95yjZdffz319fVZr0+fqtd/9sUXM/Ttt9NussB3XH193njnnWyw9trZpV+/bLj99lm/T5/0XXPN9Ntss8w/hRD05ddfz/JLL10OhpNk9ZVWSmNjY159440m/01/m3P+9rdccu21efeDDzJ23LiMnzAhK1S5r0yNjz/9NP1POin3P/JIPvnsszQ0NGTM2LF594MPknz1vVpw/vkr7u+TGztuXPpsuWW233LLnHHMMU36kqR169Y/aH5tWrdOY2Nj6uvr06ZNmx9UoyiEwwAAAD8BL7/+elbr1au8air5KgAYXVeX94cNy8ILLpi1Vlst9z/ySP74u9/loSFDcuJhh+X6f/4zgx57LF8MH57O882XrostVrX+1HxQb9umTTkYTioDkeS7w4BvTBrOzNa2bdq3a1eu88yLL6bPKquUg+FJ1Y0Zkzfefju7/fGP2WOSUHRiQ0M6tGuXJNmlX7+sv9126danTzZae+38vG/fbLD22lVfz8hRozLso4+y+korVbSvvtJKefall6b4PlTz+ptv5ohTTsmQp5/OZ198kcavV06++8EHWWayvVanxncFVFMTgp163nmpGzs2T9xxR5Ogf+y4cWn9PzyUqk3r1hljWwny1X/DS3TpUtH2/ocfTpPaLVtUxlI1NTXl/7aqmZqQb/J7S01SrvnIE09kh332ydEHHZQN1177q19U3XJLTj3//KmeV5vvCCtHjxmTXsstl6sm2YbiG9/8EulvZ5yRP+y2W+68//5cd8st6X/SSbnn2msrtoGZ1q69+eYcdOyxOfWII7Jar15pN/vsOeXcczPkqad+UL2d99svnw8fnjOPOSaLLLhgalu1ymqbbZbxX2+P813vU5LUtmqVvn365LZ//zsH77VXFph//nLf3F/v/z78yy/L79v38cWXX2a2tm0Fw1PBumoAAICZxNqrrZZBjz2WZ198MS1btkz3rl2zdu/euX/w4Dzw6KNZa9VVp3ju1HxQrxaqlEql8vHO++2XZ158MWcec0wG//OfeeaeezLXHHOUw4BynR8YrHzzJ+cXDhiQZ+65p/z1wn/+k0dvuy1J8rPllstbjz6aYw8+OGPHjUu/3/0uv9xjj+98bf+rzXbeOV98+WUuPOWUDPnXvzLkX/9Kkoq9eqfWNwHVJuutl9suvzxP3313Dv/DHyrex6kJNPqsskoaGhrKe4ZOau4558zwESMq2ubr1CkfT7JCOEk+/jq0n2+y1Ydf/MBABpKk2+KL5/HJ9r6d/PiHWG6ppXLvZKuPv4/BTzyRRRZcMIfvt19WXH75dF1sse+9Qr5rly5p07p1eX/vyf1s2WXz+ltvZZ65584SXbpUfHX4evuHJOm57LI5bN99M/jWW7NM9+65+qabqtZbqmvXPPvSS6kbM6bc9vDjj6dZs2bp9i2/7Jvcw48/nt4rrpjf77JLei67bJbo0qVi3/QkadWyZXlf+0nbkqZbiTz8+OP5w267ZZP11kuPbt1S26pVPvvii3L/cksvnfc//DCvvfHGFOfUrFmzXDFwYHotu2zW+dWvMuyjj8p9iy+6aNq3a5eXXnttql/jpF549dX0XGaZH3Ru0QiHAQAAfgKW6to1jzz5ZEUY+/Djj6fd7LNnwa+fPv/NvsOnX3hhOQhe++vVxPcPHvyt+8NOzQf17/JdYcDUWG6ppfLQkCFNHr6WJPN26pTO882XN995p0mo0mXhhcvj2rdrl2023zwXDhiQ6847L//417/yxfDhTeq1b9cuneebLw8//niT17H0t+zxObnPv/gir77xRvrvv3/W69MnS3XtmuFfflkxZkoBSrWwZWoCqqkJwVbu2TN3XHllThg4MAPOPbeir+cyyzQJVVZbccU8/8orFavB73nwwbRv1y5LL7lkue2Nt9/OuHHjBCv8YPv+5je5+Jprctn11+f1N9/McWeckedefrniLyN+iCMPPDDX3HxzjjzllLz8+ut5/uWXc1KVFbpT0rVLl7z7wQe59uab88bbb+esiy7KTXfe+b3m0Lp16xy699455Pjjc/nf/5433n47jz75ZC6++uokyQ5bbpm555wzm++6ax4aMiRvvftu7h88OH/o3z/vDxuWt959N4edcEIeeeKJvPP++7n7/vvz+ptvTnHf4R223DKta2uz83775YVXXsl9Dz+cffv3z69/+cvvtaVE1y5d8sSzz+au++/Pa2+8kT+ffHIef/bZijGLLrRQnnv55bw6dGg++/zzTJgwIfPMPXfatG6dO++7Lx9/+mlGjBxZrnfFDTfk5ddfz5CnnsoO++xT8cu/tVZbLWuuumq23mOP3PPAA3nr3Xdzx3/+U/FA0CRp3rx5rjrnnCy/9NJZ91e/ykeffJLkq+C4b58+GfTYYxXjR9fV5ZkXXsgzL7yQJHnrvffyzAsv5N3J7qEPDRmSDdZaa6rfnyITDgMAAExHI0aNKn+w/ebrvQ8+yO933jnvDRuWfQ8/PK+8/npuufPOHDlgQA7cc8/yw3Tm6Ngxyy21VK668cZyELzmqqvmqeefz2tvvvmtK4en9oP6t/muMGBq7LPrrhk5alS23WuvPPHss3n9zTdzxQ035NWhQ5MkR//xjzlx4MCcddFFee2NN/L8yy/nb9dem9O+/rPv084/P9fcdFNeef31vPbGG/n7rbdmvnnmSccOHape7+C99spJ55yT6265Ja8OHZo/HX98nnnxxey3++5TPec5OnbMXHPMkQuuvDJD33or/xk0KAcefXTFmCkFKNXClqkJqKY2BOu90kq5/YorcvRpp+WMCy8st2+49tp58bXXKkLsDdZaK0svuWR+ve++efbFF3PX/fen/0knZe9ddkntJFtQPDRkSBZbZJGKLUbg+9hhq61y2NcPtPzZhhvmrXffzS79+v3g/WO/sXbv3vn7BRfkn3ffnRXWXz/r/upXeex7rEj+xYYb5oA99sg+hx+eFdZfP4OfeCJ/nmT/76n15wMOyB9/+9scccopWWqttbLN736XTz7/PMlXe3Y/eOONWXiBBbLVbrtlqbXWym5//GPG1denfbt2adumTV4ZOjRb77FHllxjjex5yCHZe5dd8ttf/7rqtdq2bZu7rr46X3z5ZVbaZJP8cs89s94aa+Ts44//XnP+7a9/na023jjb/O53WeXnP8/nw4c32Rd6jx12SLfFF8+KG2+cTssum4cffzwtWrTIWccem/OvuCKde/bM5rvumiS5+NRTM3zEiPxsww3z6z/8IX/YbbfM8/Ue5t/4x4UXZqXll892v/99ll577Rxy3HFNflmWJC1atMg1f/1renTrlnV/9avyL7B23377XPv1A/6+8cSzz6bnBhuk5wYbJEkOPOqo9NxggxwxYEB5zAcffpjBTzyRXbfZ5nu9R0VVUxo2rPTdwwAAAPhf7bL//rns+uubtO+23Xa56NRT88Ajj+TgY4/Nsy+9lDk7dszOv/pVjjv00LSYZJuG/Y84ImdedFFefuCBdP96pdkKffvm488+y4ffEZJ8MXx4DjrmmPzz7rtTN3Zsllh00fzl//4vm/btm0uvuy77H3lkvpzkYVA333FHttxtt5SGDUuSPP3889nzkEPywquvZqHOnXPCn/6Ug445JvvvsUf2/3prh5rOnXPTxRdni403Ltfp2L17zjj66Ozy9Qf15156KQcfe2wGPfZYmjdvnhV69MilZ5xR3jf36htvzCnnnpuXXn89s7Vtm2W7d8/+e+yRLTfeOBdedVX+eumlef2tt9K8efOstPzyOeXPf07PZZet+pobGxtz7Omn58Krr84nn32Wpbt2zV8OPzwbrbNOecwKfftmi402ylEHHTTF9+7fDz6YP/z5z3nz3XfTbfHFc9axx2btrbeueK0XXXVVjjn99Hzw0Ufps8oquf8f/8inn3+eHfbeO488+WRG19XlvhtuyNq9e+eQY4/NJddem/rx47Ppeutl1V69ctSpp1a8/zfefnuOPf30vPT662k/++xZc9VV84+LLkqSLLryyhXv+4OPPppNdtwxJx52WPbdbbckySqbbprfbLttRej0zvvvZ68//Sn3Dx6c2dq2zc6/+lX+cvjhFT9jG263Xdbp3Tt/2nffKb4f8H2tv802mW+eeXLFwIEzeirMJEqlUlbZdNMcsMce5YecTo1Djzsuw0eMyAWnnPIjzm7WIRwGAACAWdC//v3vHHzssXnhvvvKq8+/y4uvvpp1f/WrvDZoUMX+qPB9jBkzJuddcUU2XHvtNG/WLNfcfHOOOf303HPttem75pozenrMRJ554YU8/8or+fUvfznV55x63nnZceutv9e2G0UmHAYAAIBZ1BkXXpitN9kkCy2wwFSN//eDD6ahsTEbrr32jzsxZmljx47NZjvvnKdffDHjxo1Lt8UXT//9989Wm2wyo6cGTEY4DAAAAABQQB5IBwAAAABQQMJhAAAAAIACEg4DAAAAABSQcBgAAAAAoICEwwAAAAAABSQcBgAAAAAoIOEwAAAAAEABCYcBAAAAAApIOAwAAAAAUED/D2wCMAPD8O7hAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(18,7))\n",
+ "gs = fig.add_gridspec(1,2)\n",
+ "gs.update(wspace=0.3, hspace=0.15)\n",
+ "ax0 = fig.add_subplot(gs[0,0])\n",
+ "ax1 = fig.add_subplot(gs[0,1])\n",
+ "\n",
+ "background_color = \"#ffe6e6\"\n",
+ "color_palette = [\"#800000\",\"#8000ff\",\"#6aac90\",\"#5833ff\",\"#da8829\"]\n",
+ "fig.patch.set_facecolor(background_color) \n",
+ "ax0.set_facecolor(background_color) \n",
+ "ax1.set_facecolor(background_color) \n",
+ "\n",
+ "# Title of the plot\n",
+ "ax0.text(0.5,0.5,\"Count of the target\\n___________\",\n",
+ " horizontalalignment = 'center',\n",
+ " verticalalignment = 'center',\n",
+ " fontsize = 18,\n",
+ " fontweight='bold',\n",
+ " fontfamily='serif',\n",
+ " color='#000000')\n",
+ "\n",
+ "ax0.set_xticklabels([])\n",
+ "ax0.set_yticklabels([])\n",
+ "ax0.tick_params(left=False, bottom=False)\n",
+ "\n",
+ "# Target Count\n",
+ "ax1.text(0.35,177,\"Output\",fontsize=14, fontweight='bold', fontfamily='serif', color=\"#000000\")\n",
+ "ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))\n",
+ "sns.countplot(ax=ax1, data=df, x = 'output',palette = color_palette)\n",
+ "ax1.set_xlabel(\"\")\n",
+ "ax1.set_ylabel(\"\")\n",
+ "ax1.set_xticklabels([\"Low chances of attack(0)\",\"High chances of attack(1)\"])\n",
+ "\n",
+ "ax0.spines[\"top\"].set_visible(False)\n",
+ "ax0.spines[\"left\"].set_visible(False)\n",
+ "ax0.spines[\"bottom\"].set_visible(False)\n",
+ "ax0.spines[\"right\"].set_visible(False)\n",
+ "ax1.spines[\"top\"].set_visible(False)\n",
+ "ax1.spines[\"left\"].set_visible(False)\n",
+ "ax1.spines[\"right\"].set_visible(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Bivariate Analysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- ## Correlation matrix of continuous features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " age | \n",
+ " trtbps | \n",
+ " chol | \n",
+ " thalachh | \n",
+ " oldpeak | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " age | \n",
+ " 1.000000 | \n",
+ " 0.279351 | \n",
+ " 0.213678 | \n",
+ " -0.398522 | \n",
+ " 0.210013 | \n",
+ "
\n",
+ " \n",
+ " trtbps | \n",
+ " 0.279351 | \n",
+ " 1.000000 | \n",
+ " 0.123174 | \n",
+ " -0.046698 | \n",
+ " 0.193216 | \n",
+ "
\n",
+ " \n",
+ " chol | \n",
+ " 0.213678 | \n",
+ " 0.123174 | \n",
+ " 1.000000 | \n",
+ " -0.009940 | \n",
+ " 0.053952 | \n",
+ "
\n",
+ " \n",
+ " thalachh | \n",
+ " -0.398522 | \n",
+ " -0.046698 | \n",
+ " -0.009940 | \n",
+ " 1.000000 | \n",
+ " -0.344187 | \n",
+ "
\n",
+ " \n",
+ " oldpeak | \n",
+ " 0.210013 | \n",
+ " 0.193216 | \n",
+ " 0.053952 | \n",
+ " -0.344187 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " age trtbps chol thalachh oldpeak\n",
+ "age 1.000000 0.279351 0.213678 -0.398522 0.210013\n",
+ "trtbps 0.279351 1.000000 0.123174 -0.046698 0.193216\n",
+ "chol 0.213678 0.123174 1.000000 -0.009940 0.053952\n",
+ "thalachh -0.398522 -0.046698 -0.009940 1.000000 -0.344187\n",
+ "oldpeak 0.210013 0.193216 0.053952 -0.344187 1.000000"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_corr = df[con_cols].corr().transpose()\n",
+ "df_corr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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