Cereberal Stroke-Imbalance Dataset Handeling.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "## Read Data:\n",
    "\n",
    "The script starts by importing necessary libraries (`pandas`, `numpy`, `seaborn`, `matplotlib.pyplot`) and reading a CSV file into a DataFrame (`df`).\n",
    "\n",
    "## Exploratory Data Analysis (EDA):\n",
    "\n",
    "Basic exploration of the dataset using `head()`, `describe()`, and checking for missing values using `isnull().sum()`.\n",
    "\n",
    "## Handling Categorical Variables:\n",
    "\n",
    "One-hot encoding is performed on categorical variables using `pd.get_dummies()`.\n",
    "\n",
    "## Handling Missing Values:\n",
    "\n",
    "Missing values are imputed using the k-nearest neighbors algorithm (`KNNImputer` from `sklearn.impute`).\n",
    "\n",
    "\n",
    "## Feature Scaling and Train-Test Split:\n",
    "\n",
    "Features are scaled using `MinMaxScaler`, and the dataset is split into training and testing sets.\n",
    "\n",
    "## Model Selection:\n",
    "\n",
    "Several classification models are chosen (`KNeighborsClassifier`, `GaussianNB`, `DecisionTreeClassifier`, `RandomForestClassifier`) for initial testing.\n",
    "\n",
    "## Model Evaluation Without Resampling:\n",
    "\n",
    "Classification reports are generated for each model to evaluate their performance on the imbalanced dataset.\n",
    "\n",
    "## OverSampling (SMOTE):\n",
    "\n",
    "The script uses the Synthetic Minority Over-sampling Technique (SMOTE) to oversample the minority class.\n",
    "\n",
    "## Model Evaluation After OverSampling:\n",
    "\n",
    "The same models are re-trained and evaluated on the oversampled dataset.\n",
    "\n",
    "## UnderSampling:\n",
    "\n",
    "Random under-sampling is performed to balance the class distribution.\n",
    "\n",
    "## Model Evaluation After UnderSampling:\n",
    "\n",
    "The models are re-trained and evaluated on the undersampled dataset.\n",
    "\n",
    "## Combining OverSampling and UnderSampling (SMOTEENN):\n",
    "\n",
    "The SMOTEENN technique, which combines SMOTE and Edited Nearest Neighbours (ENN), is applied.\n",
    "\n",
    "## Model Evaluation After Combining OverSampling and UnderSampling:\n",
    "\n",
    "The models are re-trained and evaluated on the combined dataset.\n",
    "\n",
    "## Conclusion:\n",
    "\n",
    "- The script provides classification reports for each model after different resampling techniques.\n",
    "- It highlights that resampling techniques, particularly SMOTEENN, improve the model's ability to identify cases positive for stroke.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "346a679a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c2d81ab7",
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
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       "    <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",
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       "      <th>stroke</th>\n",
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       "      <th>0</th>\n",
       "      <td>30669</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>87.96</td>\n",
       "      <td>39.2</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>16523</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "      <td>17.6</td>\n",
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       "      <th>3</th>\n",
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       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46136</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Never_worked</td>\n",
       "      <td>Rural</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32257</td>\n",
       "      <td>Female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>210.95</td>\n",
       "      <td>50.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52800</td>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>77.59</td>\n",
       "      <td>17.7</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
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       "      <th>7</th>\n",
       "      <td>41413</td>\n",
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       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>243.53</td>\n",
       "      <td>27.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
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       "      <th>8</th>\n",
       "      <td>15266</td>\n",
       "      <td>Female</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>77.67</td>\n",
       "      <td>32.3</td>\n",
       "      <td>smokes</td>\n",
       "      <td>0</td>\n",
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       "      <th>9</th>\n",
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       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>205.84</td>\n",
       "      <td>54.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0  30669    Male   3.0             0              0           No   \n",
       "1  30468    Male  58.0             1              0          Yes   \n",
       "2  16523  Female   8.0             0              0           No   \n",
       "3  56543  Female  70.0             0              0          Yes   \n",
       "4  46136    Male  14.0             0              0           No   \n",
       "5  32257  Female  47.0             0              0          Yes   \n",
       "6  52800  Female  52.0             0              0          Yes   \n",
       "7  41413  Female  75.0             0              1          Yes   \n",
       "8  15266  Female  32.0             0              0          Yes   \n",
       "9  28674  Female  74.0             1              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0       children          Rural              95.12  18.0              NaN   \n",
       "1        Private          Urban              87.96  39.2     never smoked   \n",
       "2        Private          Urban             110.89  17.6              NaN   \n",
       "3        Private          Rural              69.04  35.9  formerly smoked   \n",
       "4   Never_worked          Rural             161.28  19.1              NaN   \n",
       "5        Private          Urban             210.95  50.1              NaN   \n",
       "6        Private          Urban              77.59  17.7  formerly smoked   \n",
       "7  Self-employed          Rural             243.53  27.0     never smoked   \n",
       "8        Private          Rural              77.67  32.3           smokes   \n",
       "9  Self-employed          Urban             205.84  54.6     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "5       0  \n",
       "6       0  \n",
       "7       0  \n",
       "8       0  \n",
       "9       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\aniru\\Machine Learning\\Datasets\\Cereberal Stroke\\Cereberal_Dataset.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "adb54ecb",
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   "outputs": [
    {
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       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
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       "      <th>count</th>\n",
       "      <td>43400.000000</td>\n",
       "      <td>43400.000000</td>\n",
       "      <td>43400.000000</td>\n",
       "      <td>43400.000000</td>\n",
       "      <td>43400.000000</td>\n",
       "      <td>41938.000000</td>\n",
       "      <td>43400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36326.142350</td>\n",
       "      <td>42.217894</td>\n",
       "      <td>0.093571</td>\n",
       "      <td>0.047512</td>\n",
       "      <td>104.482750</td>\n",
       "      <td>28.605038</td>\n",
       "      <td>0.018041</td>\n",
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       "      <th>std</th>\n",
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       "      <td>22.519649</td>\n",
       "      <td>0.291235</td>\n",
       "      <td>0.212733</td>\n",
       "      <td>43.111751</td>\n",
       "      <td>7.770020</td>\n",
       "      <td>0.133103</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>18038.500000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.540000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36351.500000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.580000</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54514.250000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.070000</td>\n",
       "      <td>32.900000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72943.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>291.050000</td>\n",
       "      <td>97.600000</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "                 id           age  hypertension  heart_disease  \\\n",
       "count  43400.000000  43400.000000  43400.000000   43400.000000   \n",
       "mean   36326.142350     42.217894      0.093571       0.047512   \n",
       "std    21072.134879     22.519649      0.291235       0.212733   \n",
       "min        1.000000      0.080000      0.000000       0.000000   \n",
       "25%    18038.500000     24.000000      0.000000       0.000000   \n",
       "50%    36351.500000     44.000000      0.000000       0.000000   \n",
       "75%    54514.250000     60.000000      0.000000       0.000000   \n",
       "max    72943.000000     82.000000      1.000000       1.000000   \n",
       "\n",
       "       avg_glucose_level           bmi        stroke  \n",
       "count       43400.000000  41938.000000  43400.000000  \n",
       "mean          104.482750     28.605038      0.018041  \n",
       "std            43.111751      7.770020      0.133103  \n",
       "min            55.000000     10.100000      0.000000  \n",
       "25%            77.540000     23.200000      0.000000  \n",
       "50%            91.580000     27.700000      0.000000  \n",
       "75%           112.070000     32.900000      0.000000  \n",
       "max           291.050000     97.600000      1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec6be5b8",
   "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",
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       "bmi                   1462\n",
       "smoking_status       13292\n",
       "stroke                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9a011d6",
   "metadata": {},
   "source": [
    "BMI and Smoking Stroke has a lot of NULL| values"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "6ae80302",
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    {
     "data": {
      "text/plain": [
       "(43400, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "df.shape"
   ]
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   "cell_type": "code",
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   "id": "0b887204",
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    {
     "data": {
      "text/plain": [
       "id                     int64\n",
       "gender                object\n",
       "age                  float64\n",
       "hypertension           int64\n",
       "heart_disease          int64\n",
       "ever_married          object\n",
       "work_type             object\n",
       "Residence_type        object\n",
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       "bmi                  float64\n",
       "smoking_status        object\n",
       "stroke                 int64\n",
       "dtype: object"
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     "metadata": {},
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   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0506cdfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stroke\n",
       "0    42617\n",
       "1      783\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df['stroke'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5818e9f6",
   "metadata": {},
   "source": [
    "Very imbalace 0,1 parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a14c1b37",
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='stroke', data=df)\n",
    "plt.title(\"Imbalance data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59572453",
   "metadata": {},
   "source": [
    "## One Hot Encoding "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3627c238",
   "metadata": {},
   "source": [
    "Converting the categorical values, indicator variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "179529d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
       "       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
       "       'smoking_status', 'stroke'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9bcace24",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>...</th>\n",
       "      <th>work_type_Govt_job</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>Residence_type_Rural</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56543</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "<p>4 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id   age  hypertension  heart_disease  avg_glucose_level   bmi  stroke  \\\n",
       "0  30669   3.0             0              0              95.12  18.0       0   \n",
       "1  30468  58.0             1              0              87.96  39.2       0   \n",
       "2  16523   8.0             0              0             110.89  17.6       0   \n",
       "3  56543  70.0             0              0              69.04  35.9       0   \n",
       "\n",
       "   gender_Female  gender_Male  gender_Other  ...  work_type_Govt_job  \\\n",
       "0          False         True         False  ...               False   \n",
       "1          False         True         False  ...               False   \n",
       "2           True        False         False  ...               False   \n",
       "3           True        False         False  ...               False   \n",
       "\n",
       "   work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                   False              False                    False   \n",
       "1                   False               True                    False   \n",
       "2                   False               True                    False   \n",
       "3                   False               True                    False   \n",
       "\n",
       "   work_type_children  Residence_type_Rural  Residence_type_Urban  \\\n",
       "0                True                  True                 False   \n",
       "1               False                 False                  True   \n",
       "2               False                 False                  True   \n",
       "3               False                  True                 False   \n",
       "\n",
       "   smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
       "0                           False                        False   \n",
       "1                           False                         True   \n",
       "2                           False                        False   \n",
       "3                            True                        False   \n",
       "\n",
       "   smoking_status_smokes  \n",
       "0                  False  \n",
       "1                  False  \n",
       "2                  False  \n",
       "3                  False  \n",
       "\n",
       "[4 rows x 22 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df,columns=['gender','ever_married','work_type','Residence_type','smoking_status'])\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4494acfc",
   "metadata": {},
   "source": [
    "## Missing Value Handelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "760ee366",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>work_type_Govt_job</th>\n",
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       "      <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_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56543.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46136.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32257.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>210.95</td>\n",
       "      <td>50.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52800.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.59</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>41413.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>243.53</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15266.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.67</td>\n",
       "      <td>32.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28674.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.84</td>\n",
       "      <td>54.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id   age  hypertension  heart_disease  avg_glucose_level   bmi  \\\n",
       "0  30669.0   3.0           0.0            0.0              95.12  18.0   \n",
       "1  30468.0  58.0           1.0            0.0              87.96  39.2   \n",
       "2  16523.0   8.0           0.0            0.0             110.89  17.6   \n",
       "3  56543.0  70.0           0.0            0.0              69.04  35.9   \n",
       "4  46136.0  14.0           0.0            0.0             161.28  19.1   \n",
       "5  32257.0  47.0           0.0            0.0             210.95  50.1   \n",
       "6  52800.0  52.0           0.0            0.0              77.59  17.7   \n",
       "7  41413.0  75.0           0.0            1.0             243.53  27.0   \n",
       "8  15266.0  32.0           0.0            0.0              77.67  32.3   \n",
       "9  28674.0  74.0           1.0            0.0             205.84  54.6   \n",
       "\n",
       "   stroke  gender_Female  gender_Male  gender_Other  ...  work_type_Govt_job  \\\n",
       "0     0.0            0.0          1.0           0.0  ...                 0.0   \n",
       "1     0.0            0.0          1.0           0.0  ...                 0.0   \n",
       "2     0.0            1.0          0.0           0.0  ...                 0.0   \n",
       "3     0.0            1.0          0.0           0.0  ...                 0.0   \n",
       "4     0.0            0.0          1.0           0.0  ...                 0.0   \n",
       "5     0.0            1.0          0.0           0.0  ...                 0.0   \n",
       "6     0.0            1.0          0.0           0.0  ...                 0.0   \n",
       "7     0.0            1.0          0.0           0.0  ...                 0.0   \n",
       "8     0.0            1.0          0.0           0.0  ...                 0.0   \n",
       "9     0.0            1.0          0.0           0.0  ...                 0.0   \n",
       "\n",
       "   work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                     0.0                0.0                      0.0   \n",
       "1                     0.0                1.0                      0.0   \n",
       "2                     0.0                1.0                      0.0   \n",
       "3                     0.0                1.0                      0.0   \n",
       "4                     1.0                0.0                      0.0   \n",
       "5                     0.0                1.0                      0.0   \n",
       "6                     0.0                1.0                      0.0   \n",
       "7                     0.0                0.0                      1.0   \n",
       "8                     0.0                1.0                      0.0   \n",
       "9                     0.0                0.0                      1.0   \n",
       "\n",
       "   work_type_children  Residence_type_Rural  Residence_type_Urban  \\\n",
       "0                 1.0                   1.0                   0.0   \n",
       "1                 0.0                   0.0                   1.0   \n",
       "2                 0.0                   0.0                   1.0   \n",
       "3                 0.0                   1.0                   0.0   \n",
       "4                 0.0                   1.0                   0.0   \n",
       "5                 0.0                   0.0                   1.0   \n",
       "6                 0.0                   0.0                   1.0   \n",
       "7                 0.0                   1.0                   0.0   \n",
       "8                 0.0                   1.0                   0.0   \n",
       "9                 0.0                   0.0                   1.0   \n",
       "\n",
       "   smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
       "0                             0.0                          0.0   \n",
       "1                             0.0                          1.0   \n",
       "2                             0.0                          0.0   \n",
       "3                             1.0                          0.0   \n",
       "4                             0.0                          0.0   \n",
       "5                             0.0                          0.0   \n",
       "6                             1.0                          0.0   \n",
       "7                             0.0                          1.0   \n",
       "8                             0.0                          0.0   \n",
       "9                             0.0                          1.0   \n",
       "\n",
       "   smoking_status_smokes  \n",
       "0                    0.0  \n",
       "1                    0.0  \n",
       "2                    0.0  \n",
       "3                    0.0  \n",
       "4                    0.0  \n",
       "5                    0.0  \n",
       "6                    0.0  \n",
       "7                    0.0  \n",
       "8                    1.0  \n",
       "9                    0.0  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "imputer = KNNImputer(missing_values=np.nan)\n",
    "tab = imputer.fit_transform(df)\n",
    "df_new = pd.DataFrame(tab, columns=df.columns)\n",
    "df_new.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aafe7b3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43400, 22)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5161aafa",
   "metadata": {},
   "source": [
    "The Columns has been transformed from 12 to 22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "51078aad",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                0\n",
       "age                               0\n",
       "hypertension                      0\n",
       "heart_disease                     0\n",
       "avg_glucose_level                 0\n",
       "bmi                               0\n",
       "stroke                            0\n",
       "gender_Female                     0\n",
       "gender_Male                       0\n",
       "gender_Other                      0\n",
       "ever_married_No                   0\n",
       "ever_married_Yes                  0\n",
       "work_type_Govt_job                0\n",
       "work_type_Never_worked            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_formerly smoked    0\n",
       "smoking_status_never smoked       0\n",
       "smoking_status_smokes             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c4f6d9c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                float64\n",
       "age                               float64\n",
       "hypertension                      float64\n",
       "heart_disease                     float64\n",
       "avg_glucose_level                 float64\n",
       "bmi                               float64\n",
       "stroke                            float64\n",
       "gender_Female                     float64\n",
       "gender_Male                       float64\n",
       "gender_Other                      float64\n",
       "ever_married_No                   float64\n",
       "ever_married_Yes                  float64\n",
       "work_type_Govt_job                float64\n",
       "work_type_Never_worked            float64\n",
       "work_type_Private                 float64\n",
       "work_type_Self-employed           float64\n",
       "work_type_children                float64\n",
       "Residence_type_Rural              float64\n",
       "Residence_type_Urban              float64\n",
       "smoking_status_formerly smoked    float64\n",
       "smoking_status_never smoked       float64\n",
       "smoking_status_smokes             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cbe4130",
   "metadata": {},
   "source": [
    "## Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e04578b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_new.drop('stroke',axis=1)\n",
    "y = df_new['stroke']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "23cc2d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y ,test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "316cb512",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b1e90dc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL NAME:  KNeighborsClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      1.00      0.99     12785\n",
      "         1.0       0.00      0.00      0.00       235\n",
      "\n",
      "    accuracy                           0.98     13020\n",
      "   macro avg       0.49      0.50      0.50     13020\n",
      "weighted avg       0.96      0.98      0.97     13020\n",
      "\n",
      "MODEL NAME:  GaussianNB()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      0.15      0.26     12785\n",
      "         1.0       0.02      0.99      0.04       235\n",
      "\n",
      "    accuracy                           0.16     13020\n",
      "   macro avg       0.51      0.57      0.15     13020\n",
      "weighted avg       0.98      0.16      0.25     13020\n",
      "\n",
      "MODEL NAME:  DecisionTreeClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.98      0.98     12785\n",
      "         1.0       0.05      0.06      0.05       235\n",
      "\n",
      "    accuracy                           0.96     13020\n",
      "   macro avg       0.51      0.52      0.52     13020\n",
      "weighted avg       0.97      0.96      0.96     13020\n",
      "\n",
      "MODEL NAME:  RandomForestClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      1.00      0.99     12785\n",
      "         1.0       0.00      0.00      0.00       235\n",
      "\n",
      "    accuracy                           0.98     13020\n",
      "   macro avg       0.49      0.50      0.50     13020\n",
      "weighted avg       0.96      0.98      0.97     13020\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aniru\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\aniru\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\aniru\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "nb = GaussianNB()\n",
    "dt = DecisionTreeClassifier()\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "models = [knn, nb, dt, rf]\n",
    "\n",
    "for model in models:\n",
    "    print(\"MODEL NAME: \", model)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5195c0",
   "metadata": {},
   "source": [
    "The models accurately predicts the 0 but not 1. The precision is close to zero in all the above cases, which means the model failed to predict the cases where chances for cerebral stroke was actually present. So the above models are useless."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790fc315",
   "metadata": {},
   "source": [
    "## OverSampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fc302b1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL NAME: KNeighborsClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.87      0.93     12883\n",
      "         1.0       0.88      0.99      0.93     12688\n",
      "\n",
      "    accuracy                           0.93     25571\n",
      "   macro avg       0.94      0.93      0.93     25571\n",
      "weighted avg       0.94      0.93      0.93     25571\n",
      "\n",
      "MODEL NAME: GaussianNB()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.25      0.40     12883\n",
      "         1.0       0.57      1.00      0.72     12688\n",
      "\n",
      "    accuracy                           0.62     25571\n",
      "   macro avg       0.78      0.62      0.56     25571\n",
      "weighted avg       0.78      0.62      0.56     25571\n",
      "\n",
      "MODEL NAME: DecisionTreeClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.97      0.97     12883\n",
      "         1.0       0.97      0.98      0.97     12688\n",
      "\n",
      "    accuracy                           0.97     25571\n",
      "   macro avg       0.97      0.97      0.97     25571\n",
      "weighted avg       0.97      0.97      0.97     25571\n",
      "\n",
      "MODEL NAME: RandomForestClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.95      0.97     12883\n",
      "         1.0       0.95      0.99      0.97     12688\n",
      "\n",
      "    accuracy                           0.97     25571\n",
      "   macro avg       0.97      0.97      0.97     25571\n",
      "weighted avg       0.97      0.97      0.97     25571\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "os = SMOTE(random_state=1)\n",
    "X_os, y_os = os.fit_resample(X,y)\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_os,y_os,test_size=0.3,random_state=1)\n",
    "\n",
    "for model in models:\n",
    "    print(\"MODEL NAME:\",model)\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60f1b8b2",
   "metadata": {},
   "source": [
    "After applying SMOTE technique, the precision increased. For KNN it is 0.88, for Decision Tree it is 0.97 and RandomForest it is 0.95. So oversampling resulted in a better model that is capable of identifying the cases positive for stroke."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27960c9c",
   "metadata": {},
   "source": [
    "## UnderSampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "83d4f10f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL NAME: KNeighborsClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.67      0.72      0.70       229\n",
      "         1.0       0.72      0.66      0.69       241\n",
      "\n",
      "    accuracy                           0.69       470\n",
      "   macro avg       0.69      0.69      0.69       470\n",
      "weighted avg       0.70      0.69      0.69       470\n",
      "\n",
      "MODEL NAME: GaussianNB()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.31      0.46       229\n",
      "         1.0       0.60      0.98      0.74       241\n",
      "\n",
      "    accuracy                           0.65       470\n",
      "   macro avg       0.76      0.64      0.60       470\n",
      "weighted avg       0.76      0.65      0.61       470\n",
      "\n",
      "MODEL NAME: DecisionTreeClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.69      0.69      0.69       229\n",
      "         1.0       0.71      0.71      0.71       241\n",
      "\n",
      "    accuracy                           0.70       470\n",
      "   macro avg       0.70      0.70      0.70       470\n",
      "weighted avg       0.70      0.70      0.70       470\n",
      "\n",
      "MODEL NAME: RandomForestClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.76      0.78      0.77       229\n",
      "         1.0       0.79      0.77      0.78       241\n",
      "\n",
      "    accuracy                           0.78       470\n",
      "   macro avg       0.78      0.78      0.78       470\n",
      "weighted avg       0.78      0.78      0.78       470\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "us= RandomUnderSampler(random_state=1)\n",
    "X_us, y_us = us.fit_resample(X,y)\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_us,y_us,test_size=0.3,random_state=1)\n",
    "\n",
    "for model in models:\n",
    "    print(\"MODEL NAME:\",model)\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe28721",
   "metadata": {},
   "source": [
    "### Combining Oversampling and Undersampling "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921e83fe",
   "metadata": {},
   "source": [
    "SMOTEEN combines SMOTE and Edited Nearest Neighbours(ENN). SMOTEEN performs upsampling and downsampling at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2bc3ca80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "sample = SMOTEENN()\n",
    "X_over,y_over = sample.fit_resample(X,y)\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_over,y_over,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "70210000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL NAME: KNeighborsClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      0.96      0.98     11166\n",
      "         1.0       0.96      1.00      0.98     12315\n",
      "\n",
      "    accuracy                           0.98     23481\n",
      "   macro avg       0.98      0.98      0.98     23481\n",
      "weighted avg       0.98      0.98      0.98     23481\n",
      "\n",
      "MODEL NAME: GaussianNB()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.30      0.46     11166\n",
      "         1.0       0.61      1.00      0.76     12315\n",
      "\n",
      "    accuracy                           0.67     23481\n",
      "   macro avg       0.80      0.65      0.61     23481\n",
      "weighted avg       0.79      0.67      0.62     23481\n",
      "\n",
      "MODEL NAME: DecisionTreeClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.97      0.96      0.97     11166\n",
      "         1.0       0.97      0.98      0.97     12315\n",
      "\n",
      "    accuracy                           0.97     23481\n",
      "   macro avg       0.97      0.97      0.97     23481\n",
      "weighted avg       0.97      0.97      0.97     23481\n",
      "\n",
      "MODEL NAME: RandomForestClassifier()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      0.98      0.99     11166\n",
      "         1.0       0.98      1.00      0.99     12315\n",
      "\n",
      "    accuracy                           0.99     23481\n",
      "   macro avg       0.99      0.99      0.99     23481\n",
      "weighted avg       0.99      0.99      0.99     23481\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    print(\"MODEL NAME:\",model)\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcfe1079",
   "metadata": {},
   "source": [
    "<b>End Note</b> : For the Cerebral Stroke Imbalanced data we could make a better model using resampling techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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