Alternative_extract_using_OHE__NN_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "89e423a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import sklearn as skl\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ae28206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0   9046    Male  67.0             0              1          Yes   \n",
       "1  51676  Female  61.0             0              0          Yes   \n",
       "2  31112    Male  80.0             0              1          Yes   \n",
       "3  60182  Female  49.0             0              0          Yes   \n",
       "4   1665  Female  79.0             1              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataset in to DataFrame\n",
    "\n",
    "brain_df = pd.read_csv(\"Resources/healthcare-dataset-stroke-data.csv\")\n",
    "brain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea95d87d",
   "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>36.6</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>NaN</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>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</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>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  36.6  formerly smoked       1  \n",
       "1          Rural             202.21   NaN     never smoked       1  \n",
       "2          Rural             105.92  32.5     never smoked       1  \n",
       "3          Urban             171.23  34.4           smokes       1  \n",
       "4          Rural             174.12  24.0     never smoked       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete id column\n",
    "\n",
    "brain_df.drop(columns=[\"id\"], inplace=True)\n",
    "brain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2324e07b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               5110\n",
       "age                  5110\n",
       "hypertension         5110\n",
       "heart_disease        5110\n",
       "ever_married         5110\n",
       "work_type            5110\n",
       "Residence_type       5110\n",
       "avg_glucose_level    5110\n",
       "bmi                  4909\n",
       "smoking_status       5110\n",
       "stroke               5110\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brain_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "41bd8d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               4909\n",
       "age                  4909\n",
       "hypertension         4909\n",
       "heart_disease        4909\n",
       "ever_married         4909\n",
       "work_type            4909\n",
       "Residence_type       4909\n",
       "avg_glucose_level    4909\n",
       "bmi                  4909\n",
       "smoking_status       4909\n",
       "stroke               4909\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete rows which BMI has NA values\n",
    "\n",
    "brain_df = brain_df.dropna()\n",
    "brain_df.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb4d44ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\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_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender_Female  gender_Male  gender_Other\n",
       "0            0.0          1.0           0.0\n",
       "1            0.0          1.0           0.0\n",
       "2            1.0          0.0           0.0\n",
       "3            1.0          0.0           0.0\n",
       "4            0.0          1.0           0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder(sparse=False)\n",
    "\n",
    "# Fit the encoder and produce encoded DataFrame\n",
    "encode_df = pd.DataFrame(ohe.fit_transform(brain_df.gender.values.reshape(-1,1)))\n",
    "\n",
    "# Rename encoded columns\n",
    "encode_df.columns = ohe.get_feature_names(['gender'])\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "563052ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\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>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",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0  67.0             0              1          Yes        Private   \n",
       "2  80.0             0              1          Yes        Private   \n",
       "3  49.0             0              0          Yes        Private   \n",
       "4  79.0             1              0          Yes  Self-employed   \n",
       "5  81.0             0              0          Yes        Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \\\n",
       "0          Urban             228.69  36.6  formerly smoked       1   \n",
       "2          Rural             105.92  32.5     never smoked       1   \n",
       "3          Urban             171.23  34.4           smokes       1   \n",
       "4          Rural             174.12  24.0     never smoked       1   \n",
       "5          Urban             186.21  29.0  formerly smoked       1   \n",
       "\n",
       "   gender_Female  gender_Male  gender_Other  \n",
       "0            0.0          1.0           0.0  \n",
       "2            1.0          0.0           0.0  \n",
       "3            1.0          0.0           0.0  \n",
       "4            0.0          1.0           0.0  \n",
       "5            0.0          1.0           0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two DataFrames together and drop the Country column\n",
    "brain_df = brain_df.merge(encode_df,left_index=True,right_index=True).drop(\"gender\",1)\n",
    "brain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f47f20a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\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>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ever_married_No  ever_married_Yes\n",
       "0              0.0               1.0\n",
       "1              0.0               1.0\n",
       "2              0.0               1.0\n",
       "3              0.0               1.0\n",
       "4              0.0               1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the encoder and produce encoded DataFrame\n",
    "encode_df2 = pd.DataFrame(ohe.fit_transform(brain_df.ever_married.values.reshape(-1,1)))\n",
    "\n",
    "# Rename encoded columns\n",
    "encode_df2.columns = ohe.get_feature_names(['ever_married'])\n",
    "encode_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "880dc214",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  \"\"\"Entry point for launching an IPython kernel.\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>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</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",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  hypertension  heart_disease      work_type Residence_type  \\\n",
       "0  67.0             0              1        Private          Urban   \n",
       "2  80.0             0              1        Private          Rural   \n",
       "3  49.0             0              0        Private          Urban   \n",
       "4  79.0             1              0  Self-employed          Rural   \n",
       "5  81.0             0              0        Private          Urban   \n",
       "\n",
       "   avg_glucose_level   bmi   smoking_status  stroke  gender_Female  \\\n",
       "0             228.69  36.6  formerly smoked       1            0.0   \n",
       "2             105.92  32.5     never smoked       1            1.0   \n",
       "3             171.23  34.4           smokes       1            1.0   \n",
       "4             174.12  24.0     never smoked       1            0.0   \n",
       "5             186.21  29.0  formerly smoked       1            0.0   \n",
       "\n",
       "   gender_Male  gender_Other  ever_married_No  ever_married_Yes  \n",
       "0          1.0           0.0              0.0               1.0  \n",
       "2          0.0           0.0              0.0               1.0  \n",
       "3          0.0           0.0              0.0               1.0  \n",
       "4          1.0           0.0              0.0               1.0  \n",
       "5          1.0           0.0              0.0               1.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brain_df = brain_df.merge(encode_df2,left_index=True,right_index=True).drop(\"ever_married\",1)\n",
    "brain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26c34d34",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\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>work_type_Govt_job</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   work_type_Govt_job  work_type_Never_worked  work_type_Private  \\\n",
       "0                 0.0                     0.0                1.0   \n",
       "1                 0.0                     0.0                1.0   \n",
       "2                 0.0                     0.0                1.0   \n",
       "3                 0.0                     0.0                0.0   \n",
       "4                 0.0                     0.0                1.0   \n",
       "\n",
       "   work_type_Self-employed  work_type_children  \n",
       "0                      0.0                 0.0  \n",
       "1                      0.0                 0.0  \n",
       "2                      0.0                 0.0  \n",
       "3                      1.0                 0.0  \n",
       "4                      0.0                 0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the encoder and produce encoded DataFrame\n",
    "encode_df3 = pd.DataFrame(ohe.fit_transform(brain_df.work_type.values.reshape(-1,1)))\n",
    "\n",
    "# Rename encoded columns\n",
    "encode_df3.columns = ohe.get_feature_names(['work_type'])\n",
    "encode_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00e19f92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  \"\"\"Entry point for launching an IPython kernel.\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>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</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",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>work_type_Govt_job</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  hypertension  heart_disease Residence_type  avg_glucose_level   bmi  \\\n",
       "0  67.0             0              1          Urban             228.69  36.6   \n",
       "2  80.0             0              1          Rural             105.92  32.5   \n",
       "3  49.0             0              0          Urban             171.23  34.4   \n",
       "4  79.0             1              0          Rural             174.12  24.0   \n",
       "5  81.0             0              0          Urban             186.21  29.0   \n",
       "\n",
       "    smoking_status  stroke  gender_Female  gender_Male  gender_Other  \\\n",
       "0  formerly smoked       1            0.0          1.0           0.0   \n",
       "2     never smoked       1            1.0          0.0           0.0   \n",
       "3           smokes       1            1.0          0.0           0.0   \n",
       "4     never smoked       1            0.0          1.0           0.0   \n",
       "5  formerly smoked       1            0.0          1.0           0.0   \n",
       "\n",
       "   ever_married_No  ever_married_Yes  work_type_Govt_job  \\\n",
       "0              0.0               1.0                 0.0   \n",
       "2              0.0               1.0                 0.0   \n",
       "3              0.0               1.0                 0.0   \n",
       "4              0.0               1.0                 0.0   \n",
       "5              0.0               1.0                 0.0   \n",
       "\n",
       "   work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                     0.0                1.0                      0.0   \n",
       "2                     0.0                1.0                      0.0   \n",
       "3                     0.0                0.0                      1.0   \n",
       "4                     0.0                1.0                      0.0   \n",
       "5                     0.0                1.0                      0.0   \n",
       "\n",
       "   work_type_children  \n",
       "0                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "5                 0.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brain_df = brain_df.merge(encode_df3,left_index=True,right_index=True).drop(\"work_type\",1)\n",
    "brain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "931df341",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\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>Residence_type_Rural</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Residence_type_Rural  Residence_type_Urban\n",
       "0                   0.0                   1.0\n",
       "1                   1.0                   0.0\n",
       "2                   0.0                   1.0\n",
       "3                   1.0                   0.0\n",
       "4                   0.0                   1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the encoder and produce encoded DataFrame\n",
    "encode_df4 = pd.DataFrame(ohe.fit_transform(brain_df.Residence_type.values.reshape(-1,1)))\n",
    "\n",
    "# Rename encoded columns\n",
    "encode_df4.columns = ohe.get_feature_names(['Residence_type'])\n",
    "encode_df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0762cb5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  \"\"\"Entry point for launching an IPython kernel.\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>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>work_type_Govt_job</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>Residence_type_Rural</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  hypertension  heart_disease  avg_glucose_level   bmi  \\\n",
       "0  67.0             0              1             228.69  36.6   \n",
       "2  80.0             0              1             105.92  32.5   \n",
       "3  49.0             0              0             171.23  34.4   \n",
       "4  79.0             1              0             174.12  24.0   \n",
       "5  81.0             0              0             186.21  29.0   \n",
       "\n",
       "    smoking_status  stroke  gender_Female  gender_Male  gender_Other  \\\n",
       "0  formerly smoked       1            0.0          1.0           0.0   \n",
       "2     never smoked       1            1.0          0.0           0.0   \n",
       "3           smokes       1            1.0          0.0           0.0   \n",
       "4     never smoked       1            0.0          1.0           0.0   \n",
       "5  formerly smoked       1            0.0          1.0           0.0   \n",
       "\n",
       "   ever_married_No  ever_married_Yes  work_type_Govt_job  \\\n",
       "0              0.0               1.0                 0.0   \n",
       "2              0.0               1.0                 0.0   \n",
       "3              0.0               1.0                 0.0   \n",
       "4              0.0               1.0                 0.0   \n",
       "5              0.0               1.0                 0.0   \n",
       "\n",
       "   work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                     0.0                1.0                      0.0   \n",
       "2                     0.0                1.0                      0.0   \n",
       "3                     0.0                0.0                      1.0   \n",
       "4                     0.0                1.0                      0.0   \n",
       "5                     0.0                1.0                      0.0   \n",
       "\n",
       "   work_type_children  Residence_type_Rural  Residence_type_Urban  \n",
       "0                 0.0                   0.0                   1.0  \n",
       "2                 0.0                   0.0                   1.0  \n",
       "3                 0.0                   1.0                   0.0  \n",
       "4                 0.0                   0.0                   1.0  \n",
       "5                 0.0                   1.0                   0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brain_df = brain_df.merge(encode_df4,left_index=True,right_index=True).drop(\"Residence_type\",1)\n",
    "brain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4c818dad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\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>smoking_status_Unknown</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   smoking_status_Unknown  smoking_status_formerly smoked  \\\n",
       "0                     0.0                             1.0   \n",
       "1                     0.0                             0.0   \n",
       "2                     0.0                             0.0   \n",
       "3                     0.0                             0.0   \n",
       "4                     0.0                             1.0   \n",
       "\n",
       "   smoking_status_never smoked  smoking_status_smokes  \n",
       "0                          0.0                    0.0  \n",
       "1                          1.0                    0.0  \n",
       "2                          0.0                    1.0  \n",
       "3                          1.0                    0.0  \n",
       "4                          0.0                    0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the encoder and produce encoded DataFrame\n",
    "encode_df5 = pd.DataFrame(ohe.fit_transform(brain_df.smoking_status.values.reshape(-1,1)))\n",
    "\n",
    "# Rename encoded columns\n",
    "encode_df5.columns = ohe.get_feature_names(['smoking_status'])\n",
    "encode_df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "424f4ba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  \"\"\"Entry point for launching an IPython kernel.\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>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>...</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>Residence_type_Rural</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "      <th>smoking_status_Unknown</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  hypertension  heart_disease  avg_glucose_level   bmi  stroke  \\\n",
       "0  67.0             0              1             228.69  36.6       1   \n",
       "2  80.0             0              1             105.92  32.5       1   \n",
       "3  49.0             0              0             171.23  34.4       1   \n",
       "4  79.0             1              0             174.12  24.0       1   \n",
       "5  81.0             0              0             186.21  29.0       1   \n",
       "\n",
       "   gender_Female  gender_Male  gender_Other  ever_married_No  ...  \\\n",
       "0            0.0          1.0           0.0              0.0  ...   \n",
       "2            1.0          0.0           0.0              0.0  ...   \n",
       "3            1.0          0.0           0.0              0.0  ...   \n",
       "4            0.0          1.0           0.0              0.0  ...   \n",
       "5            0.0          1.0           0.0              0.0  ...   \n",
       "\n",
       "   work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                     0.0                1.0                      0.0   \n",
       "2                     0.0                1.0                      0.0   \n",
       "3                     0.0                0.0                      1.0   \n",
       "4                     0.0                1.0                      0.0   \n",
       "5                     0.0                1.0                      0.0   \n",
       "\n",
       "   work_type_children  Residence_type_Rural  Residence_type_Urban  \\\n",
       "0                 0.0                   0.0                   1.0   \n",
       "2                 0.0                   0.0                   1.0   \n",
       "3                 0.0                   1.0                   0.0   \n",
       "4                 0.0                   0.0                   1.0   \n",
       "5                 0.0                   1.0                   0.0   \n",
       "\n",
       "   smoking_status_Unknown  smoking_status_formerly smoked  \\\n",
       "0                     0.0                             1.0   \n",
       "2                     0.0                             0.0   \n",
       "3                     0.0                             0.0   \n",
       "4                     0.0                             1.0   \n",
       "5                     0.0                             0.0   \n",
       "\n",
       "   smoking_status_never smoked  smoking_status_smokes  \n",
       "0                          0.0                    0.0  \n",
       "2                          0.0                    1.0  \n",
       "3                          1.0                    0.0  \n",
       "4                          0.0                    0.0  \n",
       "5                          1.0                    0.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brain_df = brain_df.merge(encode_df5,left_index=True,right_index=True).drop(\"smoking_status\",1)\n",
    "brain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad4d17d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>...</th>\n",
       "      <th>never_worked</th>\n",
       "      <th>private_worker</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>minor_unemployed</th>\n",
       "      <th>Residence_type_Rural</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "      <th>smoking_status_Unknown</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  hypertension  heart_disease  avg_glucose_level   bmi  stroke  \\\n",
       "0  67.0             0              1             228.69  36.6       1   \n",
       "2  80.0             0              1             105.92  32.5       1   \n",
       "3  49.0             0              0             171.23  34.4       1   \n",
       "4  79.0             1              0             174.12  24.0       1   \n",
       "5  81.0             0              0             186.21  29.0       1   \n",
       "\n",
       "   gender_Female  gender_Male  gender_Other  ever_married_No  ...  \\\n",
       "0            0.0          1.0           0.0              0.0  ...   \n",
       "2            1.0          0.0           0.0              0.0  ...   \n",
       "3            1.0          0.0           0.0              0.0  ...   \n",
       "4            0.0          1.0           0.0              0.0  ...   \n",
       "5            0.0          1.0           0.0              0.0  ...   \n",
       "\n",
       "   never_worked  private_worker  self_employed  minor_unemployed  \\\n",
       "0           0.0             1.0            0.0               0.0   \n",
       "2           0.0             1.0            0.0               0.0   \n",
       "3           0.0             0.0            1.0               0.0   \n",
       "4           0.0             1.0            0.0               0.0   \n",
       "5           0.0             1.0            0.0               0.0   \n",
       "\n",
       "   Residence_type_Rural  Residence_type_Urban  smoking_status_Unknown  \\\n",
       "0                   0.0                   1.0                     0.0   \n",
       "2                   0.0                   1.0                     0.0   \n",
       "3                   1.0                   0.0                     0.0   \n",
       "4                   0.0                   1.0                     0.0   \n",
       "5                   1.0                   0.0                     0.0   \n",
       "\n",
       "   smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
       "0                             1.0                          0.0   \n",
       "2                             0.0                          0.0   \n",
       "3                             0.0                          1.0   \n",
       "4                             1.0                          0.0   \n",
       "5                             0.0                          1.0   \n",
       "\n",
       "   smoking_status_smokes  \n",
       "0                    0.0  \n",
       "2                    1.0  \n",
       "3                    0.0  \n",
       "4                    0.0  \n",
       "5                    0.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename columns\n",
    "\n",
    "brain_df.rename(columns={\"work_type_Govt_job\":\"govt_worker\",\"work_type_Never_worked\":\"never_worked\",\n",
    "                         \"work_type_Private\":\"private_worker\", \"work_type_Self-employed\":\"self_employed\",\n",
    "                         \"work_type_children\": \"minor_unemployed\"}, inplace=True)\n",
    "\n",
    "brain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b18e291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>...</th>\n",
       "      <th>never_worked</th>\n",
       "      <th>private_worker</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>minor_unemployed</th>\n",
       "      <th>rural_residence</th>\n",
       "      <th>urban_residence</th>\n",
       "      <th>smoking_unknown</th>\n",
       "      <th>former_smoker</th>\n",
       "      <th>never_smoked</th>\n",
       "      <th>current_smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  hypertension  heart_disease  avg_glucose_level   bmi  stroke  \\\n",
       "0  67.0             0              1             228.69  36.6       1   \n",
       "2  80.0             0              1             105.92  32.5       1   \n",
       "3  49.0             0              0             171.23  34.4       1   \n",
       "4  79.0             1              0             174.12  24.0       1   \n",
       "5  81.0             0              0             186.21  29.0       1   \n",
       "\n",
       "   gender_Female  gender_Male  gender_Other  ever_married_No  ...  \\\n",
       "0            0.0          1.0           0.0              0.0  ...   \n",
       "2            1.0          0.0           0.0              0.0  ...   \n",
       "3            1.0          0.0           0.0              0.0  ...   \n",
       "4            0.0          1.0           0.0              0.0  ...   \n",
       "5            0.0          1.0           0.0              0.0  ...   \n",
       "\n",
       "   never_worked  private_worker  self_employed  minor_unemployed  \\\n",
       "0           0.0             1.0            0.0               0.0   \n",
       "2           0.0             1.0            0.0               0.0   \n",
       "3           0.0             0.0            1.0               0.0   \n",
       "4           0.0             1.0            0.0               0.0   \n",
       "5           0.0             1.0            0.0               0.0   \n",
       "\n",
       "   rural_residence  urban_residence  smoking_unknown  former_smoker  \\\n",
       "0              0.0              1.0              0.0            1.0   \n",
       "2              0.0              1.0              0.0            0.0   \n",
       "3              1.0              0.0              0.0            0.0   \n",
       "4              0.0              1.0              0.0            1.0   \n",
       "5              1.0              0.0              0.0            0.0   \n",
       "\n",
       "   never_smoked  current_smoker  \n",
       "0           0.0             0.0  \n",
       "2           0.0             1.0  \n",
       "3           1.0             0.0  \n",
       "4           0.0             0.0  \n",
       "5           1.0             0.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename columns cont'd\n",
    "\n",
    "brain_df.rename(columns={\"Residence_type_Rural\": \"rural_residence\", \"Residence_type_Urban\": \"urban_residence\", \n",
    "                        \"smoking_status_Unknown\":\"smoking_unknown\", \"smoking_status_formerly smoked\":\"former_smoker\",\n",
    "                        \"smoking_status_never smoked\": \"never_smoked\", \"smoking_status_smokes\":\"current_smoker\"}, inplace=True)\n",
    "\n",
    "brain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "884176a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>...</th>\n",
       "      <th>never_worked</th>\n",
       "      <th>private_worker</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>minor_unemployed</th>\n",
       "      <th>rural_residence</th>\n",
       "      <th>urban_residence</th>\n",
       "      <th>smoking_unknown</th>\n",
       "      <th>former_smoker</th>\n",
       "      <th>never_smoked</th>\n",
       "      <th>current_smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4152</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.57</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4153</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>72.04</td>\n",
       "      <td>23.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4154</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125.63</td>\n",
       "      <td>57.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4155</th>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>77.94</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4156</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.99</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3327 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease  avg_glucose_level   bmi  stroke  \\\n",
       "0     67.0             0              1             228.69  36.6       1   \n",
       "2     80.0             0              1             105.92  32.5       1   \n",
       "3     49.0             0              0             171.23  34.4       1   \n",
       "4     79.0             1              0             174.12  24.0       1   \n",
       "5     81.0             0              0             186.21  29.0       1   \n",
       "...    ...           ...            ...                ...   ...     ...   \n",
       "4152  55.0             0              0              73.57  28.0       0   \n",
       "4153  79.0             1              0              72.04  23.6       0   \n",
       "4154  49.0             0              0             125.63  57.2       0   \n",
       "4155  53.0             1              0              77.94  33.0       0   \n",
       "4156  26.0             0              0              68.99  22.2       0   \n",
       "\n",
       "      gender_Female  gender_Male  gender_Other  ever_married_No  ...  \\\n",
       "0               0.0          1.0           0.0              0.0  ...   \n",
       "2               1.0          0.0           0.0              0.0  ...   \n",
       "3               1.0          0.0           0.0              0.0  ...   \n",
       "4               0.0          1.0           0.0              0.0  ...   \n",
       "5               0.0          1.0           0.0              0.0  ...   \n",
       "...             ...          ...           ...              ...  ...   \n",
       "4152            0.0          1.0           0.0              0.0  ...   \n",
       "4153            0.0          1.0           0.0              0.0  ...   \n",
       "4154            0.0          1.0           0.0              1.0  ...   \n",
       "4155            0.0          1.0           0.0              0.0  ...   \n",
       "4156            1.0          0.0           0.0              0.0  ...   \n",
       "\n",
       "      never_worked  private_worker  self_employed  minor_unemployed  \\\n",
       "0              0.0             1.0            0.0               0.0   \n",
       "2              0.0             1.0            0.0               0.0   \n",
       "3              0.0             0.0            1.0               0.0   \n",
       "4              0.0             1.0            0.0               0.0   \n",
       "5              0.0             1.0            0.0               0.0   \n",
       "...            ...             ...            ...               ...   \n",
       "4152           0.0             1.0            0.0               0.0   \n",
       "4153           0.0             1.0            0.0               0.0   \n",
       "4154           0.0             1.0            0.0               0.0   \n",
       "4155           0.0             0.0            0.0               0.0   \n",
       "4156           0.0             1.0            0.0               0.0   \n",
       "\n",
       "      rural_residence  urban_residence  smoking_unknown  former_smoker  \\\n",
       "0                 0.0              1.0              0.0            1.0   \n",
       "2                 0.0              1.0              0.0            0.0   \n",
       "3                 1.0              0.0              0.0            0.0   \n",
       "4                 0.0              1.0              0.0            1.0   \n",
       "5                 1.0              0.0              0.0            0.0   \n",
       "...               ...              ...              ...            ...   \n",
       "4152              0.0              1.0              0.0            0.0   \n",
       "4153              1.0              0.0              1.0            0.0   \n",
       "4154              0.0              1.0              0.0            1.0   \n",
       "4155              1.0              0.0              0.0            0.0   \n",
       "4156              1.0              0.0              0.0            0.0   \n",
       "\n",
       "      never_smoked  current_smoker  \n",
       "0              0.0             0.0  \n",
       "2              0.0             1.0  \n",
       "3              1.0             0.0  \n",
       "4              0.0             0.0  \n",
       "5              1.0             0.0  \n",
       "...            ...             ...  \n",
       "4152           0.0             1.0  \n",
       "4153           0.0             0.0  \n",
       "4154           0.0             0.0  \n",
       "4155           0.0             1.0  \n",
       "4156           1.0             0.0  \n",
       "\n",
       "[3327 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create DataFrame with persons who are 18yrs and older\n",
    "\n",
    "adult_df_ohe = brain_df[(brain_df[\"age\"]>17)]\n",
    "\n",
    "adult_df_ohe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c1a248",
   "metadata": {},
   "source": [
    "### Second attempt at machine learning model using binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "280ab6d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_df_ohe['age'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f16a0c17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57.0    80\n",
       "78.0    78\n",
       "52.0    75\n",
       "50.0    71\n",
       "53.0    69\n",
       "        ..\n",
       "72.0    35\n",
       "19.0    35\n",
       "70.0    34\n",
       "77.0    34\n",
       "74.0    34\n",
       "Name: age, Length: 65, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_counts = adult_df_ohe.age.value_counts()\n",
    "age_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0f2bb4a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "age_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d01968fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\pandas\\core\\generic.py:5516: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "40-59          1231\n",
       "60 or Older    1077\n",
       "Under 40       1019\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for age in adult_df_ohe['age']:\n",
    "    if age < 40:\n",
    "        adult_df_ohe.age = adult_df_ohe.age.replace(age, \"Under 40\")\n",
    "    elif (age >= 40) & (age < 60):\n",
    "        adult_df_ohe.age = adult_df_ohe.age.replace(age, \"40-59\")\n",
    "    else:\n",
    "        adult_df_ohe.age = adult_df_ohe.age.replace(age, \"60 or Older\")\n",
    "        \n",
    "adult_df_ohe.age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "210720b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114.32    4\n",
       "72.49     4\n",
       "86.06     4\n",
       "73.00     4\n",
       "91.85     4\n",
       "         ..\n",
       "62.78     1\n",
       "103.25    1\n",
       "126.99    1\n",
       "108.08    1\n",
       "68.99     1\n",
       "Name: avg_glucose_level, Length: 2859, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glucose_counts = adult_df_ohe.avg_glucose_level.value_counts()\n",
    "glucose_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dd2d92eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "glucose_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3cf80c0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.22\n",
      "271.74\n"
     ]
    }
   ],
   "source": [
    "print(min(adult_df_ohe.avg_glucose_level))\n",
    "print(max(adult_df_ohe.avg_glucose_level))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fd070992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>...</th>\n",
       "      <th>never_worked</th>\n",
       "      <th>private_worker</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>minor_unemployed</th>\n",
       "      <th>rural_residence</th>\n",
       "      <th>urban_residence</th>\n",
       "      <th>smoking_unknown</th>\n",
       "      <th>former_smoker</th>\n",
       "      <th>never_smoked</th>\n",
       "      <th>current_smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60 or Older</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60 or Older</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40-59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60 or Older</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60 or Older</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4152</th>\n",
       "      <td>40-59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.57</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4153</th>\n",
       "      <td>60 or Older</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>72.04</td>\n",
       "      <td>23.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4154</th>\n",
       "      <td>40-59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125.63</td>\n",
       "      <td>57.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4155</th>\n",
       "      <td>40-59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>77.94</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4156</th>\n",
       "      <td>Under 40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.99</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3327 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              age  hypertension  heart_disease  avg_glucose_level   bmi  \\\n",
       "0     60 or Older             0              1             228.69  36.6   \n",
       "2     60 or Older             0              1             105.92  32.5   \n",
       "3           40-59             0              0             171.23  34.4   \n",
       "4     60 or Older             1              0             174.12  24.0   \n",
       "5     60 or Older             0              0             186.21  29.0   \n",
       "...           ...           ...            ...                ...   ...   \n",
       "4152        40-59             0              0              73.57  28.0   \n",
       "4153  60 or Older             1              0              72.04  23.6   \n",
       "4154        40-59             0              0             125.63  57.2   \n",
       "4155        40-59             1              0              77.94  33.0   \n",
       "4156     Under 40             0              0              68.99  22.2   \n",
       "\n",
       "      stroke  gender_Female  gender_Male  gender_Other  ever_married_No  ...  \\\n",
       "0          1            0.0          1.0           0.0              0.0  ...   \n",
       "2          1            1.0          0.0           0.0              0.0  ...   \n",
       "3          1            1.0          0.0           0.0              0.0  ...   \n",
       "4          1            0.0          1.0           0.0              0.0  ...   \n",
       "5          1            0.0          1.0           0.0              0.0  ...   \n",
       "...      ...            ...          ...           ...              ...  ...   \n",
       "4152       0            0.0          1.0           0.0              0.0  ...   \n",
       "4153       0            0.0          1.0           0.0              0.0  ...   \n",
       "4154       0            0.0          1.0           0.0              1.0  ...   \n",
       "4155       0            0.0          1.0           0.0              0.0  ...   \n",
       "4156       0            1.0          0.0           0.0              0.0  ...   \n",
       "\n",
       "      never_worked  private_worker  self_employed  minor_unemployed  \\\n",
       "0              0.0             1.0            0.0               0.0   \n",
       "2              0.0             1.0            0.0               0.0   \n",
       "3              0.0             0.0            1.0               0.0   \n",
       "4              0.0             1.0            0.0               0.0   \n",
       "5              0.0             1.0            0.0               0.0   \n",
       "...            ...             ...            ...               ...   \n",
       "4152           0.0             1.0            0.0               0.0   \n",
       "4153           0.0             1.0            0.0               0.0   \n",
       "4154           0.0             1.0            0.0               0.0   \n",
       "4155           0.0             0.0            0.0               0.0   \n",
       "4156           0.0             1.0            0.0               0.0   \n",
       "\n",
       "      rural_residence  urban_residence  smoking_unknown  former_smoker  \\\n",
       "0                 0.0              1.0              0.0            1.0   \n",
       "2                 0.0              1.0              0.0            0.0   \n",
       "3                 1.0              0.0              0.0            0.0   \n",
       "4                 0.0              1.0              0.0            1.0   \n",
       "5                 1.0              0.0              0.0            0.0   \n",
       "...               ...              ...              ...            ...   \n",
       "4152              0.0              1.0              0.0            0.0   \n",
       "4153              1.0              0.0              1.0            0.0   \n",
       "4154              0.0              1.0              0.0            1.0   \n",
       "4155              1.0              0.0              0.0            0.0   \n",
       "4156              1.0              0.0              0.0            0.0   \n",
       "\n",
       "      never_smoked  current_smoker  \n",
       "0              0.0             0.0  \n",
       "2              0.0             1.0  \n",
       "3              1.0             0.0  \n",
       "4              0.0             0.0  \n",
       "5              1.0             0.0  \n",
       "...            ...             ...  \n",
       "4152           0.0             1.0  \n",
       "4153           0.0             0.0  \n",
       "4154           0.0             0.0  \n",
       "4155           0.0             1.0  \n",
       "4156           1.0             0.0  \n",
       "\n",
       "[3327 rows x 22 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_df_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "28767a2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\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>age_40-59</th>\n",
       "      <th>age_60 or Older</th>\n",
       "      <th>age_Under 40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_40-59  age_60 or Older  age_Under 40\n",
       "0        0.0              1.0           0.0\n",
       "1        0.0              1.0           0.0\n",
       "2        1.0              0.0           0.0\n",
       "3        0.0              1.0           0.0\n",
       "4        0.0              1.0           0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply OHE to \"age\" \n",
    "\n",
    "# Fit the encoder and produce encoded DataFrame\n",
    "encode_df_age = pd.DataFrame(ohe.fit_transform(adult_df_ohe.age.values.reshape(-1,1)))\n",
    "\n",
    "# Rename encoded columns\n",
    "encode_df_age.columns = ohe.get_feature_names(['age'])\n",
    "encode_df_age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "05908f63",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  \"\"\"Entry point for launching an IPython kernel.\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>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>minor_unemployed</th>\n",
       "      <th>rural_residence</th>\n",
       "      <th>urban_residence</th>\n",
       "      <th>smoking_unknown</th>\n",
       "      <th>former_smoker</th>\n",
       "      <th>never_smoked</th>\n",
       "      <th>current_smoker</th>\n",
       "      <th>age_40-59</th>\n",
       "      <th>age_60 or Older</th>\n",
       "      <th>age_Under 40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>220.64</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69.53</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3322</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83.93</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3325</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112.54</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3326</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.25</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2671 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hypertension  heart_disease  avg_glucose_level   bmi  stroke  \\\n",
       "0                0              1             228.69  36.6       1   \n",
       "2                0              1             105.92  32.5       1   \n",
       "3                0              0             171.23  34.4       1   \n",
       "4                1              0             174.12  24.0       1   \n",
       "5                0              0             186.21  29.0       1   \n",
       "...            ...            ...                ...   ...     ...   \n",
       "3318             1              1             220.64  30.0       0   \n",
       "3320             0              0              69.53  26.2       0   \n",
       "3322             0              0              83.93  25.6       0   \n",
       "3325             0              0             112.54  23.4       0   \n",
       "3326             0              0              75.25  39.7       0   \n",
       "\n",
       "      gender_Female  gender_Male  gender_Other  ever_married_No  \\\n",
       "0               0.0          1.0           0.0              0.0   \n",
       "2               1.0          0.0           0.0              0.0   \n",
       "3               1.0          0.0           0.0              0.0   \n",
       "4               0.0          1.0           0.0              0.0   \n",
       "5               0.0          1.0           0.0              0.0   \n",
       "...             ...          ...           ...              ...   \n",
       "3318            0.0          1.0           0.0              1.0   \n",
       "3320            0.0          1.0           0.0              0.0   \n",
       "3322            1.0          0.0           0.0              1.0   \n",
       "3325            1.0          0.0           0.0              0.0   \n",
       "3326            0.0          1.0           0.0              0.0   \n",
       "\n",
       "      ever_married_Yes  ...  minor_unemployed  rural_residence  \\\n",
       "0                  1.0  ...               0.0              0.0   \n",
       "2                  1.0  ...               0.0              0.0   \n",
       "3                  1.0  ...               0.0              1.0   \n",
       "4                  1.0  ...               0.0              0.0   \n",
       "5                  1.0  ...               0.0              1.0   \n",
       "...                ...  ...               ...              ...   \n",
       "3318               0.0  ...               1.0              0.0   \n",
       "3320               1.0  ...               0.0              0.0   \n",
       "3322               0.0  ...               0.0              1.0   \n",
       "3325               1.0  ...               0.0              1.0   \n",
       "3326               1.0  ...               0.0              0.0   \n",
       "\n",
       "      urban_residence  smoking_unknown  former_smoker  never_smoked  \\\n",
       "0                 1.0              0.0            1.0           0.0   \n",
       "2                 1.0              0.0            0.0           0.0   \n",
       "3                 0.0              0.0            0.0           1.0   \n",
       "4                 1.0              0.0            1.0           0.0   \n",
       "5                 0.0              0.0            0.0           1.0   \n",
       "...               ...              ...            ...           ...   \n",
       "3318              1.0              1.0            0.0           0.0   \n",
       "3320              1.0              0.0            1.0           0.0   \n",
       "3322              0.0              0.0            1.0           0.0   \n",
       "3325              0.0              0.0            0.0           1.0   \n",
       "3326              1.0              0.0            0.0           1.0   \n",
       "\n",
       "      current_smoker  age_40-59  age_60 or Older  age_Under 40  \n",
       "0                0.0        0.0              1.0           0.0  \n",
       "2                1.0        1.0              0.0           0.0  \n",
       "3                0.0        0.0              1.0           0.0  \n",
       "4                0.0        0.0              1.0           0.0  \n",
       "5                0.0        0.0              1.0           0.0  \n",
       "...              ...        ...              ...           ...  \n",
       "3318             0.0        0.0              1.0           0.0  \n",
       "3320             0.0        0.0              1.0           0.0  \n",
       "3322             0.0        1.0              0.0           0.0  \n",
       "3325             0.0        1.0              0.0           0.0  \n",
       "3326             0.0        0.0              0.0           1.0  \n",
       "\n",
       "[2671 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_df_ohe = adult_df_ohe.merge(encode_df_age,left_index=True,right_index=True).drop(\"age\",1)\n",
    "adult_df_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "058f7cbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.82     4\n",
       "72.49     4\n",
       "73.00     4\n",
       "97.06     3\n",
       "114.32    3\n",
       "         ..\n",
       "93.34     1\n",
       "195.16    1\n",
       "246.34    1\n",
       "96.63     1\n",
       "83.93     1\n",
       "Name: avg_glucose_level, Length: 2372, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at glucose column for binning\n",
    "glucose_counts = adult_df_ohe.avg_glucose_level.value_counts()\n",
    "glucose_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e2c5b458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'avg_glucose_level'}>]], dtype=object)"
      ]
     },
     "execution_count": 30,
     "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": [
    "\n",
    "\n",
    "adult_df_ohe.hist(column=\"avg_glucose_level\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "99459dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75-140      1615\n",
       "Under 75     571\n",
       "Over 140     485\n",
       "Name: avg_glucose_level, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bin glucose column\n",
    "\n",
    "for lvl in adult_df_ohe['avg_glucose_level']:\n",
    "    if lvl < 75:\n",
    "        adult_df_ohe.avg_glucose_level = adult_df_ohe.avg_glucose_level.replace(lvl, \"Under 75\")\n",
    "    elif (lvl >= 75) & (lvl < 140):\n",
    "        adult_df_ohe.avg_glucose_level = adult_df_ohe.avg_glucose_level.replace(lvl, \"75-140\")\n",
    "    else:\n",
    "        adult_df_ohe.avg_glucose_level = adult_df_ohe.avg_glucose_level.replace(lvl, \"Over 140\")\n",
    "        \n",
    "adult_df_ohe.avg_glucose_level.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9202497a",
   "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>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>minor_unemployed</th>\n",
       "      <th>rural_residence</th>\n",
       "      <th>urban_residence</th>\n",
       "      <th>smoking_unknown</th>\n",
       "      <th>former_smoker</th>\n",
       "      <th>never_smoked</th>\n",
       "      <th>current_smoker</th>\n",
       "      <th>age_40-59</th>\n",
       "      <th>age_60 or Older</th>\n",
       "      <th>age_Under 40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Over 140</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75-140</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Over 140</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Over 140</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Over 140</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Over 140</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Under 75</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3322</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75-140</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3325</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75-140</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3326</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75-140</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2671 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hypertension  heart_disease avg_glucose_level   bmi  stroke  \\\n",
       "0                0              1          Over 140  36.6       1   \n",
       "2                0              1            75-140  32.5       1   \n",
       "3                0              0          Over 140  34.4       1   \n",
       "4                1              0          Over 140  24.0       1   \n",
       "5                0              0          Over 140  29.0       1   \n",
       "...            ...            ...               ...   ...     ...   \n",
       "3318             1              1          Over 140  30.0       0   \n",
       "3320             0              0          Under 75  26.2       0   \n",
       "3322             0              0            75-140  25.6       0   \n",
       "3325             0              0            75-140  23.4       0   \n",
       "3326             0              0            75-140  39.7       0   \n",
       "\n",
       "      gender_Female  gender_Male  gender_Other  ever_married_No  \\\n",
       "0               0.0          1.0           0.0              0.0   \n",
       "2               1.0          0.0           0.0              0.0   \n",
       "3               1.0          0.0           0.0              0.0   \n",
       "4               0.0          1.0           0.0              0.0   \n",
       "5               0.0          1.0           0.0              0.0   \n",
       "...             ...          ...           ...              ...   \n",
       "3318            0.0          1.0           0.0              1.0   \n",
       "3320            0.0          1.0           0.0              0.0   \n",
       "3322            1.0          0.0           0.0              1.0   \n",
       "3325            1.0          0.0           0.0              0.0   \n",
       "3326            0.0          1.0           0.0              0.0   \n",
       "\n",
       "      ever_married_Yes  ...  minor_unemployed  rural_residence  \\\n",
       "0                  1.0  ...               0.0              0.0   \n",
       "2                  1.0  ...               0.0              0.0   \n",
       "3                  1.0  ...               0.0              1.0   \n",
       "4                  1.0  ...               0.0              0.0   \n",
       "5                  1.0  ...               0.0              1.0   \n",
       "...                ...  ...               ...              ...   \n",
       "3318               0.0  ...               1.0              0.0   \n",
       "3320               1.0  ...               0.0              0.0   \n",
       "3322               0.0  ...               0.0              1.0   \n",
       "3325               1.0  ...               0.0              1.0   \n",
       "3326               1.0  ...               0.0              0.0   \n",
       "\n",
       "      urban_residence  smoking_unknown  former_smoker  never_smoked  \\\n",
       "0                 1.0              0.0            1.0           0.0   \n",
       "2                 1.0              0.0            0.0           0.0   \n",
       "3                 0.0              0.0            0.0           1.0   \n",
       "4                 1.0              0.0            1.0           0.0   \n",
       "5                 0.0              0.0            0.0           1.0   \n",
       "...               ...              ...            ...           ...   \n",
       "3318              1.0              1.0            0.0           0.0   \n",
       "3320              1.0              0.0            1.0           0.0   \n",
       "3322              0.0              0.0            1.0           0.0   \n",
       "3325              0.0              0.0            0.0           1.0   \n",
       "3326              1.0              0.0            0.0           1.0   \n",
       "\n",
       "      current_smoker  age_40-59  age_60 or Older  age_Under 40  \n",
       "0                0.0        0.0              1.0           0.0  \n",
       "2                1.0        1.0              0.0           0.0  \n",
       "3                0.0        0.0              1.0           0.0  \n",
       "4                0.0        0.0              1.0           0.0  \n",
       "5                0.0        0.0              1.0           0.0  \n",
       "...              ...        ...              ...           ...  \n",
       "3318             0.0        0.0              1.0           0.0  \n",
       "3320             0.0        0.0              1.0           0.0  \n",
       "3322             0.0        1.0              0.0           0.0  \n",
       "3325             0.0        1.0              0.0           0.0  \n",
       "3326             0.0        0.0              0.0           1.0  \n",
       "\n",
       "[2671 rows x 24 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_df_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3c1dc83f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\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>avg_glucose_level_75-140</th>\n",
       "      <th>avg_glucose_level_Over 140</th>\n",
       "      <th>avg_glucose_level_Under 75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_glucose_level_75-140  avg_glucose_level_Over 140  \\\n",
       "0                       0.0                         1.0   \n",
       "1                       1.0                         0.0   \n",
       "2                       0.0                         1.0   \n",
       "3                       0.0                         1.0   \n",
       "4                       0.0                         1.0   \n",
       "\n",
       "   avg_glucose_level_Under 75  \n",
       "0                         0.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "3                         0.0  \n",
       "4                         0.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply OHE to Glucose column\n",
    "\n",
    "# Fit the encoder and produce encoded DataFrame\n",
    "encode_df_glu = pd.DataFrame(ohe.fit_transform(adult_df_ohe.avg_glucose_level.values.reshape(-1,1)))\n",
    "\n",
    "# Rename encoded columns\n",
    "encode_df_glu.columns = ohe.get_feature_names(['avg_glucose_level'])\n",
    "encode_df_glu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "30378f69",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  \"\"\"Entry point for launching an IPython kernel.\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>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>govt_worker</th>\n",
       "      <th>...</th>\n",
       "      <th>smoking_unknown</th>\n",
       "      <th>former_smoker</th>\n",
       "      <th>never_smoked</th>\n",
       "      <th>current_smoker</th>\n",
       "      <th>age_40-59</th>\n",
       "      <th>age_60 or Older</th>\n",
       "      <th>age_Under 40</th>\n",
       "      <th>avg_glucose_level_75-140</th>\n",
       "      <th>avg_glucose_level_Over 140</th>\n",
       "      <th>avg_glucose_level_Under 75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2665</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2666</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2669</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2670</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2150 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hypertension  heart_disease   bmi  stroke  gender_Female  gender_Male  \\\n",
       "0                0              1  36.6       1            0.0          1.0   \n",
       "2                0              1  32.5       1            1.0          0.0   \n",
       "3                0              0  34.4       1            1.0          0.0   \n",
       "4                1              0  24.0       1            0.0          1.0   \n",
       "5                0              0  29.0       1            0.0          1.0   \n",
       "...            ...            ...   ...     ...            ...          ...   \n",
       "2665             0              0  21.0       0            0.0          1.0   \n",
       "2666             1              0  28.9       0            0.0          1.0   \n",
       "2668             0              0  28.0       0            1.0          0.0   \n",
       "2669             0              0  29.6       0            1.0          0.0   \n",
       "2670             0              0  30.2       0            1.0          0.0   \n",
       "\n",
       "      gender_Other  ever_married_No  ever_married_Yes  govt_worker  ...  \\\n",
       "0              0.0              0.0               1.0          0.0  ...   \n",
       "2              0.0              0.0               1.0          0.0  ...   \n",
       "3              0.0              0.0               1.0          0.0  ...   \n",
       "4              0.0              0.0               1.0          0.0  ...   \n",
       "5              0.0              0.0               1.0          0.0  ...   \n",
       "...            ...              ...               ...          ...  ...   \n",
       "2665           0.0              0.0               1.0          0.0  ...   \n",
       "2666           0.0              1.0               0.0          0.0  ...   \n",
       "2668           0.0              0.0               1.0          0.0  ...   \n",
       "2669           0.0              0.0               1.0          1.0  ...   \n",
       "2670           0.0              0.0               1.0          0.0  ...   \n",
       "\n",
       "      smoking_unknown  former_smoker  never_smoked  current_smoker  age_40-59  \\\n",
       "0                 0.0            1.0           0.0             0.0        0.0   \n",
       "2                 0.0            0.0           0.0             1.0        1.0   \n",
       "3                 0.0            0.0           1.0             0.0        0.0   \n",
       "4                 0.0            1.0           0.0             0.0        0.0   \n",
       "5                 0.0            0.0           1.0             0.0        0.0   \n",
       "...               ...            ...           ...             ...        ...   \n",
       "2665              0.0            0.0           1.0             0.0        0.0   \n",
       "2666              0.0            1.0           0.0             0.0        0.0   \n",
       "2668              1.0            0.0           0.0             0.0        1.0   \n",
       "2669              0.0            0.0           1.0             0.0        0.0   \n",
       "2670              0.0            0.0           1.0             0.0        0.0   \n",
       "\n",
       "      age_60 or Older  age_Under 40  avg_glucose_level_75-140  \\\n",
       "0                 1.0           0.0                       0.0   \n",
       "2                 0.0           0.0                       0.0   \n",
       "3                 1.0           0.0                       0.0   \n",
       "4                 1.0           0.0                       0.0   \n",
       "5                 1.0           0.0                       0.0   \n",
       "...               ...           ...                       ...   \n",
       "2665              1.0           0.0                       0.0   \n",
       "2666              1.0           0.0                       0.0   \n",
       "2668              0.0           0.0                       1.0   \n",
       "2669              0.0           1.0                       1.0   \n",
       "2670              0.0           1.0                       1.0   \n",
       "\n",
       "      avg_glucose_level_Over 140  avg_glucose_level_Under 75  \n",
       "0                            1.0                         0.0  \n",
       "2                            1.0                         0.0  \n",
       "3                            1.0                         0.0  \n",
       "4                            1.0                         0.0  \n",
       "5                            0.0                         1.0  \n",
       "...                          ...                         ...  \n",
       "2665                         1.0                         0.0  \n",
       "2666                         1.0                         0.0  \n",
       "2668                         0.0                         0.0  \n",
       "2669                         0.0                         0.0  \n",
       "2670                         0.0                         0.0  \n",
       "\n",
       "[2150 rows x 26 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_df_ohe = adult_df_ohe.merge(encode_df_glu,left_index=True,right_index=True).drop(\"avg_glucose_level\",1)\n",
    "adult_df_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "275aa513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.7    24\n",
       "27.7    24\n",
       "28.1    23\n",
       "27.3    20\n",
       "26.4    18\n",
       "        ..\n",
       "50.6     1\n",
       "48.4     1\n",
       "44.6     1\n",
       "37.7     1\n",
       "14.2     1\n",
       "Name: bmi, Length: 338, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_df_ohe.bmi.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8c80ed7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 36,
     "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": [
    "adult_df_ohe.bmi.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a93bbbca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Not Obese    1157\n",
       "Obese         993\n",
       "Name: bmi, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bin BMI column\n",
    "\n",
    "for lvl in adult_df_ohe['bmi']:\n",
    "    if lvl < 30:\n",
    "        adult_df_ohe.bmi = adult_df_ohe.bmi.replace(lvl, \"Not Obese\")\n",
    "    else:\n",
    "        adult_df_ohe.bmi = adult_df_ohe.bmi.replace(lvl, \"Obese\")\n",
    "        \n",
    "adult_df_ohe.bmi.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "56bc023b",
   "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>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>govt_worker</th>\n",
       "      <th>...</th>\n",
       "      <th>smoking_unknown</th>\n",
       "      <th>former_smoker</th>\n",
       "      <th>never_smoked</th>\n",
       "      <th>current_smoker</th>\n",
       "      <th>age_40-59</th>\n",
       "      <th>age_60 or Older</th>\n",
       "      <th>age_Under 40</th>\n",
       "      <th>avg_glucose_level_75-140</th>\n",
       "      <th>avg_glucose_level_Over 140</th>\n",
       "      <th>avg_glucose_level_Under 75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Obese</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Obese</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Obese</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Obese</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Obese</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2665</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Obese</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2666</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Obese</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Obese</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2669</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Obese</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2670</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Obese</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2150 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hypertension  heart_disease        bmi  stroke  gender_Female  \\\n",
       "0                0              1      Obese       1            0.0   \n",
       "2                0              1      Obese       1            1.0   \n",
       "3                0              0      Obese       1            1.0   \n",
       "4                1              0  Not Obese       1            0.0   \n",
       "5                0              0  Not Obese       1            0.0   \n",
       "...            ...            ...        ...     ...            ...   \n",
       "2665             0              0  Not Obese       0            0.0   \n",
       "2666             1              0  Not Obese       0            0.0   \n",
       "2668             0              0  Not Obese       0            1.0   \n",
       "2669             0              0  Not Obese       0            1.0   \n",
       "2670             0              0      Obese       0            1.0   \n",
       "\n",
       "      gender_Male  gender_Other  ever_married_No  ever_married_Yes  \\\n",
       "0             1.0           0.0              0.0               1.0   \n",
       "2             0.0           0.0              0.0               1.0   \n",
       "3             0.0           0.0              0.0               1.0   \n",
       "4             1.0           0.0              0.0               1.0   \n",
       "5             1.0           0.0              0.0               1.0   \n",
       "...           ...           ...              ...               ...   \n",
       "2665          1.0           0.0              0.0               1.0   \n",
       "2666          1.0           0.0              1.0               0.0   \n",
       "2668          0.0           0.0              0.0               1.0   \n",
       "2669          0.0           0.0              0.0               1.0   \n",
       "2670          0.0           0.0              0.0               1.0   \n",
       "\n",
       "      govt_worker  ...  smoking_unknown  former_smoker  never_smoked  \\\n",
       "0             0.0  ...              0.0            1.0           0.0   \n",
       "2             0.0  ...              0.0            0.0           0.0   \n",
       "3             0.0  ...              0.0            0.0           1.0   \n",
       "4             0.0  ...              0.0            1.0           0.0   \n",
       "5             0.0  ...              0.0            0.0           1.0   \n",
       "...           ...  ...              ...            ...           ...   \n",
       "2665          0.0  ...              0.0            0.0           1.0   \n",
       "2666          0.0  ...              0.0            1.0           0.0   \n",
       "2668          0.0  ...              1.0            0.0           0.0   \n",
       "2669          1.0  ...              0.0            0.0           1.0   \n",
       "2670          0.0  ...              0.0            0.0           1.0   \n",
       "\n",
       "      current_smoker  age_40-59  age_60 or Older  age_Under 40  \\\n",
       "0                0.0        0.0              1.0           0.0   \n",
       "2                1.0        1.0              0.0           0.0   \n",
       "3                0.0        0.0              1.0           0.0   \n",
       "4                0.0        0.0              1.0           0.0   \n",
       "5                0.0        0.0              1.0           0.0   \n",
       "...              ...        ...              ...           ...   \n",
       "2665             0.0        0.0              1.0           0.0   \n",
       "2666             0.0        0.0              1.0           0.0   \n",
       "2668             0.0        1.0              0.0           0.0   \n",
       "2669             0.0        0.0              0.0           1.0   \n",
       "2670             0.0        0.0              0.0           1.0   \n",
       "\n",
       "      avg_glucose_level_75-140  avg_glucose_level_Over 140  \\\n",
       "0                          0.0                         1.0   \n",
       "2                          0.0                         1.0   \n",
       "3                          0.0                         1.0   \n",
       "4                          0.0                         1.0   \n",
       "5                          0.0                         0.0   \n",
       "...                        ...                         ...   \n",
       "2665                       0.0                         1.0   \n",
       "2666                       0.0                         1.0   \n",
       "2668                       1.0                         0.0   \n",
       "2669                       1.0                         0.0   \n",
       "2670                       1.0                         0.0   \n",
       "\n",
       "      avg_glucose_level_Under 75  \n",
       "0                            0.0  \n",
       "2                            0.0  \n",
       "3                            0.0  \n",
       "4                            0.0  \n",
       "5                            1.0  \n",
       "...                          ...  \n",
       "2665                         0.0  \n",
       "2666                         0.0  \n",
       "2668                         0.0  \n",
       "2669                         0.0  \n",
       "2670                         0.0  \n",
       "\n",
       "[2150 rows x 26 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_df_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2ba19bdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\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>bmi_Not Obese</th>\n",
       "      <th>bmi_Obese</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bmi_Not Obese  bmi_Obese\n",
       "0            0.0        1.0\n",
       "1            0.0        1.0\n",
       "2            0.0        1.0\n",
       "3            1.0        0.0\n",
       "4            1.0        0.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply OHE to bmi column\n",
    "\n",
    "# Fit the encoder and produce encoded DataFrame\n",
    "encode_df_bmi = pd.DataFrame(ohe.fit_transform(adult_df_ohe.bmi.values.reshape(-1,1)))\n",
    "\n",
    "# Rename encoded columns\n",
    "encode_df_bmi.columns = ohe.get_feature_names(['bmi'])\n",
    "encode_df_bmi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ef78d877",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bbert\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  \"\"\"Entry point for launching an IPython kernel.\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>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>govt_worker</th>\n",
       "      <th>never_worked</th>\n",
       "      <th>...</th>\n",
       "      <th>never_smoked</th>\n",
       "      <th>current_smoker</th>\n",
       "      <th>age_40-59</th>\n",
       "      <th>age_60 or Older</th>\n",
       "      <th>age_Under 40</th>\n",
       "      <th>avg_glucose_level_75-140</th>\n",
       "      <th>avg_glucose_level_Over 140</th>\n",
       "      <th>avg_glucose_level_Under 75</th>\n",
       "      <th>bmi_Not Obese</th>\n",
       "      <th>bmi_Obese</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2143</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2144</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2145</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2147</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1739 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hypertension  heart_disease  stroke  gender_Female  gender_Male  \\\n",
       "0                0              1       1            0.0          1.0   \n",
       "2                0              1       1            1.0          0.0   \n",
       "3                0              0       1            1.0          0.0   \n",
       "4                1              0       1            0.0          1.0   \n",
       "5                0              0       1            0.0          1.0   \n",
       "...            ...            ...     ...            ...          ...   \n",
       "2143             0              0       0            1.0          0.0   \n",
       "2144             0              0       0            0.0          1.0   \n",
       "2145             0              0       0            0.0          1.0   \n",
       "2147             0              0       0            0.0          1.0   \n",
       "2149             0              0       0            1.0          0.0   \n",
       "\n",
       "      gender_Other  ever_married_No  ever_married_Yes  govt_worker  \\\n",
       "0              0.0              0.0               1.0          0.0   \n",
       "2              0.0              0.0               1.0          0.0   \n",
       "3              0.0              0.0               1.0          0.0   \n",
       "4              0.0              0.0               1.0          0.0   \n",
       "5              0.0              0.0               1.0          0.0   \n",
       "...            ...              ...               ...          ...   \n",
       "2143           0.0              1.0               0.0          0.0   \n",
       "2144           0.0              0.0               1.0          0.0   \n",
       "2145           0.0              0.0               1.0          1.0   \n",
       "2147           0.0              1.0               0.0          0.0   \n",
       "2149           0.0              0.0               1.0          0.0   \n",
       "\n",
       "      never_worked  ...  never_smoked  current_smoker  age_40-59  \\\n",
       "0              0.0  ...           0.0             0.0        0.0   \n",
       "2              0.0  ...           0.0             1.0        1.0   \n",
       "3              0.0  ...           1.0             0.0        0.0   \n",
       "4              0.0  ...           0.0             0.0        0.0   \n",
       "5              0.0  ...           1.0             0.0        0.0   \n",
       "...            ...  ...           ...             ...        ...   \n",
       "2143           0.0  ...           1.0             0.0        0.0   \n",
       "2144           0.0  ...           0.0             0.0        1.0   \n",
       "2145           0.0  ...           0.0             1.0        0.0   \n",
       "2147           0.0  ...           0.0             0.0        0.0   \n",
       "2149           0.0  ...           0.0             0.0        0.0   \n",
       "\n",
       "      age_60 or Older  age_Under 40  avg_glucose_level_75-140  \\\n",
       "0                 1.0           0.0                       0.0   \n",
       "2                 0.0           0.0                       0.0   \n",
       "3                 1.0           0.0                       0.0   \n",
       "4                 1.0           0.0                       0.0   \n",
       "5                 1.0           0.0                       0.0   \n",
       "...               ...           ...                       ...   \n",
       "2143              0.0           1.0                       1.0   \n",
       "2144              0.0           0.0                       1.0   \n",
       "2145              0.0           1.0                       1.0   \n",
       "2147              1.0           0.0                       0.0   \n",
       "2149              0.0           1.0                       1.0   \n",
       "\n",
       "      avg_glucose_level_Over 140  avg_glucose_level_Under 75  bmi_Not Obese  \\\n",
       "0                            1.0                         0.0            0.0   \n",
       "2                            1.0                         0.0            0.0   \n",
       "3                            1.0                         0.0            1.0   \n",
       "4                            1.0                         0.0            1.0   \n",
       "5                            0.0                         1.0            1.0   \n",
       "...                          ...                         ...            ...   \n",
       "2143                         0.0                         0.0            1.0   \n",
       "2144                         0.0                         0.0            1.0   \n",
       "2145                         0.0                         0.0            1.0   \n",
       "2147                         1.0                         0.0            1.0   \n",
       "2149                         0.0                         0.0            0.0   \n",
       "\n",
       "      bmi_Obese  \n",
       "0           1.0  \n",
       "2           1.0  \n",
       "3           0.0  \n",
       "4           0.0  \n",
       "5           0.0  \n",
       "...         ...  \n",
       "2143        0.0  \n",
       "2144        0.0  \n",
       "2145        0.0  \n",
       "2147        0.0  \n",
       "2149        1.0  \n",
       "\n",
       "[1739 rows x 27 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_df_ohe = adult_df_ohe.merge(encode_df_bmi,left_index=True,right_index=True).drop(\"bmi\",1)\n",
    "adult_df_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2e41366d",
   "metadata": {},
   "outputs": [],
   "source": [
    "adult_df_ohe.to_csv(\"Resources/adult_OHE.csv\", index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7e32b1c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "67438034",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create our features\n",
    "\n",
    "X = adult_df_ohe.drop(columns=[\"stroke\"])\n",
    "y = adult_df_ohe[\"stroke\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5ca3993b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7b4c86c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "41/41 [==============================] - 3s 3ms/step - loss: 0.4086 - accuracy: 0.8742\n",
      "Epoch 2/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.3369 - accuracy: 0.8804\n",
      "Epoch 3/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.3158 - accuracy: 0.8819\n",
      "Epoch 4/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.3024 - accuracy: 0.8842\n",
      "Epoch 5/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2972 - accuracy: 0.8880\n",
      "Epoch 6/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2909 - accuracy: 0.8880\n",
      "Epoch 7/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2905 - accuracy: 0.8911\n",
      "Epoch 8/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2839 - accuracy: 0.8942\n",
      "Epoch 9/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2818 - accuracy: 0.8911\n",
      "Epoch 10/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2803 - accuracy: 0.8919\n",
      "Epoch 11/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2741 - accuracy: 0.8926\n",
      "Epoch 12/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2715 - accuracy: 0.8949\n",
      "Epoch 13/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2681 - accuracy: 0.8957\n",
      "Epoch 14/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2675 - accuracy: 0.8942\n",
      "Epoch 15/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2620 - accuracy: 0.8949\n",
      "Epoch 16/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.2587 - accuracy: 0.8949\n",
      "Epoch 17/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2547 - accuracy: 0.8957\n",
      "Epoch 18/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2518 - accuracy: 0.8988\n",
      "Epoch 19/100\n",
      "41/41 [==============================] - 0s 5ms/step - loss: 0.2482 - accuracy: 0.9041\n",
      "Epoch 20/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2439 - accuracy: 0.8988\n",
      "Epoch 21/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2411 - accuracy: 0.8995\n",
      "Epoch 22/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2383 - accuracy: 0.9041\n",
      "Epoch 23/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2385 - accuracy: 0.9018\n",
      "Epoch 24/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2311 - accuracy: 0.9064\n",
      "Epoch 25/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.2312 - accuracy: 0.9072\n",
      "Epoch 26/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2261 - accuracy: 0.9103\n",
      "Epoch 27/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2251 - accuracy: 0.9080\n",
      "Epoch 28/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2213 - accuracy: 0.9149\n",
      "Epoch 29/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2226 - accuracy: 0.9072\n",
      "Epoch 30/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2171 - accuracy: 0.9149\n",
      "Epoch 31/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2132 - accuracy: 0.9149\n",
      "Epoch 32/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2109 - accuracy: 0.9141\n",
      "Epoch 33/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2102 - accuracy: 0.9149\n",
      "Epoch 34/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2036 - accuracy: 0.9164\n",
      "Epoch 35/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.2034 - accuracy: 0.9210\n",
      "Epoch 36/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.2003 - accuracy: 0.9149\n",
      "Epoch 37/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1984 - accuracy: 0.9179\n",
      "Epoch 38/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1965 - accuracy: 0.9195\n",
      "Epoch 39/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1937 - accuracy: 0.9233\n",
      "Epoch 40/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1918 - accuracy: 0.9271\n",
      "Epoch 41/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1864 - accuracy: 0.9233\n",
      "Epoch 42/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1852 - accuracy: 0.9233\n",
      "Epoch 43/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1849 - accuracy: 0.9225\n",
      "Epoch 44/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1806 - accuracy: 0.9256\n",
      "Epoch 45/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1814 - accuracy: 0.9218\n",
      "Epoch 46/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1755 - accuracy: 0.9294\n",
      "Epoch 47/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.1780 - accuracy: 0.9248\n",
      "Epoch 48/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.1737 - accuracy: 0.9302\n",
      "Epoch 49/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1682 - accuracy: 0.9271\n",
      "Epoch 50/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1711 - accuracy: 0.9264\n",
      "Epoch 51/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1656 - accuracy: 0.9363\n",
      "Epoch 52/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1662 - accuracy: 0.9302\n",
      "Epoch 53/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1644 - accuracy: 0.9317\n",
      "Epoch 54/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1631 - accuracy: 0.9310\n",
      "Epoch 55/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1636 - accuracy: 0.9287\n",
      "Epoch 56/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1555 - accuracy: 0.9371\n",
      "Epoch 57/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1573 - accuracy: 0.9363\n",
      "Epoch 58/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1552 - accuracy: 0.9356\n",
      "Epoch 59/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1521 - accuracy: 0.9340\n",
      "Epoch 60/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1515 - accuracy: 0.9363\n",
      "Epoch 61/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1488 - accuracy: 0.9417\n",
      "Epoch 62/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1484 - accuracy: 0.9387\n",
      "Epoch 63/100\n",
      "41/41 [==============================] - 0s 5ms/step - loss: 0.1499 - accuracy: 0.9387\n",
      "Epoch 64/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1479 - accuracy: 0.9356\n",
      "Epoch 65/100\n",
      "41/41 [==============================] - 0s 5ms/step - loss: 0.1454 - accuracy: 0.9363\n",
      "Epoch 66/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.1458 - accuracy: 0.9371\n",
      "Epoch 67/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.1411 - accuracy: 0.9394\n",
      "Epoch 68/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1437 - accuracy: 0.9394\n",
      "Epoch 69/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1426 - accuracy: 0.9417\n",
      "Epoch 70/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.1454 - accuracy: 0.9348\n",
      "Epoch 71/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1394 - accuracy: 0.9371\n",
      "Epoch 72/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1363 - accuracy: 0.9448\n",
      "Epoch 73/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1384 - accuracy: 0.9433\n",
      "Epoch 74/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1364 - accuracy: 0.9440\n",
      "Epoch 75/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1323 - accuracy: 0.9448\n",
      "Epoch 76/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1338 - accuracy: 0.9410\n",
      "Epoch 77/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.1321 - accuracy: 0.9402\n",
      "Epoch 78/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1322 - accuracy: 0.9433\n",
      "Epoch 79/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1308 - accuracy: 0.9425\n",
      "Epoch 80/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1326 - accuracy: 0.9402\n",
      "Epoch 81/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1302 - accuracy: 0.9425\n",
      "Epoch 82/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1304 - accuracy: 0.9402\n",
      "Epoch 83/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1261 - accuracy: 0.9448\n",
      "Epoch 84/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1267 - accuracy: 0.9433\n",
      "Epoch 85/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1271 - accuracy: 0.9425\n",
      "Epoch 86/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1267 - accuracy: 0.9471\n",
      "Epoch 87/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.1236 - accuracy: 0.9448\n",
      "Epoch 88/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.1237 - accuracy: 0.9448\n",
      "Epoch 89/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1261 - accuracy: 0.9363\n",
      "Epoch 90/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1271 - accuracy: 0.9433\n",
      "Epoch 91/100\n",
      "41/41 [==============================] - 0s 3ms/step - loss: 0.1262 - accuracy: 0.9425\n",
      "Epoch 92/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1250 - accuracy: 0.9402\n",
      "Epoch 93/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1230 - accuracy: 0.9463\n",
      "Epoch 94/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1257 - accuracy: 0.9448\n",
      "Epoch 95/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1214 - accuracy: 0.9410\n",
      "Epoch 96/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1206 - accuracy: 0.9433\n",
      "Epoch 97/100\n",
      "41/41 [==============================] - 0s 4ms/step - loss: 0.1187 - accuracy: 0.9448\n",
      "Epoch 98/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1207 - accuracy: 0.9371\n",
      "Epoch 99/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1197 - accuracy: 0.9494\n",
      "Epoch 100/100\n",
      "41/41 [==============================] - 0s 2ms/step - loss: 0.1216 - accuracy: 0.9463\n"
     ]
    }
   ],
   "source": [
    "# Define the deep learning model \n",
    "nn_model = tf.keras.models.Sequential()\n",
    "nn_model.add(tf.keras.layers.Dense(units=52, activation=\"relu\", input_dim=26))\n",
    "nn_model.add(tf.keras.layers.Dense(units=52, activation=\"relu\"))\n",
    "nn_model.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Compile the Sequential model together and customize metrics\n",
    "nn_model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
    "\n",
    "# Train the model\n",
    "fit_model = nn_model.fit(X_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e1586363",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 - 1s - loss: 0.5160 - accuracy: 0.8391 - 955ms/epoch - 68ms/step\n",
      "Loss: 0.5160161256790161, Accuracy: 0.8390804529190063\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "\n",
    "model_loss, model_accuracy = nn_model.evaluate(X_test,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "687e075a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlenv",
   "language": "python",
   "name": "mlenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}