stroke_prediction_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stroke prediction model \n",
    "\n",
    "<br>\n",
    "\n",
    "A stroke is a serious medical condition that can be life-threatening, according to the American Stroke Association. Stroke is a leading cause of death in the United States and is a major cause of serious disability for adults. About _795,000_ people in the United States have a stroke each year. \n",
    "\n",
    "<br>\n",
    "<img src=\"img/gr1.jpg\" width=\"680\">\n",
    "\n",
    "<br>\n",
    "According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths.\n",
    "This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. \n",
    "Each row in the data provides relavant information about the patient.\n",
    "\n",
    "<br><br>\n",
    "<img src=\"img/leading causes death USA 2019.png\" width=\"680\">\n",
    "\n",
    "<img  style=\"float: left;\" src=\"img/about_stroke.jpg\" width=\"290\" border=200>\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "\n",
    "### What is a stroke?\n",
    "A stroke, sometimes called a brain attack, occurs when something blocks blood supply to part of the brain or when a blood vessel in the brain bursts. In either case, parts of the brain become damaged or die. A stroke can cause lasting brain damage, long-term disability, or even death.\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "\n",
    "### Stroke Risk Factors\n",
    "Eating foods low in saturated fats, trans fat, and cholesterol and high in fiber can help prevent high cholesterol. Limiting salt (sodium) in your diet can also lower your blood pressure. Smoking greatly increases your chances of having a stroke. Men should have no more than two drinks per day, and women only one.\n",
    "\n",
    "\n",
    "### Conditions that increase Risk for Stroke\n",
    "If you have already had a stroke or a transient ischemic attack (TIA), your chances of having another stroke are higher. High blood pressure is the leading cause of stroke and is the main cause for increased risk of stroke among people with diabetes. Diabetes causes sugars to build up in the blood and prevent oxygen and nutrients from getting to the various parts of your body, including your brain. A blood test can tell your doctor if you have high levels of cholesterol and triglycerides (a related kind of fat) in your blood.\n",
    "\n",
    "\n",
    "### References\n",
    "- https://www.cdc.gov/stroke/\n",
    "- https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Hope-Through-Research/Stroke-Hope-Through-Research\n",
    "- https://en.wikipedia.org/wiki/Stroke"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "**Dataset source**: Kaggle  https://www.kaggle.com/fedesoriano/stroke-prediction-dataset\n",
    "\n",
    "\n",
    "### Record fields\n",
    "\n",
    "1. **id**: unique identifier\n",
    "<br>\n",
    "2. **gender**: \"Male\", \"Female\" or \"Other\"\n",
    "<br>\n",
    "3. **age**: age of the patient\n",
    "<br>\n",
    "4. **hypertension**: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension\n",
    "<br>\n",
    "5. **heart_disease**: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease\n",
    "<br>\n",
    "6. **ever_married**: \"No\" or \"Yes\"\n",
    "<br>\n",
    "7. **work_type**: \"children\", \"Govt_jov\", \"Never_worked\", \"Private\" or \"Self-employed\"\n",
    "<br>\n",
    "8. **Residence_type**: \"Rural\" or \"Urban\"\n",
    "<br>\n",
    "9. **avg_glucose_level**: average glucose level in blood\n",
    "<br>\n",
    "10. **bmi**: body mass index\n",
    "<br>\n",
    "11. **smoking_status**: \"formerly smoked\", \"never smoked\", \"smokes\" or \"Unknown\"*\n",
    "<br>\n",
    "12. **stroke**: 1 if the patient had a stroke or 0 if not during his/her life\n",
    "\n",
    "*Note: \"Unknown\" in smoking_status means that the information is unavailable for this patient\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features\n",
    "#### Body Mass Index\n",
    "\n",
    "BMI is the most used index for obtaining an overall assessment of one's body weight. \n",
    "The Body Mass Index (BMI) or body mass index (BMI) is a widely used parameter to get a general assessment of their body weight. It relates the height with the weight of the subject with a simple mathematical formula. It is obtained by dividing the weight in kg of the subject with the square of the height expressed in meters. \n",
    "<br> \n",
    "\n",
    "<img src=\"img/BMI.png\" width=\"280\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the enviroment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import sys\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/moebius/Documents/PORTFOLIO/Development/stroke-prediction-model'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "work_dir = sys.path[0]\n",
    "work_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(work_dir+\"/data/healthcare-dataset-stroke-data.csv\")"
   ]
  },
  {
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data exploration & preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 5110 non-null   int64  \n",
      " 1   gender             5110 non-null   object \n",
      " 2   age                5110 non-null   float64\n",
      " 3   hypertension       5110 non-null   int64  \n",
      " 4   heart_disease      5110 non-null   int64  \n",
      " 5   ever_married       5110 non-null   object \n",
      " 6   work_type          5110 non-null   object \n",
      " 7   Residence_type     5110 non-null   object \n",
      " 8   avg_glucose_level  5110 non-null   float64\n",
      " 9   bmi                4909 non-null   float64\n",
      " 10  smoking_status     5110 non-null   object \n",
      " 11  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 479.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      " Categorical and Ordinal variables \n",
      "==========================================================================================\n",
      "gender \t\t ['Male' 'Female' 'Other']\n",
      "hypertension \t [0 1]\n",
      "heart_disease \t [1 0]\n",
      "ever_married \t ['Yes' 'No']\n",
      "work_type \t\t ['Private' 'Self-employed' 'Govt_job' 'children' 'Never_worked']\n",
      "Residence_type \t ['Urban' 'Rural']\n",
      "smoking_status \t ['formerly smoked' 'never smoked' 'smokes' 'Unknown']\n",
      "stroke \t\t [1 0]  yes | no \n"
     ]
    }
   ],
   "source": [
    "print(\"=\"*90)\n",
    "print(\" Categorical and Ordinal variables \")\n",
    "print(\"=\"*90)\n",
    "print(\"gender \\t\\t\",data.gender.unique())\n",
    "print(\"hypertension \\t\",data.hypertension.unique())\n",
    "print(\"heart_disease \\t\",data.heart_disease.unique())\n",
    "print(\"ever_married \\t\",data.ever_married.unique())\n",
    "print(\"work_type \\t\\t\",data.work_type.unique())\n",
    "print(\"Residence_type \\t\",data.Residence_type.unique())\n",
    "print(\"smoking_status \\t\",data.smoking_status.unique())\n",
    "print(\"stroke \\t\\t\",data.stroke.unique(), \" yes | no \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      " Numerical variables \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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>5110.0</td>\n",
       "      <td>36517.829354</td>\n",
       "      <td>21161.721625</td>\n",
       "      <td>67.00</td>\n",
       "      <td>17741.250</td>\n",
       "      <td>36932.000</td>\n",
       "      <td>54682.00</td>\n",
       "      <td>72940.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>5110.0</td>\n",
       "      <td>43.226614</td>\n",
       "      <td>22.612647</td>\n",
       "      <td>0.08</td>\n",
       "      <td>25.000</td>\n",
       "      <td>45.000</td>\n",
       "      <td>61.00</td>\n",
       "      <td>82.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>5110.0</td>\n",
       "      <td>0.097456</td>\n",
       "      <td>0.296607</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_disease</th>\n",
       "      <td>5110.0</td>\n",
       "      <td>0.054012</td>\n",
       "      <td>0.226063</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <td>5110.0</td>\n",
       "      <td>106.147677</td>\n",
       "      <td>45.283560</td>\n",
       "      <td>55.12</td>\n",
       "      <td>77.245</td>\n",
       "      <td>91.885</td>\n",
       "      <td>114.09</td>\n",
       "      <td>271.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>4909.0</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>7.854067</td>\n",
       "      <td>10.30</td>\n",
       "      <td>23.500</td>\n",
       "      <td>28.100</td>\n",
       "      <td>33.10</td>\n",
       "      <td>97.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>5110.0</td>\n",
       "      <td>0.048728</td>\n",
       "      <td>0.215320</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count          mean           std    min        25%  \\\n",
       "id                 5110.0  36517.829354  21161.721625  67.00  17741.250   \n",
       "age                5110.0     43.226614     22.612647   0.08     25.000   \n",
       "hypertension       5110.0      0.097456      0.296607   0.00      0.000   \n",
       "heart_disease      5110.0      0.054012      0.226063   0.00      0.000   \n",
       "avg_glucose_level  5110.0    106.147677     45.283560  55.12     77.245   \n",
       "bmi                4909.0     28.893237      7.854067  10.30     23.500   \n",
       "stroke             5110.0      0.048728      0.215320   0.00      0.000   \n",
       "\n",
       "                         50%       75%       max  \n",
       "id                 36932.000  54682.00  72940.00  \n",
       "age                   45.000     61.00     82.00  \n",
       "hypertension           0.000      0.00      1.00  \n",
       "heart_disease          0.000      0.00      1.00  \n",
       "avg_glucose_level     91.885    114.09    271.74  \n",
       "bmi                   28.100     33.10     97.60  \n",
       "stroke                 0.000      0.00      1.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"=\"*90)\n",
    "print(\" Numerical variables \")\n",
    "print(\"=\"*90)\n",
    "data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"dark\") # set dark for better image contrast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploration under age-related disease\n",
    "First I want to eliminate all the candidates that has a lower age in confront of the lowest patient with stroke disease. I do so because it make sense to eliminate all the patients that are too young."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender', ylabel='age'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.violinplot(x='age',y='stroke',hue='stroke',data=data) \n",
    "sns.violinplot(x=\"gender\", y=\"age\", hue=\"stroke\",data=data, size=(500,300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search for umbalanced classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='smoking_status', ylabel='stroke'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='smoking_status',y='stroke',data=data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='work_type', ylabel='stroke'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='work_type',y='stroke',data=data) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "work_type is not umbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Residence_type', ylabel='stroke'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Residence_type',y='stroke',data=data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ever_married', ylabel='stroke'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='ever_married',y='stroke',data=data) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Residence_type is not umbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3353, 1757])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ever_married.value_counts().values.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4612\n",
       "1     498\n",
       "Name: hypertension, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.hypertension.value_counts() # Un-balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2994\n",
       "Male      2115\n",
       "Other        1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.gender.value_counts() # Balanced (except for 'Other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2994\n",
       "Male      2115\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data[(data.gender != 'Other')] # remove 1 record with 'Other' in gender\n",
    "data.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4860\n",
       "1     249\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.stroke.value_counts() # Un-balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4833\n",
       "1     276\n",
       "Name: heart_disease, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.heart_disease.value_counts() # Un-balanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rebalance umbalanced classes\n",
    "\n",
    "- Theory https://towardsdatascience.com/machine-learning-multiclass-classification-with-imbalanced-data-set-29f6a177c1a\n",
    "\n",
    "- Practice https://machinelearningmastery.com/multi-class-imbalanced-classification/\n",
    "\n",
    "- Examples https://machinelearningmastery.com/imbalanced-multiclass-classification-with-the-e-coli-dataset/\n",
    "\n",
    "- Un-balanced learn Github material\n",
    "https://github.com/scikit-learn-contrib/imbalanced-learn\n",
    "\n",
    "- SMOTE\n",
    "https://www.analyticsvidhya.com/blog/2020/10/overcoming-class-imbalance-using-smote-techniques/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imblearn in /home/moebius/.local/lib/python3.8/site-packages (0.0)\n",
      "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.8/dist-packages (from imblearn) (0.8.0)\n",
      "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /home/moebius/.local/lib/python3.8/site-packages (from imbalanced-learn->imblearn) (1.19.5)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.6.3)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.0.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n"
     ]
    }
   ],
   "source": [
    "! pip3 install imblearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load libraries for over sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8.0\n"
     ]
    }
   ],
   "source": [
    "import imblearn\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "print(imblearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build over sampler istance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROS = RandomOverSampler(random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over sampling 'stroke' class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sample = data.loc[:,:\"smoking_status\"]\n",
    "Y_sample = data.stroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_res, y_res = ROS.fit_resample(X_sample, Y_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4860\n",
       "1    4860\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_res.value_counts() # Balanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8534\n",
       "1    1186\n",
       "Name: heart_disease, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_res.heart_disease.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='work_type', ylabel='Count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=data,x='work_type',hue='stroke', multiple=\"stack\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=data[(data.smoking_status != 'Unknown')]\n",
    "\n",
    "data3=pd.DataFrame(data2.smoking_status)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smoking_status:  ['formerly smoked' 'never smoked' 'smokes']\n"
     ]
    }
   ],
   "source": [
    "print(\"smoking_status: \", data3.smoking_status.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = data3.smoking_status.unique()\n",
    "counts = data3.smoking_status.value_counts()\n",
    "\n",
    "plt.bar(cols, counts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "never smoked       1892\n",
       "formerly smoked     884\n",
       "smokes              789\n",
       "Name: smoking_status, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sroke class is umbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_theme(\"notebook\")\n",
    "sns.set(rc={'figure.figsize':(12,9)} )\n",
    "sns.set_palette(sns.color_palette(\"BrBG\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]),\n",
       " [Text(0, 0, 'never smoked'),\n",
       "  Text(1, 0, 'formerly smoked'),\n",
       "  Text(2, 0, 'smokes')])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 405.725x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.catplot( x='smoking_status',\n",
    "            y='age' ,\n",
    "            data=data2,\n",
    "            hue='stroke',\n",
    "            kind=\"violin\", split=True,\n",
    "            palette=\"pastel\",\n",
    "            order=['never smoked','formerly smoked','smokes'])\n",
    "\n",
    "sns.set(rc={'figure.figsize':(14,16)})\n",
    "\n",
    "plt.title(\" Age vs Smoking status\")\n",
    "plt.ylabel('age')\n",
    "plt.xlabel('Smoking Status')\n",
    "plt.xticks(rotation=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(10,3)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The age of stroke-free patients in their medical history, by gender')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bmi_gender_stroke = data2[data2[\"stroke\"]==1]\n",
    "df_bmi_gender_not_stroke = data2[data2[\"stroke\"]==0]\n",
    "\n",
    "#newDf= df[(df['Year']== 2015) & (df[\"Month\"] == \"February\")]\n",
    "sns.histplot(data=df_bmi_gender_not_stroke,x='bmi',y='gender')#,hue='stroke')\n",
    "plt.title(\"The age of stroke-free patients in their medical history, by gender\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'the age of stroke patients in their medical history, by gender')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df_bmi_gender_stroke,x='bmi',y='gender',color='orange',bins=50)\n",
    "plt.title(\"the age of stroke patients in their medical history, by gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Residence_type'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(8,7)})\n",
    "sns.barplot(x=\"age\", y=\"Residence_type\", hue=\"stroke\",\n",
    "            palette=\"pastel\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='work_type'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(8,7)})\n",
    "sns.barplot(x=\"age\", y=\"work_type\", hue=\"stroke\",\n",
    "            palette=\"pastel\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Work type')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='age',\n",
    "               x='work_type' ,\n",
    "               data=data,\n",
    "               hue='stroke',\n",
    "               palette=\"pastel\")\n",
    "\n",
    "sns.set(rc={'figure.figsize':(14,10)})\n",
    "\n",
    "plt.title(\" Body Mass Index vs Smoking status\")\n",
    "plt.ylabel('Age')\n",
    "plt.xlabel('Work type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='work_type', ylabel='bmi'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='bmi',\n",
    "               x='work_type' ,\n",
    "               data=data,\n",
    "               hue='stroke',\n",
    "               palette=\"pastel\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ever_married', ylabel='age'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y='age',\n",
    "               x='ever_married' ,\n",
    "               data=data,\n",
    "               hue='stroke',\n",
    "               palette=\"pastel\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning & feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanup_dict = {\"gender\":{\"Male\":1,\"Female\":2,\"Other\":3},\n",
    "               \"ever_married\":{\"No\":1,\"Yes\":2},\n",
    "               \"Residence_type\":{\"Urban\":1,\"Rural\":2},\n",
    "               \"work_type\":{'Private':5, 'Self-employed':4, 'Govt_job':3, 'children':2,'Never_worked':1},\n",
    "               \"smoking_status\":{'Unknown':0, 'never smoked':1,'formerly smoked':2, 'smokes':3 }\n",
    "               }\n",
    "#data=data.replace({\"stroke\": {0:'no',1:'yes'} }) # map data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data = data.replace(cleanup_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "ever_married           0\n",
       "work_type              0\n",
       "Residence_type         0\n",
       "avg_glucose_level      0\n",
       "bmi                  201\n",
       "smoking_status         0\n",
       "stroke                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data.isnull().sum(axis = 0) #NaN values in every column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5109"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data.shape[0] # Num of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data2 = clean_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4908"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data2.shape[0] # Num of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows deleted for missing value  201\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of rows deleted for missing value \",clean_data.shape[0] - clean_data2.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data2 = clean_data2.drop([\"id\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_features = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
    "       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
    "       'smoking_status']\n",
    "\n",
    "features = [ 'age','hypertension','heart_disease', \n",
    "       'work_type', 'avg_glucose_level', 'bmi',\n",
    "       'smoking_status']\n",
    "\n",
    "target = ['stroke']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(clean_data2, columns=features)\n",
    "Y = pd.DataFrame(clean_data2, columns=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.4, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification metrics parameters\n",
    "\n",
    "<img src=\"img/metrics2.png\" width=\"340\">\n",
    "\n",
    "<img src=\"img/metrics.png\" width=\"680\">\n",
    "\n",
    "\n",
    "\n",
    "**True Positives** (TP) - These are the correctly predicted positive values which means that the value of actual class is yes and the value of predicted class is also yes. \n",
    "\n",
    "**True Negatives** (TN) - These are the correctly predicted negative values which means that the value of actual class is no and value of predicted class is also no.\n",
    "\n",
    "False positives and false negatives, these values occur when your actual class contradicts with the predicted class.\n",
    "\n",
    "**False Positives** (FP) – When actual class is no and predicted class is yes.\n",
    "\n",
    "**False Negatives** (FN) – When actual class is yes but predicted class in no.\n",
    "\n",
    "Once you understand these four parameters then we can calculate Accuracy, Precision, Recall and F1 score.\n",
    "\n",
    "**Accuracy** - Accuracy is the most intuitive performance measure and it is simply a ratio of correctly predicted observation to the total observations. One may think that, if we have high accuracy then our model is best. Yes, accuracy is a great measure but only when you have symmetric datasets where values of false positive and false negatives are almost same. Therefore, you have to look at other parameters to evaluate the performance of your model. \n",
    "\n",
    "Accuracy = TP+TN/TP+FP+FN+TN\n",
    "\n",
    "**Precision** - Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. The question that this metric answer is of all passengers that labeled as survived, how many actually survived? High precision relates to the low false positive rate. \n",
    "\n",
    "Precision = TP/TP+FP\n",
    "\n",
    "**Recall** (Sensitivity) - Recall is the ratio of correctly predicted positive observations to the all observations in actual class - yes. The question recall answers is: Of all the passengers that truly survived, how many did we label? It must be above 0.5.\n",
    "\n",
    "Recall = TP/TP+FN\n",
    "\n",
    "**F1 score** - F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, but F1 is usually more useful than accuracy, especially if you have an uneven class distribution. Accuracy works best if false positives and false negatives have similar cost. If the cost of false positives and false negatives are very different, it’s better to look at both Precision and Recall.\n",
    "\n",
    "F1 Score = 2*(Recall * Precision) / (Recall + Precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_results(y_test,y_pred):\n",
    "    accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "\n",
    "    tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "\n",
    "    precision = tp/(fp+tp)\n",
    "\n",
    "    recall = tp/(fn+tp)\n",
    "    \n",
    "    F1 = 2 * (precision * recall) / (precision + recall)\n",
    "\n",
    "    print(\" TN:\",tn,\"| FP:\", fp,\"| FN: \", fn,\"| TP:\", tp)\n",
    "\n",
    "\n",
    "    print('\\n')\n",
    "    print('precision',round(precision,3))\n",
    "    print(\"Accuracy:\",round(accuracy,3))\n",
    "    print('recall',round(recall,3))\n",
    "    print(\"F1 score\",round(F1,3))\n",
    "    print('\\n')\n",
    "    \n",
    "def get_contingency_table(y_pred):\n",
    "    (unique, counts)  = np.unique(y_pred, return_counts=True)\n",
    "    frequencies = np.asarray((unique, counts)).T\n",
    "    print(frequencies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1 - Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'clf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-9653e3bb98de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Print performance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mdisp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConfusionMatrixDisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mdisp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Blues'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxticks_rotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'horizontal'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'clf' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "model = DecisionTreeClassifier()\n",
    "\n",
    "model = model.fit(X_train,y_train) # Train Decision Tree Classifer\n",
    "\n",
    "y_pred = model.predict(X_test) #Predict the response for test dataset\n",
    "\n",
    "\n",
    "# Print performance\n",
    "cm = confusion_matrix(y_test, y_pred, labels=clf.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
    "disp = disp.plot(include_values=1, cmap='Blues', ax=None, xticks_rotation='horizontal')\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "get_results(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2 - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "forest = RandomForestClassifier(n_estimators=10)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "\n",
    "model = forest.fit(X_train, y_train.ravel())\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Print performance\n",
    "cm = confusion_matrix(y_test, y_pred, labels=clf.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
    "disp = disp.plot(include_values=1, cmap='Blues', ax=None, xticks_rotation='horizontal')\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "get_results(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 3 - Neural Network ( multi-layer perceptron MLP )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "X, y = make_classification(n_samples=100, random_state=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.6, random_state = 42)\n",
    "\n",
    "clf = MLPClassifier(random_state=1, max_iter=300).fit(X_train, y_train)\n",
    "clf.fit(X_train, y_train) \n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(\" Model accuracy score \", clf.score(X_test,y_test))\n",
    "\n",
    "\n",
    "# Print performance\n",
    "cm = confusion_matrix(y_test, y_pred, labels=clf.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
    "disp = disp.plot(include_values=1, cmap='Blues', ax=None, xticks_rotation='horizontal')\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "get_results(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 4 - Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "clf = svm.SVC(decision_function_shape='ovo')\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Print performance\n",
    "cm = confusion_matrix(y_test, y_pred, labels=clf.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
    "disp = disp.plot(include_values=1, cmap='Blues', ax=None, xticks_rotation='horizontal')\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "get_results(y_test,y_pred)"
   ]
  }
 ],
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