Stroke Prediction Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the packages, algorithms and metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "from sklearn.metrics import roc_auc_score, plot_roc_curve, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load in data and create dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5110, 12) ['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi', 'smoking_status', 'stroke']\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
    "print(df.shape, df.columns.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking out and adjusting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56669</td>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53882</td>\n",
       "      <td>Male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.4</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10434</td>\n",
       "      <td>Female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.8</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27419</td>\n",
       "      <td>Female</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60491</td>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.2</td>\n",
       "      <td>Unknown</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",
       "5  56669    Male  81.0             0              0          Yes   \n",
       "6  53882    Male  74.0             1              1          Yes   \n",
       "7  10434  Female  69.0             0              0           No   \n",
       "8  27419  Female  59.0             0              0          Yes   \n",
       "9  60491  Female  78.0             0              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "5        Private          Urban             186.21  29.0  formerly smoked   \n",
       "6        Private          Rural              70.09  27.4     never smoked   \n",
       "7        Private          Urban              94.39  22.8     never smoked   \n",
       "8        Private          Rural              76.15   NaN          Unknown   \n",
       "9        Private          Urban              58.57  24.2          Unknown   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  \n",
       "5       1  \n",
       "6       1  \n",
       "7       1  \n",
       "8       1  \n",
       "9       1  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking out the data\n",
    "# 0 is no, #1 is yes\n",
    "pd.set_option(\"display.max.columns\", None)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for nulls\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can't have nulls in the \"bmi\" value so I'll replace the nulls with the means\n",
    "\n",
    "df['bmi'].fillna(value=df['bmi'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for nulls again\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gender count\n",
    "df['gender'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Stroke count, 1 is yes and 0 is no\n",
    "df['stroke'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Work type count\n",
    "df['work_type'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "glucose_column = df[\"avg_glucose_level\"]\n",
    "bmi_column = df[\"bmi\"]\n",
    "age_column = df[\"age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of Age\n",
    "\n",
    "age_column.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of Average Glucose Level\n",
    "\n",
    "glucose_column.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAD4CAYAAAD7CAEUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWOklEQVR4nO3dfZBldX3n8ffHQQmolBgGdpyHDFgjCpQO0rLsEl2UZMGHCGTLONRGWEMcYbEiG7dWIKno/jFV7K5AQrJgRiGAEXAUedgVEoE1sqlCsRHCM8sAIzQzy3S0NqCxBoHv/nFPh8twu+cemNunm36/qm71ud97zr1ffyV8OOd3HlJVSJI0rFd13YAkaX4xOCRJrRgckqRWDA5JUisGhySplV26bmBU9tprr1q5cmXXbUjSvHLbbbf9fVUtnmmdV2xwrFy5kvHx8a7bkKR5JcmPdrSOh6okSa0YHJKkVgwOSVIrBockqRWDQ5LUisEhSWrF4JAktTKy4EiyPMl3ktyX5J4kn27qb0xyQ5IHm7979m1zRpKNSR5IclRf/ZAkdzWfnZcko+pbkjSzUe5xPAN8pqreBhwGnJrkAOB04KaqWgXc1Lyn+WwNcCBwNHB+kkXNd10ArAVWNa+jR9i3JGkGI7tyvKq2AFua5aeS3AcsBY4BjmhWuwT4G+CzTf2KqtoGPJJkI3Bokk3AHlV1C0CSS4FjgetH1ftCtPL0b3Xyu5vO+mAnvyvppZuVOY4kK4GDge8D+zShMhUuezerLQUe69tsoqktbZa3rw/6nbVJxpOMT05O7tT/DZKknpEHR5LXAVcCp1XVkzOtOqBWM9RfXKxaX1VjVTW2ePGM9+iSJL1EIw2OJK+mFxpfrapvNuUnkixpPl8CbG3qE8Dyvs2XAZub+rIBdUlSB0Y2x9Gc+XQhcF9VndP30bXAicBZzd9r+uqXJTkHeBO9SfBbq+rZJE8lOYzeoa4TgD8dVd9d6mqeQZLaGOVt1Q8HPgbcleSOpnYmvcDYkOQk4FHgIwBVdU+SDcC99M7IOrWqnm22OwW4GNiN3qS4E+OS1JFRnlX1twyenwA4cppt1gHrBtTHgYN2XneSpJfKK8clSa0YHJKkVgwOSVIrBockqRWDQ5LUisEhSWrF4JAktWJwSJJaMTgkSa0YHJKkVgwOSVIrBockqRWDQ5LUisEhSWrF4JAktWJwSJJaGVlwJLkoydYkd/fVvpbkjua1aerJgElWJvl532df7NvmkCR3JdmY5LzmkbSSpI6M8tGxFwN/Blw6Vaiqj04tJzkb+Ie+9R+qqtUDvucCYC3wPeA64Gh8dKwkdWZkexxVdTPwk0GfNXsNvwVcPtN3JFkC7FFVt1RV0QuhY3dyq5KkFrqa43g38ERVPdhX2zfJ7Um+m+TdTW0pMNG3zkRTkyR1ZJSHqmZyPC/c29gCrKiqHyc5BLg6yYHAoPmMmu5Lk6yld1iLFStW7MR2JUlTZn2PI8kuwG8CX5uqVdW2qvpxs3wb8BDwFnp7GMv6Nl8GbJ7uu6tqfVWNVdXY4sWLR9G+JC14XRyq+jXg/qr6p0NQSRYnWdQs7wesAh6uqi3AU0kOa+ZFTgCu6aBnSVJjlKfjXg7cAuyfZCLJSc1Ha3jxpPh7gDuT/B3wDeDkqpqaWD8F+DKwkd6eiGdUSVKHRjbHUVXHT1P/dwNqVwJXTrP+OHDQTm1OkvSSeeW4JKkVg0OS1IrBIUlqxeCQJLVicEiSWjE4JEmtGBySpFYMDklSKwaHJKkVg0OS1IrBIUlqxeCQJLVicEiSWjE4JEmtGBySpFYMDklSKwaHJKmVUT469qIkW5Pc3Vf7fJLHk9zRvD7Q99kZSTYmeSDJUX31Q5Lc1Xx2XvPscUlSR0a5x3ExcPSA+rlVtbp5XQeQ5AB6zyI/sNnm/CSLmvUvANYCq5rXoO+UJM2SkQVHVd0M/GTI1Y8BrqiqbVX1CLARODTJEmCPqrqlqgq4FDh2JA1LkobSxRzHp5Lc2RzK2rOpLQUe61tnoqktbZa3rw+UZG2S8STjk5OTO7tvSRKzHxwXAG8GVgNbgLOb+qB5i5qhPlBVra+qsaoaW7x48ctsVZI0yKwGR1U9UVXPVtVzwJeAQ5uPJoDlfasuAzY39WUD6pKkjsxqcDRzFlOOA6bOuLoWWJNk1yT70psEv7WqtgBPJTmsOZvqBOCa2exZkvRCu4zqi5NcDhwB7JVkAvgccESS1fQON20CPglQVfck2QDcCzwDnFpVzzZfdQq9M7R2A65vXpKkjowsOKrq+AHlC2dYfx2wbkB9HDhoJ7YmSXoZvHJcktSKwSFJasXgkCS1YnBIkloxOCRJrRgckqRWDA5JUisGhySpFYNDktSKwSFJasXgkCS1YnBIkloxOCRJrRgckqRWDA5JUisGhySpFYNDktTKUMGRpPUT+JJclGRrkrv7av8tyf1J7kxyVZI3NPWVSX6e5I7m9cW+bQ5JcleSjUnOa549LknqyLB7HF9McmuSfz/1L/shXAwcvV3tBuCgqno78H+AM/o+e6iqVjevk/vqFwBrgVXNa/vvlCTNoqGCo6p+Ffi3wHJgPMllSX59B9vcDPxku9q3q+qZ5u33gGUzfUeSJcAeVXVLVRVwKXDsMD1LkkZj6DmOqnoQ+EPgs8C/As5rDjv95kv87d8Bru97v2+S25N8N8m7m9pSYKJvnYmmNlCStUnGk4xPTk6+xLYkSTMZdo7j7UnOBe4D3gf8RlW9rVk+t+2PJvkD4Bngq01pC7Ciqg4Gfh+4LMkewKD5jJrue6tqfVWNVdXY4sWL27YlSRrCLkOu92fAl4Azq+rnU8Wq2pzkD9v8YJITgQ8BRzaHn6iqbcC2Zvm2JA8Bb6G3h9F/OGsZsLnN70mSdq5hD1V9ALhsKjSSvCrJ7gBV9ZVhfyzJ0fQOdX24qv6xr744yaJmeT96k+APV9UW4KkkhzVnU50AXDPs70mSdr5hg+NGYLe+97s3tWkluRy4Bdg/yUSSk+jtubweuGG7027fA9yZ5O+AbwAnV9XUxPopwJeBjcBDvHBeRJI0y4Y9VPVLVfXTqTdV9dOpPY7pVNXxA8oXTrPulcCV03w2DrS+jkSSNBrD7nH8LMk7p94kOQT4+QzrS5JeoYbd4zgN+HqSqYnpJcBHR9KRJGlOGyo4quoHSd4K7E/vFNn7q+oXI+1MkjQnDbvHAfAuYGWzzcFJqKpLR9KVJGnOGio4knwFeDNwB/BsU566BYgkaQEZdo9jDDhg6oI9SdLCNexZVXcD/2yUjUiS5odh9zj2Au5NcivNrUEAqurDI+lKkjRnDRscnx9lE5Kk+WPY03G/m+RXgFVVdWNz1fii0bYmSZqLhr2t+ifo3UPqz5vSUuDqEfUkSZrDhp0cPxU4HHgS/umhTnuPqilJ0tw1bHBsq6qnp94k2YUZHqgkSXrlGjY4vpvkTGC35lnjXwf+x+jakiTNVcMGx+nAJHAX8EngOnrPH5ckLTDDnlX1HL1Hx35ptO1Ikua6Ye9V9QgD5jSqar+d3pEkaU4b9lDVGL27474LeDdwHvCXM22Q5KIkW5Pc3Vd7Y5IbkjzY/N2z77MzkmxM8kCSo/rqhyS5q/nsvObZ45KkjgwVHFX1477X41X1x8D7drDZxcDR29VOB26qqlXATc17khwArAEObLY5P8nUBYYXAGuBVc1r+++UJM2iYQ9VvbPv7avo7YG8fqZtqurmJCu3Kx8DHNEsXwL8DfDZpn5FVW0DHkmyETg0ySZgj6q6penjUuBY4Pph+pYk7XzD3qvq7L7lZ4BNwG+9hN/bp6q2AFTVliRTFxEuBb7Xt95EU/tFs7x9faAka+ntnbBixYqX0J4kaUeGPavqvSPuY9C8Rc1QH6iq1gPrAcbGxrxAUZJGYNhDVb8/0+dVdc6Qv/dEkiXN3sYSYGtTnwCW9623DNjc1JcNqEuSOtLmrKpT6B0mWgqcDBxAb55jxrmO7VwLnNgsnwhc01dfk2TXJPvSmwS/tTms9VSSw5qzqU7o20aS1IE2D3J6Z1U9BZDk88DXq+p3p9sgyeX0JsL3SjIBfA44C9iQ5CTgUeAjAFV1T5INwL305lBOraqpZ5ufQu8Mrd3oTYo7MS5JHRo2OFYAT/e9fxpYOdMGVXX8NB8dOc3664B1A+rjwEFDdSlJGrlhg+MrwK1JrqI3OX0ccOnIupIkzVnDnlW1Lsn19K4aB/h4Vd0+urYkSXPVsJPjALsDT1bVnwATzSS2JGmBGfbRsZ+jd4X3GU3p1ezgXlWSpFemYfc4jgM+DPwMoKo20+40XEnSK8SwwfF0VRXNVdtJXju6liRJc9mwwbEhyZ8Db0jyCeBGfKiTJC1IOzyrqrli+2vAW4Engf2BP6qqG0bcmyRpDtphcFRVJbm6qg4BDAtJWuCGPVT1vSTvGmknkqR5Ydgrx98LnNw8WOln9G53XlX19lE1Jkmam2YMjiQrqupR4P2z1I8kaY7b0R7H1fTuivujJFdW1b+ZhZ4kSXPYjuY4+p/At98oG5EkzQ87Co6aZlmStEDt6FDVO5I8SW/PY7dmGZ6fHN9jpN1JkuacGYOjqhbt7B9Msj+9Cwqn7Af8EfAG4BPAZFM/s6qua7Y5AzgJeBb4var6653dlyRpOMOejrvTVNUDwGqAJIuAx4GrgI8D51bVF/rXT3IAsAY4EHgTcGOSt/Q9WlaSNIvaPI9jFI4EHqqqH82wzjHAFVW1raoeATYCh85Kd5KkF+k6ONYAl/e9/1SSO5NclGTPprYUeKxvnYmmJknqQGfBkeQ19J7x8fWmdAHwZnqHsbYAZ0+tOmDzgWd4JVmbZDzJ+OTk5KBVJEkvU5d7HO8HflhVTwBU1RNV9WxVPUfvlu1Th6MmgOV92y0DNg/6wqpaX1VjVTW2ePHiEbYuSQtXl8FxPH2HqZIs6fvsOODuZvlaYE2SXZvnnK8Cbp21LiVJLzDrZ1UBJNkd+HXgk33l/5pkNb3DUJumPquqe5JsAO4FngFO9YwqSepOJ8FRVf8I/PJ2tY/NsP46YN2o+5Ik7VjXZ1VJkuYZg0OS1IrBIUlqxeCQJLVicEiSWjE4JEmtGBySpFYMDklSKwaHJKkVg0OS1IrBIUlqxeCQJLVicEiSWunk7rjSlJWnf6uz39501gc7+21pPnOPQ5LUisEhSWrF4JAktdJJcCTZlOSuJHckGW9qb0xyQ5IHm7979q1/RpKNSR5IclQXPUuSerrc43hvVa2uqrHm/enATVW1CripeU+SA4A1wIHA0cD5SRZ10bAkaW4dqjoGuKRZvgQ4tq9+RVVtq6pHgI3AobPfniQJuguOAr6d5LYka5vaPlW1BaD5u3dTXwo81rftRFN7kSRrk4wnGZ+cnBxR65K0sHV1HcfhVbU5yd7ADUnun2HdDKjVoBWraj2wHmBsbGzgOpKkl6eTPY6q2tz83QpcRe/Q0xNJlgA0f7c2q08Ay/s2XwZsnr1uJUn9Zj04krw2yeunloF/DdwNXAuc2Kx2InBNs3wtsCbJrkn2BVYBt85u15KkKV0cqtoHuCrJ1O9fVlV/leQHwIYkJwGPAh8BqKp7kmwA7gWeAU6tqmc76FuSRAfBUVUPA+8YUP8xcOQ026wD1o24NUnSEObS6biSpHnA4JAktWJwSJJa8XkcA3T5jAhJmuvc45AktWJwSJJaMTgkSa0YHJKkVgwOSVIrBockqRWDQ5LUisEhSWrF4JAktWJwSJJaMTgkSa0YHJKkVgwOSVIrXTxzfHmS7yS5L8k9ST7d1D+f5PEkdzSvD/Rtc0aSjUkeSHLUbPcsSXpeF7dVfwb4TFX9MMnrgduS3NB8dm5VfaF/5SQHAGuAA4E3ATcmeYvPHZekbsz6HkdVbamqHzbLTwH3AUtn2OQY4Iqq2lZVjwAbgUNH36kkaZBO5ziSrAQOBr7flD6V5M4kFyXZs6ktBR7r22yCaYImydok40nGJycnR9W2JC1onQVHktcBVwKnVdWTwAXAm4HVwBbg7KlVB2xeg76zqtZX1VhVjS1evHjnNy1J6iY4kryaXmh8taq+CVBVT1TVs1X1HPAlnj8cNQEs79t8GbB5NvuVJD2vi7OqAlwI3FdV5/TVl/Stdhxwd7N8LbAmya5J9gVWAbfOVr+SpBfq4qyqw4GPAXcluaOpnQkcn2Q1vcNQm4BPAlTVPUk2APfSOyPrVM+okqTuzHpwVNXfMnje4roZtlkHrBtZU5KkoXnluCSpFYNDktSKwSFJasXgkCS1YnBIkloxOCRJrRgckqRWDA5JUisGhySplS5uOSLNCStP/1Ynv7vprA928rvSzuIehySpFYNDktSKwSFJasXgkCS1YnBIkloxOCRJrXg6rjTLujoNGDwVWDvHvNnjSHJ0kgeSbExyetf9SNJCNS+CI8ki4L8D7wcOoPd88gO67UqSFqb5cqjqUGBjVT0MkOQK4Bjg3k67kuYZr5bXzjBfgmMp8Fjf+wngn2+/UpK1wNrm7U+TPDALvY3SXsDfd93EHOOYDDanxyX/pbOfntPj0qGZxuVXdrTxfAmODKjViwpV64H1o29ndiQZr6qxrvuYSxyTwRyXwRyXwV7uuMyLOQ56exjL+94vAzZ31IskLWjzJTh+AKxKsm+S1wBrgGs77kmSFqR5caiqqp5J8ingr4FFwEVVdU/Hbc2GV8xht53IMRnMcRnMcRnsZY1Lql40VSBJ0rTmy6EqSdIcYXBIkloxOOaAJMuTfCfJfUnuSfLppv7GJDckebD5u2fXvXYhyaIktyf5n837BT8uSd6Q5BtJ7m/+f/MvFvq4JPkPzT8/dye5PMkvLcQxSXJRkq1J7u6rTTsOSc5obuX0QJKjhvkNg2NueAb4TFW9DTgMOLW5pcrpwE1VtQq4qXm/EH0auK/vveMCfwL8VVW9FXgHvfFZsOOSZCnwe8BYVR1E7ySaNSzMMbkYOHq72sBxaP49swY4sNnm/OYWTzMyOOaAqtpSVT9slp+i9y+BpfRuq3JJs9olwLGdNNihJMuADwJf7isv6HFJsgfwHuBCgKp6uqr+Hwt8XOidJbpbkl2A3eld67XgxqSqbgZ+sl15unE4BriiqrZV1SPARnq3eJqRwTHHJFkJHAx8H9inqrZAL1yAvTtsrSt/DPwn4Lm+2kIfl/2ASeAvmkN4X07yWhbwuFTV48AXgEeBLcA/VNW3WcBjsp3pxmHQ7ZyW7ujLDI45JMnrgCuB06rqya776VqSDwFbq+q2rnuZY3YB3glcUFUHAz9jYRyCmVZzzP4YYF/gTcBrk/x2t13NC0Pdzml7BscckeTV9ELjq1X1zab8RJIlzedLgK1d9deRw4EPJ9kEXAG8L8lf4rhMABNV9f3m/TfoBclCHpdfAx6pqsmq+gXwTeBfsrDHpN904/CSbudkcMwBSULvePV9VXVO30fXAic2yycC18x2b12qqjOqallVraQ3gfe/quq3cVz+L/BYkv2b0pH0HjGwkMflUeCwJLs3/zwdSW+ucCGPSb/pxuFaYE2SXZPsC6wCbt3Rl3nl+ByQ5FeB/w3cxfPH8s+kN8+xAVhB7x+Mj1TV9pNeC0KSI4D/WFUfSvLLLPBxSbKa3gkDrwEeBj5O7z8EF+y4JPnPwEfpnaV4O/C7wOtYYGOS5HLgCHq3Tn8C+BxwNdOMQ5I/AH6H3ridVlXX7/A3DA5JUhseqpIktWJwSJJaMTgkSa0YHJKkVgwOSVIrBockqRWDQ5LUyv8HWLSclHqr6EMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of BMI\n",
    "\n",
    "bmi_column.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  stroke\n",
       "Female  0         2853\n",
       "        1          141\n",
       "Male    0         2007\n",
       "        1          108\n",
       "Other   0            1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Stroke information by gender\n",
    "\n",
    "df.groupby([\"gender\", \"stroke\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_type      stroke\n",
       "Govt_job       0          624\n",
       "               1           33\n",
       "Never_worked   0           22\n",
       "Private        0         2776\n",
       "               1          149\n",
       "Self-employed  0          754\n",
       "               1           65\n",
       "children       0          685\n",
       "               1            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Stroke information by work type\n",
    "\n",
    "df.groupby([\"work_type\", \"stroke\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoking_status   stroke\n",
       "Unknown          0         1497\n",
       "                 1           47\n",
       "formerly smoked  0          815\n",
       "                 1           70\n",
       "never smoked     0         1802\n",
       "                 1           90\n",
       "smokes           0          747\n",
       "                 1           42\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Stroke information by smoking status\n",
    "\n",
    "df.groupby([\"smoking_status\", \"stroke\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ever_married  stroke\n",
       "No            0         1728\n",
       "              1           29\n",
       "Yes           0         3133\n",
       "              1          220\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Stroke information by married status\n",
    "\n",
    "df.groupby([\"ever_married\", \"stroke\"]).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>67.0</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>36.600000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>28.893237</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>32.500000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</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>34.400000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>24.000000</td>\n",
       "      <td>never 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             0              1          Yes        Private   \n",
       "1  Female  61.0             0              0          Yes  Self-employed   \n",
       "2    Male  80.0             0              1          Yes        Private   \n",
       "3  Female  49.0             0              0          Yes        Private   \n",
       "4  Female  79.0             1              0          Yes  Self-employed   \n",
       "\n",
       "  Residence_type  avg_glucose_level        bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.600000  formerly smoked       1  \n",
       "1          Rural             202.21  28.893237     never smoked       1  \n",
       "2          Rural             105.92  32.500000     never smoked       1  \n",
       "3          Urban             171.23  34.400000           smokes       1  \n",
       "4          Rural             174.12  24.000000     never smoked       1  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop id column as that won't be useful for our analysis\n",
    "\n",
    "df2 = df.drop(columns = ['id'],axis =1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>formerly smoked</td>\n",
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       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>32.500000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</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>34.400000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</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>24.000000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>81.0</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>29.000000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.15</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>80.43</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Rural</td>\n",
       "      <td>120.46</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>104.51</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>219.84</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>214.09</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>never 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        1  67.0             0              1          Yes        Private   \n",
       "1        0  61.0             0              0          Yes  Self-employed   \n",
       "2        1  80.0             0              1          Yes        Private   \n",
       "3        0  49.0             0              0          Yes        Private   \n",
       "4        0  79.0             1              0          Yes  Self-employed   \n",
       "5        1  81.0             0              0          Yes        Private   \n",
       "6        1  74.0             1              1          Yes        Private   \n",
       "7        0  69.0             0              0           No        Private   \n",
       "8        0  59.0             0              0          Yes        Private   \n",
       "9        0  78.0             0              0          Yes        Private   \n",
       "10       0  81.0             1              0          Yes        Private   \n",
       "11       0  61.0             0              1          Yes       Govt_job   \n",
       "12       0  54.0             0              0          Yes        Private   \n",
       "13       1  78.0             0              1          Yes        Private   \n",
       "14       0  79.0             0              1          Yes        Private   \n",
       "\n",
       "   Residence_type  avg_glucose_level        bmi   smoking_status  stroke  \n",
       "0           Urban             228.69  36.600000  formerly smoked       1  \n",
       "1           Rural             202.21  28.893237     never smoked       1  \n",
       "2           Rural             105.92  32.500000     never smoked       1  \n",
       "3           Urban             171.23  34.400000           smokes       1  \n",
       "4           Rural             174.12  24.000000     never smoked       1  \n",
       "5           Urban             186.21  29.000000  formerly smoked       1  \n",
       "6           Rural              70.09  27.400000     never smoked       1  \n",
       "7           Urban              94.39  22.800000     never smoked       1  \n",
       "8           Rural              76.15  28.893237          Unknown       1  \n",
       "9           Urban              58.57  24.200000          Unknown       1  \n",
       "10          Rural              80.43  29.700000     never smoked       1  \n",
       "11          Rural             120.46  36.800000           smokes       1  \n",
       "12          Urban             104.51  27.300000           smokes       1  \n",
       "13          Urban             219.84  28.893237          Unknown       1  \n",
       "14          Urban             214.09  28.200000     never smoked       1  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting columns with text to numbers for modelling purposes\n",
    "\n",
    "#Gender\n",
    "#Male = 1, Female = 0, Other = 2\n",
    "\n",
    "df2 = df2.replace(to_replace = 'Male', value = 1)\n",
    "df2 = df2.replace(to_replace = 'Female', value = 0)\n",
    "df2 = df2.replace(to_replace = 'Other', value = 2)\n",
    "df2.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private          2925\n",
       "Self-employed     819\n",
       "children          687\n",
       "Govt_job          657\n",
       "Never_worked       22\n",
       "Name: work_type, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.work_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
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       "      <th>Residence_type</th>\n",
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       "      <th>bmi</th>\n",
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       "      <th>stroke</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.600000</td>\n",
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       "      <td>61.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>never 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       1  67.0             0              1          Yes          2   \n",
       "1       0  61.0             0              0          Yes          3   \n",
       "2       1  80.0             0              1          Yes          2   \n",
       "3       0  49.0             0              0          Yes          2   \n",
       "4       0  79.0             1              0          Yes          3   \n",
       "\n",
       "  Residence_type  avg_glucose_level        bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.600000  formerly smoked       1  \n",
       "1          Rural             202.21  28.893237     never smoked       1  \n",
       "2          Rural             105.92  32.500000     never smoked       1  \n",
       "3          Urban             171.23  34.400000           smokes       1  \n",
       "4          Rural             174.12  24.000000     never smoked       1  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Work type\n",
    "#Govt job = 0, Never_worked = 1, Private = 2, Self-employed = 3, Children = 4\n",
    "\n",
    "df2[\"work_type\"] = df2[\"work_type\"].astype(\"category\")\n",
    "df2[\"work_type\"] = df2[\"work_type\"].cat.codes\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "never smoked       1892\n",
       "Unknown            1544\n",
       "formerly smoked     885\n",
       "smokes              789\n",
       "Name: smoking_status, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.smoking_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.15</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>80.43</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>120.46</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>104.51</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>219.84</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>214.09</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>2</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        1  67.0             0              1          Yes          2   \n",
       "1        0  61.0             0              0          Yes          3   \n",
       "2        1  80.0             0              1          Yes          2   \n",
       "3        0  49.0             0              0          Yes          2   \n",
       "4        0  79.0             1              0          Yes          3   \n",
       "5        1  81.0             0              0          Yes          2   \n",
       "6        1  74.0             1              1          Yes          2   \n",
       "7        0  69.0             0              0           No          2   \n",
       "8        0  59.0             0              0          Yes          2   \n",
       "9        0  78.0             0              0          Yes          2   \n",
       "10       0  81.0             1              0          Yes          2   \n",
       "11       0  61.0             0              1          Yes          0   \n",
       "12       0  54.0             0              0          Yes          2   \n",
       "13       1  78.0             0              1          Yes          2   \n",
       "14       0  79.0             0              1          Yes          2   \n",
       "\n",
       "   Residence_type  avg_glucose_level        bmi  smoking_status  stroke  \n",
       "0           Urban             228.69  36.600000               1       1  \n",
       "1           Rural             202.21  28.893237               2       1  \n",
       "2           Rural             105.92  32.500000               2       1  \n",
       "3           Urban             171.23  34.400000               3       1  \n",
       "4           Rural             174.12  24.000000               2       1  \n",
       "5           Urban             186.21  29.000000               1       1  \n",
       "6           Rural              70.09  27.400000               2       1  \n",
       "7           Urban              94.39  22.800000               2       1  \n",
       "8           Rural              76.15  28.893237               0       1  \n",
       "9           Urban              58.57  24.200000               0       1  \n",
       "10          Rural              80.43  29.700000               2       1  \n",
       "11          Rural             120.46  36.800000               3       1  \n",
       "12          Urban             104.51  27.300000               3       1  \n",
       "13          Urban             219.84  28.893237               0       1  \n",
       "14          Urban             214.09  28.200000               2       1  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Smoking Status\n",
    "#Unknown = 0, #Formerly smoked = 1, never smoked = 2, Smokes = 3\n",
    "\n",
    "df2[\"smoking_status\"] = df2[\"smoking_status\"].astype(\"category\")\n",
    "df2[\"smoking_status\"] = df2[\"smoking_status\"].cat.codes\n",
    "df2.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.15</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>80.43</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>120.46</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>104.51</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>219.84</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>214.09</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>2</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        1  67.0             0              1             1          2   \n",
       "1        0  61.0             0              0             1          3   \n",
       "2        1  80.0             0              1             1          2   \n",
       "3        0  49.0             0              0             1          2   \n",
       "4        0  79.0             1              0             1          3   \n",
       "5        1  81.0             0              0             1          2   \n",
       "6        1  74.0             1              1             1          2   \n",
       "7        0  69.0             0              0             0          2   \n",
       "8        0  59.0             0              0             1          2   \n",
       "9        0  78.0             0              0             1          2   \n",
       "10       0  81.0             1              0             1          2   \n",
       "11       0  61.0             0              1             1          0   \n",
       "12       0  54.0             0              0             1          2   \n",
       "13       1  78.0             0              1             1          2   \n",
       "14       0  79.0             0              1             1          2   \n",
       "\n",
       "   Residence_type  avg_glucose_level        bmi  smoking_status  stroke  \n",
       "0           Urban             228.69  36.600000               1       1  \n",
       "1           Rural             202.21  28.893237               2       1  \n",
       "2           Rural             105.92  32.500000               2       1  \n",
       "3           Urban             171.23  34.400000               3       1  \n",
       "4           Rural             174.12  24.000000               2       1  \n",
       "5           Urban             186.21  29.000000               1       1  \n",
       "6           Rural              70.09  27.400000               2       1  \n",
       "7           Urban              94.39  22.800000               2       1  \n",
       "8           Rural              76.15  28.893237               0       1  \n",
       "9           Urban              58.57  24.200000               0       1  \n",
       "10          Rural              80.43  29.700000               2       1  \n",
       "11          Rural             120.46  36.800000               3       1  \n",
       "12          Urban             104.51  27.300000               3       1  \n",
       "13          Urban             219.84  28.893237               0       1  \n",
       "14          Urban             214.09  28.200000               2       1  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Marriage status\n",
    "#Ever Married (Yes) = 1, (No) = 0\n",
    "\n",
    "df2 = df2.replace(to_replace = 'Yes', value = 1)\n",
    "df2 = df2.replace(to_replace = 'No', value = 0)\n",
    "df2.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>34.400000</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>81.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>76.15</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120.46</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>104.51</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>219.84</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>214.09</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>2</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        1  67.0             0              1             1          2   \n",
       "1        0  61.0             0              0             1          3   \n",
       "2        1  80.0             0              1             1          2   \n",
       "3        0  49.0             0              0             1          2   \n",
       "4        0  79.0             1              0             1          3   \n",
       "5        1  81.0             0              0             1          2   \n",
       "6        1  74.0             1              1             1          2   \n",
       "7        0  69.0             0              0             0          2   \n",
       "8        0  59.0             0              0             1          2   \n",
       "9        0  78.0             0              0             1          2   \n",
       "10       0  81.0             1              0             1          2   \n",
       "11       0  61.0             0              1             1          0   \n",
       "12       0  54.0             0              0             1          2   \n",
       "13       1  78.0             0              1             1          2   \n",
       "14       0  79.0             0              1             1          2   \n",
       "\n",
       "    Residence_type  avg_glucose_level        bmi  smoking_status  stroke  \n",
       "0                1             228.69  36.600000               1       1  \n",
       "1                0             202.21  28.893237               2       1  \n",
       "2                0             105.92  32.500000               2       1  \n",
       "3                1             171.23  34.400000               3       1  \n",
       "4                0             174.12  24.000000               2       1  \n",
       "5                1             186.21  29.000000               1       1  \n",
       "6                0              70.09  27.400000               2       1  \n",
       "7                1              94.39  22.800000               2       1  \n",
       "8                0              76.15  28.893237               0       1  \n",
       "9                1              58.57  24.200000               0       1  \n",
       "10               0              80.43  29.700000               2       1  \n",
       "11               0             120.46  36.800000               3       1  \n",
       "12               1             104.51  27.300000               3       1  \n",
       "13               1             219.84  28.893237               0       1  \n",
       "14               1             214.09  28.200000               2       1  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Residence_type \n",
    "#(Urban) = 1, (Rural) = 0\n",
    "\n",
    "df2 = df2.replace(to_replace = 'Urban', value = 1)\n",
    "df2 = df2.replace(to_replace = 'Rural', value = 0)\n",
    "df2.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the X and Y variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping out the outcome variable to prevent any errors\n",
    "\n",
    "y = df2['stroke']\n",
    "x = df2.drop(['stroke'],axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train-Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "smote = SMOTE()\n",
    "\n",
    "x_oversample, y_oversample = smote.fit_resample(x, y)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_oversample, y_oversample, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the top features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier: 1.00\n"
     ]
    }
   ],
   "source": [
    "#fit the decision tree and output accuracy\n",
    "clf = DecisionTreeClassifier(criterion='entropy').fit(X, y)\n",
    "print('Accuracy of Decision Tree classifier: {:.2f}'\n",
    "     .format(clf.score(x, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age : 0.30359560583099515\n",
      "avg_glucose_level : 0.302533316129952\n",
      "bmi : 0.19171073952022952\n",
      "smoking_status : 0.05778710983949938\n",
      "work_type : 0.033444331256026895\n",
      "gender : 0.02997910327369959\n",
      "Residence_type : 0.024959803897717254\n",
      "heart_disease : 0.022846405290433667\n",
      "hypertension : 0.017640477038948256\n",
      "ever_married : 0.015503107922498264\n"
     ]
    }
   ],
   "source": [
    "# summarize feature importance, and store values under feature names in scores dictionary\n",
    "importance = clf.feature_importances_\n",
    "scores = {}\n",
    "for i,v in enumerate(importance):\n",
    "    #print('Feature: %s, Score: %.5f' % (xcols[i],v))\n",
    "    scores[xcols[i]] = v\n",
    "\n",
    "#reverse sort 'scores' dictionary by values\n",
    "import operator\n",
    "sorted_scores = sorted(scores.items(), key=operator.itemgetter(1),reverse=True)\n",
    "\n",
    "#print top 10 features\n",
    "for feature, score in sorted_scores[0:10]:\n",
    "    print(feature, ':', score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-133-0822777bc287>:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(labels = top_feats, rotation=90)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_feats, top_vals = [], []\n",
    "for feat_name, value in sorted_scores[0 : 10]:\n",
    "    top_feats.append(feat_name)\n",
    "    top_vals.append(value)\n",
    "\n",
    "# plot feature importance for top 10 features\n",
    "# plotting function\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "feats_plot = ax.bar(top_feats, top_vals)\n",
    "ax.set_xticklabels(labels = top_feats, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.80      0.81       973\n",
      "           1       0.80      0.83      0.82       972\n",
      "\n",
      "    accuracy                           0.82      1945\n",
      "   macro avg       0.82      0.82      0.82      1945\n",
      "weighted avg       0.82      0.82      0.82      1945\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tricky/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "log = LogisticRegression()\n",
    "log.fit(x_train, y_train)\n",
    "y_pred_log = log.predict(x_test)\n",
    "cr = classification_report(y_test, y_pred_log)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score:  0.8\n",
      "Recall Score:  0.83\n",
      "F1 Score:  0.82\n",
      "Accuracy Score:  0.82\n",
      "ROC AUC:  0.82\n"
     ]
    }
   ],
   "source": [
    "print('Precision Score: ', round(precision_score(y_test, y_pred_log), 2))\n",
    "print('Recall Score: ', round(recall_score(y_test, y_pred_log), 2))\n",
    "print('F1 Score: ', round(f1_score(y_test, y_pred_log), 2))\n",
    "print('Accuracy Score: ', round(accuracy_score(y_test, y_pred_log), 2))\n",
    "print('ROC AUC: ', round(roc_auc_score(y_test, y_pred_log), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Model: Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 10, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9512720119240924\n",
      "std dev of accuracies: 1.3483032988480203e-05\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 10, 'subsample': 1}\n",
      "mean of accuracies: 0.9512720119240924\n",
      "std dev of accuracies: 1.3483032988480203e-05\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 100, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9512720119240924\n",
      "std dev of accuracies: 1.3483032988480203e-05\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 100, 'subsample': 1}\n",
      "mean of accuracies: 0.9512720119240924\n",
      "std dev of accuracies: 1.3483032988480203e-05\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 250, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9506849277304067\n",
      "std dev of accuracies: 1.3645479169041602e-05\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 250, 'subsample': 1}\n",
      "mean of accuracies: 0.9499019956497646\n",
      "std dev of accuracies: 0.0005672620625994845\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 10, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9512720119240924\n",
      "std dev of accuracies: 1.3483032988480203e-05\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 10, 'subsample': 1}\n",
      "mean of accuracies: 0.9512720119240924\n",
      "std dev of accuracies: 1.3483032988480203e-05\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 100, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9510763937707277\n",
      "std dev of accuracies: 0.00026316281254620605\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 100, 'subsample': 1}\n",
      "mean of accuracies: 0.9504891947102462\n",
      "std dev of accuracies: 0.0002838771058794365\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 250, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9489233305489622\n",
      "std dev of accuracies: 0.0017385255877430362\n",
      "\n",
      "params: {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 250, 'subsample': 1}\n",
      "mean of accuracies: 0.9483364760888683\n",
      "std dev of accuracies: 0.0017321161520086276\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 10, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9512718970572965\n",
      "std dev of accuracies: 0.0004910271639702201\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 10, 'subsample': 1}\n",
      "mean of accuracies: 0.9510763937707277\n",
      "std dev of accuracies: 0.00026316281254620605\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9461843318014697\n",
      "std dev of accuracies: 0.0016710968717350456\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100, 'subsample': 1}\n",
      "mean of accuracies: 0.946183298000307\n",
      "std dev of accuracies: 0.0024736894953544496\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 250, 'subsample': 0.5}\n",
      "mean of accuracies: 0.942269326797872\n",
      "std dev of accuracies: 0.004087294814085326\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 250, 'subsample': 1}\n",
      "mean of accuracies: 0.9440302347785413\n",
      "std dev of accuracies: 0.003889286455543422\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 10, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9489232156821664\n",
      "std dev of accuracies: 0.0029240558384439827\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 10, 'subsample': 1}\n",
      "mean of accuracies: 0.9499021105165606\n",
      "std dev of accuracies: 0.000735100414614734\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9414863947172302\n",
      "std dev of accuracies: 0.00446549945985563\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100, 'subsample': 1}\n",
      "mean of accuracies: 0.9434432654516516\n",
      "std dev of accuracies: 0.0046672681020631665\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 250, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9410949286769091\n",
      "std dev of accuracies: 0.005000606568870971\n",
      "\n",
      "params: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 250, 'subsample': 1}\n",
      "mean of accuracies: 0.941095158410501\n",
      "std dev of accuracies: 0.003734872895550184\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 3, 'n_estimators': 10, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9491195230363062\n",
      "std dev of accuracies: 0.0011001551936235783\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 3, 'n_estimators': 10, 'subsample': 1}\n",
      "mean of accuracies: 0.9459876798469423\n",
      "std dev of accuracies: 0.003003956075884029\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 3, 'n_estimators': 100, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9360058701527362\n",
      "std dev of accuracies: 0.0071100214827175615\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 3, 'n_estimators': 100, 'subsample': 1}\n",
      "mean of accuracies: 0.9412900873630902\n",
      "std dev of accuracies: 0.005459469411099047\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 3, 'n_estimators': 250, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9338533812649499\n",
      "std dev of accuracies: 0.006525157126582446\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 3, 'n_estimators': 250, 'subsample': 1}\n",
      "mean of accuracies: 0.9387456729677996\n",
      "std dev of accuracies: 0.007000203041815906\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 5, 'n_estimators': 10, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9293529002028089\n",
      "std dev of accuracies: 0.005794351838263569\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 5, 'n_estimators': 10, 'subsample': 1}\n",
      "mean of accuracies: 0.938942439789123\n",
      "std dev of accuracies: 0.003158510735571681\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 5, 'n_estimators': 100, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9256350066209222\n",
      "std dev of accuracies: 0.005272491466633145\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 5, 'n_estimators': 100, 'subsample': 1}\n",
      "mean of accuracies: 0.936593528680401\n",
      "std dev of accuracies: 0.005089294474764879\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 5, 'n_estimators': 250, 'subsample': 0.5}\n",
      "mean of accuracies: 0.9144778798713858\n",
      "std dev of accuracies: 0.018170969513997895\n",
      "\n",
      "params: {'learning_rate': 0.25, 'max_depth': 5, 'n_estimators': 250, 'subsample': 1}\n",
      "mean of accuracies: 0.9416817831370029\n",
      "std dev of accuracies: 0.004468734107558691\n",
      "\n",
      "***best parameters: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 10, 'subsample': 0.5}\n",
      "best score: 0.9512720119240924\n"
     ]
    }
   ],
   "source": [
    "# set-up grid of parameters to search\n",
    "param_grid = {'n_estimators': [10, 100, 250], 'learning_rate': [.01, .1, .25],\n",
    "              'subsample': [.5, 1],\n",
    "              'max_depth': [3, 5]} \n",
    "\n",
    "# n_estimators controls how many models to aggregate (i.e., how many weak learners)\n",
    "# learning rate shrinks contribution from individual models (how vigorously errors are corrected);\n",
    "\n",
    "# max_depth controls tree depth/complexity of individual models\n",
    "\n",
    "# instantiate grid search object\n",
    "grid = GridSearchCV(GradientBoostingClassifier(), param_grid, cv = 3)\n",
    "\n",
    "# fitting the model for grid search \n",
    "grid.fit(x, y)\n",
    "\n",
    "# print parameters, mean, and standard deviation of scores by iteration\n",
    "for z in range(0, len(grid.cv_results_['params'])):\n",
    "    print('\\nparams:', grid.cv_results_['params'][z])\n",
    "    print('mean of accuracies:', grid.cv_results_['mean_test_score'][z])\n",
    "    print('std dev of accuracies:', grid.cv_results_['std_test_score'][z])\n",
    "\n",
    "# print best parameter after tuning \n",
    "print('\\n***best parameters:', grid.best_params_)\n",
    "print('best score:', grid.best_score_)\n",
    "\n",
    "# store the best estimator\n",
    "best_sgb = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Model: Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.93      0.95       973\n",
      "           1       0.93      0.96      0.95       972\n",
      "\n",
      "    accuracy                           0.95      1945\n",
      "   macro avg       0.95      0.95      0.95      1945\n",
      "weighted avg       0.95      0.95      0.95      1945\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train, y_train)\n",
    "y_pred_rf = rf.predict(x_test)\n",
    "cr_rf = classification_report(y_test, y_pred_rf)\n",
    "print(cr_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score:  0.93\n",
      "Recall Score:  0.96\n",
      "F1 Score:  0.95\n",
      "Accuracy Score:  0.95\n",
      "ROC AUC:  0.95\n"
     ]
    }
   ],
   "source": [
    "print('Precision Score: ', round(precision_score(y_test, y_pred_rf), 2))\n",
    "print('Recall Score: ', round(recall_score(y_test, y_pred_rf), 2))\n",
    "print('F1 Score: ', round(f1_score(y_test, y_pred_rf), 2))\n",
    "print('Accuracy Score: ', round(accuracy_score(y_test, y_pred_rf), 2))\n",
    "print('ROC AUC: ', round(roc_auc_score(y_test, y_pred_rf), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's Start Predicting (Using the model after grid search[best_logreg])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(57, 10) ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi', 'smoking_status'] \n",
      "\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>101.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>88.92</td>\n",
       "      <td>22.9</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>79.79</td>\n",
       "      <td>29.7</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>71.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>96.97</td>\n",
       "      <td>29.1</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>111.81</td>\n",
       "      <td>27.3</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>59.35</td>\n",
       "      <td>32.3</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>86.94</td>\n",
       "      <td>41.1</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Male</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>98.55</td>\n",
       "      <td>25.6</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Female</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>226.98</td>\n",
       "      <td>29.8</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</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>72.81</td>\n",
       "      <td>26.3</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Female</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>68.02</td>\n",
       "      <td>37.5</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Female</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>68.56</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>64.14</td>\n",
       "      <td>29.4</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Female</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>235.63</td>\n",
       "      <td>32.3</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Female</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.34</td>\n",
       "      <td>24.4</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Male</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>240.59</td>\n",
       "      <td>31.4</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>78.92</td>\n",
       "      <td>27.7</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Female</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>82.81</td>\n",
       "      <td>28.0</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Male</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>74.10</td>\n",
       "      <td>28.8</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>86.30</td>\n",
       "      <td>31.7</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Male</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>162.14</td>\n",
       "      <td>32.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Female</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>88.20</td>\n",
       "      <td>28.4</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>91.92</td>\n",
       "      <td>35.9</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Male</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.46</td>\n",
       "      <td>21.2</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>197.28</td>\n",
       "      <td>34.5</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Male</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>233.94</td>\n",
       "      <td>42.4</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Female</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>247.51</td>\n",
       "      <td>40.5</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>84.96</td>\n",
       "      <td>36.7</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>57.93</td>\n",
       "      <td>30.9</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Female</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>78.80</td>\n",
       "      <td>29.3</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Male</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>87.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Female</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>78.81</td>\n",
       "      <td>19.6</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>95.12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Male</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>87.96</td>\n",
       "      <td>39.2</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Female</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>110.89</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Female</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Never_worked</td>\n",
       "      <td>Rural</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>210.95</td>\n",
       "      <td>50.1</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Female</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>73.73</td>\n",
       "      <td>34.4</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Female</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>79.58</td>\n",
       "      <td>22.2</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Female</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Urban</td>\n",
       "      <td>77.88</td>\n",
       "      <td>13.8</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>205.26</td>\n",
       "      <td>40.3</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>58.63</td>\n",
       "      <td>28.4</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>139.90</td>\n",
       "      <td>31.7</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Male</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>211.03</td>\n",
       "      <td>34.2</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Female</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>72.79</td>\n",
       "      <td>54.7</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Rural</td>\n",
       "      <td>91.65</td>\n",
       "      <td>24.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Female</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Rural</td>\n",
       "      <td>79.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Female</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>225.47</td>\n",
       "      <td>36.9</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Male</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>94.48</td>\n",
       "      <td>31.1</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>82.34</td>\n",
       "      <td>31.9</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Female</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>180.63</td>\n",
       "      <td>31.8</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>92.48</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Male</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>116.66</td>\n",
       "      <td>28.5</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Male</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>112.09</td>\n",
       "      <td>29.5</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>130.34</td>\n",
       "      <td>22.0</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0   Female   66             0              0          Yes  Self-employed   \n",
       "1   Female   79             0              0           No        Private   \n",
       "2   Female   68             1              0           No  Self-employed   \n",
       "3     Male   58             0              0          Yes        Private   \n",
       "4     Male   54             0              0          Yes        Private   \n",
       "5     Male   61             0              1          Yes        Private   \n",
       "6   Female   70             0              1          Yes        Private   \n",
       "7     Male   47             0              0          Yes        Private   \n",
       "8     Male   74             0              0          Yes        Private   \n",
       "9   Female   79             0              1          Yes        Private   \n",
       "10    Male   81             0              0          Yes        Private   \n",
       "11  Female   57             0              0           No  Self-employed   \n",
       "12  Female   80             1              0          Yes  Self-employed   \n",
       "13    Male   45             0              0          Yes        Private   \n",
       "14  Female   78             0              0          Yes        Private   \n",
       "15  Female   70             0              0          Yes  Self-employed   \n",
       "16    Male   58             0              1          Yes        Private   \n",
       "17    Male   57             1              0          Yes       Govt_job   \n",
       "18  Female   69             0              0          Yes       Govt_job   \n",
       "19    Male   64             0              1          Yes       Govt_job   \n",
       "20    Male   57             0              0          Yes        Private   \n",
       "21    Male   77             0              0          Yes  Self-employed   \n",
       "22  Female   51             1              0          Yes        Private   \n",
       "23    Male   60             0              1          Yes        Private   \n",
       "24    Male   66             0              0          Yes        Private   \n",
       "25    Male   57             0              0          Yes        Private   \n",
       "26    Male   68             0              0          Yes        Private   \n",
       "27  Female   68             1              1          Yes        Private   \n",
       "28    Male   57             0              0          Yes        Private   \n",
       "29  Female   14             0              0           No       children   \n",
       "30  Female   75             0              0          Yes  Self-employed   \n",
       "31    Male   71             1              0          Yes  Self-employed   \n",
       "32  Female   78             0              0          Yes        Private   \n",
       "33    Male    3             0              0           No       children   \n",
       "34    Male   58             1              0          Yes        Private   \n",
       "35  Female    8             0              0           No        Private   \n",
       "36  Female   70             0              0          Yes        Private   \n",
       "37    Male   14             0              0           No   Never_worked   \n",
       "38  Female   47             0              0          Yes        Private   \n",
       "39  Female   52             0              0          Yes        Private   \n",
       "40  Female   68             0              0          Yes        Private   \n",
       "41  Female    5             0              0           No       children   \n",
       "42  Female   56             1              0          Yes        Private   \n",
       "43  Female   43             0              0          Yes        Private   \n",
       "44  Female   40             0              0          Yes        Private   \n",
       "45    Male   53             0              0          Yes        Private   \n",
       "46  Female   52             0              1          Yes        Private   \n",
       "47    Male   31             0              0          Yes       Govt_job   \n",
       "48  Female   63             0              0          Yes       Govt_job   \n",
       "49  Female   69             0              0          Yes  Self-employed   \n",
       "50    Male   64             0              0          Yes       Govt_job   \n",
       "51  Female   64             0              0          Yes        Private   \n",
       "52  Female   62             0              0          Yes  Self-employed   \n",
       "53  Female    2             0              0           No       children   \n",
       "54    Male   53             0              0          Yes        Private   \n",
       "55    Male   65             1              0          Yes        Private   \n",
       "56  Female   22             0              0           No        Private   \n",
       "\n",
       "   Residence_type  avg_glucose_level   bmi   smoking_status  \n",
       "0           Urban             101.45   NaN          Unknown  \n",
       "1           Rural              88.92  22.9     never smoked  \n",
       "2           Urban              79.79  29.7     never smoked  \n",
       "3           Urban              71.20   NaN          Unknown  \n",
       "4           Rural              96.97  29.1           smokes  \n",
       "5           Rural             111.81  27.3           smokes  \n",
       "6           Rural              59.35  32.3  formerly smoked  \n",
       "7           Urban              86.94  41.1  formerly smoked  \n",
       "8           Urban              98.55  25.6          Unknown  \n",
       "9           Urban             226.98  29.8     never smoked  \n",
       "10          Urban              72.81  26.3     never smoked  \n",
       "11          Urban              68.02  37.5     never smoked  \n",
       "12          Urban              68.56  26.2          Unknown  \n",
       "13          Rural              64.14  29.4     never smoked  \n",
       "14          Rural             235.63  32.3     never smoked  \n",
       "15          Rural              76.34  24.4  formerly smoked  \n",
       "16          Rural             240.59  31.4           smokes  \n",
       "17          Urban              78.92  27.7  formerly smoked  \n",
       "18          Urban              82.81  28.0     never smoked  \n",
       "19          Urban              74.10  28.8          Unknown  \n",
       "20          Urban              86.30  31.7          Unknown  \n",
       "21          Rural             162.14  32.6  formerly smoked  \n",
       "22          Urban              88.20  28.4     never smoked  \n",
       "23          Urban              91.92  35.9           smokes  \n",
       "24          Rural              76.46  21.2  formerly smoked  \n",
       "25          Rural             197.28  34.5  formerly smoked  \n",
       "26          Rural             233.94  42.4     never smoked  \n",
       "27          Urban             247.51  40.5  formerly smoked  \n",
       "28          Rural              84.96  36.7          Unknown  \n",
       "29          Rural              57.93  30.9          Unknown  \n",
       "30          Rural              78.80  29.3  formerly smoked  \n",
       "31          Rural              87.80   NaN          Unknown  \n",
       "32          Rural              78.81  19.6          Unknown  \n",
       "33          Rural              95.12  18.0          Unknown  \n",
       "34          Urban              87.96  39.2     never smoked  \n",
       "35          Urban             110.89  17.6          Unknown  \n",
       "36          Rural              69.04  35.9  formerly smoked  \n",
       "37          Rural             161.28  19.1          Unknown  \n",
       "38          Urban             210.95  50.1          Unknown  \n",
       "39          Urban              73.73  34.4  formerly smoked  \n",
       "40          Urban              79.58  22.2     never smoked  \n",
       "41          Urban              77.88  13.8          Unknown  \n",
       "42          Rural             205.26  40.3     never smoked  \n",
       "43          Rural              58.63  28.4           smokes  \n",
       "44          Rural             139.90  31.7           smokes  \n",
       "45          Urban             211.03  34.2  formerly smoked  \n",
       "46          Urban              72.79  54.7     never smoked  \n",
       "47          Rural              91.65  24.6  formerly smoked  \n",
       "48          Rural              79.92   NaN           smokes  \n",
       "49          Rural             225.47  36.9     never smoked  \n",
       "50          Urban              94.48  31.1     never smoked  \n",
       "51          Urban              82.34  31.9     never smoked  \n",
       "52          Urban             180.63  31.8  formerly smoked  \n",
       "53          Rural              92.48  18.0          Unknown  \n",
       "54          Rural             116.66  28.5  formerly smoked  \n",
       "55          Urban             112.09  29.5     never smoked  \n",
       "56          Urban             130.34  22.0     never smoked  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bringing in new randomized health data\n",
    "\n",
    "mydata = pd.read_csv('Health_Data_For_Predictions.csv')\n",
    "print(mydata.shape, mydata.columns.to_list(),'\\n')\n",
    "mydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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                  4\n",
       "smoking_status       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for nulls\n",
    "mydata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can't have nulls in the \"bmi\" value so I'll replace the nulls with the means\n",
    "\n",
    "mydata['bmi'].fillna(value=mydata['bmi'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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                  0\n",
       "smoking_status       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for nulls\n",
    "mydata.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>101.45</td>\n",
       "      <td>30.328302</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>88.92</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>79.79</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>71.20</td>\n",
       "      <td>30.328302</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>96.97</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>111.81</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>59.35</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>86.94</td>\n",
       "      <td>41.100000</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>98.55</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>226.98</td>\n",
       "      <td>29.800000</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</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>72.81</td>\n",
       "      <td>26.300000</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>68.02</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>68.56</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>64.14</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>235.63</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0        0   66             0              0          Yes  Self-employed   \n",
       "1        0   79             0              0           No        Private   \n",
       "2        0   68             1              0           No  Self-employed   \n",
       "3        1   58             0              0          Yes        Private   \n",
       "4        1   54             0              0          Yes        Private   \n",
       "5        1   61             0              1          Yes        Private   \n",
       "6        0   70             0              1          Yes        Private   \n",
       "7        1   47             0              0          Yes        Private   \n",
       "8        1   74             0              0          Yes        Private   \n",
       "9        0   79             0              1          Yes        Private   \n",
       "10       1   81             0              0          Yes        Private   \n",
       "11       0   57             0              0           No  Self-employed   \n",
       "12       0   80             1              0          Yes  Self-employed   \n",
       "13       1   45             0              0          Yes        Private   \n",
       "14       0   78             0              0          Yes        Private   \n",
       "\n",
       "   Residence_type  avg_glucose_level        bmi   smoking_status  \n",
       "0           Urban             101.45  30.328302          Unknown  \n",
       "1           Rural              88.92  22.900000     never smoked  \n",
       "2           Urban              79.79  29.700000     never smoked  \n",
       "3           Urban              71.20  30.328302          Unknown  \n",
       "4           Rural              96.97  29.100000           smokes  \n",
       "5           Rural             111.81  27.300000           smokes  \n",
       "6           Rural              59.35  32.300000  formerly smoked  \n",
       "7           Urban              86.94  41.100000  formerly smoked  \n",
       "8           Urban              98.55  25.600000          Unknown  \n",
       "9           Urban             226.98  29.800000     never smoked  \n",
       "10          Urban              72.81  26.300000     never smoked  \n",
       "11          Urban              68.02  37.500000     never smoked  \n",
       "12          Urban              68.56  26.200000          Unknown  \n",
       "13          Rural              64.14  29.400000     never smoked  \n",
       "14          Rural             235.63  32.300000     never smoked  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting columns with text to numbers for predicting purposes\n",
    "\n",
    "#Gender\n",
    "#Male = 1, Female = 0, Other = 2\n",
    "\n",
    "mydata = mydata.replace(to_replace = 'Male', value = 1)\n",
    "mydata = mydata.replace(to_replace = 'Female', value = 0)\n",
    "mydata = mydata.replace(to_replace = 'Other', value = 2)\n",
    "mydata.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Urban</td>\n",
       "      <td>101.45</td>\n",
       "      <td>30.328302</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>88.92</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Urban</td>\n",
       "      <td>79.79</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Urban</td>\n",
       "      <td>71.20</td>\n",
       "      <td>30.328302</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>96.97</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>smokes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  age  hypertension  heart_disease ever_married  work_type  \\\n",
       "0       0   66             0              0          Yes          3   \n",
       "1       0   79             0              0           No          2   \n",
       "2       0   68             1              0           No          3   \n",
       "3       1   58             0              0          Yes          2   \n",
       "4       1   54             0              0          Yes          2   \n",
       "\n",
       "  Residence_type  avg_glucose_level        bmi smoking_status  \n",
       "0          Urban             101.45  30.328302        Unknown  \n",
       "1          Rural              88.92  22.900000   never smoked  \n",
       "2          Urban              79.79  29.700000   never smoked  \n",
       "3          Urban              71.20  30.328302        Unknown  \n",
       "4          Rural              96.97  29.100000         smokes  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Work Type\n",
    "\n",
    "#Govt job = 0, Never_worked = 1, Private = 2, Self-employed = 3, Children = 4\n",
    "\n",
    "mydata[\"work_type\"] = mydata[\"work_type\"].astype(\"category\")\n",
    "mydata[\"work_type\"] = mydata[\"work_type\"].cat.codes\n",
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>59.35</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>72.81</td>\n",
       "      <td>26.300000</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  age  hypertension  heart_disease ever_married  work_type  \\\n",
       "0        0   66             0              0          Yes          3   \n",
       "1        0   79             0              0           No          2   \n",
       "2        0   68             1              0           No          3   \n",
       "3        1   58             0              0          Yes          2   \n",
       "4        1   54             0              0          Yes          2   \n",
       "5        1   61             0              1          Yes          2   \n",
       "6        0   70             0              1          Yes          2   \n",
       "7        1   47             0              0          Yes          2   \n",
       "8        1   74             0              0          Yes          2   \n",
       "9        0   79             0              1          Yes          2   \n",
       "10       1   81             0              0          Yes          2   \n",
       "11       0   57             0              0           No          3   \n",
       "12       0   80             1              0          Yes          3   \n",
       "13       1   45             0              0          Yes          2   \n",
       "14       0   78             0              0          Yes          2   \n",
       "\n",
       "   Residence_type  avg_glucose_level        bmi  smoking_status  \n",
       "0           Urban             101.45  30.328302               0  \n",
       "1           Rural              88.92  22.900000               2  \n",
       "2           Urban              79.79  29.700000               2  \n",
       "3           Urban              71.20  30.328302               0  \n",
       "4           Rural              96.97  29.100000               3  \n",
       "5           Rural             111.81  27.300000               3  \n",
       "6           Rural              59.35  32.300000               1  \n",
       "7           Urban              86.94  41.100000               1  \n",
       "8           Urban              98.55  25.600000               0  \n",
       "9           Urban             226.98  29.800000               2  \n",
       "10          Urban              72.81  26.300000               2  \n",
       "11          Urban              68.02  37.500000               2  \n",
       "12          Urban              68.56  26.200000               0  \n",
       "13          Rural              64.14  29.400000               2  \n",
       "14          Rural             235.63  32.300000               2  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Smoking status\n",
    "\n",
    "#Unknown = 0, #Formerly smoked = 1, never smoked = 2, Smokes = 3\n",
    "\n",
    "mydata[\"smoking_status\"] = mydata[\"smoking_status\"].astype(\"category\")\n",
    "mydata[\"smoking_status\"] = mydata[\"smoking_status\"].cat.codes\n",
    "mydata.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "      <td>30.328302</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
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       "      <td>32.300000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <th>10</th>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Urban</td>\n",
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       "      <td>0</td>\n",
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       "      <th>13</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>64.14</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>235.63</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "0        0   66             0              0             1          3   \n",
       "1        0   79             0              0             0          2   \n",
       "2        0   68             1              0             0          3   \n",
       "3        1   58             0              0             1          2   \n",
       "4        1   54             0              0             1          2   \n",
       "5        1   61             0              1             1          2   \n",
       "6        0   70             0              1             1          2   \n",
       "7        1   47             0              0             1          2   \n",
       "8        1   74             0              0             1          2   \n",
       "9        0   79             0              1             1          2   \n",
       "10       1   81             0              0             1          2   \n",
       "11       0   57             0              0             0          3   \n",
       "12       0   80             1              0             1          3   \n",
       "13       1   45             0              0             1          2   \n",
       "14       0   78             0              0             1          2   \n",
       "\n",
       "   Residence_type  avg_glucose_level        bmi  smoking_status  \n",
       "0           Urban             101.45  30.328302               0  \n",
       "1           Rural              88.92  22.900000               2  \n",
       "2           Urban              79.79  29.700000               2  \n",
       "3           Urban              71.20  30.328302               0  \n",
       "4           Rural              96.97  29.100000               3  \n",
       "5           Rural             111.81  27.300000               3  \n",
       "6           Rural              59.35  32.300000               1  \n",
       "7           Urban              86.94  41.100000               1  \n",
       "8           Urban              98.55  25.600000               0  \n",
       "9           Urban             226.98  29.800000               2  \n",
       "10          Urban              72.81  26.300000               2  \n",
       "11          Urban              68.02  37.500000               2  \n",
       "12          Urban              68.56  26.200000               0  \n",
       "13          Rural              64.14  29.400000               2  \n",
       "14          Rural             235.63  32.300000               2  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Marriage Status\n",
    "\n",
    "#Ever Married (Yes) = 1, (No) = 0\n",
    "\n",
    "mydata = mydata.replace(to_replace = 'Yes', value = 1)\n",
    "mydata = mydata.replace(to_replace = 'No', value = 0)\n",
    "mydata.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>101.45</td>\n",
       "      <td>30.328302</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>88.92</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>79.79</td>\n",
       "      <td>29.700000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>71.20</td>\n",
       "      <td>30.328302</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>96.97</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>111.81</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>59.35</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>86.94</td>\n",
       "      <td>41.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>98.55</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>226.98</td>\n",
       "      <td>29.800000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>72.81</td>\n",
       "      <td>26.300000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>68.02</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>68.56</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>64.14</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>235.63</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "0        0   66             0              0             1          3   \n",
       "1        0   79             0              0             0          2   \n",
       "2        0   68             1              0             0          3   \n",
       "3        1   58             0              0             1          2   \n",
       "4        1   54             0              0             1          2   \n",
       "5        1   61             0              1             1          2   \n",
       "6        0   70             0              1             1          2   \n",
       "7        1   47             0              0             1          2   \n",
       "8        1   74             0              0             1          2   \n",
       "9        0   79             0              1             1          2   \n",
       "10       1   81             0              0             1          2   \n",
       "11       0   57             0              0             0          3   \n",
       "12       0   80             1              0             1          3   \n",
       "13       1   45             0              0             1          2   \n",
       "14       0   78             0              0             1          2   \n",
       "\n",
       "    Residence_type  avg_glucose_level        bmi  smoking_status  \n",
       "0                1             101.45  30.328302               0  \n",
       "1                0              88.92  22.900000               2  \n",
       "2                1              79.79  29.700000               2  \n",
       "3                1              71.20  30.328302               0  \n",
       "4                0              96.97  29.100000               3  \n",
       "5                0             111.81  27.300000               3  \n",
       "6                0              59.35  32.300000               1  \n",
       "7                1              86.94  41.100000               1  \n",
       "8                1              98.55  25.600000               0  \n",
       "9                1             226.98  29.800000               2  \n",
       "10               1              72.81  26.300000               2  \n",
       "11               1              68.02  37.500000               2  \n",
       "12               1              68.56  26.200000               0  \n",
       "13               0              64.14  29.400000               2  \n",
       "14               0             235.63  32.300000               2  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Residence_type \n",
    "#(Urban) = 1, (Rural) = 0\n",
    "\n",
    "mydata = mydata.replace(to_replace = 'Urban', value = 1)\n",
    "mydata = mydata.replace(to_replace = 'Rural', value = 0)\n",
    "mydata.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gender', 'age', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi', 'smoking_status'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Creating the list for which we will use to predict\n",
    "\n",
    "xcols4 = mydata.columns.to_list()\n",
    "print(xcols4,'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction\n",
    "\n",
    "pred = log.predict(mydata[xcols4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 0 0 0 1 0 1 1 1 0 0 0 1 1 0 0 1 0 0 1 0 0 1 1 1 0 0 0 1 0 1 0 0 0 1\n",
      " 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "#As we can see, the model predicts some people are at risk at having a stroke (1) while others are not (0)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata['Stroke'] = pred.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    gender  age  hypertension  heart_disease  ever_married  work_type  \\\n",
      "0        0   66             0              0             1          3   \n",
      "1        0   79             0              0             0          2   \n",
      "2        0   68             1              0             0          3   \n",
      "3        1   58             0              0             1          2   \n",
      "4        1   54             0              0             1          2   \n",
      "5        1   61             0              1             1          2   \n",
      "6        0   70             0              1             1          2   \n",
      "7        1   47             0              0             1          2   \n",
      "8        1   74             0              0             1          2   \n",
      "9        0   79             0              1             1          2   \n",
      "10       1   81             0              0             1          2   \n",
      "11       0   57             0              0             0          3   \n",
      "12       0   80             1              0             1          3   \n",
      "13       1   45             0              0             1          2   \n",
      "14       0   78             0              0             1          2   \n",
      "15       0   70             0              0             1          3   \n",
      "16       1   58             0              1             1          2   \n",
      "17       1   57             1              0             1          0   \n",
      "18       0   69             0              0             1          0   \n",
      "19       1   64             0              1             1          0   \n",
      "20       1   57             0              0             1          2   \n",
      "21       1   77             0              0             1          3   \n",
      "22       0   51             1              0             1          2   \n",
      "23       1   60             0              1             1          2   \n",
      "24       1   66             0              0             1          2   \n",
      "25       1   57             0              0             1          2   \n",
      "26       1   68             0              0             1          2   \n",
      "27       0   68             1              1             1          2   \n",
      "28       1   57             0              0             1          2   \n",
      "29       0   14             0              0             0          4   \n",
      "30       0   75             0              0             1          3   \n",
      "31       1   71             1              0             1          3   \n",
      "32       0   78             0              0             1          2   \n",
      "33       1    3             0              0             0          4   \n",
      "34       1   58             1              0             1          2   \n",
      "35       0    8             0              0             0          2   \n",
      "36       0   70             0              0             1          2   \n",
      "37       1   14             0              0             0          1   \n",
      "38       0   47             0              0             1          2   \n",
      "39       0   52             0              0             1          2   \n",
      "40       0   68             0              0             1          2   \n",
      "41       0    5             0              0             0          4   \n",
      "42       0   56             1              0             1          2   \n",
      "43       0   43             0              0             1          2   \n",
      "44       0   40             0              0             1          2   \n",
      "45       1   53             0              0             1          2   \n",
      "46       0   52             0              1             1          2   \n",
      "47       1   31             0              0             1          0   \n",
      "48       0   63             0              0             1          0   \n",
      "49       0   69             0              0             1          3   \n",
      "50       1   64             0              0             1          0   \n",
      "51       0   64             0              0             1          2   \n",
      "52       0   62             0              0             1          3   \n",
      "53       0    2             0              0             0          4   \n",
      "54       1   53             0              0             1          2   \n",
      "55       1   65             1              0             1          2   \n",
      "56       0   22             0              0             0          2   \n",
      "\n",
      "    Residence_type  avg_glucose_level        bmi  smoking_status  Stroke  \n",
      "0                1             101.45  30.328302               0       1  \n",
      "1                0              88.92  22.900000               2       1  \n",
      "2                1              79.79  29.700000               2       0  \n",
      "3                1              71.20  30.328302               0       0  \n",
      "4                0              96.97  29.100000               3       0  \n",
      "5                0             111.81  27.300000               3       0  \n",
      "6                0              59.35  32.300000               1       1  \n",
      "7                1              86.94  41.100000               1       0  \n",
      "8                1              98.55  25.600000               0       1  \n",
      "9                1             226.98  29.800000               2       1  \n",
      "10               1              72.81  26.300000               2       1  \n",
      "11               1              68.02  37.500000               2       0  \n",
      "12               1              68.56  26.200000               0       0  \n",
      "13               0              64.14  29.400000               2       0  \n",
      "14               0             235.63  32.300000               2       1  \n",
      "15               0              76.34  24.400000               1       1  \n",
      "16               0             240.59  31.400000               3       0  \n",
      "17               1              78.92  27.700000               1       0  \n",
      "18               1              82.81  28.000000               2       1  \n",
      "19               1              74.10  28.800000               0       0  \n",
      "20               1              86.30  31.700000               0       0  \n",
      "21               0             162.14  32.600000               1       1  \n",
      "22               1              88.20  28.400000               2       0  \n",
      "23               1              91.92  35.900000               3       0  \n",
      "24               0              76.46  21.200000               1       1  \n",
      "25               0             197.28  34.500000               1       1  \n",
      "26               0             233.94  42.400000               2       1  \n",
      "27               1             247.51  40.500000               1       0  \n",
      "28               0              84.96  36.700000               0       0  \n",
      "29               0              57.93  30.900000               0       0  \n",
      "30               0              78.80  29.300000               1       1  \n",
      "31               0              87.80  30.328302               0       0  \n",
      "32               0              78.81  19.600000               0       1  \n",
      "33               0              95.12  18.000000               0       0  \n",
      "34               1              87.96  39.200000               2       0  \n",
      "35               1             110.89  17.600000               0       0  \n",
      "36               0              69.04  35.900000               1       1  \n",
      "37               0             161.28  19.100000               0       0  \n",
      "38               1             210.95  50.100000               0       0  \n",
      "39               1              73.73  34.400000               1       0  \n",
      "40               1              79.58  22.200000               2       0  \n",
      "41               1              77.88  13.800000               0       0  \n",
      "42               0             205.26  40.300000               2       0  \n",
      "43               0              58.63  28.400000               3       0  \n",
      "44               0             139.90  31.700000               3       0  \n",
      "45               1             211.03  34.200000               1       0  \n",
      "46               1              72.79  54.700000               2       0  \n",
      "47               0              91.65  24.600000               1       0  \n",
      "48               0              79.92  30.328302               3       1  \n",
      "49               0             225.47  36.900000               2       1  \n",
      "50               1              94.48  31.100000               2       0  \n",
      "51               1              82.34  31.900000               2       0  \n",
      "52               1             180.63  31.800000               1       1  \n",
      "53               0              92.48  18.000000               0       0  \n",
      "54               0             116.66  28.500000               1       0  \n",
      "55               1             112.09  29.500000               2       0  \n",
      "56               1             130.34  22.000000               2       0  \n"
     ]
    }
   ],
   "source": [
    "print(mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Storing the results\n",
    "\n",
    "mydata.to_csv(r'/Users/tricky/Desktop/Stroke Prediction Project/Stroke_Predictions.csv')"
   ]
  }
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