XGBoost_diabetes_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5bab4a71",
   "metadata": {},
   "source": [
    "# Midterm Project: Diabetes Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b737d608",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c2b91e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"diabetes_prediction_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c94370f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>smoking_history</th>\n",
       "      <th>bmi</th>\n",
       "      <th>HbA1c_level</th>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>never</td>\n",
       "      <td>25.19</td>\n",
       "      <td>6.6</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Info</td>\n",
       "      <td>27.32</td>\n",
       "      <td>6.6</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>never</td>\n",
       "      <td>27.32</td>\n",
       "      <td>5.7</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>current</td>\n",
       "      <td>23.45</td>\n",
       "      <td>5.0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>current</td>\n",
       "      <td>20.14</td>\n",
       "      <td>4.8</td>\n",
       "      <td>155</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Info</td>\n",
       "      <td>27.32</td>\n",
       "      <td>6.2</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>Female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Info</td>\n",
       "      <td>17.37</td>\n",
       "      <td>6.5</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>Male</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>former</td>\n",
       "      <td>27.83</td>\n",
       "      <td>5.7</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>Female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>never</td>\n",
       "      <td>35.42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>Female</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>current</td>\n",
       "      <td>22.43</td>\n",
       "      <td>6.6</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender   age  hypertension  heart_disease smoking_history    bmi  \\\n",
       "0      Female  80.0             0              1           never  25.19   \n",
       "1      Female  54.0             0              0         No Info  27.32   \n",
       "2        Male  28.0             0              0           never  27.32   \n",
       "3      Female  36.0             0              0         current  23.45   \n",
       "4        Male  76.0             1              1         current  20.14   \n",
       "...       ...   ...           ...            ...             ...    ...   \n",
       "99995  Female  80.0             0              0         No Info  27.32   \n",
       "99996  Female   2.0             0              0         No Info  17.37   \n",
       "99997    Male  66.0             0              0          former  27.83   \n",
       "99998  Female  24.0             0              0           never  35.42   \n",
       "99999  Female  57.0             0              0         current  22.43   \n",
       "\n",
       "       HbA1c_level  blood_glucose_level  diabetes  \n",
       "0              6.6                  140         0  \n",
       "1              6.6                   80         0  \n",
       "2              5.7                  158         0  \n",
       "3              5.0                  155         0  \n",
       "4              4.8                  155         0  \n",
       "...            ...                  ...       ...  \n",
       "99995          6.2                   90         0  \n",
       "99996          6.5                  100         0  \n",
       "99997          5.7                  155         0  \n",
       "99998          4.0                  100         0  \n",
       "99999          6.6                   90         0  \n",
       "\n",
       "[100000 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "535631ec",
   "metadata": {},
   "source": [
    "## Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37ba7643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "smoking_history        0\n",
       "bmi                    0\n",
       "HbA1c_level            0\n",
       "blood_glucose_level    0\n",
       "diabetes               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a745a37e",
   "metadata": {},
   "source": [
    "## Checking the data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b2b255e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                  object\n",
       "age                    float64\n",
       "hypertension             int64\n",
       "heart_disease            int64\n",
       "smoking_history         object\n",
       "bmi                    float64\n",
       "HbA1c_level            float64\n",
       "blood_glucose_level      int64\n",
       "diabetes                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab3aa37",
   "metadata": {},
   "source": [
    "**Age should be changed to int**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb45beee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"age\"] = df[\"age\"].astype(\"int64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b6caf2",
   "metadata": {},
   "source": [
    "## Checking unique values for columns that might require cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b5c054",
   "metadata": {},
   "source": [
    "### For Column \"gender\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53de9b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    58552\n",
       "Male      41430\n",
       "Other        18\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0cd318c",
   "metadata": {},
   "source": [
    "#### Since the value of \"Other\" is very low, we will replace it with the mode of the column \"gender\" which is \"Female\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e667e32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"gender\"] = df[\"gender\"].replace({\"Other\":\"Female\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6ae48d",
   "metadata": {},
   "source": [
    "### For Column \"age\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83dfbb40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([80, 54, 28, 36, 76, 20, 44, 79, 42, 32, 53, 78, 67, 15, 37, 40,  5,\n",
       "       69, 72,  4, 30, 45, 43, 50, 41, 26, 34, 73, 77, 66, 29, 60, 38,  3,\n",
       "       57, 74, 19, 46, 21, 59, 27, 13, 56,  2,  7, 11,  6, 55,  9, 62, 47,\n",
       "       12, 68, 75, 22, 58, 18, 24, 17, 25,  0, 33, 16, 61, 31,  8, 49, 39,\n",
       "       65, 14, 70, 48, 51, 71, 64, 63, 52, 10, 35, 23,  1], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"age\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ec7921",
   "metadata": {},
   "source": [
    "### For Column \"smoking_history\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f7cc920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoking_history\n",
       "No Info        35816\n",
       "never          35095\n",
       "former          9352\n",
       "current         9286\n",
       "not current     6447\n",
       "ever            4004\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"smoking_history\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b46612",
   "metadata": {},
   "source": [
    "#### We can notice that we have a lot of NaN's and some entries need cleaning. \n",
    "1. **\"ever\" should be replaced with \"never\"**\n",
    "2. **\"not current\" should be replaced with \"former\"**\n",
    "3. **Rows containing \"No Info\" should be dropped since smoking is an important factor that influences the probability of having diabetes and replacing it with the mode or median would skew our results**\n",
    "4. **Rows the include a value of 0,1,2,3 for column \"age\" should be dropped.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a8fa821",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"smoking_history\"] = df[\"smoking_history\"].replace({\"ever\":\"never\",\"not current\":\"former\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ede018cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[(df[\"smoking_history\"]==\"No Info\")].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1273403c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoking_history\n",
       "never      39099\n",
       "former     15799\n",
       "current     9286\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"smoking_history\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "12c99788",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[(df[\"age\"].isin([0,1,2,3]))].index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3ce8dc",
   "metadata": {},
   "source": [
    "### **Reset the index**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea74cbe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e89bdb",
   "metadata": {},
   "source": [
    "### Saving the cleaned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b8152694",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"cleaned_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f668d504",
   "metadata": {},
   "source": [
    "## Exploring the dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824a6ec8",
   "metadata": {},
   "source": [
    "### 1. Plotting our target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ace8c2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='diabetes', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"diabetes\",data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583e7146",
   "metadata": {},
   "source": [
    "### We can notice that our dataset is imbalanced. So the XGBoost Classifier will be used since it tackles data imbalance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f90f314",
   "metadata": {},
   "source": [
    "### 2. Splitting the dataframe into numerical and categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8a3c655",
   "metadata": {},
   "outputs": [],
   "source": [
    "num = df.select_dtypes([\"int64\",\"float64\"])\n",
    "cat = df.select_dtypes(\"object\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c2a2f19",
   "metadata": {},
   "source": [
    "### 3. Checking Correlations between the independent numerical continuous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f1dca351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>bmi</th>\n",
       "      <th>HbA1c_level</th>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.19</td>\n",
       "      <td>6.6</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.32</td>\n",
       "      <td>5.7</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.45</td>\n",
       "      <td>5.0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20.14</td>\n",
       "      <td>4.8</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.32</td>\n",
       "      <td>6.6</td>\n",
       "      <td>85</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63706</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.34</td>\n",
       "      <td>6.5</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63707</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.69</td>\n",
       "      <td>3.5</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63708</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.83</td>\n",
       "      <td>5.7</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63709</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63710</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.43</td>\n",
       "      <td>6.6</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63711 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease    bmi  HbA1c_level  \\\n",
       "0       80             0              1  25.19          6.6   \n",
       "1       28             0              0  27.32          5.7   \n",
       "2       36             0              0  23.45          5.0   \n",
       "3       76             1              1  20.14          4.8   \n",
       "4       20             0              0  27.32          6.6   \n",
       "...    ...           ...            ...    ...          ...   \n",
       "63706   26             0              0  34.34          6.5   \n",
       "63707   40             0              0  40.69          3.5   \n",
       "63708   66             0              0  27.83          5.7   \n",
       "63709   24             0              0  35.42          4.0   \n",
       "63710   57             0              0  22.43          6.6   \n",
       "\n",
       "       blood_glucose_level  diabetes  \n",
       "0                      140         0  \n",
       "1                      158         0  \n",
       "2                      155         0  \n",
       "3                      155         0  \n",
       "4                       85         0  \n",
       "...                    ...       ...  \n",
       "63706                  160         0  \n",
       "63707                  155         0  \n",
       "63708                  155         0  \n",
       "63709                  100         0  \n",
       "63710                   90         0  \n",
       "\n",
       "[63711 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a2097443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>HbA1c_level</th>\n",
       "      <th>blood_glucose_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.141985</td>\n",
       "      <td>0.115796</td>\n",
       "      <td>0.117020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.141985</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.084350</td>\n",
       "      <td>0.094415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HbA1c_level</th>\n",
       "      <td>0.115796</td>\n",
       "      <td>0.084350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <td>0.117020</td>\n",
       "      <td>0.094415</td>\n",
       "      <td>0.195586</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          age       bmi  HbA1c_level  blood_glucose_level\n",
       "age                  1.000000  0.141985     0.115796             0.117020\n",
       "bmi                  0.141985  1.000000     0.084350             0.094415\n",
       "HbA1c_level          0.115796  0.084350     1.000000             0.195586\n",
       "blood_glucose_level  0.117020  0.094415     0.195586             1.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = num.loc[:,[\"age\",\"bmi\",\"HbA1c_level\",\"blood_glucose_level\"]].corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50294cb4",
   "metadata": {},
   "source": [
    "### 4. Plotting the heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5605f8da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = np.triu(corr)\n",
    "np.fill_diagonal(matrix,False)\n",
    "sns.heatmap(corr, annot=True, mask=matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4cbb50f",
   "metadata": {},
   "source": [
    "#### Conclusion: No independent numerical variables are highly correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c3788a4",
   "metadata": {},
   "source": [
    "### 5. Checking the distribution of the continuous numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6ad704e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in num.columns[3:6]:\n",
    "    sns.displot(num[column], kde=True)\n",
    "    plt.title(column)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f19572",
   "metadata": {},
   "source": [
    "**Our independent variables do not follow a normal distribution. So, the MinMaxScaler will be used.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076e71e4",
   "metadata": {},
   "source": [
    "## Transforming the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1f7b34",
   "metadata": {},
   "source": [
    "**1. Creating dummies for the categorical columns \"gender\" and \"smoking_histroy\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70dd49a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = pd.get_dummies(cat, dtype=\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2402bca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>smoking_history_current</th>\n",
       "      <th>smoking_history_former</th>\n",
       "      <th>smoking_history_never</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63706</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63707</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63708</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63709</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63710</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63711 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender_Female  gender_Male  smoking_history_current  \\\n",
       "0                  1            0                        0   \n",
       "1                  0            1                        0   \n",
       "2                  1            0                        1   \n",
       "3                  0            1                        1   \n",
       "4                  1            0                        0   \n",
       "...              ...          ...                      ...   \n",
       "63706              1            0                        0   \n",
       "63707              1            0                        0   \n",
       "63708              0            1                        0   \n",
       "63709              1            0                        0   \n",
       "63710              1            0                        1   \n",
       "\n",
       "       smoking_history_former  smoking_history_never  \n",
       "0                           0                      1  \n",
       "1                           0                      1  \n",
       "2                           0                      0  \n",
       "3                           0                      0  \n",
       "4                           0                      1  \n",
       "...                       ...                    ...  \n",
       "63706                       0                      1  \n",
       "63707                       0                      1  \n",
       "63708                       1                      0  \n",
       "63709                       0                      1  \n",
       "63710                       0                      0  \n",
       "\n",
       "[63711 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b52f100",
   "metadata": {},
   "source": [
    "**2. Concat our numerical and categorical data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f8b58828",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.concat([num,cat],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "83384272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>bmi</th>\n",
       "      <th>HbA1c_level</th>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>smoking_history_current</th>\n",
       "      <th>smoking_history_former</th>\n",
       "      <th>smoking_history_never</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.19</td>\n",
       "      <td>6.6</td>\n",
       "      <td>140</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.32</td>\n",
       "      <td>5.7</td>\n",
       "      <td>158</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.45</td>\n",
       "      <td>5.0</td>\n",
       "      <td>155</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20.14</td>\n",
       "      <td>4.8</td>\n",
       "      <td>155</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.32</td>\n",
       "      <td>6.6</td>\n",
       "      <td>85</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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63706</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.34</td>\n",
       "      <td>6.5</td>\n",
       "      <td>160</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63707</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.69</td>\n",
       "      <td>3.5</td>\n",
       "      <td>155</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63708</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.83</td>\n",
       "      <td>5.7</td>\n",
       "      <td>155</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63709</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63710</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.43</td>\n",
       "      <td>6.6</td>\n",
       "      <td>90</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63711 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease    bmi  HbA1c_level  \\\n",
       "0       80             0              1  25.19          6.6   \n",
       "1       28             0              0  27.32          5.7   \n",
       "2       36             0              0  23.45          5.0   \n",
       "3       76             1              1  20.14          4.8   \n",
       "4       20             0              0  27.32          6.6   \n",
       "...    ...           ...            ...    ...          ...   \n",
       "63706   26             0              0  34.34          6.5   \n",
       "63707   40             0              0  40.69          3.5   \n",
       "63708   66             0              0  27.83          5.7   \n",
       "63709   24             0              0  35.42          4.0   \n",
       "63710   57             0              0  22.43          6.6   \n",
       "\n",
       "       blood_glucose_level  diabetes  gender_Female  gender_Male  \\\n",
       "0                      140         0              1            0   \n",
       "1                      158         0              0            1   \n",
       "2                      155         0              1            0   \n",
       "3                      155         0              0            1   \n",
       "4                       85         0              1            0   \n",
       "...                    ...       ...            ...          ...   \n",
       "63706                  160         0              1            0   \n",
       "63707                  155         0              1            0   \n",
       "63708                  155         0              0            1   \n",
       "63709                  100         0              1            0   \n",
       "63710                   90         0              1            0   \n",
       "\n",
       "       smoking_history_current  smoking_history_former  smoking_history_never  \n",
       "0                            0                       0                      1  \n",
       "1                            0                       0                      1  \n",
       "2                            1                       0                      0  \n",
       "3                            1                       0                      0  \n",
       "4                            0                       0                      1  \n",
       "...                        ...                     ...                    ...  \n",
       "63706                        0                       0                      1  \n",
       "63707                        0                       0                      1  \n",
       "63708                        0                       1                      0  \n",
       "63709                        0                       0                      1  \n",
       "63710                        1                       0                      0  \n",
       "\n",
       "[63711 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c673382c",
   "metadata": {},
   "source": [
    "### Dropping the target variable and storing it into a seperate dataframe (y)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ab0a8813",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_new[[\"diabetes\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2df70e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_new.drop(columns=\"diabetes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06436b1d",
   "metadata": {},
   "source": [
    "## Splitting the data into test and train and applying XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a38a15d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea230132",
   "metadata": {},
   "source": [
    "## Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ac08af61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37210d3",
   "metadata": {},
   "source": [
    "## Creating the model using XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7751df9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0ff261e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-aucpr:0.86584\n",
      "[1]\tvalidation_0-aucpr:0.87085\n",
      "[2]\tvalidation_0-aucpr:0.87324\n",
      "[3]\tvalidation_0-aucpr:0.87277\n",
      "[4]\tvalidation_0-aucpr:0.87433\n",
      "[5]\tvalidation_0-aucpr:0.87574\n",
      "[6]\tvalidation_0-aucpr:0.87655\n",
      "[7]\tvalidation_0-aucpr:0.87652\n",
      "[8]\tvalidation_0-aucpr:0.87846\n",
      "[9]\tvalidation_0-aucpr:0.87932\n",
      "[10]\tvalidation_0-aucpr:0.88513\n",
      "[11]\tvalidation_0-aucpr:0.88491\n",
      "[12]\tvalidation_0-aucpr:0.88567\n",
      "[13]\tvalidation_0-aucpr:0.88636\n",
      "[14]\tvalidation_0-aucpr:0.88601\n",
      "[15]\tvalidation_0-aucpr:0.88646\n",
      "[16]\tvalidation_0-aucpr:0.88594\n",
      "[17]\tvalidation_0-aucpr:0.88613\n",
      "[18]\tvalidation_0-aucpr:0.88587\n",
      "[19]\tvalidation_0-aucpr:0.88428\n",
      "[20]\tvalidation_0-aucpr:0.88414\n",
      "[21]\tvalidation_0-aucpr:0.88329\n",
      "[22]\tvalidation_0-aucpr:0.88322\n",
      "[23]\tvalidation_0-aucpr:0.88339\n",
      "[24]\tvalidation_0-aucpr:0.88342\n",
      "[25]\tvalidation_0-aucpr:0.88347\n"
     ]
    }
   ],
   "source": [
    "XG_boost = XGBClassifier(scale_pos_weight= 100, objective = \"binary:logistic\", n_estimators=500, eval_metric='aucpr',\n",
    "                         early_stopping_rounds=10, max_depth=8, random_state=42)\n",
    "\n",
    "model = XG_boost.fit(X_train_scaled, y_train, eval_set=[(X_test_scaled, y_test)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea13fcc5",
   "metadata": {},
   "source": [
    "### Generating our predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d884ddda",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e32be9",
   "metadata": {},
   "source": [
    "### Checking our accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "00752c0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 75.00784765093648 %\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\",(accuracy * 100.0),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad67b979",
   "metadata": {},
   "source": [
    "### Printing a classification report and confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8f16a09c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b59cab2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.72      0.84     17035\n",
      "           1       0.30      0.99      0.46      2079\n",
      "\n",
      "    accuracy                           0.75     19114\n",
      "   macro avg       0.65      0.85      0.65     19114\n",
      "weighted avg       0.92      0.75      0.80     19114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rep = classification_report(y_test,y_pred)\n",
    "print(rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "721bc06f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred, labels=XG_boost.classes_)\n",
    "color = 'white'\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=XG_boost.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0503cde3",
   "metadata": {},
   "source": [
    "### Even though this lead to our model accuracy being low, we focused on decreasing the value of false negatives as much as possible (increasing recall) because falsely diagnosing patients who indeed have diabetes is very dangerous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0df364cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "probs = model.predict_proba(X_test_scaled)\n",
    "preds = probs[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}