{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brain Stroke prediction- DecisionTree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context:\n",
    "\n",
    "A stroke is a medical condition in which poor blood flow to the brain causes cell death. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Both cause parts of the brain to stop functioning properly. Signs and symptoms of a stroke may include an inability to move or feel on one side of the body, problems understanding or speaking, dizziness, or loss of vision to one side. Signs and symptoms often appear soon after the stroke has occurred. If symptoms last less than one or two hours, the stroke is a transient ischemic attack (TIA), also called a mini-stroke. A hemorrhagic stroke may also be associated with a severe headache. The symptoms of a stroke can be permanent. Long-term complications may include pneumonia and loss of bladder control.\n",
    "\n",
    "The main risk factor for stroke is high blood pressure. Other risk factors include high blood cholesterol, tobacco smoking, obesity, diabetes mellitus, a previous TIA, end-stage kidney disease, and atrial fibrillation. An ischemic stroke is typically caused by blockage of a blood vessel, though there are also less common causes. A hemorrhagic stroke is caused by either bleeding directly into the brain or into the space between the brain's membranes. Bleeding may occur due to a ruptured brain aneurysm. Diagnosis is typically based on a physical exam and supported by medical imaging such as a CT scan or MRI scan. A CT scan can rule out bleeding, but may not necessarily rule out ischemia, which early on typically does not show up on a CT scan. Other tests such as an electrocardiogram (ECG) and blood tests are done to determine risk factors and rule out other possible causes. Low blood sugar may cause similar symptoms.\n",
    "\n",
    "Prevention includes decreasing risk factors, surgery to open up the arteries to the brain in those with problematic carotid narrowing, and warfarin in people with atrial fibrillation. Aspirin or statins may be recommended by physicians for prevention. A stroke or TIA often requires emergency care. An ischemic stroke, if detected within three to four and half hours, may be treatable with a medication that can break down the clot. Some hemorrhagic strokes benefit from surgery. Treatment to attempt recovery of lost function is called stroke rehabilitation, and ideally takes place in a stroke unit; however, these are not available in much of the world.\n",
    "\n",
    "### Attribute Information:\n",
    "\n",
    "1) gender: \"Male\", \"Female\" or \"Other\"\n",
    "2) age: age of the patient\n",
    "3) hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension\n",
    "4) heartdisease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease 5) evermarried: \"No\" or \"Yes\"\n",
    "6) worktype: \"children\", \"Govtjov\", \"Neverworked\", \"Private\" or \"Self-employed\" 7) Residencetype: \"Rural\" or \"Urban\"\n",
    "8) avgglucoselevel: average glucose level in blood\n",
    "9) bmi: body mass index\n",
    "10) smoking_status: \"formerly smoked\", \"never smoked\", \"smokes\" or \"Unknown\"*\n",
    "11) stroke: 1 if the patient had a stroke or 0 if not\n",
    "\n",
    "*Note: \"Unknown\" in smoking_status means that the information is unavailable for this patient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IMPORTING LIBRARIES AND LOADING DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import joblib as joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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>2</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>3</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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.4</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.8</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>80.43</td>\n",
       "      <td>29.7</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Rural</td>\n",
       "      <td>120.46</td>\n",
       "      <td>36.8</td>\n",
       "      <td>smokes</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    Male  80.0             0              1          Yes        Private   \n",
       "2  Female  49.0             0              0          Yes        Private   \n",
       "3  Female  79.0             1              0          Yes  Self-employed   \n",
       "4    Male  81.0             0              0          Yes        Private   \n",
       "5    Male  74.0             1              1          Yes        Private   \n",
       "6  Female  69.0             0              0           No        Private   \n",
       "7  Female  78.0             0              0          Yes        Private   \n",
       "8  Female  81.0             1              0          Yes        Private   \n",
       "9  Female  61.0             0              1          Yes       Govt_job   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "1          Rural             105.92  32.5     never smoked       1  \n",
       "2          Urban             171.23  34.4           smokes       1  \n",
       "3          Rural             174.12  24.0     never smoked       1  \n",
       "4          Urban             186.21  29.0  formerly smoked       1  \n",
       "5          Rural              70.09  27.4     never smoked       1  \n",
       "6          Urban              94.39  22.8     never smoked       1  \n",
       "7          Urban              58.57  24.2          Unknown       1  \n",
       "8          Rural              80.43  29.7     never smoked       1  \n",
       "9          Rural             120.46  36.8           smokes       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('full_data.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA EXPLORATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4981 entries, 0 to 4980\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             4981 non-null   object \n",
      " 1   age                4981 non-null   float64\n",
      " 2   hypertension       4981 non-null   int64  \n",
      " 3   heart_disease      4981 non-null   int64  \n",
      " 4   ever_married       4981 non-null   object \n",
      " 5   work_type          4981 non-null   object \n",
      " 6   Residence_type     4981 non-null   object \n",
      " 7   avg_glucose_level  4981 non-null   float64\n",
      " 8   bmi                4981 non-null   float64\n",
      " 9   smoking_status     4981 non-null   object \n",
      " 10  stroke             4981 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 428.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.419859</td>\n",
       "      <td>0.096165</td>\n",
       "      <td>0.055210</td>\n",
       "      <td>105.943562</td>\n",
       "      <td>28.498173</td>\n",
       "      <td>0.049789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.662755</td>\n",
       "      <td>0.294848</td>\n",
       "      <td>0.228412</td>\n",
       "      <td>45.075373</td>\n",
       "      <td>6.790464</td>\n",
       "      <td>0.217531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.230000</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.850000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>113.860000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age  hypertension  heart_disease  avg_glucose_level  \\\n",
       "count  4981.000000   4981.000000    4981.000000        4981.000000   \n",
       "mean     43.419859      0.096165       0.055210         105.943562   \n",
       "std      22.662755      0.294848       0.228412          45.075373   \n",
       "min       0.080000      0.000000       0.000000          55.120000   \n",
       "25%      25.000000      0.000000       0.000000          77.230000   \n",
       "50%      45.000000      0.000000       0.000000          91.850000   \n",
       "75%      61.000000      0.000000       0.000000         113.860000   \n",
       "max      82.000000      1.000000       1.000000         271.740000   \n",
       "\n",
       "               bmi       stroke  \n",
       "count  4981.000000  4981.000000  \n",
       "mean     28.498173     0.049789  \n",
       "std       6.790464     0.217531  \n",
       "min      14.000000     0.000000  \n",
       "25%      23.700000     0.000000  \n",
       "50%      28.100000     0.000000  \n",
       "75%      32.600000     0.000000  \n",
       "max      48.900000     1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Male' 'Female']\n",
      "['Private' 'Self-employed' 'Govt_job' 'children']\n",
      "['Urban' 'Rural']\n",
      "['formerly smoked' 'never smoked' 'smokes' 'Unknown']\n",
      "['Yes' 'No']\n"
     ]
    }
   ],
   "source": [
    "print(df['gender'].unique())\n",
    "print(df['work_type'].unique())\n",
    "print(df['Residence_type'].unique())\n",
    "print(df['smoking_status'].unique())\n",
    "print(df['ever_married'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIZUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.16.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cufflinks as cf\n",
    "\n",
    "cf.go_offline()\n",
    "cf.set_config_file(offline=False, world_readable=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Proportion of Stroke among Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "labels": [
          "Female",
          "Male"
         ],
         "marker": {
          "colors": [
           "rgba(255, 182, 193, 1.0)",
           "rgba(135, 206, 250, 1.0)"
          ]
         },
         "name": "",
         "type": "pie",
         "values": [
          140,
          108
         ]
        }
       ],
       "layout": {
        "legend": {
         "bgcolor": "#F5F6F9",
         "font": {
          "color": "#4D5663"
         }
        },
        "paper_bgcolor": "#F5F6F9",
        "plot_bgcolor": "#F5F6F9",
        "title": "The Proportion of Stroke among Gender",
        "titlefont": {
         "color": "#4D5663"
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"9c1441d9-9692-438e-976a-ccc21b9c9783\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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    "gender = df.groupby(df['gender'])['stroke'].sum()\n",
    "df_gender = pd.DataFrame({'labels': gender.index,'values': gender.values})\n",
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    "df_gender.iplot(kind='pie',labels='labels',values='values', title='The Proportion of Stroke among Gender', colors = colors)"
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   "source": [
    "### Work type of people who had stroke"
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    "job = df.groupby(df['work_type'])['stroke'].sum()\n",
    "df_job = pd.DataFrame({'labels': job.index,'values': job.values})\n",
    "colors2= ['palegreen','paleturquoise','thistle','moccasin']\n",
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    "### Smoking status of people who had stroke"
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    "smoke = df.groupby(df['smoking_status'])['stroke'].sum()\n",
    "df_smoke = pd.DataFrame({'labels': smoke.index,'values': smoke.values})\n",
    "df_smoke.iplot(kind='pie',labels='labels',values='values', title='Smoking status of people who had stroke', colors = colors2, pull=[0.02, 0.02, 0.1, 0.02])"
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    "### Residence area of people who had stroke"
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    "Residence = df.groupby(df['Residence_type'])['stroke'].sum()\n",
    "df_Residence = pd.DataFrame({'labels': Residence.index,'values': Residence.values})\n",
    "df_Residence.iplot(kind='pie',labels='labels',values='values', title='Residence area of people who had stroke', colors = colors2, pull=[0.02, 0.02],hole = 0.3)"
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   "source": [
    "### Marriage status of people who had stroke"
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    "Married = df.groupby(df['ever_married'])['stroke'].sum()\n",
    "df_Married = pd.DataFrame({'labels': Married.index,'values': Married.values})\n",
    "df_Married.iplot(kind='pie',labels='labels',values='values', title='Marriage status of people who had stroke', colors = colors2, pull=[0.02, 0.02],hole = 0.3)"
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    "stroke = df.loc[df['stroke']== 1].reset_index()\n",
    "\n",
    "stroke[\"male_age\"]=stroke[stroke[\"gender\"]==\"Male\"][\"age\"]\n",
    "stroke[\"female_age\"]=stroke[stroke[\"gender\"]==\"Female\"][\"age\"]\n",
    "stroke[[\"male_age\",\"female_age\"]].iplot(kind=\"histogram\", bins=20, theme=\"white\", title=\"Stroke Ages\",xTitle='Ages', yTitle='Count')"
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   "source": [
    "## DATA PREPROCESSING"
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   "execution_count": 13,
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       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
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       "      <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",
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       "      <th>0</th>\n",
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       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
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       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>3</th>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
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      "text/plain": [
       "   gender   age  hypertension  heart_disease  ever_married      work_type  \\\n",
       "0       0  67.0             0              1             1        Private   \n",
       "1       0  80.0             0              1             1        Private   \n",
       "2       1  49.0             0              0             1        Private   \n",
       "3       1  79.0             1              0             1  Self-employed   \n",
       "4       0  81.0             0              0             1        Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "1          Rural             105.92  32.5     never smoked       1  \n",
       "2          Urban             171.23  34.4           smokes       1  \n",
       "3          Rural             174.12  24.0     never smoked       1  \n",
       "4          Urban             186.21  29.0  formerly smoked       1  "
      ]
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     "execution_count": 13,
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   "source": [
    "df['ever_married'] = [ 0 if i !='Yes' else 1 for i in df['ever_married'] ]\n",
    "df['gender'] = [0 if i != 'Female' else 1 for i in df['gender']]\n",
    "df.head(5)"
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   "execution_count": 14,
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       "<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>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>work_type_Govt_job</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>2217</th>\n",
       "      <td>0</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115.12</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3177</th>\n",
       "      <td>1</td>\n",
       "      <td>51.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>85.59</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3788</th>\n",
       "      <td>1</td>\n",
       "      <td>61.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>71.40</td>\n",
       "      <td>29.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2380</th>\n",
       "      <td>0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92.02</td>\n",
       "      <td>38.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2177</th>\n",
       "      <td>0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>145.37</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender    age  hypertension  heart_disease  ever_married  \\\n",
       "2217       0   1.64             0              0             0   \n",
       "3177       1  51.00             0              0             1   \n",
       "3788       1  61.00             0              0             1   \n",
       "2380       0  49.00             0              0             1   \n",
       "2177       0  62.00             0              0             1   \n",
       "\n",
       "      avg_glucose_level   bmi  stroke  work_type_Govt_job  work_type_Private  \\\n",
       "2217             115.12  21.1       0                   0                  0   \n",
       "3177              85.59  30.5       0                   1                  0   \n",
       "3788              71.40  29.2       0                   0                  1   \n",
       "2380              92.02  38.1       0                   0                  1   \n",
       "2177             145.37  33.3       0                   0                  1   \n",
       "\n",
       "      work_type_Self-employed  work_type_children  Residence_type_Rural  \\\n",
       "2217                        0                   1                     0   \n",
       "3177                        0                   0                     1   \n",
       "3788                        0                   0                     1   \n",
       "2380                        0                   0                     0   \n",
       "2177                        0                   0                     0   \n",
       "\n",
       "      Residence_type_Urban  smoking_status_Unknown  \\\n",
       "2217                     1                       1   \n",
       "3177                     0                       0   \n",
       "3788                     0                       0   \n",
       "2380                     1                       0   \n",
       "2177                     1                       1   \n",
       "\n",
       "      smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
       "2217                               0                            0   \n",
       "3177                               0                            1   \n",
       "3788                               1                            0   \n",
       "2380                               0                            1   \n",
       "2177                               0                            0   \n",
       "\n",
       "      smoking_status_smokes  \n",
       "2217                      0  \n",
       "3177                      0  \n",
       "3788                      0  \n",
       "2380                      0  \n",
       "2177                      0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns = ['work_type', 'Residence_type','smoking_status'])\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                            0\n",
       "age                               0\n",
       "hypertension                      0\n",
       "heart_disease                     0\n",
       "ever_married                      0\n",
       "avg_glucose_level                 0\n",
       "bmi                               0\n",
       "stroke                            0\n",
       "work_type_Govt_job                0\n",
       "work_type_Private                 0\n",
       "work_type_Self-employed           0\n",
       "work_type_children                0\n",
       "Residence_type_Rural              0\n",
       "Residence_type_Urban              0\n",
       "smoking_status_Unknown            0\n",
       "smoking_status_formerly smoked    0\n",
       "smoking_status_never smoked       0\n",
       "smoking_status_smokes             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target and Feature values / Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['stroke'], axis = 1)\n",
    "y = df['stroke']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3337, 17), (1644, 17))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train , y_test = train_test_split(X,y, test_size = 0.33, random_state = 42)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL BUILDING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier and Gini method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, random_state=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_gini = DecisionTreeClassifier(criterion='gini', random_state=0,max_depth= 5)\n",
    "clf_gini.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction Model File Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Uncomment the lines below when any changes are made to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = DecisionTreeClassifier()\n",
    "# model.fit(X_train,y_train)\n",
    "# joblib.dump(model, 'stroke-prediction-model.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction Model File Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load('stroke-prediction-model.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model accuracy score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score with criterion gini index: 0.9507\n"
     ]
    }
   ],
   "source": [
    "y_pred_gini = clf_gini.predict(X_test)\n",
    "print('Model accuracy score with criterion gini index: {0:0.4f}'. format(accuracy_score(y_test, y_pred_gini)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train_gini = clf_gini.predict(X_train)\n",
    "\n",
    "y_pred_train_gini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training-set accuracy score: 0.9527\n"
     ]
    }
   ],
   "source": [
    "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train_gini)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.9527\n",
      "Test set score: 0.9507\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(clf_gini.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(clf_gini.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL WORKING GRAPH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(model, out_file='stroke-prediction-visual-model.dot', \n",
    "                        feature_names=['gender', 'age',\t'hypertension',\t'heart_disease',\t'ever_married',\t'avg_glucose_level',\t'bmi',\t'work_type_Govt_job',\t'work_type_Private',\t'work_type_Self-employed',\t'work_type_children',\t'Residence_type_Rural',\t'Residence_type_Urban',\t'smoking_status_Unknown',\t'smoking_status_formerly smoked',\t'smoking_status_never smoked',\t'smoking_status_smokes'],\n",
    "                        label='all',\n",
    "                        rounded=True,\n",
    "                        filled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PREDICTION TESTING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender = Male \n",
      "Age = 40 \n",
      "Hypertention = 0 \n",
      "Heart Disease = 0 \n",
      "Ever Married = 0 \n",
      "Avg Glucose Level = 100 \n",
      "bmi = 35 \n",
      "Work Type Govt Job = 0 \n",
      "Work Type Private = 1 \n",
      "Work Type Self-Employed = 0 \n",
      "Work Type Children = 0 \n",
      "Residence Type Rural = 0 \n",
      "Residence Type Urban = 1 \n",
      "Smoking Status Unknown = 0 \n",
      "Smoking Status Formerly Smoked = 0 \n",
      "Smoking Status Never Smoked = 1 \n",
      "Smoking Status Smoked = 0\n",
      "\n",
      "Model predicts NO STROKE\n"
     ]
    }
   ],
   "source": [
    "print(\"Gender = Male \\nAge = 40 \\nHypertention = 0 \\nHeart Disease = 0 \\nEver Married = 0 \\n\\\n",
    "Avg Glucose Level = 100 \\nbmi = 35 \\nWork Type Govt Job = 0 \\nWork Type Private = 1 \\nWork Type Self-Employed = 0 \\n\\\n",
    "Work Type Children = 0 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0 \\nSmoking Status Formerly Smoked = 0 \\n\\\n",
    "Smoking Status Never Smoked = 1 \\nSmoking Status Smoked = 0\")\n",
    "\n",
    "predictionOutcome = model.predict([[1,40,0,0,0,100,35,0,1,0,0,0,1,0,1,0,0]])\n",
    "\n",
    "if predictionOutcome == 0:\n",
    "    print(\"\\nModel predicts NO STROKE\")\n",
    "else:\n",
    "    print(\"\\nModel predicts STROKE\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender = Female \n",
      "Age = 35 \n",
      "Hypertention = 1 \n",
      "Heart Disease = 0 \n",
      "Ever Married = 1 \n",
      "Avg Glucose Level = 80 \n",
      "bmi = 45 \n",
      "Work Type Govt Job = 1 \n",
      "Work Type Private = 0 \n",
      "Work Type Self-Employed = 0 \n",
      "Work Type Children = 0 \n",
      "Residence Type Rural = 0 \n",
      "Residence Type Urban = 1 \n",
      "Smoking Status Unknown = 0  \n",
      "Smoking Status Formerly Smoked = 1 \n",
      "Smoking Status Never Smoked = 0 \n",
      "Smoking Status Smoked = 0\n",
      "\n",
      "Model predicts NO STROKE\n"
     ]
    }
   ],
   "source": [
    "print(\"Gender = Female \\nAge = 35 \\nHypertention = 1 \\nHeart Disease = 0 \\nEver Married = 1 \\n\\\n",
    "Avg Glucose Level = 80 \\nbmi = 45 \\nWork Type Govt Job = 1 \\nWork Type Private = 0 \\nWork Type Self-Employed = 0 \\n\\\n",
    "Work Type Children = 0 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0  \\nSmoking Status Formerly Smoked = 1 \\n\\\n",
    "Smoking Status Never Smoked = 0 \\nSmoking Status Smoked = 0\")\n",
    "\n",
    "predictionOutcome = model.predict([[1,35,1,0,1,80,45,1,0,0,0,0,1,1,0,0,0]])\n",
    "\n",
    "if predictionOutcome == 0:\n",
    "    print(\"\\nModel predicts NO STROKE\")\n",
    "else:\n",
    "    print(\"\\nModel predicts STROKE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender = Male \n",
      "Age = 20 \n",
      "Hypertention = 0 \n",
      "Heart Disease = 0 \n",
      "Ever Married = 0 \n",
      "Avg Glucose Level = 80 \n",
      "bmi = 27 \n",
      "Work Type Govt Job = 0 \n",
      "Work Type Private = 0 \n",
      "Work Type Self-Employed = 0 \n",
      "Work Type Children = 1 \n",
      "Residence Type Rural = 0 \n",
      "Residence Type Urban = 1 \n",
      "Smoking Status Unknown = 0  \n",
      "Smoking Status Formerly Smoked = 0 \n",
      "Smoking Status Never Smoked = 0 \n",
      "Smoking Status Smokes = 1\n",
      "\n",
      "Model predicts NO STROKE\n"
     ]
    }
   ],
   "source": [
    "print(\"Gender = Male \\nAge = 20 \\nHypertention = 0 \\nHeart Disease = 0 \\nEver Married = 0 \\n\\\n",
    "Avg Glucose Level = 80 \\nbmi = 27 \\nWork Type Govt Job = 0 \\nWork Type Private = 0 \\nWork Type Self-Employed = 0 \\n\\\n",
    "Work Type Children = 1 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0  \\nSmoking Status Formerly Smoked = 0 \\n\\\n",
    "Smoking Status Never Smoked = 0 \\nSmoking Status Smokes = 1\")\n",
    "\n",
    "predictionOutcome = model.predict([[0,20,0,0,0,80,27,0,0,0,1,0,1,0,0,0,1]])\n",
    "\n",
    "if predictionOutcome == 0:\n",
    "    print(\"\\nModel predicts NO STROKE\")\n",
    "else:\n",
    "    print(\"\\nModel predicts STROKE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender = Male \n",
      "Age = 20 \n",
      "Hypertention = 0 \n",
      "Heart Disease = 0 \n",
      "Ever Married = 0 \n",
      "Avg Glucose Level = 80 \n",
      "bmi = 22 \n",
      "Work Type Govt Job = 0 \n",
      "Work Type Private = 0 \n",
      "Work Type Self-Employed = 0 \n",
      "Work Type Children = 1 \n",
      "Residence Type Rural = 0 \n",
      "Residence Type Urban = 1 \n",
      "Smoking Status Unknown = 0  \n",
      "Smoking Status Formerly Smoked = 0 \n",
      "Smoking Status Never Smoked = 0 \n",
      "Smoking Status Smokes = 1\n",
      "\n",
      "Model predicts NO STROKE\n"
     ]
    }
   ],
   "source": [
    "print(\"Gender = Male \\nAge = 20 \\nHypertention = 0 \\nHeart Disease = 0 \\nEver Married = 0 \\n\\\n",
    "Avg Glucose Level = 80 \\nbmi = 22 \\nWork Type Govt Job = 0 \\nWork Type Private = 0 \\nWork Type Self-Employed = 0 \\n\\\n",
    "Work Type Children = 1 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0  \\nSmoking Status Formerly Smoked = 0 \\n\\\n",
    "Smoking Status Never Smoked = 0 \\nSmoking Status Smokes = 1\")\n",
    "\n",
    "predictionOutcome = model.predict([[0,20,0,0,0,80,22,0,0,0,1,0,1,0,0,0,1]])\n",
    "\n",
    "if predictionOutcome == 0:\n",
    "    print(\"\\nModel predicts NO STROKE\")\n",
    "else:\n",
    "    print(\"\\nModel predicts STROKE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender = Male \n",
      "Age = 20 \n",
      "Hypertention = 1 \n",
      "Heart Disease = 0 \n",
      "Ever Married = 0 \n",
      "Avg Glucose Level = 80 \n",
      "bmi = 26 \n",
      "Work Type Govt Job = 0 \n",
      "Work Type Private = 0 \n",
      "Work Type Self-Employed = 0 \n",
      "Work Type Children = 1 \n",
      "Residence Type Rural = 0 \n",
      "Residence Type Urban = 1 \n",
      "Smoking Status Unknown = 0  \n",
      "Smoking Status Formerly Smoked = 0 \n",
      "Smoking Status Never Smoked = 0 \n",
      "Smoking Status Smokes = 1\n",
      "\n",
      "Model predicts NO STROKE\n"
     ]
    }
   ],
   "source": [
    "print(\"Gender = Male \\nAge = 20 \\nHypertention = 1 \\nHeart Disease = 0 \\nEver Married = 0 \\n\\\n",
    "Avg Glucose Level = 80 \\nbmi = 26 \\nWork Type Govt Job = 0 \\nWork Type Private = 0 \\nWork Type Self-Employed = 0 \\n\\\n",
    "Work Type Children = 1 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0  \\nSmoking Status Formerly Smoked = 0 \\n\\\n",
    "Smoking Status Never Smoked = 0 \\nSmoking Status Smokes = 1\")\n",
    "\n",
    "predictionOutcome = model.predict([[0,20,1,0,0,80,26,0,0,0,1,0,1,0,0,0,1]])\n",
    "\n",
    "if predictionOutcome == 0:\n",
    "    print(\"\\nModel predicts NO STROKE\")\n",
    "else:\n",
    "    print(\"\\nModel predicts STROKE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automated Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "######################################################### PROGRAM STARTED #########################################################\n",
      "\n",
      "Array 0 = [  0  20   0   0   0 111  17   1   0   0   0   1   0   1   0   0   0]\n",
      "Model predicts NO STROKE =  [0]\n",
      "###################################################################################################################################\n",
      "\n",
      "Array 1 = [ 1 19  0  1  1 85 98  1  0  0  0  1  0  1  0  0  0]\n",
      "Model predicts NO STROKE =  [0]\n",
      "###################################################################################################################################\n",
      "\n",
      "Array 2 = [ 0 80  0  0  1 27 89  1  0  0  0  1  0  1  0  0  0]\n",
      "Model predicts STROKE =  [1]\n",
      "######################################################### PROGRAM FINISHED #########################################################\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "count=0\n",
    "array=[]\n",
    "predictionOutcome=[0]\n",
    "\n",
    "print(\"######################################################### PROGRAM STARTED #########################################################\")\n",
    "\n",
    "while predictionOutcome == [0]:\n",
    "    arr1=np.random.randint(2, size=1) #Gender\n",
    "    arr2=np.random.randint(83, size=1) #Age\n",
    "    arr3=np.random.randint(2, size=1) #Hypertention\n",
    "    arr4=np.random.randint(2, size=1) #Heart Disease\n",
    "    arr5=np.random.randint(2, size=1) #Ever Married\n",
    "    arr6=np.random.randint(272, size=1) #Avg Glucose Level\n",
    "    arr7=np.random.randint(100, size=1) #bmi\n",
    "    \n",
    "    worktype= random.randint(0,4) #Work Type\n",
    "    \n",
    "    arr8=[0] #Govt Job\n",
    "    arr9=[0] #Private\n",
    "    arr10=[0] #Self-Employed\n",
    "    arr11=[0] #Children\n",
    "    \n",
    "    if worktype == 0:\n",
    "        arr8=[1] #Govt Job\n",
    "        arr9=[0] #Private\n",
    "        arr10=[0] #Self-Employed\n",
    "        arr11=[0] #Children\n",
    "    elif worktype == 1:\n",
    "        arr8=[0] #Govt Job\n",
    "        arr9=[1] #Private\n",
    "        arr10=[0] #Self-Employed\n",
    "        arr11=[0] #Children\n",
    "    elif worktype == 2:\n",
    "        arr8=[0] #Govt Job\n",
    "        arr9=[0] #Private\n",
    "        arr10=[1] #Self-Employed\n",
    "        arr10=[0] #Children\n",
    "    elif worktype == 3:\n",
    "        arr8=[0] #Govt Job\n",
    "        arr10=[0] #Self-Employed\n",
    "        arr10=[0] #Self-Employed\n",
    "        arr11=[1] #Children\n",
    "    \n",
    "    residencetype= random.randint(0,2) #Residence Type\n",
    "    \n",
    "    arr12=[0] #Rural\n",
    "    arr17=[0] #Urban\n",
    "    \n",
    "    if worktype == 0:\n",
    "        arr12=[1] #Rural\n",
    "        arr17=[0] #Urban\n",
    "    elif worktype == 1:\n",
    "        arr12=[0] #Rural\n",
    "        arr17=[1] #Urban\n",
    "    \n",
    "    smokingtype= random.randint(0,4) #Smoking Type\n",
    "    \n",
    "    arr13=[0] #Unknown\n",
    "    arr14=[0] #Formerly Smoked\n",
    "    arr15=[0] #Never Smoked\n",
    "    arr16=[0] #Smokes\n",
    "    \n",
    "    if worktype == 0:\n",
    "        arr13=[1] #Unknown\n",
    "        arr14=[0] #Formerly Smoked\n",
    "        arr15=[0] #Never Smoked\n",
    "        arr16=[0] #Smokes\n",
    "    elif worktype == 1:\n",
    "        arr13=[0] #Unknown\n",
    "        arr14=[1] #Formerly Smoked\n",
    "        arr15=[0] #Never Smoked\n",
    "        arr16=[0] #Smokes\n",
    "    elif worktype == 2:\n",
    "        arr13=[0] #Unknown\n",
    "        arr14=[0] #Formerly Smoked\n",
    "        arr15=[1] #Never Smoked\n",
    "        arr16=[0] #Smokes\n",
    "    elif worktype == 3:\n",
    "        arr13=[0] #Unknown\n",
    "        arr14=[0] #Formerly Smoked\n",
    "        arr15=[0] #Never Smoked\n",
    "        arr16=[1] #Smokes\n",
    "    \n",
    "    array = np.concatenate((arr1, arr2, arr3, arr4, arr5, arr6, arr7, arr8, arr9, arr10, arr11, arr12, arr17, arr13, arr14, arr15, arr16), axis=0)\n",
    "    \n",
    "    print(\"\\nArray %d =\" %count, array)\n",
    "    \n",
    "    predictionOutcome = model.predict([array])\n",
    "    \n",
    "    if predictionOutcome == 0:\n",
    "        print(\"Model predicts NO STROKE = \", predictionOutcome)\n",
    "        print(\"###################################################################################################################################\")\n",
    "    else:\n",
    "        print(\"Model predicts STROKE = \", predictionOutcome)\n",
    "    \n",
    "    count+=1\n",
    "else:\n",
    "    print(\"######################################################### PROGRAM FINISHED #########################################################\")\n"
   ]
  }
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