Stroke Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A stroke occurs when the blood supply to part of your brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. Brain cells begin to die in minutes. A stroke is a medical emergency, and prompt treatment is crucial. Early action can reduce brain damage and other complications. The good news is that many fewer Americans die of stroke now than in the past. Effective treatments can also help prevent disability from stroke.\n",
    "\n",
    "This project for understand what are the reasons that cause stroke to peoeple and see if we can succefully detect stroke on some features using ML technics. There are several myths about stroke.  We will test the validity of a few of them:\n",
    "\n",
    "1. “Is stroke only old people’s problem?”\n",
    "2. “People living in the city are at high risk of stroke?”\n",
    "3. “Smokers are more prone to stroke?”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stroke Prediction Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Context\n",
    "According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths.\n",
    "This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1) id: unique identifier<br>\n",
    "2) gender: \"Male\", \"Female\" or \"Other\"<br>\n",
    "3) age: age of the patient<br>\n",
    "4) hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension<br>\n",
    "5) heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease<br>\n",
    "6) ever_married: \"No\" or \"Yes\"<br>\n",
    "7) work_type: \"children\", \"Govt_jov\", \"Never_worked\", \"Private\" or \"Self-employed\"<br>\n",
    "8) Residence_type: \"Rural\" or \"Urban\"<br>\n",
    "9) avg_glucose_level: average glucose level in blood<br>\n",
    "10) bmi: body mass index<br>\n",
    "11) smoking_status: \"formerly smoked\", \"never smoked\", \"smokes\" or \"Unknown\"*<br>\n",
    "12) stroke: 1 if the patient had a stroke or 0 if not<br><br>\n",
    "*Note: \"Unknown\" in smoking_status means that the information is unavailable for this patient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/fedesoriano/stroke-prediction-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "from sklearn.tree import DecisionTreeRegressor,DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import classification_report,confusion_matrix,f1_score\n",
    "from plotly.subplots import make_subplots\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, ConfusionMatrixDisplay, precision_score, recall_score, f1_score, classification_report, roc_curve, plot_roc_curve, auc, precision_recall_curve, plot_precision_recall_curve, average_precision_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"healthcare-dataset-stroke-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</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",
       "4   1665  Female  79.0             1              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",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5110, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "id\n",
      "16380    1\n",
      "58061    1\n",
      "69918    1\n",
      "23238    1\n",
      "4807     1\n",
      "        ..\n",
      "35085    1\n",
      "44375    1\n",
      "5464     1\n",
      "50522    1\n",
      "49152    1\n",
      "Name: id, Length: 5110, dtype: int64\n",
      "\n",
      "gender\n",
      "Female    2994\n",
      "Male      2115\n",
      "Other        1\n",
      "Name: gender, dtype: int64\n",
      "\n",
      "age\n",
      "78.00    102\n",
      "57.00     95\n",
      "52.00     90\n",
      "54.00     87\n",
      "51.00     86\n",
      "        ... \n",
      "1.40       3\n",
      "0.48       3\n",
      "0.16       3\n",
      "0.08       2\n",
      "0.40       2\n",
      "Name: age, Length: 104, dtype: int64\n",
      "\n",
      "hypertension\n",
      "0    4612\n",
      "1     498\n",
      "Name: hypertension, dtype: int64\n",
      "\n",
      "heart_disease\n",
      "0    4834\n",
      "1     276\n",
      "Name: heart_disease, dtype: int64\n",
      "\n",
      "ever_married\n",
      "Yes    3353\n",
      "No     1757\n",
      "Name: ever_married, dtype: int64\n",
      "\n",
      "work_type\n",
      "Private          2925\n",
      "Self-employed     819\n",
      "children          687\n",
      "Govt_job          657\n",
      "Never_worked       22\n",
      "Name: work_type, dtype: int64\n",
      "\n",
      "Residence_type\n",
      "Urban    2596\n",
      "Rural    2514\n",
      "Name: Residence_type, dtype: int64\n",
      "\n",
      "avg_glucose_level\n",
      "93.88     6\n",
      "83.16     5\n",
      "91.85     5\n",
      "91.68     5\n",
      "73.00     5\n",
      "         ..\n",
      "162.14    1\n",
      "111.77    1\n",
      "90.91     1\n",
      "103.50    1\n",
      "88.02     1\n",
      "Name: avg_glucose_level, Length: 3979, dtype: int64\n",
      "\n",
      "bmi\n",
      "28.7    41\n",
      "28.4    38\n",
      "26.7    37\n",
      "27.7    37\n",
      "26.1    37\n",
      "        ..\n",
      "13.9     1\n",
      "49.9     1\n",
      "40.6     1\n",
      "52.3     1\n",
      "44.1     1\n",
      "Name: bmi, Length: 418, dtype: int64\n",
      "\n",
      "smoking_status\n",
      "never smoked       1892\n",
      "Unknown            1544\n",
      "formerly smoked     885\n",
      "smokes              789\n",
      "Name: smoking_status, dtype: int64\n",
      "\n",
      "stroke\n",
      "0    4861\n",
      "1     249\n",
      "Name: stroke, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# All unique values and value counts of every column.\n",
    "for column in df.columns:\n",
    "    print(\"\\n\" + column)\n",
    "    print(df[column].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is there any null values?\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'bmi' column has 201 nan values. \n",
    "# This is not too much, so we will drop this rows\n",
    "# Drop null rows by 'bmi' column\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop 'id' column\n",
    "df = df.drop('id', axis=1)\n",
    "\n",
    "# Round 'age' column and convert data type to integer\n",
    "df.age = df.age.round().astype('int')\n",
    "\n",
    "# Drop 'Other' gendered individuals for simplify the mathematical compuations. Because only one patient has 'other'\n",
    "df.drop(df.loc[df['gender']=='Other'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis -EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation of values with each other\n",
    "correlation = df.corr()\n",
    "fig, axes = plt.subplots(figsize=(7, 7))\n",
    "sns.heatmap(correlation, vmax=.8, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4699\n",
       "1     209\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GfqWqHknyWuBG4JIp/j7p2/gEt/QcJPkBBtOg7AQ+XlWfSfIFYP2RsEhyCDi5qg4neSfwPVX1rrbtPcACg7m27mYQGj9bVXuSvKRte3joK0+uqldO6edJx/DIQnoOqurf2tHCTwG/n+TvR+z2P1V1uC2Pmu79iP9i8HdDXg/sYXB6+CtV9erj2bP0nfCahfQcJHkZ8N9V9WHgfcCPAAeBlz7DkE8Dm5K8KMmLgZ8BPtO2fQPYBFyR5Oer6qvAo0kub9+VJK+a4M+RujyykJ6bHwL+IMk3gf8FrgJeB9yRZH9VvWF456r6bJLtwD2t9MGq+lyStW3715O8CbgzydeBXwBuSvLbwEkMrmd8fvI/SxrNaxaSpC5PQ0mSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpK7/A4Uduza0WCJNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize stroke distribution\n",
    "sns.countplot(x='stroke', data=df, palette='cool')\n",
    "df.stroke.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stroke positive patient ratio: 0.04258353708231459\n",
      "Stroke negative patient ratio: 0.9574164629176855\n"
     ]
    }
   ],
   "source": [
    "# ratio of stroke positive and stroke negative patient counts:\n",
    "print(\"Stroke positive patient ratio: \" + str(df.stroke.value_counts()[1]/len(df)))\n",
    "print(\"Stroke negative patient ratio: \" + str(df.stroke.value_counts()[0]/len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TCSYAYLI\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize age distribution\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.distplot(df['age'], bins=max(df['age']), kde=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age distribution of data is balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize age and stroke correlation.\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "sns.distplot(df[df['stroke'] == 0][\"age\"], bins=max(df['age']), kde=False)\n",
    "sns.distplot(df[df['stroke'] == 1][\"age\"], bins=max(df['age']), color='red', kde=False)\n",
    "\n",
    "plt.title('Stroke Negative vs Stroke Positive Patient Counts by Age', fontsize=15)\n",
    "plt.xlim([min(df['age']),max(df['age'])])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to above plot, we have balanced age distribution in stroke negative group. But stroke positive patients are stacked to the left.\n",
    "\n",
    "Let's look age ratio distribution in each groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TCSYAYLI\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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2L/QnOd/brZvAaNqvm+vwt1bvMrMTskqntgfTvQx/QZFrfYF5Ax/0/pTWul/ZtuJPMvnqpdQH7+32r8BIWrcr8CfGPzjnfp0ZYGandSOfuW7FB8hH4+v3hPF1RjO24m8d/mue37Z0Me3bgHvNbBL+pJgphQHAObcdXxL1bTP7EL4rlb+Z2RJXYMtl55wzs8uDZdgXHxB2+bOc793Zz74A/DlYrlOy7iRsxZfsHIW/6MiV72SY7f2skqhcXeYvCIb/Hfh3M5uOP+n/j5ktdc492MW8u3InfpnPwFdbGUfb4GwrPgjpaYvl1R0s+zT8rfdTspchT/2wQhWyH/a2F/AV+E+i7ba/DHbW8cynO8e7TOl6d84/HZ3LdrYm3cVzao+o5CwQbBin429jfizn9V38H/+xIPki4MScHeOTOZNcit9JpzjnFud5belBHifgD7xPOOeWdpXeObfJOXc1vi5R5sRfUOlOAQ4JTjSZvB2F36AzJ/u1wft+WWkm4m+rZSv0Kmwh8KmcWyKfxl9gLOhe1j3nXCK4NfQb/FVS0fuCyncbNbjtsjdtrwa7c3W6EBgVVFzNTLMSX3cr37q5AH/L8wEz+yDsvA36HL4+SL7tteDgzDnXgq+H8hXal5I8hm8t+3qeeWS26UXAGMtqxGG+EU7urdUKsoKiYNvILW0qeHsPgqDX8MHT5/BVF7L308cI6uvlyfurXUz+YXxd1rOCac/NuTWXnY8l+AYHIXyQ1Y75Dmjzba/Tg/fMttTd/b07+9la/C3M6cDc4GIH/Ik3DAzuYFuK03PdOg44597Bl6620PHFT3e2kW34/zKzjbwZ/F8Zj+FLznbkW/aupt+JzLkme3ufjA+AsxV63ChkPyxUQfN0zjXiS7kvNLP9ukrfiYXAxy2rEYz5/hWn0LoNFHr+AX/sPDIr3SR8467ncxN2cE4tCpWctToDXwH1d865hdkjzOxpfAu0c/BF+P+DrwNxr5n9Fr8zXoq/KkiDv31mZt8D/hrcInkAvxHvhb+q+pdgY+1IlZkdjr9PPwRfnH0B/qrm/I5+ZGY/x18dzMNfIRyMb8mSifBX4xskfNnMaoFEDw8aG/Gtuy7D75i/wtdDexDAObfWzBYBvzCzRvyJ5se0v/p+CzjDzM7E71DrOwgE/gNfAnmXmf0ZXzT+K+Ah51yHpYi5glKJK/BXuyvwt/P+D/CKc65bJXA9dE1wi+IOfH2zofj/80Da1kl6CzjNzB7E3y5f6pzLe0XrnHso2EZvM987/Bb8CakCf5skN33azL6Er6z/sJkdE5zEfgg8ZmbpYFw9vt7kafj+tN7uxnJejf+/j8TXycr4Db6V7ONm9gf8Bcxo/Da6wDl3C77k7hV8/bcf4bfXn+EDjuySmEfwB/pl+O3qQnyDmWyZE803zOxWoLGLQOo24Dv4llhfzxl3I34fnGf+SQsr8CU2s/B15H7b0USdcwkzuxN/oTeWnI5FzWwBvlTmNXxJwdfxda7anSACg4G3zewGfIvnWvyJ50f4dXpnD5e/W/uZc26FmZ2Arxd2k5md45xbamZXAbea2a/xpY3l+GBghnPua53Mvytd5i9Yzy8E6ZrwLQsjdNzBaXePibfhS4drad//2CP4SvqPmNmv8KUvNfiuH8qdcz3tWPst/PHxv83s3/ANBn5O663C7HSjzbegfQ3fUGdVnukVsh92J2+FzBP8eXQW8KyZ/REf5DTjq+t8Gd+wpau+9n6DL71+KFjH1fhGAq/ij6vdOf+AP0/+NVivTfiOnTfib8MXck4tDrebWyCU6gtfkvB2J+P/hL/yLQu+fwzfz1kLvsLgR/Ab1cU5vzsFvwE24ItBX8YfYCKdzOt6WlvzpfAb1NPBxjA4J+2xtG1RdDr+qmhTkJ+lwe+yW5t+AV/5OU4HffrkzCNfS8W5+BNV5sD2ADAx53fTgrQNQT7OoH1rmRH4E8lW2rZ2bZMuGHY8rf0bbaSA/pdyp4Uv3fsr/sSa6XvuFmBSF9tH3nXQ2X/RwXQy9ZhWZs3/MbL6KwrSfRhfktVAVkuy3HxkpR+JDx62Bf/HfODQnDSraNvPWRm+FODdzH+H767iQfy22oCv/Psbcra7nOleRtBaM2d4pjuIeTnDx+Fvr76P339W4esZfSArzeQgH81B/mYHeb0rK83oYNupC6b1a3xQs7MlXpDue8E0knTQz1nONuuC+bZbZnxQ9Dt8o5I4/oT5D+CoAo4xJwTTXkfQT1TWuMvxJ5d6/C3mJ4CPdDKtGH6/fjJY9iZ8/a+ryOlrrzvL39P9DH/C2o7v9sCC18X44KQFfzyaD3ypi3W0ijytE7uZvx/gA8LaYH0uBM7IGt9u2enGMREfGDWSp+Vh1n718+D/iOP38QeB07pzjMkz/lB8sN6Eb+R0Hu1bYZbj961MK97rg+Ft0hWyH9LaWvP0nN8VNM9OlqMMf5GyGH/h2Rwsz1W0PQbkXf9Z29vjwf+wHd+dx+icNIWcf64P8vHp4P9vwZ9rs7ftLs+pxXhlOs2TXWT+sTxPAcc5557o6/yI9Cfmnym7Avijc+5nfZ0fEdnzmdn1+ECskJa0u5Vua/ZQUJz6Ev6qaB98XbAltL2FIyI9YGYX4G9hvoMvFfwu/or72r7Ml4jI7qDgrOfK8LciRuOLzh8Gvuv6oLWfSD/Ugq8LOAl/a+N54ASX1beeiEh/pduaIiIiIiVEXWmIiIiIlBAFZyIiIiIlpF/VORsxYoSbMmVKX2dDREREpEsvvPDCZudcuyei9KvgbMqUKSxevCudMIuIiIjsHmaWt5GTbmuKiIiIlBAFZyIiIiIlRMGZiIiISAlRcCYiIiJSQhSciYiIiJQQBWciIiIiJUTBmYiIiEgJUXAmIiIiUkIUnImIiIiUEAVnIiIiIiVEwZmIiIhICelXz9YUERER6ZE5c/IPnz278PQdpe0mlZyJiIiIlBAFZyIiIiIlRMGZiIiISAlRcCYiIiJSQhSciYiIiJQQBWciIiIiJUTBmYiIiEgJUXAmIiIiUkIUnImIiIiUEAVnIiIiIiVEwZmIiIhICVFwJiIiIlJCFJyJiIiIlBAFZyIiIiIlRMGZiIiISAlRcCYiIiJSQhSciYiIiJQQBWciIiIiJUTBmYiIiEgJUXAmIiIiUkIUnImIiIiUEAVnIiIiIiVEwZmIiIhICVFwJiIiIlJCFJyJiIiIlBAFZyIiIiIlRMGZiIiISAlRcCYiIiJSQhSciYiIiJQQBWciIiIiJaSowZmZnWxmS81smZldmme8mdnvg/FLzOyQrHGXmNnrZvaamd1iZuXFzKuIiIhIKYgUa8JmFgauBE4E1gKLzOwe59wbWclOAaYHr8OAPwOHmdl44NvA/s65JjP7O3A2cH2x8isiIiKyS+bMyT989uxuTaaYJWezgGXOuRXOuThwK3BGTpozgBud9xwwxMzGBuMiQIWZRYBKYH0R8yoiIiJSEooZnI0H1mR9XxsM6zKNc24dcAWwGtgA1DrnHs43EzObbWaLzWzxpk2bei3zIiIiIn2hmMGZ5RnmCkljZkPxpWpTgXFAlZmdm28mzrk5zrmZzrmZI0eO3KUMi4iIiPS1YgZna4GJWd8n0P7WZEdpTgBWOuc2OecSwD+AI4uYVxEREZGSUMzgbBEw3cymmlkMX6H/npw09wBfClptHo6/fbkBfzvzcDOrNDMDjgfeLGJeRUREREpC0VprOueSZnYR8BAQBq51zr1uZhcE468C7gdOBZYBjcD5wbiFZjYXeBFIAi8BHTSBEBEREek/ihacATjn7scHYNnDrsr67IALO/jtz4CfFTN/IiIiIqVGTwgQERERKSEKzkRERERKiIIzERERkRKi4ExERESkhCg4ExERESkhCs5ERERESoiCMxEREZESouBMREREpIQUtRNaERERkT3anN3/gCKVnImIiIiUEAVnIiIiIiVEwZmIiIhICVFwJiIiIlJCFJyJiIiIlBAFZyIiIiIlRMGZiIiISAlRcCYiIiJSQhSciYiIiJQQBWciIiIiJUTBmYiIiEgJUXAmIiIiUkIUnImIiIiUEAVnIiIiIiVEwZmIiIhICVFwJiIiIlJCFJyJiIiIlBAFZyIiIiIlRMGZiIiISAlRcCYiIiJSQhSciYiIiJQQBWciIiIiJUTBmYiIiEgJUXAmIiIiUkIUnImIiIiUEAVnIiIiIiVEwZmIiIhICVFwJiIiIlJCFJyJiIiIlBAFZyIiIiIlRMGZiIiISAlRcCYiIiJSQhSciYiIiJQQBWciIiIiJUTBmYiIiEgJUXAmIiIiUkIUnImIiIiUEAVnIiIiIiWkqMGZmZ1sZkvNbJmZXZpnvJnZ74PxS8zskKxxQ8xsrpm9ZWZvmtkRxcyriIiISCkoWnBmZmHgSuAUYH/gHDPbPyfZKcD04DUb+HPWuN8BDzrn9gUOBN4sVl5FRERESkUxS85mAcuccyucc3HgVuCMnDRnADc67zlgiJmNNbMa4BjgLwDOubhzbnsR8yoiIiJSEooZnI0H1mR9XxsMKyTNXsAm4Doze8nMrjGzqiLmVURERKQkFDM4szzDXIFpIsAhwJ+dcwcDDUC7OmsAZjbbzBab2eJNmzbtSn5FRERE+lwxg7O1wMSs7xOA9QWmWQusdc4tDIbPxQdr7Tjn5jjnZjrnZo4cObJXMi4iIiLSV4oZnC0CppvZVDOLAWcD9+SkuQf4UtBq83Cg1jm3wTn3HrDGzPYJ0h0PvFHEvIqIiIiUhEixJuycS5rZRcBDQBi41jn3upldEIy/CrgfOBVYBjQC52dN4lvA34LAbkXOOBEREZF+qWjBGYBz7n58AJY97Kqszw64sIPfvgzMLGb+REREREqNnhAgIiIiUkKKWnImIiJSdHPmtB82e/buz4dIL1HJmYiIiEgJUXAmIiIiUkIUnImIiIiUEAVnIiIiIiVEwZmIiIhICVFwJiIiIlJCFJyJiIiIlBAFZyIiIiIlpKDgzMzuMLPTzEzBnIiIiEgRFRps/Rn4PPCOmf3SzPYtYp5EREREBqyCgjPn3KPOuS8AhwCrgEfM7BkzO9/MosXMoIiIiMhAUvBtSjMbDpwHfA14CfgdPlh7pCg5ExERERmACnrwuZn9A9gX+CvwCefchmDUbWa2uFiZExERERloCgrOgGucc/dnDzCzMudci3NuZhHyJSIiIjIgFXpb8z/yDHu2NzMiIiIiIl2UnJnZGGA8UGFmBwMWjKoBKoucNxERESmmOXPaD5s9e/fno7/Lt5470dVtzY/jGwFMAH6TNbwe+HG35iQiIiIiXeo0OHPO3QDcYGafcc7dsZvyJCIiIjJgdXVb81zn3E3AFDP7bu5459xv8vxMRERERHqoq9uaVcF7dbEzIiIiIiJd39a8Onj/+e7JjoiIiMjAVuiDz39tZjVmFjWzx8xss5mdW+zMiYiIiAw0hfZzdpJzrg44HVgLzAB+ULRciYiIiAxQhQZnmYebnwrc4pzbWqT8iIiIiAxohT6+6V4zewtoAr5pZiOB5uJlS0RERGRgKqjkzDl3KXAEMNM5lwAagDOKmTERERGRgajQkjOA/fD9nWX/5sZezo+IiIjIgFZQcGZmfwX2Bl4GUsFgh4IzERERkV5VaMnZTGB/55wrZmZEREREBrpCW2u+BowpZkZEREREpPCSsxHAG2b2PNCSGeic+2RRciUiIiIyQBUanF1WzEyIiIiIiFdQcOacm29mk4HpzrlHzawSCBc3ayIiIiIDT6HP1vw6MBe4Ohg0HrirWJkSERERGagKbRBwIXAUUAfgnHsHGFWsTImIiIgMVIUGZy3OuXjmS9ARrbrVEBEREellhQZn883sx0CFmZ0I3A7cW7xsiYiIiAxMhQZnlwKbgFeBbwD3Az8tVqZEREREBqpCW2umzewu4C7n3KYi50lERERkwOq05My8y8xsM/AWsNTMNpnZv++e7ImIiIgMLF3d1rwY30rzUOfccOfcMOAw4Cgzu6TouRMREREZYLoKzr4EnOOcW5kZ4JxbAZwbjBMRERGRXtRVcBZ1zm3OHRjUO4sWJ0siIiIiA1dXwVm8h+NEREREpAe6aq15oJnV5RluQHkR8iMiIiIyoHVacuacC1f+hX4AACAASURBVDvnavK8BjnnurytaWYnm9lSM1tmZpfmGW9m9vtg/BIzOyRnfNjMXjKz+7q/aCIiIiJ7nkI7oe02MwsDVwKnAPsD55jZ/jnJTgGmB6/ZwJ9zxn8HeLNYeRQREREpNUULzoBZwDLn3IrguZy3AmfkpDkDuNF5zwFDzGwsgJlNAE4DriliHkVERERKSkFPCOih8cCarO9r8X2kdZVmPLAB+B/gh8CgIuZRRERk95szp/2w2bN3fz5KWb51BN1bT70xjT5QzJIzyzPMFZLGzE4HNjrnXuhyJmazzWyxmS3etElPlhIREZE9WzGDs7XAxKzvE4D1BaY5Cvikma3C3w49zsxuyjcT59wc59xM59zMkSNH9lbeRURERPpEMYOzRcB0M5tqZjHgbOCenDT3AF8KWm0eDtQ65zY4537knJvgnJsS/O5x59y5RcyriIiISEkoWp0z51zSzC4CHgLCwLXOudfN7IJg/FXA/cCpwDKgETi/WPkRERERyWvbNli5ElxQ+yoUgjFjoLxvunQtZoMAnHP34wOw7GFXZX12wIVdTGMeMK8I2RMREZGBqL4ebr8dHnwQXngBVqxon8YMxo+HvfaCQw6Bfff1w3aDogZnIiIiIiVjxQqYPx8uuQQaG2HSJJg1C77+dVizBsJhny6ZhNWrfWna88/Dk0/CxIlwwglw6KGt6YpEwZmIiIj0b2vWwN13w6uv+luVX/oSnHceHH54a2lYbrcbBx3k3xMJWLgQHn0UrrvOl7addx5MmVK07Co4ExERkf6prg7mzvWlXxUVcOaZcNxx8K1vFT6NaBSOPhqOPBKWLIFbboFf/QpOPdW/ilCKpuBMRERE+hfn4Kab4LLLoKUFPv5xOOkkqKrq+TRDIV+aNn063HYb3HcfvP46XHghDOrd/vKL2ZWGiIiIyO61YQOcdpq/dTl6NPz0p/CpT+1aYJatqgq+8hVfT23tWrj8cti6tXemHVDJmYiIiPQPDz8MX/yib435u99BLOZLvIph5kwYMgT++Ed/m/M734Fx43pl0io5ExERkT1bMgk//rG/fTlyJCxaBN/+dvECs4xp0+D734d0Gq64wpfa9QIFZyIiIrLn2rTJ1yf7r//ytxqffx4+8IHdN/8JE+CHP/QNA/7wB6it3eVJKjgTERGRPdNLL/nbi888Azfc4LvDqKzc/fkYORIuusjfTv3jH6G5eZcmp+BMRERE9jy33QZHHeVvKS5Y4BsA9KXJk2H2bN+n2jXXQCrV40mpQYCIiPSd3I4/wZ/gBmo+SkG+ddGZ3b2enINf/AJ+9jPf/9jcub5VZin44AfhnHPg5pvhnnt8K9EeUHAmIiIie4ZEwrfG/NvffEnZnDlQVtbXuWrrox/1j3568EGYMaNH9d90W1NERERKX309/Pa3PjD7z/+E668vvcAs43Of891qXHddjxoIKDgTERGR0rZ+Pfzyl75E6u9/991mZJ6JWYpiMd9ytLkZ/vIXXy+uGxSciYiISOl64w3fyWs8Dt/7Hnz2s32do8KMG+frny1dCg891K2fqs6ZiIiIlKb58+HWW2HsWN9VxbBhfZ2j7jnySP/8zfvugwMPLPgJAio5ExERkdKSSvnblzff7CvU//CHe15gBv7W69ln+7pxN95Y8O1NBWciIiJSOurq4Iwz4LHH4Ljj4JvfhPLyvs5Vz9XU+AYCK1fC448X9BMFZyIiIlIa3nkHDj/cd0Nxzjk+qCn28zF3h1mzfB9od93lHzfVhX6wxCIiIrLHe+ghH8Rs3AgPPwzHHtvXOeo9ZvCFL/jnb950k+9ItxMKzkRERKTvOAeXXw6nngqTJsGiRf52Zn8zdCiceSa89Ra8+GKnSRWciYiISN9oaoJzz/UV/j/zGf8A86lT+zpXxfPRj8LEiXD77dDS0mEyBWciIiKy+61e7Z+Necstvsf/226Dqqq+zlVxhUK+9ea2bfDAAx0mUz9nIiIisnvdfTecfz4kk/7zJz7R1znafaZN840eHnmkwyQqORMREZHdo7kZvvUtX/dq6lR44YWBFZhlfPrTEOm4fEzBmYiIiBTf0qVwxBHwxz/CxRf7+mXTp/d1rvrG4MFw+ukdjtZtTREREem5OXO6TvPss75uWXU13Htvp4FJ0fJQao49FubOzTtKwZmIiIgUR2OjD8qefx5mzPA95I8f39e5Kg3RaIejFJyJiIhI73v1Vd/hal2dr1d26qkKzAqk4ExERER6T0MD3HEHPP00jBvnn405eXJf52qPouBMREREdp1z8NxzPjBraIBTToHTTuv09p3kp+BMREREds369XDzzf7B5XvtBd/5ju8JX3pEwZmIiIj0TEsL/POfvkPVigr44hfhyCN9T/jSYwrOREREpHuc8w/vnjsXtm71AdlnPuO7ypBdpuBMRERECrdsmQ/KVq70Ff5/8AP/SCLpNQrOREREpGvvvQd33gkvv+x7uNctzKJRcCYiIiIdq6uD++6Dp57yLS8/+Uk44QQoK+vrnPVbCs5ERESkvfp6X9F/3jxIJOCYY3zXGDU1fZ2zfk/BmYiIiLSqr4eHH4b58yEeh0MP9c/CHD26r3M2YCg4ExERkdagLFNSduih/pFLY8f2dc4GHAVnIiIiA9nmzfDYY7BgQWtQdtppMGZMX+dswFJwJiIiMhC9+CJccw288IL/PmuWf+SSgrI+p+BMREQGjjlzipN29uzu56UvOAcPPghXXAGPPw7l5XD88XDccTBsWN/lqzvregBQcCYiItLfbd8Of/0rXHUVvPEGjB8Pl1/uu8aoqOjr3EkO9RwnIiLSXy1aBF/9qu/J/9vf9o9XuuEGWLECvv99BWYlSiVnIiIi/cn27XD77XD11b4+WVWV783/G9+AQw7p69xJARSciYiI7OkaGuDee+GWW3ydsngcDjgArrwSvvAF/7gl2WMU9bammZ1sZkvNbJmZXZpnvJnZ74PxS8zskGD4RDN7wszeNLPXzew7xcyniIjIHqe5Ge6+G84+G0aNgnPOgcWL4cILYeFCWLIEvvlNBWZ7oKKVnJlZGLgSOBFYCywys3ucc29kJTsFmB68DgP+HLwnge855140s0HAC2b2SM5vRUREBg7nYNky31Hs/ffDE09AUxOMGAFf/rIP0o4+Wg8i7weKeVtzFrDMObcCwMxuBc4AsgOsM4AbnXMOeM7MhpjZWOfcBmADgHOu3szeBMbn/FZERKRnUilIJiGdhnAYIpHSC2oaGuCVV3y9sQUL4Mkn4b33/Li994avfc0/VuljH/OtLqXfKGZwNh5Yk/V9Lb5UrKs04wkCMwAzmwIcDCwsRiZFRKQfSadh0ya46y5YuRJWrfKv99+HLVtg/Xp/OzCdbv9bs9ZALRz2fYBVVLS+53tlxj31lH8g+ODB/lVT46fRVV4bG6G21r/+8hd45x14+2146y1YurQ1n+PH+77IjjnGB2MzZvT2mpMSUszgzPIMc91JY2bVwB3Axc65urwzMZsNzAaYNGlSz3IqIiJ7Hudg9Wp4/nlYvtwHYevW+UcQZQwaBFOm+F7vp071JU/l5RCLtZaWpVKtJWnJZOvn5mb/amryLSA3bPCfm5raB3dXXtk+f1VVrcFaKNQ67aYmHyg2N7f/TTQK06b54Ouss3zryoMPhgkTfPAoA0Ixg7O1wMSs7xOA9YWmMbMoPjD7m3PuHx3NxDk3B5gDMHPmzNzgT0RE+pP6ev8cyIce8q+VK/3wsjIfhB1zjC9luugiH+QMGdI2qOmNnuid8wFgJlBravKlWnV1raVgtbVtvzvXtkRu9Wpf4lZZ6YO3IUPgX/8VJk706aTfWLhya7d/U8wtYBEw3cymAuuAs4HP56S5B7goqI92GFDrnNtgZgb8BXjTOfebIuZRRERK3bZtcM89cMcdvjJ8S4vvTPW44+CSS/wty/Hj295GPPTQ4uXHzJe8xWKtLSFPPLF708gXJE6duut5k36haMGZcy5pZhcBDwFh4Frn3OtmdkEw/irgfuBUYBnQCJwf/Pwo4IvAq2b2cjDsx865+3s7nzcvXN3huM8fptukIiJ9IpGABx6AG2/0/XfF475U6YIL4Iwz4KijfHAEei6j9DtFLTsNgqn7c4ZdlfXZARfm+d0C8tdHExGR/mzTJrj0Urj2Wv955EjfV9c55/jSMNW7kgFAN7ZFRKRvOQevv+7rkr3xhr89+YlP+GdCfvzj6iZCBhwFZyIi0iu6XU2kpQWefhoefdR3cTFkiO+366qrfB2yHlq4civLO8hLbsVnkVKk4ExEBoaO6iXNnr1789GRfPkrZt56o55WN/KXHbhF62qZfudN7PP366jYssl3E3H++TBzpm+pmBWYZf9u77tubjPNw6YO24XM9wPd+Q9LZTvvDQOgjqGCMxER6ZZM1wAdlU51pHzLJvb/65/Z++5biDY1suGwY1h1wifY72OzVJdMJIuCMxGREpLdJ1Ju8LM7W5D3pG+mjpRt28J+N13NjLk3EEomePekM3jz87PZPn0/9r7rZhau2tYmfXeDPpH+RsGZiIgAkHRQmw5RmwrxjquggTA7CLd5byZMHCNOiO3PrCKRSpNIpUmmHcmUCx7x4nAOBjfWce7Tczlz4V2UJeI8eOBx3HDcubw3ehKR943oplUMdpOIkaaCNNWkqCJF07tbqYiGqYhFqIiFqYqFqSrT6UqKr7OLkt15G11bu4i001HFbvX9V/pSaUd9c4LtjQlqmzp4NSaofW8Y29MhatNGXRCQ7XDZD/4e3W7aZaSpIEUMR4w0riVJJGyURcNUh4xwyDAzqhrrOXPebXxy3u2UtzSx4JDjue2U83l31CSSqTSxlCORTtOSSNJAGQmMxiD4S2Pw4rp28w4ZDHH7MpQEw0gwlCSLtkUYHUkzIZJkUjTFmHCKsO6OSj+g4ExE9jh7eufR+fK/98qtJJwPUpoI00iI5Zt20JJI05JM0ZxIsa0xTn1zkvrmBDtakuxoTvrvLX5YbVOC+uZkp/Mui4QYXBFlSCJCKBGnihQjgxKr6g7eM58j1vYJecs/1rbtY6Shnn1vu5Z9b76G2I463j3+NF776sXU7jWDo/C9i7db7qxK/s5BEyHeOOnTNCVSNMZTNCVSNLQkqWtOwNKlbCXKe5TxJlU8vK3tKSxMmpEkGEUN5S+tY1hVjBHVMUYOKmdYVYxwSJGb7BkUnImIdKGrksR4Mr0zWKrLDpxaEsG7D6J2NCfZ0ZLkrQ11NCfTtCRSNCfTNCdSxN0HSBJqO4MFK9t8vXfJBmLhEIPKI1SXR6guizCoPALOMaK6jIlDK6mIhYNbgv79kweNY0hFlMEVUWoqopRHg0cczZnTa/XKIg07mDH3Bvb729WU1dWy5qMf59WvXcL26ft1azpmUEmaoVUxhuYZv/fbj7f5HnfGVqJsJMpGYjtf7xNj2fpaGuOpnWnDZgyrjvFkahh7x5JMiyaYFkuyVzRJVag4j2Xu9CKiKHOU/kLBmYgMeM45rn961c6SmqaEL6lqigfvifTOYZnhTYkUv3vsbeqakjQlUl3OIxIyqst9MJVMOcoiYWoqooyMhCiPhhmz8i0qSFNJiorg9uH2oz9GWTRMeSREWTTMl4+cTFkk3G7anQUBh04pXj2ZcFMjM+64kf1uupry7VtZd9TxLPn6JWzb94NFm2e2mDnGEGcMcaChzbjlp32e5kSKzTta2Fjfwqbg9fbmCI80lpPKegjNuHCSvWNJ9o0l2C94TYsliaqgTfqIgjMR6VduXrga5xwtyTQNLUn/iqeoWOEYPHQQW9NhtqZCbAleW//rMbY0xIkn0x1O04CyaMiXSEXDlEfDjBxUxgfG1VBT7kukBpVHGFQe3VmaNSgo2aouj1BTHqUsEsKC7iLy3tZcNb/dsOUjq9t8zxeY9QVLJBj90kI+dPV/U7FtM+sP/yivfv27bPnAQX2dtTbKo2EmDK1kwtDKncM+/8qbxB2sTkRYloiwLB5heSLKO/EI19dWkQhKLyOkmUALk2lmMk2cPDbK/rEEQ8LFKWUTyabgTEQK1ld1vZxz1DUl2dLQwtaGOG+sr6WhJUVD3N8mzARg2cFYKp17Ep0A26CMFDWkqCFJDXHGjhnNtFHVVMUiVMZabweWR1tvD8YiIUIF9MMVT6bZmoyztSFenBXRxyyZYNTLixj33DxiO+rZcOjRPPX1S9j8oZl9nbVuiRlMiyWZFktCVevwZ1ZsZQNlvEs5q6jgXcp5hWrmM5QbN/g048JJ9itLcEAswYHlcT4YS7BizWZ1/yG9SsGZiOx2qbRje6MPYrY0ZL3viLO1oWXnsMzwbQ1xku2CLS8WCVFdFqEqFmZwRZRxgyuoKvNdL1SVRaiKRagqC7P/vH9SQ5Ky3ErtH85XTV2yWTLJyCWLGf/sE8Tq66ibOJVlnzyHl7/9k77OWq8KG0yghQm0cBS1O4dvdxEqxozkzXiUN+JR3miJ8kRjuW9ZCgxnOGMWvsv4IRVMGFrJ+CEVVMRKo5RT9kwKzopA3RDIQJF2juag/tXLa7ZT25Rge2OcuqbWrhy2NyV4fV2tb3UXlG41xVN0dHNoUHmE4VUxhlXFmDC0kgMnDGFYdWznsGFVMRat3LYzAIuGQx1Mqa2Rlui9BR8gLJVixKsvMP6ZJyir2079hMksP/0s6ibv3ddZ262GWBLe38BBQObGbTMhVlLOcipZQQVv1Tbz+vq6nb8ZXhVj/FAfrE0aWsG4IRVEsrbVfI0xMqVvOleUpt3ZB9qACM46uxUj0lPFuMXXF4F9MpWmPmhlWN+cpK4pwWtBMJWp+N4UT9GYSNEcT9GYSO4c3pxoraf134+83W7alUFplnNQEQszalAZVcOrWku2YhE+ceA4hlXFGF4dY2hljFik62Br/fbmXl0H0palUgx//WXGP/0Y5bXb2DFuIitO+TR1U6bpMUuBckuzH43sRyMAy0/6CE3xFOu2N7F2WyNrtzXx7pZGlqz1JXDhkDFucDmThlUyaXgVQ12EYdZ5tycycA2I4Eykv3LO0ZxIU9+coK45QW1TMvgcvDdlgi7/OTOuLugPq6450aa7gXxCRmtv7dEQg8qijBpU3qa7hspYmJMPGMOQSt9lw+CKGIMrojsDrc4C2aOnj+jVddKf9eRCs6Pf7J2nFMBSSUa89hLjnnnCB2VjxvPWSZ+kdq99FJQVoCIWZtqoaqaNam3IUd+cYM3WRt7d2sjqrY0sXLmVp5dv4Rb2Y7iLM4PGna9UOk0kVFhJsPRvCs6kJPW0tLPUbwfEk2ma4r4ie2M8tfNz5v2Fd7cST6b9K9X6vnDlFhpaUjQlkjtLtzIBWCLVeeuxSMh2tib0LQsjjKyupqbCty6sKQ/GZaV5etlmXzk+6ivDWwEn5uP3a9+jvOwZMnXKxj03n7K67ewYO4GlJ3yC7dP27XdBWW8+M7QQg8qj7D9uMPuPGwxAMp1mw/ZmGuc/xdtU8g6VPMsQACL3vsH4oRW8u6WBgycN5ZDJQxg1qLzN9LKPjbkB9u58vJAUl4KzPcCe3ht6qSj0lmE67WhOpnwwFNzGy3zOBFKN8RQL3tnUJoCKJx3xZIp4Ks1dL62jMeEDsMaWFI1BMNZRpfZ8DF/ZPRYOsam+hYqgNeHwqhhThlflDa5qgu4cBmeNK48WFlxlW7m5oetEssezRIJRrzzPuIVPEquvo378JFae/Clqp07vd0FZqYiEQkwcVsnetoVT2QLAVhfhbSpZtNeHWb21keueXsXVT64AYOKwCg6ZNHTnK5V2etLBAKDgTApWikFiMp0OHm/jH3GzaNVWdrQk2wRE/pXkxXe3EU9lAqjgPZnmxmdXtZZetaQK6lA0WyRkO4OoaCREWSREItVCLByiKhbZWY8qFg61fe9iWCR4TiHsGUG46naWpuzHI2WE4nFGvbSQsc8/SaxhB3UTp7L8tM/6iv7dCMryTXv5mfn7vs+XtjvT7c+GWZLDqePw5U8AcNCUYbzWEuOl5hgvtjTx3Ks7uPvl9QBESbMXTUynkU3UMJ1GhqvuWr+j4Ez6lHOO+pYktY0JtjXG2d7oW/c9u2ILTVmlVI1BT+2ZIMy/p9v1ZfWHx5flnY8ZPujJCqCiYd8z++ThlVQGpVK+nyvfLYP/3tr3VVVZhIqof6+Mhfnnkg1Ew6HdchVbjNu8CqYGnnBzE6NfWsiY5xcQbWqgdvLeLDvjHOon7dXXWZMsZQYfLo/z4XLfX55zsD4V5qXmGA9sTPMOFTzEcO5jJADDXILpNDJtRSPTaWQvmtp1GXPYbl8K2RUKzqRX5J7ok6n0zucJHjRxCJvqm9lY38LGuhY2Bp9Xb2mkIZ6kszt9sUjIB0nRMGXRMEMro5RFyymLhCiLhCmLhlo/R0Kc8sExVJX5ntkzFdWryiKURULc8vyavPPoaanUzmcUyi5RkFh8sbpaxix+mlEvLyQcj7N96nTWHXU8OyZM7uusSQHMYHwkxfjqJkZu8vXMks5YRTnvBPXWllHBQny9thCOCa6ZvWhiKs1MpYkPxpNUxnTKL5bersuof0q6JZ5Mt2kNWN+cDF6JIBjzw/K1AAwZDK8uY9Qg/8ouhcqUUmVaAGZKq7rbcukj00f21qKK7PEGr3ibve67neFvvIw52LLfB9lw2DE0jh7X11mTThRyoo+YYxpNTKOJU4K6a3UuvDNYW0kFL1LDPHwjgct+9hB7jazmgHE17D+uhumjBzFj9CDGOVUvLEUKzoRkKs3Wxjib6+Ns3tGy8+VLuVpLutZta6Ilz/MHw9b6QOdhVWVMGV618zmDg8ojnDVzIqMGlTGsKtamE8ZilJioFKYtrY8ByDlGL36afW+9lvFPP0YqGmXjwYez4dCjiQ8Z2te5kyKqsRQfpp4PUw/426HbiLCCCuLHncjr62t5dsUW7grqrwFU21imxZLMiCWYHk0yLZZgSjTJ+EiKWA+Dtk47a+3ZJAccBWf9TKZD0dqmRN7X5h0tbNmRHYTF2dYYx+W5tVgeDTG6ppxRg8rYb0wNo2vKqSlrDboy7xWxcKfPHTxg/OAiLvGuK8WGDiLdFW5uYsqDd7HP7dcxZPlSmocOZ8ns75KoqCRZUdX1BKTfMYNhJBlGPYedOGPn8G0Ncd7ZuIO336/nncef4+14lMcby/l7qrWqhuEYF0kxMZJiUjTJpEiSidEUo8MpRobTjAinqAk5lboViYKz3cR3Fpqivtk/qHlHc5LmpO+eoTmRojmZpjmeojnpvzfF0zs/v7aulkTKkUilg5cjGbzPeXI5zYl00Ft7Km/JVraqWJgRg8oYUV3G1BFVHDplGMOryxhZHWNEdRkvrd5Odbmvs1VWYP9WXdmTS2/25LzLwFD5/nqm3/FXpt11M2V129k6Y3+e/ekVvHviJ0iXlQ+4lo/StaFVMWZNHcasqcNgyUM7h29NhVgWj7AmGWZ1IsKL21NsTMZY2hxjG+0D/Jg5RgbB2shwiiGhNNUhR62LUkGKCtI738tIE8ERXb2NaMg3pIqEjUjICIcMlwiTckYaSAMpB2ms3ee0gxSQDtK++V49BsFTTl3WZzhg7CCqQo5Kc1SH0lQGn/eEnkgUnHWiqxKVeDLN+3XNbKxv5v26Ft6v8+8LV2yhrjlBQ0uKlqR/xE1LMsVP7nqtW/OPhUOURUPgIBp0rRANt7YyHFQeYvroasojYcqjIcqDTkN9D+2tr5qsz11VYt/WqOcPipS8dJoxixYw7c6/MeGpR8A51h5zEks/9xU2HTRLlYikR4aF08yqiDMr+L6wtvX2ZNwZG4mxnQjbiTB42GA2pUJsSoXZlAqxJhnhtZSxIx1iB1VBmJTHn57pYO5jepjrTp4wsiH/4EpLU/WfjzK8KsbIQWWMHFTGmJpyJtVV0uBaGEOcYST6NIhTcFaARMoHYRu2N/N+fTObd7Rw1fzlrN3W2K6lYTRsVJX5XtaHVkYpj5ZTFg1THglx2F7Dfd2sssjObhkqYr6lYXnUB1gV0czn8M4uGnTbTUQAyrdsYq9/3s7ed9/CoHWraR4yjLfO/hrvfOaLNIyb2NfZkxLWYSfcBf4+Zo4JtDCBFgAOG9Lxhf5zK7bSQoimna8wLYRIAtPP/xzJlCOZTpNMu+CzIzTvCUL4hmNhXNZnCJn/HgbC5sM+/xneWF+HgzYvf1MW9hlbQ0PaaEwbDc5oSIdoyHzeZzKbd8TZWN/C8o072FjfQjI9FPD1MstIM9E1M5lmJtHMNBqZQjMRK7wj8V2h4CyHc44tO+LBc9AaWL21kU31LTuDsFg4xIjqGIdMHsqZB41jwtBKRtWUMbqmnNE15QytjPZ6lw0iMnBZKsXoxc8w7e6bmTD/YUKpJO8ffDhLvvF91hx7MulYWV9nUaQNMygnTTlpcpugHLbPqPw/eqGpR/NKWmOH4w6r6GTf+PSH2k4nlWbDVdfx4JpG3qeMdZSxmnIWUsNjQYvXKGn2dk3sQwMHsIN9aSRWpGBNwRlQ25Rg2cYdLNtYz7JNDTS0+N6Wy6MhJg2rZP+xNYwdXMHYweUMrYoRMlOgJSJFVbH5fQ688pdMffBOKje9R0vNEN7+7JdZdubnqZsyra+zJ9KrOm3huRueGRoJh5gYTfEhawBaH1/nHGwlwjtU8jZVvE0l9zGCuxlFlDT7ugYOZAeHUscYi/defnptSnsQ5xzrtjfx5oY63txQz3t1zQBUlUWYNrKKvUZUM2l4JSMHlXXaCrEU9OSWpyq5i5SmSOMOhr+xhBGvvUj1e+tIh8NsOPyjvPidf2PtR04gXVbe9UREuqGnnaf29HcdnX/27tHUOtcbXXqYwXCSDKeOw6kDoNmFeJMqXqWaV6jmJsZyE2OZ4Jo5lDqOZDuTrGWX8j5ggjPnHBtqm3l5zXZeXVdLbVMCAyYPr+KUA8YwbVQ1Y2rKe6V1oohIoWK125gw/yH2ue1aat5dUdUQygAAD2JJREFUTiidpmH0ON49/jRe+O7PaR6ujpVFSkm5pTmYeg4O+pPb6KIspobF1HA3I7mTUUx2TXyE7RzFdob14Nmn/T44W7O1kSeWbuTlNdvZVN9C2IwZo6s5cf/R7Dt6EJVlPVsFe0Lp056QR5GBKFa7jYnzHmTS4/czevEzhFJJmgcP5b1Dj2bzBw6maZRvuabATKT0jbIEp7KFU9lCnQvzDEN4iiHcxFj+xhgOcDv4CNs5jDrKrfPurjL6ZXBW25jgniXrufuldSx+dxsAU4ZXcuRB4/jg+MEl+XwxBVIi/Vv1mlWMf/oxxi94jFEvPUcolaJ+/CTe/PzXWX38aQx7c4m6wBDJo7efW1lMNZbiZLZwMltY72IsYAgLGMKfmMhfSHO4q+UEtjCdpk5399KLUnZBYzzF929/hXtfWU9LMs2M0dX84OP7ADC0MtbHuRORgcSSCUa+sojxCx5j/NOPU7N6BQDbp07nzS98g9XHncq2fQ7YGZANe+vVvsyuyICSr0Bk714OAsdZnLPYyGfdRt6mkieDQG0+Q5lMEye6jufXr4Kz5Zt2cP+rG/jMhyfw+VmT+MC4GsxMpVIiUnzOUbPqHcY8/zRjFj/NqBefI9ZQTyoa4/0PH8Hbn/0y6446joZxauktMpCYwT40sg+NnOveYwFDeIRhXMP4Dn/Tr4KzcUMqWPjj4xlUHu3rrAw4+R4Rs/z/t3fvMXJe5R3Hv8/Oxd6dvfmyvu3GXt+I4zrEhOA4JAqXOJDQNBe1qIkaoFUpQkoaqFpVlH9aWtGLVFWlooDSNBRESUWAhIgiCE2aAqkAJyGxHV/qJV47a3t3ndje+2UuT/94z+zOmHWyTnZ3xvP+PtLoPe95zztz5tnR+PH7njnn9tlObyjV6nxL/1TL33ahP3dlr+fOooEzNB07QsvRLpqP/pL0cDRAeLx1Kcd23cKJd76H3ndcR67hja1teaFLL1XL30VEZlZvBW7kNLv8NIep58PnaVdTydmyTFqJmYjMi7rsJJkTx2jqOUrT8WM09hwlPRIlY9mGDAPrNjLYuYmBdZuYbF2iRElEzssM3sL5J96tqeSs2un2qsjFIz1whrY9z7J877O07XmGpQdeIDkRzV003rKEwc6NDLWvY6hjHWNtK8HqKtxjEakVSs5EJPbSA2dZenAvSw/tjbYH9tB4sgeAQiLJ6Uu30XXH3STGxxhqX0e2qbnCPRaRWlZTydnpkUldnRKR15QcG6Gh9wSZ3uNkeo/za1/9Ao0nptfDHWpfy6tbr+DwHXfzyuVXcvqyt5JfXA9c+BgwEZE3oqaSMxGRIstlaT76EksO76e16yBr/vdJGvp7SQ8PTrUZb11K39uv4fDtv8PpLZdz5tJtTLa0VrDXIiJKzkSkBqSGh2g4dZL6/l5WvLCb1q6DNB85TCKXBSCfSjO+ZBkDnRsZbVvN6MrVjKxcQ76+QQP3RaTqKDkTkYuDO4tf7aelu4vm7i5ajhxm5e6naXiln9To8FSz0bZVnN20hRM738XZTVs4u3krg2vXs+G7D1ew8yIis6fkTESqS6FApvc4Ld2HaT7SFW27u2jp7iI9NH1LcrKxmYnmFs5s2sLoilWMtq1mbMVKDt31BxXsvIjIm6fkTEQWXrgK1tRzlKaebhpf7o7KLx+h+dhLJMen5/8ZW9rGYOdGum+8lcH1mxno3MRg52bGlq9g43cequCbEBGZH0rORGReJEeGyPSdJNPbQ0PvCRqPH4sSsZ6jNB0/WpaAFRJJRlZ3MNSxjv4rdzLQuZmB9VESpgH6IhI3Ss5E5MK4kxoeZPGrp6h/9RT1r/RFU1P0HSfTe4KGvqhcegsSokH5w+1rGeropO8d1zLU0cnQJZ0MdXQyunINntTXkYgIzHNyZmY3AZ8DEsAD7v635xy3cPwDwCjwu+7+3GzOFZE54k5ybJT04FnSg2dZVNwOnGXN00+SGhkiNTIcbYeHSI8McfXfffpXnmaiuZWRVWsYWd1B//YdjK5aw8jK9rBdw/iyFXgiUYE3KCJycZm35MzMEsA/AzcCPcBuM3vM3feXNLsZ2BweVwNfBK6e5bkyxyyfx3JZ6nJZEtksddksViznslAolLd3nyrX95/EvPz5Wg/vx+sSeCJBIZGkkEziiWS0nyxuU1PHqdPyNxfMncTEBMnRYZJjo6RGh0mNjJAcHSY1GrZjoyRHhkkPDZAeHJhKvqYTsQES2cnzvkS2PkO2sZFsponhjnVMZprovfp6xpe1MbasjbHlKxhdsYZcpnEB37iISO2azytnO4Aud38JwMz+A7gNKE2wbgO+6u4O/NTMWs1sNdA5i3Pnjju4Y4UC4FHSUSiEZCPUu5eUo8TEvDB9bihbwcueoy6fx/I56nK5qW1dPoeFbWn9jHXnnpvNUpedpC6XDfuTUV1++lgim43aF4/lctTlonIim41ep3Q/HyVideckX2/W5V/+pwv7M5hRSKaixC2ZnCoXEikKqWJ9qEumytrmU+lwTjiWSFJIpcrbp0J9MhW1TU0/R7Eunyw9J4m/ZsJoM70LLF+I/sb5fNjmos9BrrSuZJvLkpicoG5iguTEOHWTEySKj4kJEhPjU/t1oS45PkZqdJjk6Ah1+fys4putb2CyuZXJ5lYmmlsYXL+ZibA/2dwyVZ5oWTLVrv1Hj894tUtzg4mIzJ/5TM7agZdL9nuIro69Xpv2WZ57QX7jt66noe9kSKQcvBASLH/9k6tM8SpUIZUuTzCK+4mScnoRuYZMSdskhWQoT51bUp7p+UKbmRIVtyhBWfXzH4ea6YSld8d1mBemEpMoKQ3J6q8kKCUJaEg8rZiAhqt5xQQ2kZ0MCWaO5PhYWfup5DOfnU5mc1FdcULSalVIJMkvWkQ+HR6LFpNPL6IQ6rINjeSXLCefXkRucT25hgy5hgzZqW1jKDeSzYRtfcPUvidTF9wn3YYUEVl45vOUnJjZB4H3u/tHw/6HgB3u/oclbf4T+Bt3/0nYfwL4U2DD651b8hwfAz4WdrcB++blDV2clgOvVLoTVUTxKKd4lFM8yike5RSPcopHuTcaj3Xu3nZu5XxeOesBLinZ7wBOzLJNehbnAuDu9wP3A5jZM+5+1Zvrdu1QPMopHuUUj3KKRznFo5ziUU7xKDfX8ZjPEdi7gc1mtt7M0sCdwGPntHkM+LBFdgID7n5ylueKiIiI1Jx5u3Lm7jkzuxf4AdF0GA+6+4tm9vFw/EvA94im0egimkrj917r3Pnqq4iIiEi1mNd5ztz9e0QJWGndl0rKDtwz23Nn4f4L7WONUzzKKR7lFI9yikc5xaOc4lFO8Sg3p/GYtx8EiIiIiMiF06yfIiIiIlWkJpIzM7vJzA6ZWZeZfarS/VloZvagmfWb2b6SuqVm9kMzOxy2SyrZx4VkZpeY2X+b2QEze9HMPhHqYxkTM1tsZj83sxdCPD4T6mMZjyIzS5jZL8zsu2E/tvEws24z22tmz5vZM6EuzvFoNbNvmtnB8D1yTVzjYWaXhs9F8TFoZp+MazwAzOyPwnfpPjN7KHzHzmk8LvrkrGSpp5uBrcBdZra1sr1acP8G3HRO3aeAJ9x9M/BE2I+LHPDH7n4ZsBO4J3wm4hqTCeC97n4FsB24Kfw6Oq7xKPoEcKBkP+7xeI+7by+ZDiDO8fgc8H133wJcQfQ5iWU83P1Q+FxsB95O9OO9R4hpPMysHbgPuMrdtxH9aPFO5jgeF31yRskyUe4+CRSXeooNd/8RcPqc6tuAr4TyV4DbF7RTFeTuJ939uVAeIvpibSemMfHIcNhNhYcT03gAmFkH8OvAAyXVsY3HecQyHmbWDFwP/CuAu0+6+1liGo9z3AD80t2PEu94JIF6M0sCDUTzsM5pPGohOTvfElBxtzLMGUfYrqhwfyrCzDqBtwE/I8YxCbfwngf6gR+6e6zjAfwj0WokpQvKxjkeDjxuZs+GVVcgvvHYAJwCvhxuez9gZhniG49SdwIPhXIs4+Hux4G/B44BJ4nmZ32cOY5HLSRnM68+LbFnZo3At4BPuvtgpftTSe6eD7clOoAdZrat0n2qFDO7Beh392cr3Zcqcq27X0k0POQeM7u+0h2qoCRwJfBFd38bMEJMbtm9ljAh/K3Aw5XuSyWFsWS3AeuBNUDGzO6e69epheRsNstExVGfma0GCNv+CvdnQZlZiigx+3d3/3aojnVMAMLtmaeIxijGNR7XAreaWTfRMIj3mtnXiG88cPcTYdtPNJ5oB/GNRw/QE64uA3yTKFmLazyKbgaec/e+sB/XeOwCjrj7KXfPAt8G3skcx6MWkjMt9TSzx4CPhPJHgO9UsC8LysyMaLzIAXf/h5JDsYyJmbWZWWso1xN9uRwkpvFw9z9z9w537yT6vnjS3e8mpvEws4yZNRXLwPuAfcQ0Hu7eC7xsZpeGqhuA/cQ0HiXuYvqWJsQ3HseAnWbWEP6tuYFoXPOcxqMmJqE1sw8QjSEpLvX02Qp3aUGZ2UPAu4HlQB/w58CjwDeAtUQfpg+6+7k/GqhJZnYd8GNgL9Njij5NNO4sdjExs7cSDVBNEP2H7Bvu/pdmtowYxqOUmb0b+BN3vyWu8TCzDURXyyC6pfd1d/9sXOMBYGbbiX4skgZeIlpasI74xqOBaGz3BncfCHVx/nx8BvhtopkBfgF8FGhkDuNRE8mZiIiISK2ohduaIiIiIjVDyZmIiIhIFVFyJiIiIlJFlJyJiIiIVBElZyIiIiJVRMmZiIiISBVRciYiIiJSRZSciUjsmdmjYdHvF4sLf5vZ75vZ/5nZU2b2L2b2+VDfZmbfMrPd4XFtZXsvIrVGk9CKSOyZ2VJ3Px2Wt9oNvB94mmhNxSHgSeAFd7/XzL4OfMHdf2Jma4EfuPtlFeu8iNScZKU7ICJSBe4zsztC+RLgQ8D/FJdfMbOHgbeE47uArdGyegA0m1mTuw8tZIdFpHYpORORWAvra+4CrnH3UTN7CjgEnO9qWF1oO7YwPRSRuNGYMxGJuxbgTEjMtgA7gQbgXWa2xMySwG+WtH8cuLe4ExbJFhGZM0rORCTuvg8kzWwP8FfAT4HjwF8DPwP+C9gPDIT29wFXmdkeM9sPfHzhuywitUw/CBARmYGZNbr7cLhy9gjwoLs/Uul+iUjt05UzEZGZ/YWZPQ/sA44Aj1a4PyISE7pyJiIiIlJFdOVMREREpIooORMRERGpIkrORERERKqIkjMRERGRKqLkTERERKSKKDkTERERqSL/Dzf9zRqVkq0hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize age and stroke correlation\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "sns.distplot(df[df['stroke'] == 0][\"age\"], bins=max(df['age']), kde=True)\n",
    "sns.distplot(df[df['stroke'] == 1][\"age\"], bins=max(df['age']), color='red', kde=True)\n",
    "\n",
    "plt.title('Age Distributions in Stroke Negative vs Stroke Positive Patient Groups', fontsize=15)\n",
    "plt.xlim([min(df['age']),max(df['age'])])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer of Question 1:**\n",
    "\n",
    "We can say that stroke ratio in age groups, elder people are more prone to stroke. But even a teenager can have a stroke. So, age has a direct impact on stroke but being young doesn’t mean you won’t have a stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data distributions\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, sharey=True)\n",
    "sns.countplot(x='gender', data=df, hue='stroke', ax=ax1, palette='cool');\n",
    "sns.countplot(x='ever_married', data=df, hue='stroke', ax=ax2, palette='cool');\n",
    "sns.countplot(x='heart_disease', data=df, hue='stroke', ax=ax3, palette='cool');\n",
    "fig.set_figwidth(15)\n",
    "fig.set_figheight(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rate of stroke is higher in married people, but this may be a result of bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)\n",
    "sns.countplot(x='heart_disease', data=df, hue='stroke', ax=ax1, palette='cool')\n",
    "sns.countplot(x='stroke', data=df, hue='heart_disease', ax=ax2, palette='cool')\n",
    "fig.set_figwidth(10)\n",
    "fig.set_figheight(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stroke and heart disesase surprisingly negative correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)\n",
    "sns.countplot(x='Residence_type', data=df, hue='stroke', ax=ax1, palette='cool')\n",
    "sns.countplot(x='stroke', data=df, hue='Residence_type', ax=ax2, palette='cool')\n",
    "fig.set_figwidth(10)\n",
    "fig.set_figheight(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer of Question 2:**\n",
    "\n",
    "First plot shows that the numbers and rates of stroke and non-stroke patients are very similar in both localization types.\n",
    "The second plot also supports this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Work type and stroke relationship\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)\n",
    "sns.countplot(x='work_type', data=df, hue='stroke', ax=ax1, palette='cool')\n",
    "sns.countplot(x='stroke', data=df, hue='work_type', ax=ax2, palette='cool')\n",
    "fig.set_figwidth(15)\n",
    "fig.set_figheight(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Smoking status and stroke relationship\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)\n",
    "sns.countplot(x='smoking_status', data=df, hue='stroke', ax=ax1, palette='cool')\n",
    "sns.countplot(x='stroke', data=df, hue='smoking_status', ax=ax2, palette='cool')\n",
    "fig.set_figwidth(15)\n",
    "fig.set_figheight(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer of Question 3:**\n",
    "    \n",
    "In the non-stroke patient group; the most common smoking status is never smoked, followed by formerly smoked and smokers. This is an expected result. But the situation of smokers is not at all as expected.\n",
    "According to data of smokers, most common pattern in the stroke group is non-smokers. Strange but true. This may be due to other factors affecting stroke and a biased assessment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "stroke_df = df[df[\"stroke\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VVzcua1PkIV2tD/cXDct9RevD/WU9zKmT+iVJkuajuXbIUpIkad4xkEmSJDVmIJuDktydZFWS1Uk+mmTbGfp9aZbrX5Tk9zesSm2oJG9OcnWSK/u/95Puo+9p/XX6SPKMfrlVSbYZU60/GfH67/n9JM0dM/3fTPKwJOf00wck+fQMy38ryYJR17k5MJDNTT+rqn2q6vHAXcCrB2f2t5iiqp46y/UvAgxkDSV5CvBC4IlVtTfwHOA7Qy7+B8C7+n3kZ6OqUZu+JLsm+UiSbya5LMmXk/zuLNZzQJL7fL1J8uokr7ifPhck8bpUm4GquqWqZvUhKh3zxxT+g8x9FwO/0b8gfj7JR4Cr4FejFkn+Kcl/nVyg/0Tze/1I2MVJLu9/Jl9QTwSe0Y+wvCHJFknemeSr/WjNH437l5yHFgK3V9WdAFV1e1XdkmTfJBf2b56fTXKvG6Yl+UPgJcBbk5wxdaVJjkhyaf+3ff9keE/ykyTv6Nf7b0n2698cv5nkoL7PkUnOTfKZJNclOX6a9affV1YnuSrJS/v205McPNDvjCQHzbRv9ev5uyTXJPlnYJeN9Q+rTpIAnwQuqqpHVtW+dJcS2n0WqzsAuM9AVlUnV9WHZ7FuzSFJXtH/X/1aktP75v2TfKl/vZgcqV+UZPU0y++c5PwkVyR5P/0F3/v+1yY5Cbgc2CPJnw28Nvz5lH7/0B8JOH9cRwKaqyp/5tgP8JP+cUvgXOA1dC+IPwUeMU2/3wWW9dMPpBtp2QbYFti6b98TWNlPHwB8emA9RwNv6acfBKwc3I4/I/kbbwesAr4BnAQ8E9gK+BIw0fd5Kd2lXwBOAw6dOj1lnY8FPgVs1T8/CXhFP13AC/rpTwDn99v7LWBV334ksAbYud9/VgOLp+xrvwcsp7ssza7ATXTh8pnAJ/s+2wM39vvvtPsW8OKB9TwM+OF0v5M/G7SPLQEunGHe1sCpdB/urgCe1bd/BdhroN8FwL7A94Dv9vvsM2ZY59uAP+2n9wEuAa7s97cdB9b3N/1+vhrYr/W/kz/3+hvuRXc7wgX9853615uP0g3gPI7uftPQHWlZ3U8fQP+eAvwt8NZ++nf6154Fff9fAk/u5z2P7rIY6df9aWD/vt86YJ++39nAEa3/bcbxM6euQ6Z7bJNkVT99MfBBuk+nl1bVjdP0/1fgb5M8CDiQ7hPxz5JsD/xdkn2Au4FHz7C95wF7D5wnsD1dgJtuW9oIquonSfYFngE8C/gn4H8DjweWd4MbbEEXkIa1hO7N86v98tsAt/Xz7gI+009fBdxZVb9IchXdC+Ck5VV1B0CSjwNPpwtRk54OnFlVdwO3JrkQ+O2qOi/J+5LsQhe2PlbddQVn2rf2H1jPLUk+tx6/p4azF91IxHSOAaiq/5LkN4HzkzwaOItuBPb4fnT2YVV1WZKT6UL5u4bc9oeB11XVhUn+AjgeeH0/78FV9dQk+wMfotvnNTc8Gzinqm4HqKrv968ln6yqXwLXJNn1ftaxP91rAFX1z0l+MDDv21V1ST/9vP7niv75dnSvDTcBN1bV5HvgZdz7NWqzZSCbm35WVfsMNvT/KX46Xeeq+nmSC4Dn042qnNnPegNwK90oyAOAn8+wvdC9eH52gyvX0PowcgFwQR+MjgGurqqnDLN8kj3oRsQATqb7Oy6rquOm6f6L6j9u0n1KnTxU+sskg68DUy9MOPX5dPebnXQ63flthwNHDfT/tX2rP8TuRRDHKMn76AL1XXS3qXsvQFV9Pcm36T6wnU03cnk8XTD76Cy2sz2wQ1Vd2Dctm7KeM/vtXpTkoUl2qKofzu630kYWpv9/eeeUPvdnpv/bg+9hAf5PVb3/XgUki6Zs7266D5ebPc8h23ycBbySbsRl8s1ve2BN/8nm5XQjLgA/Bh4ysOxngdck2QogyaOTPHgsVc9TSR6TZM+Bpn2Aa4GJdCf8k2SrJHvNtI6q+k51J/bvU1UnAyuAQ/tRKpLslOTh61nac/vltgEOAb44Zf5FwEv7c8Mm6D4NX9rPO41+FKR+dYeNmfati4DD+/UspBsl1MZ1NfDEySdVdQzdKOoEM7ypVtV3gTuS7E334e6sEdR1f6Ff7awAXpJkZ+heQ2axjovoPpiR5AXAjjP0+yxwVJLt+r67Tb52zVcGss3H+XRvjv9WVXf1bScBS5NcQvfpd/LTyZXAuv6kzTcAHwCuAS7vT9J8P46ejtp2wLL+pPYr6c7NeCtwKPCOJF+jO19n6G/SVtU1wFvoDj9dSTfSsfC+l/o1X6Ab6VpFd9hx5ZT5n6Dbf74GfA54Y1V9r9/+rXSh8tSB/jPtW58Arqc7fPr3wIVoY/scsHWS1wy0TV5CZ/BN89HAf6Y7dwi6EPZGYPuquqpvm/ohbkZV9SPgB0me0Te9nHv/fSe/CPJ04Ed9f80B/QepE4AL+9egd89iNX9O9yWAy+kOSd40w7bOBz4CfLk/QnAOQ+5jmytvnSQJ6L5lSXcS/2tnufy2dAHrib7Jzg396ONfA08C1tJ9KDuZ7stCJ9Odc7gO+B9V9fl+mV3pTuB/e1VNfvPt0XRvmL+kOwR98TTbehvw46r6q/681ZPpAuA3gVdW1Q/6Uyu+TPclkIcCR1XVpVPXJc1HjoJI2mBJnkN3gva7DWNzR1WtoTunbzpHzrDMrUx5b6iqbwB738/mdga+3fdfBTx5mnUfcD/rkOYtR8gkSRskydvpvlT0gslv6UpaP46QSZKGluTNwGFTmj9aVfu1qEfaXDhCJkmS1JjfspQkSWrMQCZJktSYgUzSvJTkW0kWDNFvhyT/fRw1SZq/DGSS5p0kW9x/r3vsABjIJI2UgUzSJiXJG5P8cT/915M3Jk+yJMk/JnlZkquSrE7yjoHlfpLkL5J8BXjKQPs2ST6T5L/NsMkTgUclWZXknUlOT3LwwPJnJDkoyZFJzu3XdV2S4wf6HJHk0n4d71/PQChpHjCQSdrUXER3z1aAxcB2/b0yn053O6Z3AM+muz/obyc5pO/7YGB1VT2pqr7Qt21Hd4P2j1TVP8ywvWOB/9ffM/TP6G4H9Uq450baTwX+pe+7H90tifYBDkuyOMlj6W4X9LSq2ofuZsl/sKH/CJI2LwYySZuay4B9kzwEuJPuVjyL6ULaD4ELqmptVa0DzqC7xyt0QehjU9Z1LnBqVX142I1X1YXAb/Q3Qn4Z3T0/1/Wzl1fVHVX1M+DjdCFxCd0tir6aZFX//JHr+0tL2rx5YVhJm5Sq+kWSb9GNUn2J7mbnzwIeRXcj431nWPTnVXX3lLYvAi9I8pFav4synk43ynU4cNRgeVPLBQIsq6rj1mP9kuYZR8gkbYouAv60f7wYeDWwCrgEeGaSBf15Wi8DLryP9bwVuAM46T76/Bh4yJS204DXA1TV1QPtz02yU5JtgEPoAt8K4NB+RI1+/sOH+SUlzR8GMkmboouBhcCX+5th/xy4uL+Z9nHA54GvAZdX1bn3s67XA1sn+b/TzezvzfjF/ksC7+zbbgWuBU6d0v0LdKNnq+gOZa6sqmuAtwDnJ7kSWN7XLkn38NZJkrSekmwLXAU8sap+1LcdCSyuqte2rE3SpskRMklaD0meA3wdeO9kGJOkDeUImSQBSXamO99rqiX9YUtJGhkDmSRJUmMespQkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTG/gOjmtwwPx+iyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Work type distribution in stroke patients\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.countplot(data=stroke_df,x='work_type', palette='cool');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Smoking status distribution in stroke patients\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.countplot(data=stroke_df,x='smoking_status',palette='cool');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to data of smokers, most common pattern in stroke group is non-smokers. Strange but true. This may be due to other factors affecting stroke and a biased assesment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing for Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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>Male</td>\n",
       "      <td>67</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>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>80</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>Female</td>\n",
       "      <td>49</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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0    Male   67             0              1          Yes        Private   \n",
       "2    Male   80             0              1          Yes        Private   \n",
       "3  Female   49             0              0          Yes        Private   \n",
       "4  Female   79             1              0          Yes  Self-employed   \n",
       "5    Male   81             0              0          Yes        Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "2          Rural             105.92  32.5     never smoked       1  \n",
       "3          Urban             171.23  34.4           smokes       1  \n",
       "4          Rural             174.12  24.0     never smoked       1  \n",
       "5          Urban             186.21  29.0  formerly smoked       1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encode 'work_type','Residence_type', 'smoking_status' columns\n",
    "df = pd.get_dummies(df, columns=[\"work_type\", \"smoking_status\"], prefix=[\"work_type\", \"smoking_status\"])\n",
    "\n",
    "# Convert 'gender' column to boolean\n",
    "df[\"gender\"] = df[\"gender\"].apply(lambda x: 1 if x==\"Male\" else 0)\n",
    "\n",
    "# Convert 'ever_married' column to boolean\n",
    "df[\"ever_married\"] = df[\"ever_married\"].apply(lambda x: 1 if x==\"Yes\" else 0)\n",
    "\n",
    "# Encode \"Residence_type\" column to boolean\n",
    "df[\"Residence_type\"] = df[\"Residence_type\"].apply(lambda x: 1 if x==\"Urban\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>gender</th>\n",
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       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
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       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>smoking_status_Unknown</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
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       "      <th>smoking_status_smokes</th>\n",
       "    </tr>\n",
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       "      <td>174.12</td>\n",
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      ],
      "text/plain": [
       "   gender  age  hypertension  heart_disease  ever_married  Residence_type  \\\n",
       "0       1   67             0              1             1               1   \n",
       "2       1   80             0              1             1               0   \n",
       "3       0   49             0              0             1               1   \n",
       "4       0   79             1              0             1               0   \n",
       "5       1   81             0              0             1               1   \n",
       "\n",
       "   avg_glucose_level   bmi  stroke  work_type_Govt_job  \\\n",
       "0             228.69  36.6       1                   0   \n",
       "2             105.92  32.5       1                   0   \n",
       "3             171.23  34.4       1                   0   \n",
       "4             174.12  24.0       1                   0   \n",
       "5             186.21  29.0       1                   0   \n",
       "\n",
       "   work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                       0                  1                        0   \n",
       "2                       0                  1                        0   \n",
       "3                       0                  1                        0   \n",
       "4                       0                  0                        1   \n",
       "5                       0                  1                        0   \n",
       "\n",
       "   work_type_children  smoking_status_Unknown  smoking_status_formerly smoked  \\\n",
       "0                   0                       0                               1   \n",
       "2                   0                       0                               0   \n",
       "3                   0                       0                               0   \n",
       "4                   0                       0                               0   \n",
       "5                   0                       0                               1   \n",
       "\n",
       "   smoking_status_never smoked  smoking_status_smokes  \n",
       "0                            0                      0  \n",
       "2                            1                      0  \n",
       "3                            0                      1  \n",
       "4                            1                      0  \n",
       "5                            0                      0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4908 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  hypertension  heart_disease  ever_married  Residence_type  \\\n",
       "0          1             0              1             1               1   \n",
       "2          1             0              1             1               0   \n",
       "3          0             0              0             1               1   \n",
       "4          0             1              0             1               0   \n",
       "5          1             0              0             1               1   \n",
       "...      ...           ...            ...           ...             ...   \n",
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       "5107       0             0              0             1               0   \n",
       "5108       1             0              0             1               0   \n",
       "5109       0             0              0             1               1   \n",
       "\n",
       "      stroke  work_type_Govt_job  work_type_Never_worked  work_type_Private  \\\n",
       "0          1                   0                       0                  1   \n",
       "2          1                   0                       0                  1   \n",
       "3          1                   0                       0                  1   \n",
       "4          1                   0                       0                  0   \n",
       "5          1                   0                       0                  1   \n",
       "...      ...                 ...                     ...                ...   \n",
       "5104       0                   0                       0                  0   \n",
       "5106       0                   0                       0                  0   \n",
       "5107       0                   0                       0                  0   \n",
       "5108       0                   0                       0                  1   \n",
       "5109       0                   1                       0                  0   \n",
       "\n",
       "      work_type_Self-employed  work_type_children  smoking_status_Unknown  \\\n",
       "0                           0                   0                       0   \n",
       "2                           0                   0                       0   \n",
       "3                           0                   0                       0   \n",
       "4                           1                   0                       0   \n",
       "5                           0                   0                       0   \n",
       "...                       ...                 ...                     ...   \n",
       "5104                        0                   1                       1   \n",
       "5106                        1                   0                       0   \n",
       "5107                        1                   0                       0   \n",
       "5108                        0                   0                       0   \n",
       "5109                        0                   0                       1   \n",
       "\n",
       "      smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
       "0                                  1                            0   \n",
       "2                                  0                            1   \n",
       "3                                  0                            0   \n",
       "4                                  0                            1   \n",
       "5                                  1                            0   \n",
       "...                              ...                          ...   \n",
       "5104                               0                            0   \n",
       "5106                               0                            1   \n",
       "5107                               0                            1   \n",
       "5108                               1                            0   \n",
       "5109                               0                            0   \n",
       "\n",
       "      smoking_status_smokes       age  avg_glucose_level       bmi  \n",
       "0                         0  1.069941           2.777797  0.981145  \n",
       "2                         0  1.646347           0.014016  0.459086  \n",
       "3                         1  0.271842           1.484266  0.701016  \n",
       "4                         0  1.602008           1.549325 -0.623231  \n",
       "5                         0  1.690685           1.821493  0.013426  \n",
       "...                     ...       ...                ...       ...  \n",
       "5104                      0 -1.324357          -0.049918 -1.310821  \n",
       "5106                      0  1.690685           0.448045  1.414072  \n",
       "5107                      0 -0.348902          -0.502181  0.217156  \n",
       "5108                      0  0.360520           1.373057 -0.419501  \n",
       "5109                      0  0.050148          -0.450629 -0.343102  \n",
       "\n",
       "[4908 rows x 18 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling the variance in features\n",
    "\n",
    "std=StandardScaler()\n",
    "\n",
    "columns = ['age','avg_glucose_level','bmi']\n",
    "scaled = std.fit_transform(df[['age','avg_glucose_level','bmi']])\n",
    "scaled = pd.DataFrame(scaled, columns=columns, index= df.index.values)\n",
    "\n",
    "df2=df.drop(columns=columns,axis=1)\n",
    "df2=df2.merge(scaled, left_index=True, right_index=True, how = \"left\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, df.columns != 'stroke']\n",
    "y = df['stroke']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "#X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n",
    "\n",
    "X_train, y_train = SMOTE().fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def report(y_test, y_pred):\n",
    "    \"\"\"\n",
    "    Creates confusion matrix and classification report\n",
    "    \n",
    "    Args:\n",
    "    y_test: splitted test dataframe labels\n",
    "    y_pred: predictions for test dataframe\n",
    "    \n",
    "    Returns:\n",
    "    None\n",
    "    \"\"\"\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    sns.heatmap(cm, annot=True,fmt='d',cmap=\"flare\");\n",
    "\n",
    "    print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Adaboost Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.92      0.94       929\n",
      "           1       0.12      0.19      0.15        53\n",
      "\n",
      "    accuracy                           0.88       982\n",
      "   macro avg       0.54      0.56      0.54       982\n",
      "weighted avg       0.91      0.88      0.90       982\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "#create adaboost classification obj\n",
    "ab_clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, \n",
    "                            learning_rate=0.5, random_state=100)\n",
    "\n",
    "#training via adaboost classficiation model\n",
    "ab_clf.fit(X_train, y_train)\n",
    "\n",
    "#make prediction using the test set\n",
    "y_pred= ab_clf.predict(X_test)\n",
    "\n",
    "#score\n",
    "ab_clf_score = ab_clf.score(X_test, y_test)\n",
    "\n",
    "\n",
    "score = cross_val_score(ab_clf, X_train, y_train, cv = 6)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "roc = roc_auc_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# print ('Train score of AdaBoostClassifier is', score.mean(),'%')\n",
    "# print ('--')\n",
    "# print ('Precision score is ', precision)\n",
    "# print ('--')\n",
    "# print ('ROC Score is', roc)\n",
    "# print ('--')\n",
    "# print ('Recall Score is ', recall)\n",
    "sns.heatmap(cm, annot=True,fmt='d',cmap=\"Reds\");\n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. XGboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.94      0.94       929\n",
      "           1       0.12      0.15      0.13        53\n",
      "\n",
      "    accuracy                           0.89       982\n",
      "   macro avg       0.53      0.54      0.54       982\n",
      "weighted avg       0.91      0.89      0.90       982\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "#create xgboost classification obj\n",
    "xgboost_clf = GradientBoostingClassifier(random_state=0)\n",
    "\n",
    "#training via xgboost classficiation model\n",
    "xgboost_clf.fit(X_train, y_train)\n",
    "\n",
    "#make prediction using the test set\n",
    "y_pred= xgboost_clf.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True,fmt='d',cmap=\"Reds\");\n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.64      0.77       929\n",
      "           1       0.08      0.55      0.14        53\n",
      "\n",
      "    accuracy                           0.64       982\n",
      "   macro avg       0.52      0.59      0.46       982\n",
      "weighted avg       0.91      0.64      0.74       982\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD4CAYAAADSIzzWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUAUlEQVR4nO3df7RV5X3n8ff3nAvaoJlgFIJAAypRwRUTgtSRRK1O1CSdQEJUEmWopaEaUjEmNhjTadMO6rRdU6erYw0TfxCNUmpipJmZJgZjbGsSNRONIqHQongLQoIxURjRy/3OH/cUrwLnHuRyn3s271fWXmefffaP56518uHxu5/9nMhMJEkDr1a6AZJ0oDKAJakQA1iSCjGAJakQA1iSCunY3xe4ON7oMAvt4roLp5Ruggahg2+9N/b1HHuTOTfkL/f5evvCHrAkFbLfe8CSNJDaqVdpAEuqlI4oWlXYKwawpEqptU/+GsCSqsUShCQVUrMEIUll2AOWpEKsAUtSIXVLEJJUhiUISSrEEoQkFWIPWJIKcRiaJBXS0T75awBLqhZLEJJUSI326QIbwJIqxVEQklSIJQhJKsQesCQV4oTsklSIJQhJKsQShCQV4jA0SSrEHrAkFVI3gCWpDEsQklSIJQhJKsRhaJJUSBt1gA1gSdXihOySVIglCEkqpH36vwawpIqJNipBtFNvXZL6FHux9HmuiCcj4rGIeCQiHm5sOywi7omINY3X4b32vzIi1kbE6og4u6/zG8CSKqW2F0uLfj0z35GZUxrvFwIrMnMCsKLxnoiYCMwCJgHnANdHRL2vtkpSZUS0vrxO04EljfUlwIxe25dm5vbMXAesBaY2O5EBLKlSakTLS0TMi4iHey3zXnO6BL4VET/s9dnIzNwI0Hgd0dg+Gni617GdjW175E04SZWyNx3bzFwMLG6yy7TM3BARI4B7IuIne3npbHZ9e8CSKqUWrS99ycwNjdfNwF30lBQ2RcQogMbr5sbuncDYXoePATY0beve/nGSNJjFXvyv6XkihkXEof+2DpwFPA4sB+Y0dpsD3N1YXw7MioiDImI8MAF4sNk1LEFIqpR+HAU8ErirMa64A7g9M/8uIh4ClkXEXGA9cC5AZq6MiGXAE0AXMD8zdzS7gAEsqVL6azrKzPwX4MTdbN8CnLmHYxYBi1q9hgEsqVKckF2SCmmf+DWAJVVMG00FYQBLqpY2yl8DWFK19DW8bDAxgCVVij9LL0mFtFH+GsCSqsUSxAFq0brHePH5F+jesYPuri6uOel0Rr/9BC644ToOOmQYW55cz00X/DYvPv88Uz92Hu+94tKdx45++wlcPfk9dD76WMG/QP1uyBCGXvXfYcgQqNXpfui7dH1tCR0zL6I2+RTIJH/5HC8v/q/w3BZqJ7yLjvM+Dh0d0NVF19Iv0v3Ej0r/FW2lnUZBRGbTyXr22cXxxv17gUFk0brHuHrKaWzd8uzObQsfvI+vfuYq1tz/j5xy0YW8efw4/vY//5dXHXfkCRO55O47+P2jd3noprKuu3BK3ztVxUEHw/YXoV5n6O//BS/f+pfkvz4FL24DoH7Wh4gj30rXLdcRbz2G/MXP4bktxJhxDL3iT9i+4LzCf8DAOfjWe/c5Pr//lrEtZ87JzzxdNK6djGc/G3nsMay5/x8BWHXPd5g884O77HPSRz/Cw3fcOdBN00DZ/mLPa72jZyF3hi/QE9AN+dRaeG5Lz3rnkz09544hA9fWCujPnyTa3/osQUTEcfTM9D6anrktNwDLM3PVfm5b28lMFnzr62Qmf//Fm/mH/3kLGx5fxYkffD+PLv/fTD53BsPH7jo/85TzZ/JX02cVaLEGRNQY+sc3ECNHs+PbXyf/uWdK2Y6P/Bb1d59F/r+tvHT15bscVjvpVLqfWgtdLw90i9tarY1qEE17wBHxWWApPf9YPAg81Fi/IyIWNjlu5yzzT/BSf7Z3UPvTaWdx9btO5S/fN5PT53+cY95zCl/+rU9w2vx5XPnwdzn40EPpeunV/2caN3UKL23bxoaV/ntWWdnNS5+fx/YF5xFHHUeMGQdA1503sf2yWex44Nt0vHfGqw6J0ePoOH8eXTf/eYEGt7d26gH3VYKYC5yUmddm5m2N5Vp6JiWeu6eDMnNxZk7JzCkTGdqf7R3UfrHxGQCe/+nPeOSubzB+6rvYtHoNf3H2DK6ZchoP3XEnP/vnda865qRZM3nI8sOBYdtWun/yKLW3v/pnwnY8cC+1k059ZcPwwxmy4Au8/MVryM1N5/PWbkREy0tpfQVwN3DkbraPanymhqFveAMHHXLIzvXjzzqDf318FYcecTjQ86V4/+ev4P4bbtx5TEQw+dwZPLz0q0XarAFw6L+DNwzrWR8ylPqkyeSG9cTIV0pR9cmnkBvW97x5wzCGfuYaupZ9iVyzskCD219//iLG/tZXDfgyYEVErOGVH5v7VeAY4JP7s2Ht5o0jR3DxXV8BoNbRwUO3/w1PfPPbnHHpJZw2/+MA/Ohry3ng5tt2HjPh1Gn8vHMDP1v3ZIkmawDEm97MkHmfhVoNajV2/OA+uh/5PkMu/UNi1Fjo7ia3bOblRqmh/t4PESOPpGPGbJgxG4CX/uT34JfPlfwz2koMhmRtUZ/D0CKiRk/JYTQ9ZZNO4KG+Znr/NwfSMDS17oAahqaW9ccwtB+/dVzLmfP2p54smtZ9joLIzG7g+wPQFknaZ4Ohttsqn4STVCltlL8GsKRqsQcsSYW0Uf4awJKqpZ2ehDOAJVVKrY2GoRnAkiol2miKMQNYUqV4E06SCmmj/DWAJVWLPWBJKqSN8tcAllQt9TYaBdFG9wslqW/9PR9wRNQj4kcR8Y3G+8Mi4p6IWNN4Hd5r3ysjYm1ErI6Is/s6twEsqVIiWl9atADo/ZM1C4EVmTkBWNF4T0RMBGYBk4BzgOsjot7sxAawpErpzwCOiDHAB4Av9do8HVjSWF8CzOi1fWlmbs/MdcBaeqby3SMDWFKlRC1aX3r9fmVjmfea010H/B6v/gWgkZm5EaDxOqKxfTSv/HAF9Mydvuuv8PbiTThJlbI3N+EyczGweHefRcRvAJsz84cRcXoLp9vdhZtODm8AS6qUfhyGNg34YES8HzgYeGNE3AZsiohRmbkxIkYBmxv7dwJjex0/Bmj6q6qWICRVSn+NgsjMKzNzTGaOo+fm2r2ZeSGwHJjT2G0OcHdjfTkwKyIOiojxwATgwWbXsAcsqVIG4EGMa4FlETEXWA+cC5CZKyNiGfAE0AXM7+u3Mw1gSZWyPx5Fzsz7gPsa61uAM/ew3yJgUavnNYAlVYqPIktSIbV6+ySwASypUpwNTZJKaaPJeAxgSdViD1iSyrAEIUml1Nvn+TIDWFKlhDVgSSrEEoQklWEPWJJKsQcsSYXYA5akMsJREJJUiCUISSoj2qcDbABLqhh7wJJUhsPQJKkUe8CSVIajICSpFEsQklSIJQhJKsP5gCWpFEsQklSGN+EkqRRLEJJUhg9iSFIp9oAlqRB7wK+4YevT+/sSakfZXboFqqh2GobWPrcLJakV9VrrSxMRcXBEPBgRj0bEyoj4QmP7YRFxT0SsabwO73XMlRGxNiJWR8TZfTXVAJZULRGtL81tB87IzBOBdwDnRMTJwEJgRWZOAFY03hMRE4FZwCTgHOD6iKg3u4ABLKla+imAs8cLjbdDGksC04Elje1LgBmN9enA0szcnpnrgLXA1GbXMIAlVUut1vISEfMi4uFey7zep4qIekQ8AmwG7snMHwAjM3MjQON1RGP30UDvm16djW175CgISdWyFzfhMnMxsLjJ5zuAd0TEm4C7IuKEZlfe3SmaXd8esKRq6b8a8E6Z+RxwHz213U0RMarnUjGKnt4x9PR4x/Y6bAywodl5DWBJ1VKvt740ERFHNHq+RMSvAP8B+AmwHJjT2G0OcHdjfTkwKyIOiojxwATgwWbXsAQhqVr6bxzwKGBJYyRDDViWmd+IiO8ByyJiLrAeOBcgM1dGxDLgCaALmN8oYeyRASypWvopgDPzx8A7d7N9C3DmHo5ZBCxq9RoGsKRqaaMn4QxgSdVSa59bWwawpGoxgCWpEEsQklRG2AOWpELsAUtSIQawJBViAEtSIX08YjyYGMCSqsUesCQVYgBLUiEOQ5OkQuwBS1IhBrAkFeIoCEkqxB6wJBViAEtSIY6CkKRC7AFLUiE1b8JJUhk1e8CSVEZYA5akMqwBS1IhjoKQpELsAUtSIY6CkKRCLEFIUiGWICSpkDYahtY+LZWkVtSi9aWJiBgbEd+JiFURsTIiFjS2HxYR90TEmsbr8F7HXBkRayNidUSc3WdT9/mPlaTBpFZvfWmuC/h0Zh4PnAzMj4iJwEJgRWZOAFY03tP4bBYwCTgHuD4iml7EAJZULVFrfWkiMzdm5v9trD8PrAJGA9OBJY3dlgAzGuvTgaWZuT0z1wFrganNrmEAS6qWvShBRMS8iHi41zJvd6eMiHHAO4EfACMzcyP0hDQworHbaODpXod1NrbtkTfhJFXLXoyCyMzFwOLmp4tDgK8Cl2XmL2PP59/dB9ns3AawpGrpx1EQETGEnvD9SmZ+rbF5U0SMysyNETEK2NzY3gmM7XX4GGBDs/NbgpBULf03CiKAG4FVmfnfen20HJjTWJ8D3N1r+6yIOCgixgMTgAebXcMesKRq6b9HkacBs4HHIuKRxrbPAdcCyyJiLrAeOBcgM1dGxDLgCXpGUMzPzB3NLmAAS6qWfipBZOY/sPu6LsCZezhmEbCo1WsYwJKqxV/EkKRCfBRZG5/ZxOyPX8L7PnweH5h5PktuX/qqz2/88m0c+86pPPvz5wq1UANt4zObmD3vE7zvw+fzgY98lCW3/zUAP/mnNZw/57f5j+ddwMULPs0LL2wt3NI2F9H6Upg94P2kXq+z8PIFTDr+OF7YupWZH/tPTPu1qRxz9FFsfGYTD3z/Bxz5lreUbqYGUL1eZ+GnLn3lO3HBbzLt5Klc9UdX89lP/S5T3zWZO7/+t3zpy7dx2Sd+p3Rz21cbTUfZPi1tMyOOOJxJxx8HwCHDhnHU+PFs+ulPAbjmz/6cKxb8Lk0GdKuCdv1OjGPT5s2se+opTpr8TgCmnTyVb634TsFWVkD/zQWx/5taugEHgs4NG1i1ejUnnjCJFffdz4gRR3DcsW8r3SwV1POd+CdOPOEE3nb00az47t8D8HffXsHGTZv7OFpNtVEJ4nUHcERc1OSznc9XL77pltd7iUrYum0bl35mIZ/7zOXU6x3ccOPNLLjE/7w8kPV8J67kc5++jEMOGcaiP7iK25fdyYc/NoetW7cxdIiVwX1Sq7W+FBaZTR9V3vOBEesz81f73HHbL17fBSrg5Ze7uHjBp3j3vz+Zi2ZfwOo1a/nN35nPrxx8MADPbN7MiCMO529uvZkjDj+8cGsHWHaXbkERPd+JT/PuU36Niy782C6fr3tqPVd8/g+589abCrRuEBg2fJ+7pTtW3Npy5tTPnF20G9z0n9qI+PGePgJG9n9zqiMzueoLf8xR48dz0ewLADh2wjF8795v7tznjPdP586vLOGw4W8q1UwNoMzkqj9axFHjx70qfLc8+yxvPuwwuru7+asv3cysmR8q18gqaKNhaH39t85I4Gzg56/ZHsAD+6VFFfHDRx7l7v/1f3jbhGOYfn5PAF/+yU9w2numFW6ZStn5nTjmaKbPmg3A5Z+8hCfXP83ty+4E4L1nnM7M6b9RsJUVMAhurrWqaQkiIm4Ebm48kvfaz27PzF3/G+q1DuAShJo4QEsQ6kN/lCDu/+vWSxCnnj94SxCZObfJZ32HryQNtAqVICSpvQyC4WWtMoAlVYs9YEkqo52eMDWAJVVLrX1irX1aKkmtcD5gSSrEGrAkFWINWJIKsQcsSYXYA5akQurtMxeEASypWixBSFIhliAkqRB7wJJUiD1gSSqk3j6x1j4tlaQWOBmPJJXSRjXg9mmpJLUiovWlz1PFTRGxOSIe77XtsIi4JyLWNF6H9/rsyohYGxGrI+Lsvs5vAEuqlqi1vvTtFuCc12xbCKzIzAnAisZ7ImIiMAuY1Djm+oho+lSIASypWvqxB5yZ9wPPvmbzdGBJY30JMKPX9qWZuT0z1wFrganNzm8AS6qWer3lJSLmRcTDvZZ5LVxhZGZuBGi8jmhsHw083Wu/zsa2PfImnKRq2YubcJm5GFjcX1fe3SWaHWAPWFK19GMJYg82RcSonkvFKGBzY3snMLbXfmOADc1OZABLqpjYi+V1WQ7MaazPAe7utX1WRBwUEeOBCcCDzU5kCUJStfTjgxgRcQdwOnB4RHQCfwBcCyyLiLnAeuBcgMxcGRHLgCeALmB+Zu5oev7MpiWKfbftF/v5AmpL2V26BRqMhg3f5/TMzlUtZ06MOb7oY3P2gCVVSxs9CWcAS6qW9pkKwgCWVDXtk8AGsKRqcTY0SSrEAJakQrwJJ0ml2AOWpDIsQUhSIQawJJViAEtSEf4opySV4igISSrEHrAkFWIAS1IpBrAklWEPWJIKaZ/8NYAlVYyjICSpEEsQklSKASxJZdgDlqRCDGBJKqSNbsJFZpZuwwEjIuZl5uLS7dDg4vfiwNU+/1RUw7zSDdCg5PfiAGUAS1IhBrAkFWIADyzrfNodvxcHKG/CSVIh9oAlqRADWJIKMYAHSEScExGrI2JtRCws3R6VFxE3RcTmiHi8dFtUhgE8ACKiDvwP4H3AROCjETGxbKs0CNwCnFO6ESrHAB4YU4G1mfkvmfkSsBSYXrhNKiwz7weeLd0OlWMAD4zRwNO93nc2tkk6gBnAA2N30zM5/k86wBnAA6MTGNvr/RhgQ6G2SBokDOCB8RAwISLGR8RQYBawvHCbJBVmAA+AzOwCPgl8E1gFLMvMlWVbpdIi4g7ge8CxEdEZEXNLt0kDy0eRJakQe8CSVIgBLEmFGMCSVIgBLEmFGMCSVIgBLEmFGMCSVMj/B0/ty1P7pgGIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create SVC classification obj\n",
    "svc_obj = SVC()\n",
    "parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10, 100]}\n",
    "svc = GridSearchCV(svc_obj, parameters)\n",
    "\n",
    "#training via SVC classficiation model\n",
    "svc.fit(X_train, y_train)\n",
    "\n",
    "#make prediction using the test set\n",
    "y_pred= svc.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True,fmt='d',cmap=\"Reds\");\n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.78      0.86       929\n",
      "           1       0.11      0.47      0.18        53\n",
      "\n",
      "    accuracy                           0.77       982\n",
      "   macro avg       0.54      0.63      0.52       982\n",
      "weighted avg       0.92      0.77      0.83       982\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create RandomForest classification obj\n",
    "forest_obj = RandomForestClassifier()\n",
    "parameters = {'n_estimators':[10, 20, 50, 100], 'max_depth':[2, 4, 6]}\n",
    "forest = GridSearchCV(forest_obj, parameters)\n",
    "\n",
    "#training via RandomForest classficiation model\n",
    "forest.fit(X_train, y_train)\n",
    "\n",
    "#make prediction using the test set\n",
    "y_pred= forest.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True,fmt='d',cmap=\"Reds\");\n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.14      0.24       929\n",
      "           1       0.06      1.00      0.12        53\n",
      "\n",
      "    accuracy                           0.19       982\n",
      "   macro avg       0.53      0.57      0.18       982\n",
      "weighted avg       0.95      0.19      0.24       982\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create LogisticRegression classification obj\n",
    "regression = LogisticRegression()\n",
    "\n",
    "#training via LogisticRegression classficiation model\n",
    "regression.fit(X_train, y_train)\n",
    "\n",
    "#make prediction using the test set\n",
    "y_pred= regression.predict(X_test)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True,fmt='d',cmap=\"Reds\");\n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to accuracy, XGboost seems as best predictor model. But according to f1-scores, best model is random forest classifier with grad-searched parameters. Accuracy value can be misleading for unbalanced datasets. f1-score gives better insight such this situations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Best model:**  Random Forest Classifier ,  **f1-score:**  0.19, **Accuracy:** 0.79"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this article, we took a look at stroke predictor factors according to Kaggle’s 2021 Stroke Prediction data.\n",
    "\n",
    "1. We analyzed data for “stroke only old people’s problem” myth, which showed that although stroke is common in older patients, it is also seen in younger patients.\n",
    "2. We then looked at the “people living in the city are at high risk of stroke” myth. The data showed us that there is no significant difference between the two groups.\n",
    "3. Finally, we looked at the “smokers are more prone to stroke”. We found that, surprisingly, stroke is more prone in the group of non-smokers. This may be a biased assessment due to other factors.\n",
    "\n",
    "In this study, we analyzed the causes of stroke and questioned three items that most of us misunderstood by the population. Our analysis may have been biased, as we had data for 5000 patients. The real question remains:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# My Medium blog post about data analysis of this project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data-Driven Analysis of Mythes About Stroke Causes**\n",
    "\n",
    "https://medium.com/@sahika.betul/data-driven-analysis-of-mythes-about-stroke-causes-dd347899bba5"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}