stroke_classifier_optimization_1.ipynb

{
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   "metadata": {
    "colab": {
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    },
    "id": "XRJ04-sruixq",
    "outputId": "28de195d-7c7e-4d4a-e840-693367ecdafd"
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   "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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import dependencies\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import numpy as np\n",
    "\n",
    "# Import and read the healthcare-dataset-stroke-data.csv.\n",
    "import pandas as pd \n",
    "stroke_df = pd.read_csv(\"data/healthcare-dataset-stroke-data.csv\")\n",
    "stroke_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_lPgQqUqAhz6"
   },
   "source": [
    "## **EDA & Preprocessing**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CfYBDKU6IjtF",
    "outputId": "e50a77fe-4aff-4e53-da70-2b0f249b57df"
   },
   "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": [
    "# Examine the info for the dataframe\n",
    "stroke_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2nDEn0MvLi1S",
    "outputId": "c6e4032e-c67c-4b31-b625-25740ee038d1"
   },
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the total NaN values for each column\n",
    "stroke_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XM3VsqShDZYZ",
    "outputId": "2c2af84e-6347-4209-fcaf-0340e060c379"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "0.08     15.500000\n",
       "0.16     14.766667\n",
       "0.24     17.400000\n",
       "0.32     18.080000\n",
       "0.40     15.850000\n",
       "           ...    \n",
       "78.00    27.929032\n",
       "79.00    27.862338\n",
       "80.00    28.783582\n",
       "81.00    27.951667\n",
       "82.00    28.029091\n",
       "Name: bmi, Length: 104, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series of average BMI for each age\n",
    "avg_bmi_by_age = stroke_df.groupby('age')['bmi'].mean()\n",
    "avg_bmi_by_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "YbUXJ5SwRI_f"
   },
   "outputs": [],
   "source": [
    "# Replace NaN values in the 'bmi' column with the average BMI of the corresponding age\n",
    "def replace_bmi(row):\n",
    "    if pd.isna(row['bmi']):\n",
    "        return avg_bmi_by_age[row['age']]\n",
    "    else:\n",
    "        return row['bmi']\n",
    "\n",
    "stroke_df['bmi'] = stroke_df.apply(replace_bmi, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xYFPW9v7WQAS",
    "outputId": "adb3a10e-f830-4c3b-91b0-d725e19caba9"
   },
   "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                5110 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": [
    "# Check the info of the dataframe (if the NaN values in the 'bmi' column are replaced)\n",
    "stroke_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "4mUUkMtQtp0n",
    "outputId": "d44e32f1-bad9-4f88-e988-fb81316e2249"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine the distribution of 'age', 'bmi' and 'avg_glucose_level'\n",
    "sns.histplot(stroke_df[['age', 'bmi', 'avg_glucose_level']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 569
    },
    "id": "sMdpHfIa-s40",
    "outputId": "1d8596bb-4a49-43e5-cf20-884001b87cba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'BMI')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xHG7qiIgJ4Pmqc0jzWAO8XHUIqY5fysyeegeWRblLy1lEjGVmf9U5pEY4LSNJBbLcJalAlrv0+vZWHUBqlHPuklQgz9wlqUCWuyQVyHKX5hERX4yI0xFxtOosUqMsd2l+XwY+WHUIqRmWuzSPzPwmcKbqHFIzLHdJKpDlLkkFstwlqUCWuyQVyHKX5hERw8C3gLdHxIsRMVB1JmmhXH5AkgrkmbskFchyl6QCWe6SVCDLXZIKZLlLUoEsd133ImImIp6JiGcj4umIeF9tfENEZER8ds5z10TEdET8TW3/wYi4v6rs0nwsdwnOZ+a7MvOdwKeAP5tz7LvAh+bs3wUcW8pwUjMsd+lKq4Gzc/bPA+MR0V/b/yjwyJKnkhrUWXUAaRl4Y0Q8A6wC1gJbfub4V4C7I+IkMAO8BKxb0oRSgyx3qTYtAxAR7wX+PiL65hx/DPgscAr4p6WPJzXOaRlpjsz8FrAG6Jkz9hPgMPBJ4KsVRZMa4pm7NEdEvAPoAH4IvGnOob8A/iMzfxgRlWSTGmG5S6/NuQMEsD0zZ+aWeGYew6tk1EZcFVKSCuScuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBfp/p4sKdTvRTe4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at the distribution of bmi column in closer detail\n",
    "plt.boxplot(stroke_df['bmi'], showmeans=True)\n",
    "plt.xlabel('BMI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "NnmhNLQPAQMA"
   },
   "outputs": [],
   "source": [
    "# Create bins for the bmi column \n",
    "bins = [0, 18, 25, 30, 100]\n",
    "labels = ['underweight', 'normal', 'overweight', 'obese']\n",
    "stroke_df['binned_bmi'] = pd.cut(stroke_df['bmi'], bins = bins, labels = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 557
    },
    "id": "hlfb4iHXCUcX",
    "outputId": "9f7a8dcc-9b7a-4907-b906-9a4c07a00b78"
   },
   "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",
       "      <th>binned_bmi</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>36.600000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>obese</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>30.190000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>obese</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>32.500000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>obese</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>34.400000</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "      <td>obese</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>24.000000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>18234</td>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>83.75</td>\n",
       "      <td>28.783582</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>44873</td>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>19723</td>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>obese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>37544</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>44679</td>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>overweight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5110 rows × 13 columns</p>\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",
       "5105  18234  Female  80.0             1              0          Yes   \n",
       "5106  44873  Female  81.0             0              0          Yes   \n",
       "5107  19723  Female  35.0             0              0          Yes   \n",
       "5108  37544    Male  51.0             0              0          Yes   \n",
       "5109  44679  Female  44.0             0              0          Yes   \n",
       "\n",
       "          work_type Residence_type  avg_glucose_level        bmi  \\\n",
       "0           Private          Urban             228.69  36.600000   \n",
       "1     Self-employed          Rural             202.21  30.190000   \n",
       "2           Private          Rural             105.92  32.500000   \n",
       "3           Private          Urban             171.23  34.400000   \n",
       "4     Self-employed          Rural             174.12  24.000000   \n",
       "...             ...            ...                ...        ...   \n",
       "5105        Private          Urban              83.75  28.783582   \n",
       "5106  Self-employed          Urban             125.20  40.000000   \n",
       "5107  Self-employed          Rural              82.99  30.600000   \n",
       "5108        Private          Rural             166.29  25.600000   \n",
       "5109       Govt_job          Urban              85.28  26.200000   \n",
       "\n",
       "       smoking_status  stroke  binned_bmi  \n",
       "0     formerly smoked       1       obese  \n",
       "1        never smoked       1       obese  \n",
       "2        never smoked       1       obese  \n",
       "3              smokes       1       obese  \n",
       "4        never smoked       1      normal  \n",
       "...               ...     ...         ...  \n",
       "5105     never smoked       0  overweight  \n",
       "5106     never smoked       0       obese  \n",
       "5107     never smoked       0       obese  \n",
       "5108  formerly smoked       0  overweight  \n",
       "5109          Unknown       0  overweight  \n",
       "\n",
       "[5110 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if bins are created successfully \n",
    "stroke_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "MoMw1ODMCcRK"
   },
   "outputs": [],
   "source": [
    "# Drop the id column\n",
    "stroke_df = stroke_df.drop(columns = ['id', 'bmi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FfnMB2TBEkXf",
    "outputId": "683f48eb-d7ad-433e-a51e-2f919f328b5b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                  3\n",
       "age                   104\n",
       "hypertension            2\n",
       "heart_disease           2\n",
       "ever_married            2\n",
       "work_type               5\n",
       "Residence_type          2\n",
       "avg_glucose_level    3979\n",
       "smoking_status          4\n",
       "stroke                  2\n",
       "binned_bmi              4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the number of unique values in each column.\n",
    "stroke_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "nxjSG65qEUXl",
    "outputId": "569f2158-3384-47f8-ef84-6dbd9d6a7d60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of the bmi bins\n",
    "stroke_df['binned_bmi'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t2C3oHHuEz3p",
    "outputId": "f0efad3b-2a7c-444a-b26f-0db0031925cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2994\n",
       "Male      2115\n",
       "Other        1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at gender value counts\n",
    "gender_counts = stroke_df['gender'].value_counts()\n",
    "gender_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "pkDdV-1ovJY5"
   },
   "outputs": [],
   "source": [
    "# Drop the record with gender = 'Other' (since there is only 1 record)\n",
    "stroke_df = stroke_df.drop(stroke_df[stroke_df['gender'] == 'Other'].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S2m56O0hTUTx",
    "outputId": "047ed178-ab96-48e4-c236-0b08b787a4dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if 'Other' is dropped on gender column\n",
    "stroke_df['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4Qm_GAW2FMdD",
    "outputId": "71a1ae02-e5f9-4f74-b0ce-23c12d516d63"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private          2924\n",
       "Self-employed     819\n",
       "children          687\n",
       "Govt_job          657\n",
       "Never_worked       22\n",
       "Name: work_type, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at work type value counts\n",
    "work_counts = stroke_df['work_type'].value_counts()\n",
    "work_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tlzqqwnLFXhi",
    "outputId": "220ab79c-ffd9-4b94-81e9-673b15a45728"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "never smoked       1892\n",
       "Unknown            1544\n",
       "formerly smoked     884\n",
       "smokes              789\n",
       "Name: smoking_status, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at smoking status value counts\n",
    "smoke_counts = stroke_df['smoking_status'].value_counts()\n",
    "smoke_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H_xUW3kv7j8O",
    "outputId": "76e877bf-a3c3-42d5-a91c-bd08307a24b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4860\n",
       "1     249\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the stroke outcome value counts\n",
    "stroke_counts = stroke_df['stroke'].value_counts()\n",
    "stroke_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Oh4iuAwvTtOC"
   },
   "source": [
    "**Findings:** The `0`s and `1`s in stroke column is highly imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "ea782UxzgtsM"
   },
   "outputs": [],
   "source": [
    "# Split the data into X and y\n",
    "y = stroke_df['stroke']\n",
    "X = stroke_df.drop(columns=['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "qy76XUrCgKyW"
   },
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN\n",
    "from imblearn.combine import SMOTEENN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "YrQWgB4EhMcW"
   },
   "outputs": [],
   "source": [
    "# Perform oversampling with RandomOverSampler\n",
    "ros = RandomOverSampler()\n",
    "X_ros, y_ros = ros.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "WsAI5tRl4uu9"
   },
   "outputs": [],
   "source": [
    "# Convert categorical data to numeric with `pd.get_dummies` (only on X, bc y is already 0s and 1s)\n",
    "X_encoded = pd.get_dummies(X_ros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "X4FuF4MllrNS"
   },
   "outputs": [],
   "source": [
    "# Create a StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "GQzO_VGdiSxP"
   },
   "outputs": [],
   "source": [
    "# Split the processed data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y_ros, test_size = 0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eVFpoxYy1pi2"
   },
   "source": [
    "## **Train and Evaluate the Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J_h4Ixyi0ALC"
   },
   "source": [
    "### **Train model on resampled data using `RandomOverSampler`** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "TClVwbub4stn"
   },
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "MXcGjd3G14Zp"
   },
   "outputs": [],
   "source": [
    "# Instantiate a Random Forest Classifier model\n",
    "rf_model = RandomForestClassifier(n_estimators=100, class_weight='balanced', random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "HUk52msW9kur",
    "outputId": "59065760-7610-4cca-fc35-3e5682d81e15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', random_state=1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model with training data\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "yfJNHvqY9k__"
   },
   "outputs": [],
   "source": [
    "# Make predictions on the testing set\n",
    "predictions = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "Z5QOHWlf-JAN"
   },
   "outputs": [],
   "source": [
    "# Evaluate the model with Confusion Matrix and accuracy score\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"]\n",
    ")\n",
    "\n",
    "acc_score = accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "1zRTU2yN-YLX",
    "outputId": "6fcaf3ca-9f17-4859-b530-01c84ef0de56"
   },
   "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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>951</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>0</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0          951           21\n",
       "Actual 1            0          972"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a confusion matrix for the model\n",
    "display(cm_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "izCf-YWO-dyb",
    "outputId": "67a0430a-8bc8-4eba-b4e2-569ad2fa3d4f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.9891975308641975\n"
     ]
    }
   ],
   "source": [
    "# Look at the accuracy score \n",
    "print(f\"Accuracy Score : {acc_score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "53TdixsEo_ni",
    "outputId": "80f5a2be-0975-4d7a-8514-a8dcd4aa6ab0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99       972\n",
      "           1       0.98      1.00      0.99       972\n",
      "\n",
      "    accuracy                           0.99      1944\n",
      "   macro avg       0.99      0.99      0.99      1944\n",
      "weighted avg       0.99      0.99      0.99      1944\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Look at the classification report\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A-0fyLE4ui--"
   },
   "source": [
    "**Evaluation:** In this optimization attempt, we divided the `bmi` column into four categories(or bins) and applied the `RandomOverSampler` technique to oversample the data and utilized it for training our model. As a result, we obtained an accuracy score of approximately 98.9% for predicting both class `0` and `1`. When we compared this outcome with our initial model that didn't involve binning the bmi, we observed that the overall performance decreased slight by 0.2%, while sensitivity and specificity scores all remained unchanged. This suggests that binning the `bmi` column has little to no affect to the overall performance of the model."
   ]
  }
 ],
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