Full.ipynb

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      "interpreter": {
        "hash": "dcacb0086e9a4f4eabd41c33bf4faac5ea0a3337ed3f5eff0680afa930572c04"
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    "colab": {
      "name": "Full.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "9QtR_KjRKZD-",
        "outputId": "5aa16dfa-2d3e-4685-e028-12bb4492e16e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "## Import package\n",
        "\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import f1_score, roc_auc_score, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "## Read dataset\n",
        "\n",
        "raw_df = pd.read_csv('./Dataset/train.csv')\n",
        "raw_df.head()\n",
        "\n",
        "# Drop `id` feature because it's useless\n",
        "\n",
        "df = raw_df.copy()\n",
        "df.drop(columns=['id'], inplace=True)\n",
        "df\n",
        "\n",
        "## Analyze Data\n",
        "\n",
        "df.describe().transpose()\n",
        "\n",
        "df.info()\n",
        "\n",
        "def draw_missing_data_table(data):\n",
        "    total = data.isnull().sum().sort_values(ascending=False)\n",
        "    percent = (data.isnull().sum()/data.isnull().count()).sort_values(ascending=False)\n",
        "    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "    return missing_data\n",
        "\n",
        "# sns.set(style=\"ticks\");\n",
        "# pal = [\"#FA5858\", \"#58D3F7\"]\n",
        "\n",
        "# sns.pairplot(df, hue=\"stroke\", palette=pal);\n",
        "# plt.title(\"stroke\");\n",
        "\n",
        "df.corr()[\"stroke\"].sort_values(ascending=False)\n",
        "\n",
        "df.corr()[\"bmi\"].sort_values(ascending=False)\n",
        "\n",
        "plt.figure(figsize=(12,7))\n",
        "sns.set_context(\"paper\", font_scale=1.5)\n",
        "sns.heatmap(df.corr(), cmap=\"viridis\")\n",
        "\n",
        "sns.boxplot(x='avg_glucose_level',data=df, color='Red')\n",
        "\n",
        "sns.boxplot(x='bmi',data=df, color = 'Green')\n",
        "\n",
        "df.loc[df['bmi'] > 45]\n",
        "\n",
        "sns.boxplot(x='age',data=df , color = 'Blue')\n",
        "\n",
        "df.columns\n",
        "\n",
        "# unknown_feature = ['gender', 'hypertension', 'heart_disease', 'Residence_type', 'smoking_status','ever_married', 'blood', 'work_type', 'stroke']\n",
        "unknown_feature = df.columns\n",
        "print('Value counts: \\n')\n",
        "for i in unknown_feature:\n",
        "    print(df[i].value_counts())\n",
        "    print('\\n')\n",
        "\n",
        "print('Value percent: ')\n",
        "for i in unknown_feature:\n",
        "    print(df[i].value_counts(normalize=True)*100)\n",
        "    print('\\n')\n",
        "\n",
        "## Preprocessing Data\n",
        "\n",
        "### fill 'age' and 'bmi' mean values\n",
        "\n",
        "df['age'] = df['age'].fillna(np.mean(df['age']))\n",
        "df\n",
        "\n",
        "labels = [1,2,3,4,5]\n",
        "df[\"age_bin\"] = pd.cut(df[\"age\"], 5, labels=labels)\n",
        "df['age_bin'].value_counts()\n",
        "\n",
        "gb_bmi = df.groupby(\"age_bin\")[\"bmi\"].mean()\n",
        "gb_bmi\n",
        "\n",
        "def impute_bmi(cols):\n",
        "    bmi = cols[0]\n",
        "    age_bin = cols[1]\n",
        "    \n",
        "    if pd.isnull(bmi):\n",
        "        if age_bin == 1:\n",
        "            return 20.715509\n",
        "        elif age_bin == 2:\n",
        "            return 28.814368\n",
        "        elif age_bin == 3:\n",
        "            return 31.020136\n",
        "        elif age_bin == 4:\n",
        "            return 31.480000\n",
        "        elif age_bin == 5:\n",
        "            return 29.288200\n",
        "    else:\n",
        "        return bmi\n",
        "\n",
        "df[\"bmi\"] = df[[\"bmi\",\"age_bin\"]].apply(impute_bmi, axis=1)\n",
        "df['bmi']\n",
        "\n",
        "draw_missing_data_table(df)\n",
        "\n",
        "df.describe()\n",
        "\n",
        "# Xoá dữ liệu có `bmi` <= 45\n",
        "\n",
        "df = df.loc[df['bmi'] < 45]\n",
        "df\n",
        "\n",
        "\n",
        "\n",
        "draw_missing_data_table(df)\n",
        "\n",
        "## Drop height\n",
        "\n",
        "df.drop(columns=['height'], inplace=True)\n",
        "\n",
        "df.head()\n",
        "\n",
        "draw_missing_data_table(df)\n",
        "\n",
        "## Check correlation for avg_glucose_level\n",
        "\n",
        "df.corr()['avg_glucose_level'].sort_values(ascending=False)\n",
        "\n",
        "### Hmm, this isn't really that much correlation so mean all time\n",
        "\n",
        "df['avg_glucose_level'] = df['avg_glucose_level'].fillna(np.mean(df['avg_glucose_level']))\n",
        "\n",
        "df.head()\n",
        "\n",
        "draw_missing_data_table(df)\n",
        "\n",
        "## Fill hypertension and blood with the one that has the most value count\n",
        "\n",
        "df['hypertension'] = df['hypertension'].fillna(0.0)\n",
        "df['hypertension'] = df['hypertension'].astype(int)\n",
        "df.head()\n",
        "\n",
        "draw_missing_data_table(df)\n",
        "\n",
        "df['heart_disease'] = df['heart_disease'].fillna(0.0)\n",
        "df['heart_disease'] = df['heart_disease'].astype(int)\n",
        "df.head()\n",
        "\n",
        "draw_missing_data_table(df)\n",
        "\n",
        "sns.countplot(x=df['blood'], hue=df['stroke'])\n",
        "\n",
        "df['blood'] = df['blood'].fillna('AB')\n",
        "\n",
        "df.head()\n",
        "\n",
        "df['Residence_type'].unique()\n",
        "df.loc[df['Residence_type']=='Unknown']\n",
        "\n",
        "# - We will convert ever_married to 0 and 1\n",
        "# - We will one-hot-encode work_type, avg_glucose_level, blood\n",
        "# - We will drop Residence_type\n",
        "# - smoking_status????\n",
        "\n",
        "df.drop(columns='age_bin', inplace=True)\n",
        "\n",
        "df\n",
        "\n",
        "for i in df.columns:\n",
        "    print(str(i) + '= ' + str(df[i].unique()))\n",
        "\n",
        "marriage_mapping = {'No': 0, 'Yes': 1}\n",
        "df['ever_married'] = df['ever_married'].map(marriage_mapping)\n",
        "\n",
        "df.head()\n",
        "\n",
        "oh = OneHotEncoder()\n",
        "\n",
        "categorical_features_oh = ['gender', 'blood', 'smoking_status', 'Residence_type', 'work_type']\n",
        "\n",
        "for i in categorical_features_oh:\n",
        "    df[i] = pd.Categorical(df[i])\n",
        "    dfDummies = pd.get_dummies(df[i], prefix= i)\n",
        "    df = pd.concat([df, dfDummies], axis=1)\n",
        "df\n",
        "\n",
        "for i in categorical_features_oh:\n",
        "    df.drop(columns=i, axis=1, inplace=True)\n",
        "df\n",
        "\n",
        "draw_missing_data_table(df)\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "std = StandardScaler()\n",
        "columns = ['avg_glucose_level','bmi','age']\n",
        "scaled = std.fit_transform(df[columns])\n",
        "scaled = pd.DataFrame(scaled,columns=columns)\n",
        "df=df.drop(columns=columns,axis=1)\n",
        "\n",
        "df = df.reset_index(drop=True)\n",
        "df = df.merge(scaled, left_index=True, right_index=True, how = \"left\")\n",
        "df\n",
        "\n",
        "draw_missing_data_table(df)\n",
        "df.to_csv('./Dataset/preprocessed_train.csv', index=False)\n",
        "# x = df.copy()\n",
        "# x = x.drop(columns='stroke')\n",
        "# y = df['stroke']\n",
        "\n",
        "# x[0:5]\n",
        "\n",
        "# y[0:5]\n",
        "\n",
        "# ## TREATING IMBALANCE CLASS USING SMOTE\n",
        "\n",
        "# smote = SMOTE()\n",
        "# x_smote, y_smote = smote.fit_resample(x, y)\n",
        "\n",
        "# x_train, x_test, y_train, y_test = train_test_split(x_smote, y_smote, test_size= 0.2, random_state= 42)\n",
        "\n",
        "# print(len(x_train))\n",
        "# print(len(y_train))\n",
        "# print(len(x_test))\n",
        "# print(len(y_test))\n",
        "\n",
        "# ## Training model\n",
        "\n",
        "# ### Using XGBoost\n",
        "\n",
        "# import xgboost as xgb\n",
        "# model_xgb = xgb.XGBClassifier()\n",
        "# model_xgb.fit(x_train, y_train)\n",
        "# y_xgb_pred = model_xgb.predict(x_test)\n",
        "# f1_score(y_test, y_xgb_pred)\n",
        "\n",
        "# cm = confusion_matrix(y_test, y_xgb_pred)\n",
        "# cm\n",
        "\n",
        "# tn = cm[0,0]\n",
        "# fp = cm[0,1]\n",
        "# tp = cm[1,1]\n",
        "# fn = cm[1,0]\n",
        "# accuracy  = (tp + tn) / (tp + fp + tn + fn)\n",
        "# precision = tp / (tp + fp)\n",
        "# recall    = tp / (tp + fn)\n",
        "# f1score  = 2 * precision * recall / (precision + recall)\n",
        "# print(f1score)\n",
        "# print(accuracy)"
      ],
      "execution_count": 445,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4000 entries, 0 to 3999\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   gender             4000 non-null   object \n",
            " 1   age                3893 non-null   float64\n",
            " 2   hypertension       3770 non-null   float64\n",
            " 3   heart_disease      3910 non-null   float64\n",
            " 4   ever_married       4000 non-null   object \n",
            " 5   work_type          4000 non-null   object \n",
            " 6   Residence_type     4000 non-null   object \n",
            " 7   avg_glucose_level  3829 non-null   float64\n",
            " 8   bmi                2884 non-null   float64\n",
            " 9   smoking_status     4000 non-null   object \n",
            " 10  blood              3990 non-null   object \n",
            " 11  height             2928 non-null   float64\n",
            " 12  stroke             4000 non-null   int64  \n",
            "dtypes: float64(6), int64(1), object(6)\n",
            "memory usage: 406.4+ KB\n",
            "Value counts: \n",
            "\n",
            "Female    2218\n",
            "Male      1625\n",
            "Other      157\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "\n",
            "52.00    78\n",
            "57.00    71\n",
            "53.00    70\n",
            "78.00    69\n",
            "50.00    69\n",
            "         ..\n",
            "0.72      2\n",
            "0.08      2\n",
            "0.48      2\n",
            "1.16      2\n",
            "0.40      1\n",
            "Name: age, Length: 104, dtype: int64\n",
            "\n",
            "\n",
            "0.0    3402\n",
            "1.0     368\n",
            "Name: hypertension, dtype: int64\n",
            "\n",
            "\n",
            "0.0    3694\n",
            "1.0     216\n",
            "Name: heart_disease, dtype: int64\n",
            "\n",
            "\n",
            "Yes    2626\n",
            "No     1374\n",
            "Name: ever_married, dtype: int64\n",
            "\n",
            "\n",
            "Private          2305\n",
            "Self-employed     631\n",
            "children          539\n",
            "Govt_job          508\n",
            "Never_worked       17\n",
            "Name: work_type, dtype: int64\n",
            "\n",
            "\n",
            "Unknown    1987\n",
            "Urban      1018\n",
            "Rural       995\n",
            "Name: Residence_type, dtype: int64\n",
            "\n",
            "\n",
            "93.88     5\n",
            "91.85     5\n",
            "93.55     4\n",
            "71.06     4\n",
            "114.32    4\n",
            "         ..\n",
            "99.96     1\n",
            "69.88     1\n",
            "57.77     1\n",
            "210.78    1\n",
            "94.00     1\n",
            "Name: avg_glucose_level, Length: 3179, dtype: int64\n",
            "\n",
            "\n",
            "27.6    25\n",
            "27.7    24\n",
            "27.0    24\n",
            "26.9    23\n",
            "28.7    23\n",
            "        ..\n",
            "44.3     1\n",
            "15.7     1\n",
            "39.8     1\n",
            "45.5     1\n",
            "47.5     1\n",
            "Name: bmi, Length: 381, dtype: int64\n",
            "\n",
            "\n",
            "never smoked       1467\n",
            "Unknown            1226\n",
            "formerly smoked     686\n",
            "smokes              621\n",
            "Name: smoking_status, dtype: int64\n",
            "\n",
            "\n",
            "AB    1544\n",
            "A      841\n",
            "B      829\n",
            "O      776\n",
            "Name: blood, dtype: int64\n",
            "\n",
            "\n",
            "1.66    94\n",
            "1.68    91\n",
            "1.71    83\n",
            "1.64    81\n",
            "1.70    78\n",
            "        ..\n",
            "1.16     1\n",
            "1.11     1\n",
            "0.91     1\n",
            "1.05     1\n",
            "1.17     1\n",
            "Name: height, Length: 78, dtype: int64\n",
            "\n",
            "\n",
            "0    3870\n",
            "1     130\n",
            "Name: stroke, dtype: int64\n",
            "\n",
            "\n",
            "Value percent: \n",
            "Female    55.450\n",
            "Male      40.625\n",
            "Other      3.925\n",
            "Name: gender, dtype: float64\n",
            "\n",
            "\n",
            "52.00    2.003596\n",
            "57.00    1.823786\n",
            "53.00    1.798099\n",
            "78.00    1.772412\n",
            "50.00    1.772412\n",
            "           ...   \n",
            "0.72     0.051374\n",
            "0.08     0.051374\n",
            "0.48     0.051374\n",
            "1.16     0.051374\n",
            "0.40     0.025687\n",
            "Name: age, Length: 104, dtype: float64\n",
            "\n",
            "\n",
            "0.0    90.238727\n",
            "1.0     9.761273\n",
            "Name: hypertension, dtype: float64\n",
            "\n",
            "\n",
            "0.0    94.475703\n",
            "1.0     5.524297\n",
            "Name: heart_disease, dtype: float64\n",
            "\n",
            "\n",
            "Yes    65.65\n",
            "No     34.35\n",
            "Name: ever_married, dtype: float64\n",
            "\n",
            "\n",
            "Private          57.625\n",
            "Self-employed    15.775\n",
            "children         13.475\n",
            "Govt_job         12.700\n",
            "Never_worked      0.425\n",
            "Name: work_type, dtype: float64\n",
            "\n",
            "\n",
            "Unknown    49.675\n",
            "Urban      25.450\n",
            "Rural      24.875\n",
            "Name: Residence_type, dtype: float64\n",
            "\n",
            "\n",
            "93.88     0.130582\n",
            "91.85     0.130582\n",
            "93.55     0.104466\n",
            "71.06     0.104466\n",
            "114.32    0.104466\n",
            "            ...   \n",
            "99.96     0.026116\n",
            "69.88     0.026116\n",
            "57.77     0.026116\n",
            "210.78    0.026116\n",
            "94.00     0.026116\n",
            "Name: avg_glucose_level, Length: 3179, dtype: float64\n",
            "\n",
            "\n",
            "27.6    0.866852\n",
            "27.7    0.832178\n",
            "27.0    0.832178\n",
            "26.9    0.797503\n",
            "28.7    0.797503\n",
            "          ...   \n",
            "44.3    0.034674\n",
            "15.7    0.034674\n",
            "39.8    0.034674\n",
            "45.5    0.034674\n",
            "47.5    0.034674\n",
            "Name: bmi, Length: 381, dtype: float64\n",
            "\n",
            "\n",
            "never smoked       36.675\n",
            "Unknown            30.650\n",
            "formerly smoked    17.150\n",
            "smokes             15.525\n",
            "Name: smoking_status, dtype: float64\n",
            "\n",
            "\n",
            "AB    38.696742\n",
            "A     21.077694\n",
            "B     20.776942\n",
            "O     19.448622\n",
            "Name: blood, dtype: float64\n",
            "\n",
            "\n",
            "1.66    3.210383\n",
            "1.68    3.107923\n",
            "1.71    2.834699\n",
            "1.64    2.766393\n",
            "1.70    2.663934\n",
            "          ...   \n",
            "1.16    0.034153\n",
            "1.11    0.034153\n",
            "0.91    0.034153\n",
            "1.05    0.034153\n",
            "1.17    0.034153\n",
            "Name: height, Length: 78, dtype: float64\n",
            "\n",
            "\n",
            "0    96.75\n",
            "1     3.25\n",
            "Name: stroke, dtype: float64\n",
            "\n",
            "\n",
            "gender= ['Female' 'Male' 'Other']\n",
            "age= [4.30712561e+01 5.50000000e+01 4.20000000e+01 3.80000000e+01\n",
            " 2.40000000e+01 8.00000000e+01 3.30000000e+01 8.00000000e+00\n",
            " 2.00000000e+01 4.30000000e+01 2.30000000e+01 5.90000000e+01\n",
            " 3.10000000e+01 7.90000000e+01 4.10000000e+01 4.50000000e+01\n",
            " 1.60000000e+01 6.50000000e+01 5.80000000e+01 4.40000000e+01\n",
            " 7.80000000e+01 4.90000000e+01 4.80000000e+01 3.70000000e+01\n",
            " 4.60000000e+01 2.80000000e+01 1.20000000e+01 3.00000000e+00\n",
            " 5.40000000e+01 1.30000000e+01 1.40000000e+01 6.90000000e+01\n",
            " 5.20000000e+01 8.20000000e+01 1.00000000e+01 7.30000000e+01\n",
            " 3.90000000e+01 8.00000000e-01 7.40000000e+01 3.20000000e+01\n",
            " 6.00000000e+01 7.00000000e+00 1.80000000e+01 7.00000000e+01\n",
            " 6.10000000e+01 1.50000000e+01 6.20000000e+01 1.88000000e+00\n",
            " 1.70000000e+01 3.50000000e+01 6.80000000e+01 2.90000000e+01\n",
            " 7.70000000e+01 6.40000000e+01 3.00000000e+01 6.30000000e+01\n",
            " 4.00000000e+01 4.70000000e+01 7.50000000e+01 6.70000000e+01\n",
            " 2.50000000e+01 4.00000000e+00 5.00000000e+00 1.90000000e+01\n",
            " 5.60000000e+01 3.40000000e+01 5.70000000e+01 5.00000000e+01\n",
            " 5.10000000e+01 7.20000000e+01 5.30000000e+01 8.10000000e+01\n",
            " 3.60000000e+01 2.60000000e+01 7.10000000e+01 6.60000000e+01\n",
            " 2.00000000e+00 2.20000000e+01 2.70000000e+01 2.10000000e+01\n",
            " 7.60000000e+01 9.00000000e+00 6.00000000e+00 5.60000000e-01\n",
            " 1.10000000e+01 6.40000000e-01 1.60000000e-01 1.80000000e+00\n",
            " 2.40000000e-01 1.08000000e+00 1.00000000e+00 1.32000000e+00\n",
            " 3.20000000e-01 1.56000000e+00 1.72000000e+00 1.64000000e+00\n",
            " 8.80000000e-01 1.48000000e+00 8.00000000e-02 1.16000000e+00\n",
            " 1.24000000e+00 4.80000000e-01 4.00000000e-01 1.40000000e+00\n",
            " 7.20000000e-01]\n",
            "hypertension= [0 1]\n",
            "heart_disease= [0 1]\n",
            "ever_married= ['No' 'Yes']\n",
            "work_type= ['children' 'Private' 'Govt_job' 'Self-employed' 'Never_worked']\n",
            "Residence_type= ['Unknown' 'Urban' 'Rural']\n",
            "avg_glucose_level= [85.81 89.17 98.53 ... 69.3  75.3  62.55]\n",
            "bmi= [18.6      31.48     18.5      31.020136 26.2      29.2882   20.715509\n",
            " 27.6      37.6      28.814368 31.6      24.9      39.7      25.7\n",
            " 26.8      41.3      37.       24.1      23.9      31.       23.1\n",
            " 20.9      31.5      36.2      20.1      22.8      40.1      26.5\n",
            " 24.3      33.1      27.3      23.4      24.2      30.       22.2\n",
            " 28.7      26.6      27.1      38.8      28.5      18.9      40.8\n",
            " 21.5      32.2      23.5      31.4      30.1      42.6      35.9\n",
            " 20.7      26.1      24.       42.2      31.7      38.       29.7\n",
            " 35.3      30.9      33.5      28.1      36.       29.8      29.6\n",
            " 32.3      26.7      21.4      24.4      34.5      32.       31.8\n",
            " 28.2      18.       36.1      29.       25.8      20.2      31.9\n",
            " 29.4      32.4      16.7      32.1      17.       23.8      17.3\n",
            " 35.8      22.9      18.1      30.5      28.4      32.7      27.8\n",
            " 17.1      33.       11.3      39.5      23.6      23.7      27.\n",
            " 20.4      30.4      22.4      24.6      32.8      15.3      28.8\n",
            " 23.3      26.9      26.       36.4      27.7      21.6      26.3\n",
            " 21.8      43.8      27.2      30.8      38.9      38.2      26.4\n",
            " 34.3      24.5      31.3      28.       37.8      20.8      25.5\n",
            " 19.4      30.2      27.9      29.1      30.7      35.2      16.6\n",
            " 30.3      22.1      21.2      21.7      28.3      37.7      35.7\n",
            " 31.2      32.5      35.5      23.       14.8      16.8      39.4\n",
            " 29.9      38.1      22.3      27.4      34.2      30.6      24.8\n",
            " 15.1      13.8      18.8      40.3      19.2      22.6      21.3\n",
            " 18.7      18.3      38.6      18.2      38.3      40.4      41.6\n",
            " 17.6      17.4      34.       25.1      34.7      23.2      28.9\n",
            " 33.9      20.3      19.5      34.4      18.4      22.7      13.9\n",
            " 40.6      24.7      17.8      17.9      33.4      16.4      27.5\n",
            " 19.8      36.6      39.3      17.5      20.6      31.1      35.6\n",
            " 37.9      25.4      22.       34.6      42.       39.       25.6\n",
            " 25.3      14.6      33.3      39.2      35.       44.6      29.5\n",
            " 33.6      15.9      32.9      32.6      20.       16.3      14.2\n",
            " 20.5      29.3      43.7      36.8      14.5      33.7      42.1\n",
            " 44.8      19.7      44.3      36.9      15.       15.8      41.2\n",
            " 39.1      29.2      41.8      25.2      28.6      42.4      43.6\n",
            " 40.9      41.1      17.7      19.9      43.9      41.5      36.7\n",
            " 21.       19.1      21.9      37.2      37.4      36.3      37.1\n",
            " 39.9      41.4      25.9      25.       33.8      14.1      21.1\n",
            " 34.1      43.3      39.6      12.       19.       38.4      40.\n",
            " 38.7      15.5      22.5      16.       33.2      34.8      16.2\n",
            " 41.       44.       36.5      40.2      16.5      43.1      37.5\n",
            " 42.7      17.2      42.3      16.9      19.3      41.7      44.7\n",
            " 14.3      43.       44.5      19.6      13.       40.5      34.9\n",
            " 44.2      16.1      12.3      15.2      42.8      41.9      35.1\n",
            " 43.2      13.7      38.5      37.3      43.4      35.4      13.2\n",
            " 14.4      15.4      12.8      13.5      15.7      11.5      40.7\n",
            " 42.9      39.8     ]\n",
            "smoking_status= ['Unknown' 'never smoked' 'smokes' 'formerly smoked']\n",
            "blood= ['O' 'B' 'AB' 'A']\n",
            "stroke= [0 1]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rvk8wNuAIHvR"
      },
      "source": [
        "## Test submission"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SegVz8WoIHvR",
        "outputId": "c9c4ff83-080f-43b5-ba6a-fd4206656504",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "raw_df = pd.read_csv('./Dataset/public_test.csv')\n",
        "raw_df.head()"
      ],
      "execution_count": 419,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>blood</th>\n",
              "      <th>height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>Female</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>92.97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>AB</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>210</td>\n",
              "      <td>Male</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>91.54</td>\n",
              "      <td>31.4</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>A</td>\n",
              "      <td>1.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>242</td>\n",
              "      <td>Male</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>children</td>\n",
              "      <td>Urban</td>\n",
              "      <td>98.56</td>\n",
              "      <td>17.5</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>711</td>\n",
              "      <td>Male</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>92.96</td>\n",
              "      <td>22.2</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>AB</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>724</td>\n",
              "      <td>Male</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>81.77</td>\n",
              "      <td>44.7</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>O</td>\n",
              "      <td>1.34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    id  gender   age  hypertension  ...   bmi   smoking_status blood height\n",
              "0   67  Female  17.0           0.0  ...   NaN  formerly smoked    AB    NaN\n",
              "1  210    Male  81.0           0.0  ...  31.4     never smoked     A   1.60\n",
              "2  242    Male   4.0           NaN  ...  17.5          Unknown     O    NaN\n",
              "3  711    Male  81.0           0.0  ...  22.2     never smoked    AB    NaN\n",
              "4  724    Male  17.0           0.0  ...  44.7     never smoked     O   1.34\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 419
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hD-jCXmIHvS"
      },
      "source": [
        "df = raw_df.copy()\n",
        "df['age'] = df['age'].fillna(np.mean(df['age']))"
      ],
      "execution_count": 420,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEgtWsOnIHvS",
        "outputId": "2785f711-e6a1-4ab8-e732-b463019d8625",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "labels = [1,2,3,4,5]\n",
        "df[\"age_bin\"] = pd.cut(df[\"age\"], 5, labels=labels)\n",
        "df['age_bin'].value_counts()"
      ],
      "execution_count": 421,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    129\n",
              "4    111\n",
              "5     94\n",
              "2     86\n",
              "1     80\n",
              "Name: age_bin, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 421
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2G6nfclIHvS",
        "outputId": "f2ea547b-0914-4942-ee01-673314a7a129",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "gb_bmi = df.groupby(\"age_bin\")[\"bmi\"].mean()\n",
        "gb_bmi"
      ],
      "execution_count": 422,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age_bin\n",
              "1    21.190909\n",
              "2    28.552632\n",
              "3    32.482645\n",
              "4    30.884000\n",
              "5    29.695181\n",
              "Name: bmi, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 422
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfE28kCNIHvS"
      },
      "source": [
        "def impute_bmi(cols):\n",
        "    bmi = cols[0]\n",
        "    age_bin = cols[1]\n",
        "    if pd.isnull(bmi):\n",
        "        if age_bin == 1:\n",
        "            return 21.190909\n",
        "        elif age_bin == 2:\n",
        "            return 28.552632\n",
        "        elif age_bin == 3:\n",
        "            return 32.456890\n",
        "        elif age_bin == 4:\n",
        "            return 30.884000\n",
        "        elif age_bin == 5:\n",
        "            return 29.695181\n",
        "    else:\n",
        "        return bmi"
      ],
      "execution_count": 423,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HiwHNgq5IHvT",
        "outputId": "54395019-a409-4b69-8260-3aa594bbe207",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df[\"bmi\"] = df[[\"bmi\",\"age_bin\"]].apply(impute_bmi, axis=1)\n",
        "df['bmi']"
      ],
      "execution_count": 424,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      28.552632\n",
              "1      31.400000\n",
              "2      17.500000\n",
              "3      22.200000\n",
              "4      44.700000\n",
              "         ...    \n",
              "495    24.200000\n",
              "496    29.200000\n",
              "497    32.800000\n",
              "498    30.884000\n",
              "499    17.600000\n",
              "Name: bmi, Length: 500, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 424
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71kAIwmiIHvT",
        "outputId": "45320cc7-3c26-43bf-b9b9-50ec2465e76a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "source": [
        "df = df.drop(['age_bin'],axis=1)\n",
        "draw_missing_data_table(df)"
      ],
      "execution_count": 425,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Total</th>\n",
              "      <th>Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>heart_disease</th>\n",
              "      <td>134</td>\n",
              "      <td>0.268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>height</th>\n",
              "      <td>124</td>\n",
              "      <td>0.248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hypertension</th>\n",
              "      <td>45</td>\n",
              "      <td>0.090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <td>6</td>\n",
              "      <td>0.012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>blood</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoking_status</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residence_type</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>work_type</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ever_married</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Total  Percent\n",
              "heart_disease        134    0.268\n",
              "height               124    0.248\n",
              "hypertension          45    0.090\n",
              "avg_glucose_level      6    0.012\n",
              "blood                  0    0.000\n",
              "smoking_status         0    0.000\n",
              "bmi                    0    0.000\n",
              "Residence_type         0    0.000\n",
              "work_type              0    0.000\n",
              "ever_married           0    0.000\n",
              "age                    0    0.000\n",
              "gender                 0    0.000\n",
              "id                     0    0.000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 425
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mALfaiLbIHvT"
      },
      "source": [
        "df = df.drop(['height'],axis=1)"
      ],
      "execution_count": 426,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kdNEUpd1IHvT"
      },
      "source": [
        "df['avg_glucose_level'] = df['avg_glucose_level'].fillna(np.mean(df['avg_glucose_level']))"
      ],
      "execution_count": 427,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJYMut4CIHvU",
        "outputId": "7c22474f-197e-429b-d596-c37fd001467b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "unknown_feature = df.columns\n",
        "print('Value counts: \\n')\n",
        "for i in unknown_feature:\n",
        "    print(df[i].value_counts())\n",
        "    print('\\n')"
      ],
      "execution_count": 428,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Value counts: \n",
            "\n",
            "46079    1\n",
            "8553     1\n",
            "21830    1\n",
            "37192    1\n",
            "51532    1\n",
            "        ..\n",
            "12482    1\n",
            "28367    1\n",
            "66258    1\n",
            "724      1\n",
            "56322    1\n",
            "Name: id, Length: 500, dtype: int64\n",
            "\n",
            "\n",
            "Female    305\n",
            "Male      175\n",
            "Other      20\n",
            "Name: gender, dtype: int64\n",
            "\n",
            "\n",
            "54.00    13\n",
            "43.00    12\n",
            "37.00    12\n",
            "66.00    11\n",
            "40.00    11\n",
            "         ..\n",
            "1.48      1\n",
            "1.72      1\n",
            "9.00      1\n",
            "0.72      1\n",
            "1.80      1\n",
            "Name: age, Length: 87, dtype: int64\n",
            "\n",
            "\n",
            "0.0    404\n",
            "1.0     51\n",
            "Name: hypertension, dtype: int64\n",
            "\n",
            "\n",
            "0.0    345\n",
            "1.0     21\n",
            "Name: heart_disease, dtype: int64\n",
            "\n",
            "\n",
            "Yes    327\n",
            "No     173\n",
            "Name: ever_married, dtype: int64\n",
            "\n",
            "\n",
            "Private          274\n",
            "Self-employed     88\n",
            "children          71\n",
            "Govt_job          65\n",
            "Never_worked       2\n",
            "Name: work_type, dtype: int64\n",
            "\n",
            "\n",
            "Urban      245\n",
            "Rural      232\n",
            "Unknown     23\n",
            "Name: Residence_type, dtype: int64\n",
            "\n",
            "\n",
            "106.47502    6\n",
            "112.02000    2\n",
            "90.11000     2\n",
            "91.89000     2\n",
            "81.51000     2\n",
            "            ..\n",
            "64.62000     1\n",
            "56.90000     1\n",
            "110.20000    1\n",
            "93.72000     1\n",
            "83.75000     1\n",
            "Name: avg_glucose_level, Length: 481, dtype: int64\n",
            "\n",
            "\n",
            "29.695181    11\n",
            "30.884000    11\n",
            "28.552632    10\n",
            "32.456890     8\n",
            "23.500000     8\n",
            "             ..\n",
            "17.300000     1\n",
            "38.900000     1\n",
            "40.400000     1\n",
            "57.200000     1\n",
            "17.500000     1\n",
            "Name: bmi, Length: 240, dtype: int64\n",
            "\n",
            "\n",
            "never smoked       199\n",
            "Unknown            143\n",
            "formerly smoked     82\n",
            "smokes              76\n",
            "Name: smoking_status, dtype: int64\n",
            "\n",
            "\n",
            "AB    217\n",
            "O     200\n",
            "A      44\n",
            "B      39\n",
            "Name: blood, dtype: int64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kJKfcrSIHvU"
      },
      "source": [
        "df['hypertension'] = df['hypertension'].fillna(0.0)\n",
        "df['hypertension'] = df['hypertension'].astype(int)\n",
        "df['heart_disease'] = df['heart_disease'].fillna(0)\n",
        "df['heart_disease'] = df['heart_disease'].astype(int)"
      ],
      "execution_count": 429,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYFBKWIgIHvU",
        "outputId": "7fa037fb-5a97-40c0-a70d-5228a2a1434a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "for i in df.columns:\n",
        "    print(str(i) + '= ' + str(df[i].unique()))"
      ],
      "execution_count": 430,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id= [   67   210   242   711   724   751   841   917  1119  1192  1552  1847\n",
            "  1953  2267  2304  2424  2456  2580  2647  2692  2818  3013  3318  3512\n",
            "  3700  4148  4929  5170  5294  5647  5654  5714  5987  6202  6390  6422\n",
            "  6472  6574  6599  6605  6625  6805  7047  7054  7538  7653  7841  7871\n",
            "  7924  8022  8045  8122  8543  8553  8623  8920  8964  9189  9394 10018\n",
            " 10056 10135 10166 10321 10696 10870 10950 11312 11325 11382 11632 11762\n",
            " 11816 11872 11891 11908 11933 12345 12367 12482 12594 12668 12834 12857\n",
            " 12900 12963 13031 13172 13237 13286 13540 13598 13768 13846 13902 14444\n",
            " 14553 14658 14789 14892 14943 14996 15062 15422 15464 15978 16024 16263\n",
            " 16320 16377 16446 16590 16617 16627 16629 16817 17098 17174 17238 17308\n",
            " 17478 17569 18178 18283 18352 18398 18578 18696 18833 19498 19814 19907\n",
            " 20237 20387 20426 20439 20979 21067 21110 21346 21366 21407 21677 21830\n",
            " 22151 22282 22330 22515 22614 22880 23194 23210 23238 23339 23439 23449\n",
            " 23568 23989 24106 24163 24246 24262 24421 24585 24669 24736 24783 24854\n",
            " 24955 24972 25051 25404 25446 25476 25636 25643 25718 26025 26062 26594\n",
            " 26884 26935 27119 27416 27480 27596 27789 27859 28011 28367 28382 28513\n",
            " 28873 29267 29276 29281 29764 30002 30650 30683 30734 31092 31131 31153\n",
            " 31426 31563 31642 31697 31703 31708 31795 31911 32126 32503 32523 32571\n",
            " 32610 32638 32669 32726 33102 33439 33560 33657 33886 33924 34026 34045\n",
            " 34130 34184 34285 34415 34416 34567 34900 35095 35210 35217 35941 35999\n",
            " 36045 36087 36162 36210 36633 36710 36922 37192 37289 37440 37651 37728\n",
            " 37866 38074 38207 38287 38397 38617 38661 38980 39616 39796 39940 39977\n",
            " 40163 40447 40705 40887 40980 40998 41241 41263 41481 41654 41715 41942\n",
            " 42400 42760 42806 42899 43035 43054 43138 43364 43433 43487 43507 43656\n",
            " 44047 44288 44662 44799 44992 44993 45252 45279 45325 45372 45395 45563\n",
            " 45817 45965 46048 46079 46454 46503 46517 46647 46670 46703 46987 47057\n",
            " 47113 47144 47356 47410 47456 47668 47799 47802 47886 48721 48824 49053\n",
            " 49190 49261 49277 49509 49605 49762 49930 49955 50009 50070 50141 50210\n",
            " 50410 50412 50931 51329 51374 51408 51476 51512 51532 51651 51823 51856\n",
            " 52092 52172 52247 52361 52368 52447 52461 52619 52681 52960 53004 53105\n",
            " 53219 53426 53440 53636 53802 53821 53954 53976 53998 54117 54127 54400\n",
            " 54869 54871 55244 55337 55361 55681 55824 56139 56254 56277 56322 56339\n",
            " 56450 56547 56567 56575 56594 56600 56692 56939 56961 56979 57080 57322\n",
            " 57343 57645 57870 58313 58477 58978 59027 59058 59126 59272 59405 59437\n",
            " 59522 59691 59908 60455 60602 60635 60675 60934 60964 60981 61238 61384\n",
            " 61477 61667 61684 61830 61987 61997 62167 62296 62395 62715 62738 62798\n",
            " 62861 63026 63043 63312 63333 63404 63453 63478 63773 63804 63915 64155\n",
            " 64416 64435 64670 64864 64931 65103 65351 65636 65729 65966 66006 66258\n",
            " 66306 66690 66786 66945 67356 67711 67921 67981 68020 68034 68059 68371\n",
            " 68789 68853 69010 69059 69222 69528 69551 69647 69732 69768 70318 70428\n",
            " 70615 70670 71057 71143 71151 71279 71515 71551 71622 71777 71900 71966\n",
            " 72081 72184 72215 72393 72491 72562 72792 72940]\n",
            "gender= ['Female' 'Male' 'Other']\n",
            "age= [17.         81.          4.          5.         34.         32.\n",
            " 47.         31.         73.         20.          0.72       51.\n",
            " 60.         66.         55.         80.         79.         18.\n",
            " 42.88539877 37.          8.         42.         11.         49.\n",
            " 78.         12.         48.         35.         64.         52.\n",
            " 54.         57.         33.         50.         40.         67.\n",
            " 16.         74.         53.         58.          3.         19.\n",
            " 22.          2.         76.         46.         69.         68.\n",
            " 28.         45.         29.         61.         59.         38.\n",
            " 43.          7.         62.         71.         36.         63.\n",
            " 15.          9.         72.         44.         41.         82.\n",
            " 39.         30.         77.         24.          1.48       14.\n",
            " 56.         21.         25.          1.8        75.         13.\n",
            "  6.         10.         27.         26.         23.          1.72\n",
            " 65.         70.          0.24      ]\n",
            "hypertension= [0 1]\n",
            "heart_disease= [0 1]\n",
            "ever_married= ['No' 'Yes']\n",
            "work_type= ['Private' 'Self-employed' 'children' 'Govt_job' 'Never_worked']\n",
            "Residence_type= ['Urban' 'Rural' 'Unknown']\n",
            "avg_glucose_level= [ 92.97        91.54        98.56        92.96        81.77\n",
            "  75.1         83.75        85.18       101.81        70.66\n",
            " 101.58        79.53       112.19        82.31        95.19\n",
            "  80.67       100.2         70.28        80.35        73.87\n",
            " 230.74        83.7        101.12       106.47502024 232.29\n",
            "  71.18        78.48        67.97        92.74        99.01\n",
            "  94.77        98.9         89.42        87.          73.99\n",
            " 108.51       101.76       103.29        85.66       235.06\n",
            "  70.43       107.74        69.72       112.83        94.75\n",
            "  83.16        91.68        86.78       101.46        82.95\n",
            "  94.12       105.28       195.74        78.79        76.35\n",
            "  94.37        80.27        92.24        56.33        98.02\n",
            " 112.02        77.91        73.94        81.32       232.64\n",
            " 112.75       208.99       111.08        98.07        96.02\n",
            " 207.28        71.12        69.24       106.54        83.55\n",
            " 169.67        73.18        94.39        77.82       105.9\n",
            " 195.43       115.52        73.57        80.08        67.56\n",
            "  91.16       203.76       112.37        81.         115.68\n",
            " 227.23        77.2         88.          74.8         90.71\n",
            "  76.63        77.1        117.63        81.58        79.62\n",
            "  85.98        82.46        80.57       221.24        80.48\n",
            "  80.54        77.92       102.76        89.06        76.12\n",
            "  81.76       192.5        110.38       112.08       130.54\n",
            " 116.06        78.9         85.         221.79       101.66\n",
            "  93.52        86.06        81.38       108.32       108.96\n",
            " 121.66       168.68        84.43        99.44        71.77\n",
            "  97.05       104.9        206.09       103.68        90.11\n",
            " 110.1         93.3         70.13       103.72       117.03\n",
            "  75.13        82.63        77.99       116.62        82.94\n",
            " 118.55        82.62       125.43        74.43        77.95\n",
            "  95.23       194.75        75.          90.44       153.24\n",
            "  86.4         84.68       116.04        77.76        70.91\n",
            " 113.08       115.07       231.56        94.27        87.91\n",
            "  79.42       102.          91.32        93.72        79.98\n",
            "  83.84       201.96       119.9        100.8        103.22\n",
            "  78.04        92.75       103.78       213.43       104.16\n",
            "  86.92        86.19       115.71        73.          86.95\n",
            " 197.36        69.47        73.81        88.98        74.24\n",
            " 229.94        72.76       194.37        96.62        83.59\n",
            " 216.19       199.2         94.24        88.56        76.78\n",
            " 189.82       216.07       101.45        85.9         76.42\n",
            "  85.82        84.03        73.24        98.44        91.89\n",
            "  76.57       217.74        85.27        94.89       124.78\n",
            "  76.08        69.2         86.21        95.87        87.12\n",
            "  80.94       207.84        87.15       116.44        76.52\n",
            "  92.59        79.57        74.34        74.02        85.08\n",
            " 104.02       112.96       234.5        167.16        86.85\n",
            " 119.4        103.65       111.24       112.7         73.08\n",
            "  62.21        62.68        72.99       203.87       208.39\n",
            "  72.17        68.99       193.61       131.42        76.64\n",
            " 106.53       111.48        73.98        56.64       107.33\n",
            "  99.72        60.73        64.62        87.4        222.52\n",
            "  82.42        66.16       135.82        72.04        58.01\n",
            "  68.43        75.06        64.45       110.2         74.\n",
            " 247.87        94.92        57.46       200.66       145.18\n",
            " 102.16        55.79        59.62        63.74        63.49\n",
            " 253.93        80.2        207.37        95.24        66.3\n",
            " 126.57       141.37        83.03        61.13        76.09\n",
            "  57.79       142.63        60.64       203.27        78.8\n",
            "  63.57       106.8        196.58       228.5        197.06\n",
            " 223.83        57.52        76.47       110.42        88.62\n",
            " 111.76        58.89       125.63       191.94       256.74\n",
            "  56.94       190.67       120.22       120.56       112.55\n",
            " 106.52        70.87        78.5         74.39       123.89\n",
            " 236.04        78.98        81.51       261.67        91.3\n",
            " 113.41        96.47        57.92        68.01       138.44\n",
            "  77.94        78.85        57.4         81.36       114.46\n",
            " 123.08        56.9         77.72        85.77        89.68\n",
            "  78.11        60.32       131.81       205.78       254.95\n",
            "  98.14       228.26        63.9         63.98        58.42\n",
            " 190.14        88.79       125.32       100.47        69.45\n",
            "  58.66       103.5        106.76       128.61       116.98\n",
            " 119.13        65.29       126.12       202.06       140.1\n",
            " 129.66       108.82        83.8         88.97        65.58\n",
            "  65.36        57.56        60.7         69.94        77.77\n",
            "  84.04       248.24        92.98        70.56        61.42\n",
            "  59.11       102.97       134.65       132.46        89.41\n",
            "  60.67        81.24       239.07       108.63        68.66\n",
            " 126.09        79.22       150.74       221.89       229.73\n",
            "  86.07       121.15        76.19        68.68        99.96\n",
            " 221.08        57.38       105.72       130.07       122.23\n",
            " 117.77        68.07       144.14       151.26        78.05\n",
            " 101.06       102.03       115.98       108.38        78.43\n",
            "  70.38       100.54        67.29        79.33        61.8\n",
            "  80.55       110.33        87.71        76.34       146.1\n",
            "  55.93        87.39       200.25        62.66        76.03\n",
            "  71.79        60.17       131.05       141.84        82.26\n",
            " 123.98        89.14        86.67       195.71        82.57\n",
            "  69.46        94.14        85.33        80.8         88.85\n",
            "  55.35       151.16       111.84        83.73       103.08\n",
            " 134.76        62.44       149.8         83.2         86.3\n",
            "  57.09        56.48       182.99        63.42        66.69\n",
            "  88.06        76.98       179.14        57.96        70.19\n",
            " 179.67        82.64       263.32       200.91        85.07\n",
            " 144.33        77.16        77.43        81.73        67.41\n",
            "  89.73       212.92        84.93        74.66        64.37\n",
            " 102.92      ]\n",
            "bmi= [28.552632 31.4      17.5      22.2      44.7      20.7      37.\n",
            " 28.4      27.2      35.9      20.1      31.9      24.3      33.5\n",
            " 28.5      29.695181 28.7      33.7      30.2      34.2      40.8\n",
            " 23.9      16.1      23.8      25.5      22.7      35.5      24.1\n",
            " 19.       16.3      33.3      20.6      39.9      20.8      30.884\n",
            " 39.5      18.2      27.9      20.2      22.4      25.9      21.4\n",
            " 21.8      23.1      32.7      22.6      38.1      29.4      20.4\n",
            " 29.1      39.1      24.8      27.6      45.2      25.1      23.2\n",
            " 24.       34.9      27.3      16.7      28.3      27.5      28.6\n",
            " 28.9      33.9      19.6      38.       33.8      27.1      40.\n",
            " 23.4      30.6      50.6      45.8      17.3      55.9      36.2\n",
            " 21.6      28.       25.3      28.2      24.2      17.7      18.5\n",
            " 24.9      20.3      34.8      16.8      32.45689  29.       27.4\n",
            " 23.       16.       30.       35.4      31.5      34.1      14.2\n",
            " 32.4      27.7      26.9      45.7      25.       23.6      30.9\n",
            " 17.8      40.3      31.1      17.9      32.       29.3      38.4\n",
            " 32.2      35.2      21.190909 25.7      38.2      32.6      34.7\n",
            " 18.1      41.8      36.9      14.       40.4      20.9      41.4\n",
            " 35.       30.1      37.6      39.3      20.5      34.5      36.7\n",
            " 21.5      22.       26.2      18.9      19.8      23.5      18.8\n",
            " 27.       18.6      30.3      26.6      45.3      28.8      43.4\n",
            " 34.6      27.8      25.8      23.3      35.6      19.3      38.9\n",
            " 24.5      14.8      31.8      14.6      26.1      25.4      43.7\n",
            " 18.3      22.9      17.       38.8      29.6      18.4      46.4\n",
            " 26.4      36.       36.8      19.5      30.4      29.9      24.7\n",
            " 22.3      35.1      21.7      42.6      36.3      21.3      50.8\n",
            " 29.5      40.2      47.5      26.       32.9      29.7      41.9\n",
            " 31.6      32.1      55.7      31.       31.3      43.       25.6\n",
            " 43.2      48.8      92.       38.7      14.1      41.7      46.\n",
            " 45.4      26.8      32.8      36.5      24.4      41.3      32.3\n",
            " 19.1      42.4      37.3      32.5      25.2      57.2      33.1\n",
            " 30.5      29.8      24.6      15.       34.       22.8      57.9\n",
            " 37.9      33.2      46.1      41.5      37.2      47.6      21.2\n",
            " 19.4      35.3      64.4      30.7      21.9      29.2      26.3\n",
            " 16.2      17.6     ]\n",
            "smoking_status= ['formerly smoked' 'never smoked' 'Unknown' 'smokes']\n",
            "blood= ['AB' 'A' 'O' 'B']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znTWNNuHIHvU"
      },
      "source": [
        "marriage_mapping = {'No': 0, 'Yes': 1}\n",
        "df['ever_married'] = df['ever_married'].map(marriage_mapping)"
      ],
      "execution_count": 431,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xnLZ54QIHvU"
      },
      "source": [
        "oh = OneHotEncoder()"
      ],
      "execution_count": 432,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMc7gzGPIHvV"
      },
      "source": [
        "categorical_features_oh = ['gender', 'blood', 'smoking_status', 'Residence_type', 'work_type']"
      ],
      "execution_count": 433,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ue0_mzZIHvV",
        "outputId": "7f937281-9366-4963-ce51-57ec5d3a49f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        }
      },
      "source": [
        "for i in categorical_features_oh:\n",
        "    df[i] = pd.Categorical(df[i])\n",
        "    dfDummies = pd.get_dummies(df[i], prefix= i)\n",
        "    df = pd.concat([df, dfDummies], axis=1)\n",
        "df"
      ],
      "execution_count": 434,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>blood</th>\n",
              "      <th>gender_Female</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>blood_A</th>\n",
              "      <th>blood_AB</th>\n",
              "      <th>blood_B</th>\n",
              "      <th>blood_O</th>\n",
              "      <th>smoking_status_Unknown</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "      <th>Residence_type_Rural</th>\n",
              "      <th>Residence_type_Unknown</th>\n",
              "      <th>Residence_type_Urban</th>\n",
              "      <th>work_type_Govt_job</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>Female</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>92.97</td>\n",
              "      <td>28.552632</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>AB</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>210</td>\n",
              "      <td>Male</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>91.54</td>\n",
              "      <td>31.400000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>242</td>\n",
              "      <td>Male</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>children</td>\n",
              "      <td>Urban</td>\n",
              "      <td>98.56</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>O</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>711</td>\n",
              "      <td>Male</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>92.96</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>AB</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>724</td>\n",
              "      <td>Male</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>81.77</td>\n",
              "      <td>44.700000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>O</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>495</th>\n",
              "      <td>72393</td>\n",
              "      <td>Female</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Govt_job</td>\n",
              "      <td>Rural</td>\n",
              "      <td>84.93</td>\n",
              "      <td>24.200000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>A</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>72491</td>\n",
              "      <td>Male</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>74.66</td>\n",
              "      <td>29.200000</td>\n",
              "      <td>smokes</td>\n",
              "      <td>O</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>72562</td>\n",
              "      <td>Female</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>64.37</td>\n",
              "      <td>32.800000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>AB</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>72792</td>\n",
              "      <td>Female</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>77.94</td>\n",
              "      <td>30.884000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>AB</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>72940</td>\n",
              "      <td>Female</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>children</td>\n",
              "      <td>Urban</td>\n",
              "      <td>102.92</td>\n",
              "      <td>17.600000</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>AB</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  gender  ...  work_type_Self-employed  work_type_children\n",
              "0       67  Female  ...                        0                   0\n",
              "1      210    Male  ...                        1                   0\n",
              "2      242    Male  ...                        0                   1\n",
              "3      711    Male  ...                        0                   0\n",
              "4      724    Male  ...                        0                   0\n",
              "..     ...     ...  ...                      ...                 ...\n",
              "495  72393  Female  ...                        0                   0\n",
              "496  72491    Male  ...                        0                   0\n",
              "497  72562  Female  ...                        0                   0\n",
              "498  72792  Female  ...                        0                   0\n",
              "499  72940  Female  ...                        0                   1\n",
              "\n",
              "[500 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 434
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E13U__mFIHvV",
        "outputId": "87f9d2ba-b257-4bc1-f58a-158f22097a59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "source": [
        "for i in categorical_features_oh:\n",
        "    df.drop(columns=i, axis=1, inplace=True)\n",
        "df"
      ],
      "execution_count": 435,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <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>ever_married</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>gender_Female</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>blood_A</th>\n",
              "      <th>blood_AB</th>\n",
              "      <th>blood_B</th>\n",
              "      <th>blood_O</th>\n",
              "      <th>smoking_status_Unknown</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "      <th>Residence_type_Rural</th>\n",
              "      <th>Residence_type_Unknown</th>\n",
              "      <th>Residence_type_Urban</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",
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              "  <tbody>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>210</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>91.54</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>242</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>98.56</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>711</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>92.96</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <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>495</th>\n",
              "      <td>72393</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>84.93</td>\n",
              "      <td>24.200000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>72491</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>74.66</td>\n",
              "      <td>29.200000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>72562</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>64.37</td>\n",
              "      <td>32.800000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>72792</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>77.94</td>\n",
              "      <td>30.884000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>72940</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>102.92</td>\n",
              "      <td>17.600000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id   age  ...  work_type_Self-employed  work_type_children\n",
              "0       67  17.0  ...                        0                   0\n",
              "1      210  81.0  ...                        1                   0\n",
              "2      242   4.0  ...                        0                   1\n",
              "3      711  81.0  ...                        0                   0\n",
              "4      724  17.0  ...                        0                   0\n",
              "..     ...   ...  ...                      ...                 ...\n",
              "495  72393  23.0  ...                        0                   0\n",
              "496  72491  53.0  ...                        0                   0\n",
              "497  72562  57.0  ...                        0                   0\n",
              "498  72792  53.0  ...                        0                   0\n",
              "499  72940   2.0  ...                        0                   1\n",
              "\n",
              "[500 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 435
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6615M5MIHvV"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "std = StandardScaler()\n",
        "columns = ['avg_glucose_level','bmi','age']\n",
        "scaled = std.fit_transform(df[columns])\n",
        "scaled = pd.DataFrame(scaled,columns=columns)\n",
        "df=df.drop(columns=columns,axis=1)"
      ],
      "execution_count": 436,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtqMfA1nIHvW",
        "outputId": "f84e27e7-5755-40de-de52-b74ba5f59c4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "source": [
        "df = df.reset_index(drop=True)\n",
        "df = df.merge(scaled, left_index=True, right_index=True, how = \"left\")\n",
        "df"
      ],
      "execution_count": 437,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
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              "    }\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>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>gender_Female</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>blood_A</th>\n",
              "      <th>blood_AB</th>\n",
              "      <th>blood_B</th>\n",
              "      <th>blood_O</th>\n",
              "      <th>smoking_status_Unknown</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "      <th>Residence_type_Rural</th>\n",
              "      <th>Residence_type_Unknown</th>\n",
              "      <th>Residence_type_Urban</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>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>age</th>\n",
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              "      <td>-0.288097</td>\n",
              "      <td>-0.071492</td>\n",
              "      <td>-1.162120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>210</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.318603</td>\n",
              "      <td>0.286883</td>\n",
              "      <td>1.711148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>242</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.168848</td>\n",
              "      <td>-1.462595</td>\n",
              "      <td>-1.745753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>711</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>-0.871045</td>\n",
              "      <td>1.711148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0</td>\n",
              "      <td>-0.527022</td>\n",
              "      <td>1.960843</td>\n",
              "      <td>-1.162120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <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",
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              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>-0.678697</td>\n",
              "      <td>0.009987</td>\n",
              "      <td>0.454093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>72562</td>\n",
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              "      <td>0</td>\n",
              "      <td>-0.898210</td>\n",
              "      <td>0.463089</td>\n",
              "      <td>0.633672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>72792</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>-0.608726</td>\n",
              "      <td>0.221938</td>\n",
              "      <td>0.454093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>72940</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>-0.075838</td>\n",
              "      <td>-1.450009</td>\n",
              "      <td>-1.835542</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  hypertension  heart_disease  ...  avg_glucose_level       bmi       age\n",
              "0       67             0              0  ...          -0.288097 -0.071492 -1.162120\n",
              "1      210             0              0  ...          -0.318603  0.286883  1.711148\n",
              "2      242             0              0  ...          -0.168848 -1.462595 -1.745753\n",
              "3      711             0              0  ...          -0.288311 -0.871045  1.711148\n",
              "4      724             0              0  ...          -0.527022  1.960843 -1.162120\n",
              "..     ...           ...            ...  ...                ...       ...       ...\n",
              "495  72393             0              0  ...          -0.459611 -0.619321 -0.892751\n",
              "496  72491             0              0  ...          -0.678697  0.009987  0.454093\n",
              "497  72562             0              0  ...          -0.898210  0.463089  0.633672\n",
              "498  72792             0              0  ...          -0.608726  0.221938  0.454093\n",
              "499  72940             0              0  ...          -0.075838 -1.450009 -1.835542\n",
              "\n",
              "[500 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 437
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v61CgcM0IHvW"
      },
      "source": [
        "dir_path = \"./Dataset/ouput_processed_test.csv\""
      ],
      "execution_count": 438,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4aD4kTWIHvW"
      },
      "source": [
        "df.to_csv(dir_path, index= False)"
      ],
      "execution_count": 439,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9xzd0ECIHvW",
        "outputId": "b077815c-6099-45d7-e81e-e49c56cc37e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "source": [
        "test_df = pd.read_csv(dir_path)\n",
        "test_df"
      ],
      "execution_count": 440,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>hypertension</th>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.318603</td>\n",
              "      <td>0.286883</td>\n",
              "      <td>1.711148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>242</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.168848</td>\n",
              "      <td>-1.462595</td>\n",
              "      <td>-1.745753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>711</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.288311</td>\n",
              "      <td>-0.871045</td>\n",
              "      <td>1.711148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>724</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.527022</td>\n",
              "      <td>1.960843</td>\n",
              "      <td>-1.162120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>72393</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.459611</td>\n",
              "      <td>-0.619321</td>\n",
              "      <td>-0.892751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>72491</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.678697</td>\n",
              "      <td>0.009987</td>\n",
              "      <td>0.454093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>72562</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.898210</td>\n",
              "      <td>0.463089</td>\n",
              "      <td>0.633672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>72792</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.608726</td>\n",
              "      <td>0.221938</td>\n",
              "      <td>0.454093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>72940</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.075838</td>\n",
              "      <td>-1.450009</td>\n",
              "      <td>-1.835542</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  hypertension  heart_disease  ...  avg_glucose_level       bmi       age\n",
              "0       67             0              0  ...          -0.288097 -0.071492 -1.162120\n",
              "1      210             0              0  ...          -0.318603  0.286883  1.711148\n",
              "2      242             0              0  ...          -0.168848 -1.462595 -1.745753\n",
              "3      711             0              0  ...          -0.288311 -0.871045  1.711148\n",
              "4      724             0              0  ...          -0.527022  1.960843 -1.162120\n",
              "..     ...           ...            ...  ...                ...       ...       ...\n",
              "495  72393             0              0  ...          -0.459611 -0.619321 -0.892751\n",
              "496  72491             0              0  ...          -0.678697  0.009987  0.454093\n",
              "497  72562             0              0  ...          -0.898210  0.463089  0.633672\n",
              "498  72792             0              0  ...          -0.608726  0.221938  0.454093\n",
              "499  72940             0              0  ...          -0.075838 -1.450009 -1.835542\n",
              "\n",
              "[500 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 440
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7h8VIeiIHvX"
      },
      "source": [
        "# test_df.drop(columns= 'Unnamed: 0', inplace=True)"
      ],
      "execution_count": 441,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDD4t9-9IHvX"
      },
      "source": [
        "x = test_df.copy()\n",
        "x.drop(columns='id', inplace=True)\n",
        "id_test = test_df['id']"
      ],
      "execution_count": 442,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJqlJmtfIHvX",
        "outputId": "be65b1f6-7277-47f5-fb08-ead08ed893db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "source": [
        "x"
      ],
      "execution_count": 443,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>gender_Female</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>blood_A</th>\n",
              "      <th>blood_AB</th>\n",
              "      <th>blood_B</th>\n",
              "      <th>blood_O</th>\n",
              "      <th>smoking_status_Unknown</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "      <th>Residence_type_Rural</th>\n",
              "      <th>Residence_type_Unknown</th>\n",
              "      <th>Residence_type_Urban</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>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.288097</td>\n",
              "      <td>-0.071492</td>\n",
              "      <td>-1.162120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.318603</td>\n",
              "      <td>0.286883</td>\n",
              "      <td>1.711148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.168848</td>\n",
              "      <td>-1.462595</td>\n",
              "      <td>-1.745753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.288311</td>\n",
              "      <td>-0.871045</td>\n",
              "      <td>1.711148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.527022</td>\n",
              "      <td>1.960843</td>\n",
              "      <td>-1.162120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.459611</td>\n",
              "      <td>-0.619321</td>\n",
              "      <td>-0.892751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.678697</td>\n",
              "      <td>0.009987</td>\n",
              "      <td>0.454093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.898210</td>\n",
              "      <td>0.463089</td>\n",
              "      <td>0.633672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.608726</td>\n",
              "      <td>0.221938</td>\n",
              "      <td>0.454093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.075838</td>\n",
              "      <td>-1.450009</td>\n",
              "      <td>-1.835542</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 25 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     hypertension  heart_disease  ...       bmi       age\n",
              "0               0              0  ... -0.071492 -1.162120\n",
              "1               0              0  ...  0.286883  1.711148\n",
              "2               0              0  ... -1.462595 -1.745753\n",
              "3               0              0  ... -0.871045  1.711148\n",
              "4               0              0  ...  1.960843 -1.162120\n",
              "..            ...            ...  ...       ...       ...\n",
              "495             0              0  ... -0.619321 -0.892751\n",
              "496             0              0  ...  0.009987  0.454093\n",
              "497             0              0  ...  0.463089  0.633672\n",
              "498             0              0  ...  0.221938  0.454093\n",
              "499             0              0  ... -1.450009 -1.835542\n",
              "\n",
              "[500 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 443
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pmWqNGImIHvX",
        "outputId": "2d7e5bab-52fa-429f-d727-e8be96f69118",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "y_xgb_pred_test = model_xgb.predict(x)"
      ],
      "execution_count": 444,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-444-6c55ca7e5a04>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_xgb_pred_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_xgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, data, output_margin, ntree_limit, validate_features)\u001b[0m\n\u001b[1;32m    789\u001b[0m                                                  \u001b[0moutput_margin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_margin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    790\u001b[0m                                                  \u001b[0mntree_limit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mntree_limit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 791\u001b[0;31m                                                  validate_features=validate_features)\n\u001b[0m\u001b[1;32m    792\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moutput_margin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    793\u001b[0m             \u001b[0;31m# If output_margin is active, simply return the scores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, data, output_margin, ntree_limit, pred_leaf, pred_contribs, approx_contribs, pred_interactions, validate_features)\u001b[0m\n\u001b[1;32m   1282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1283\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalidate_features\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1284\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1286\u001b[0m         \u001b[0mlength\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc_bst_ulong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36m_validate_features\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1689\u001b[0m                 raise ValueError(msg.format(self.feature_names,\n\u001b[0;32m-> 1690\u001b[0;31m                                             data.feature_names))\n\u001b[0m\u001b[1;32m   1691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1692\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_split_value_histogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_pandas\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: feature_names mismatch: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f20', 'f21', 'f22', 'f23', 'f24'] ['hypertension', 'heart_disease', 'ever_married', 'gender_Female', 'gender_Male', 'gender_Other', 'blood_A', 'blood_AB', 'blood_B', 'blood_O', 'smoking_status_Unknown', 'smoking_status_formerly smoked', 'smoking_status_never smoked', 'smoking_status_smokes', 'Residence_type_Rural', 'Residence_type_Unknown', 'Residence_type_Urban', 'work_type_Govt_job', 'work_type_Never_worked', 'work_type_Private', 'work_type_Self-employed', 'work_type_children', 'avg_glucose_level', 'bmi', 'age']\nexpected f10, f12, f11, f2, f20, f15, f24, f16, f3, f22, f21, f23, f17, f9, f7, f5, f19, f13, f18, f8, f0, f6, f4, f1, f14 in input data\ntraining data did not have the following fields: work_type_Govt_job, smoking_status_Unknown, Residence_type_Unknown, Residence_type_Rural, blood_A, smoking_status_never smoked, hypertension, heart_disease, avg_glucose_level, blood_AB, work_type_children, work_type_Never_worked, work_type_Self-employed, gender_Male, work_type_Private, Residence_type_Urban, smoking_status_smokes, bmi, smoking_status_formerly smoked, age, gender_Female, blood_O, ever_married, blood_B, gender_Other"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MQcVbL0IHvX"
      },
      "source": [
        "y_xgb_pred_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCugKS0cIHvY"
      },
      "source": [
        "data_test_submit = {'id': id_test, 'stroke': y_xgb_pred_test}\n",
        "df_test_submit = pd.DataFrame(data= data_test_submit)\n",
        "df_test_submit"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dhw4VhHrIHvY"
      },
      "source": [
        "df_test_submit.to_csv('./Dataset/Submission.csv', index= False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1BW0cU5AIHvY"
      },
      "source": [
        "## Save Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsgvpMAGIHvY"
      },
      "source": [
        "from model_utils import save_model, load_model\n",
        "save_model(model_xgb, './models/xgb')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}