Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0f38f91e",
   "metadata": {},
   "source": [
    "The Diabetes prediction dataset is a collection of medical and demographic data from patients, along with their diabetes status (positive or negative). The data includes features such as age, gender, body mass index (BMI), hypertension, heart disease, smoking history, HbA1c level, and blood glucose level. This dataset can be used to build machine learning models to predict diabetes in patients based on their medical history and demographic information. This can be useful for healthcare professionals in identifying patients who may be at risk of developing diabetes and in developing personalized treatment plans. Additionally, the dataset can be used by researchers to explore the relationships between various medical and demographic factors and the likelihood of developing diabetes.\n",
    "\n",
    "https://www.kaggle.com/datasets/iammustafatz/diabetes-prediction-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8490b3b",
   "metadata": {},
   "source": [
    "Column description:\n",
    "\n",
    "1) gender: Gender refers to the biological sex of the individual, which can have an impact on their susceptibility to diabetes. There are three categories in it male ,female and other.\n",
    "\n",
    "2) age: Age is an important factor as diabetes is more commonly diagnosed in older adults.Age ranges from 0-80 in our dataset.\n",
    "\n",
    "3) hypertension: Hypertension is a medical condition in which the blood pressure in the arteries is persistently elevated. It has values a 0 or 1 where 0 indicates they don’t have hypertension and for 1 it means they have hypertension.\n",
    "\n",
    "4) heart_diesease: Heart disease is another medical condition that is associated with an increased risk of developing diabetes. It has values a 0 or 1 where 0 indicates they don’t have heart disease and for 1 it means they have heart disease.\n",
    "\n",
    "5) smoking_history: Smoking history is also considered a risk factor for diabetes and can exacerbate the complications associated with diabetes.In our dataset we have 5 categories i.e not current,former,No Info,current,never and ever.\n",
    "\n",
    "6) bmi: BMI (Body Mass Index) is a measure of body fat based on weight and height. Higher BMI values are linked to a higher risk of diabetes. The range of BMI in the dataset is from 10.16 to 71.55. BMI less than 18.5 is underweight, 18.5-24.9 is normal, 25-29.9 is overweight, and 30 or more is obese.\n",
    "\n",
    "7) HbA1c_level: HbA1c (Hemoglobin A1c) level is a measure of a person's average blood sugar level over the past 2-3 months. Higher levels indicate a greater risk of developing diabetes. Mostly more than 6.5% of HbA1c Level indicates diabetes.\n",
    "\n",
    "8) blood_glucose_level: Blood glucose level refers to the amount of glucose in the bloodstream at a given time. High blood glucose levels are a key indicator of diabetes.\n",
    "\n",
    "9) diabetes: Diabetes is the target variable being predicted, with values of 1 indicating the presence of diabetes and 0 indicating the absence of diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "84a5860b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17439c84",
   "metadata": {},
   "source": [
    "# Reading .csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "230a2c08",
   "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</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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/diabetes_prediction_dataset.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f32283a",
   "metadata": {},
   "source": [
    "# Exploring data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "80cc86f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 9)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1593f7d1",
   "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": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "cbd861d5",
   "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": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "db84237c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   gender               100000 non-null  object \n",
      " 1   age                  100000 non-null  float64\n",
      " 2   hypertension         100000 non-null  int64  \n",
      " 3   heart_disease        100000 non-null  int64  \n",
      " 4   smoking_history      100000 non-null  object \n",
      " 5   bmi                  100000 non-null  float64\n",
      " 6   HbA1c_level          100000 non-null  float64\n",
      " 7   blood_glucose_level  100000 non-null  int64  \n",
      " 8   diabetes             100000 non-null  int64  \n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6d5b3a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'age', 'hypertension', 'heart_disease', 'smoking_history',\n",
       "       'bmi', 'HbA1c_level', 'blood_glucose_level', 'diabetes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5d93b4f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming columns\n",
    "\n",
    "data.columns = ['gender', 'age', 'hypertension', \n",
    "                'heart_disease', 'smoking_history','bmi_score', \n",
    "                'haemoglobin_level', 'blood_glucose_level', 'is_diabetic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "054e0ccd",
   "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</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>smoking_history</th>\n",
       "      <th>bmi_score</th>\n",
       "      <th>haemoglobin_level</th>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <th>is_diabetic</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease smoking_history  bmi_score  \\\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",
       "   haemoglobin_level  blood_glucose_level  is_diabetic  \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  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "481815fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "79bb902c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    58552\n",
       "Male      41430\n",
       "Other        18\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gender\n",
    "\n",
    "data.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "210d937f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.00    5621\n",
       "51.00    1619\n",
       "47.00    1574\n",
       "48.00    1568\n",
       "53.00    1542\n",
       "         ... \n",
       "0.48       83\n",
       "1.00       83\n",
       "0.40       66\n",
       "0.16       59\n",
       "0.08       36\n",
       "Name: age, Length: 102, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# age\n",
    "\n",
    "data.age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "189ded42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.0\n",
      "0.08\n"
     ]
    }
   ],
   "source": [
    "print(data.age.max())\n",
    "print(data.age.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe22dce",
   "metadata": {},
   "source": [
    "🎯 Decision: Converting age from float into int. Datatype of Age as float is illogical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c8aba754",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.age = data.age.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "221946d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.age.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5c01102a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80    5621\n",
       "51    1619\n",
       "47    1574\n",
       "48    1568\n",
       "53    1542\n",
       "      ... \n",
       "76     733\n",
       "77     730\n",
       "75     721\n",
       "78     682\n",
       "79     621\n",
       "Name: age, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "359b17a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(data.age.max())\n",
    "print(data.age.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "41599cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    92515\n",
       "1     7485\n",
       "Name: hypertension, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hypertension\n",
    "\n",
    "data.hypertension.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "1944550b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    96058\n",
       "1     3942\n",
       "Name: heart_disease, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# heart_disease\n",
    "\n",
    "data.heart_disease.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9d694469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Info        35816\n",
       "never          35095\n",
       "former          9352\n",
       "current         9286\n",
       "not current     6447\n",
       "ever            4004\n",
       "Name: smoking_history, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# smoking_history\n",
    "\n",
    "data.smoking_history.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37096f3",
   "metadata": {},
   "source": [
    "🎯 Decision: Some on the values in smoking history are illogical. e.g not current, ever. \n",
    "          Hence combining not current into former, ever into current."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f7d88fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaning Smoking_history column\n",
    "\n",
    "def clean_smoking_history(value):\n",
    "    \n",
    "    if value == \"No Info\":\n",
    "        return \"no info\"\n",
    "    elif value == \"ever\":\n",
    "        return \"current\"\n",
    "    elif value == \"not current\":\n",
    "        return \"former\"\n",
    "    else:\n",
    "        return value\n",
    "    \n",
    "data.smoking_history = data.smoking_history.apply(clean_smoking_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "94616800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no info    35816\n",
       "never      35095\n",
       "former     15799\n",
       "current    13290\n",
       "Name: smoking_history, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.smoking_history.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "d73211ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.32    25495\n",
       "23.00      103\n",
       "27.12      101\n",
       "27.80      100\n",
       "24.96      100\n",
       "         ...  \n",
       "58.23        1\n",
       "48.18        1\n",
       "55.57        1\n",
       "57.07        1\n",
       "60.52        1\n",
       "Name: bmi_score, Length: 4247, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bmi_score\n",
    "\n",
    "data.bmi_score.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "93fb2c8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.69\n",
      "10.01\n"
     ]
    }
   ],
   "source": [
    "print(data.bmi_score.max())\n",
    "print(data.bmi_score.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8169b3b0",
   "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</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>smoking_history</th>\n",
       "      <th>bmi_score</th>\n",
       "      <th>haemoglobin_level</th>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <th>is_diabetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>80</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</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</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</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</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  age  hypertension  heart_disease smoking_history  bmi_score  \\\n",
       "0  Female   80             0              1           never      25.19   \n",
       "1  Female   54             0              0         no info      27.32   \n",
       "2    Male   28             0              0           never      27.32   \n",
       "3  Female   36             0              0         current      23.45   \n",
       "4    Male   76             1              1         current      20.14   \n",
       "\n",
       "   haemoglobin_level  blood_glucose_level  is_diabetic  \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  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "5507b62a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.6    8540\n",
       "5.7    8413\n",
       "6.5    8362\n",
       "5.8    8321\n",
       "6.0    8295\n",
       "6.2    8269\n",
       "6.1    8048\n",
       "3.5    7662\n",
       "4.8    7597\n",
       "4.5    7585\n",
       "4.0    7542\n",
       "5.0    7471\n",
       "8.8     661\n",
       "8.2     661\n",
       "9.0     654\n",
       "7.5     643\n",
       "6.8     642\n",
       "7.0     634\n",
       "Name: haemoglobin_level, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# haemoglobin_level\n",
    "\n",
    "data.haemoglobin_level.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "7b1f45a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.0\n",
      "3.5\n"
     ]
    }
   ],
   "source": [
    "print(data.haemoglobin_level.max())\n",
    "print(data.haemoglobin_level.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "2eef3d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130    7794\n",
       "159    7759\n",
       "140    7732\n",
       "160    7712\n",
       "126    7702\n",
       "145    7679\n",
       "200    7600\n",
       "155    7575\n",
       "90     7112\n",
       "80     7106\n",
       "158    7026\n",
       "100    7025\n",
       "85     6901\n",
       "280     729\n",
       "300     674\n",
       "240     636\n",
       "260     635\n",
       "220     603\n",
       "Name: blood_glucose_level, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# blood_glucose_level\n",
    "\n",
    "data.blood_glucose_level.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "270a9148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300\n",
      "80\n"
     ]
    }
   ],
   "source": [
    "print(data.blood_glucose_level.max())\n",
    "print(data.blood_glucose_level.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d5e81f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    91500\n",
       "1     8500\n",
       "Name: is_diabetic, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is_diabetic\n",
    "\n",
    "data.is_diabetic.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66e3c3c1",
   "metadata": {},
   "source": [
    "💡Insight: This dataset contains 91500 records of nondiabetic people and 8500 records of diabetic people.\n",
    "            This results that our data is imbalanced, so our model will also is trained for looking at people with no diabtees sign.\n",
    "            Hence it will not be efficient model.\n",
    "            Therefore the dataset has to be balanced using any one of the techniques Over sampling/Undersampling/SMOTE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "f4d6bf38",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yl/blp7v0ys1lzdfkls51wcv3l40000gn/T/ipykernel_90388/2627137660.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  data.corr()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>bmi_score</th>\n",
       "      <th>haemoglobin_level</th>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <th>is_diabetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.251093</td>\n",
       "      <td>0.233254</td>\n",
       "      <td>0.337747</td>\n",
       "      <td>0.101328</td>\n",
       "      <td>0.110631</td>\n",
       "      <td>0.257933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>0.251093</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.121262</td>\n",
       "      <td>0.147666</td>\n",
       "      <td>0.080939</td>\n",
       "      <td>0.084429</td>\n",
       "      <td>0.197823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_disease</th>\n",
       "      <td>0.233254</td>\n",
       "      <td>0.121262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061198</td>\n",
       "      <td>0.067589</td>\n",
       "      <td>0.070066</td>\n",
       "      <td>0.171727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi_score</th>\n",
       "      <td>0.337747</td>\n",
       "      <td>0.147666</td>\n",
       "      <td>0.061198</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082997</td>\n",
       "      <td>0.091261</td>\n",
       "      <td>0.214357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>haemoglobin_level</th>\n",
       "      <td>0.101328</td>\n",
       "      <td>0.080939</td>\n",
       "      <td>0.067589</td>\n",
       "      <td>0.082997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166733</td>\n",
       "      <td>0.400660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood_glucose_level</th>\n",
       "      <td>0.110631</td>\n",
       "      <td>0.084429</td>\n",
       "      <td>0.070066</td>\n",
       "      <td>0.091261</td>\n",
       "      <td>0.166733</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.419558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_diabetic</th>\n",
       "      <td>0.257933</td>\n",
       "      <td>0.197823</td>\n",
       "      <td>0.171727</td>\n",
       "      <td>0.214357</td>\n",
       "      <td>0.400660</td>\n",
       "      <td>0.419558</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          age  hypertension  heart_disease  bmi_score  \\\n",
       "age                  1.000000      0.251093       0.233254   0.337747   \n",
       "hypertension         0.251093      1.000000       0.121262   0.147666   \n",
       "heart_disease        0.233254      0.121262       1.000000   0.061198   \n",
       "bmi_score            0.337747      0.147666       0.061198   1.000000   \n",
       "haemoglobin_level    0.101328      0.080939       0.067589   0.082997   \n",
       "blood_glucose_level  0.110631      0.084429       0.070066   0.091261   \n",
       "is_diabetic          0.257933      0.197823       0.171727   0.214357   \n",
       "\n",
       "                     haemoglobin_level  blood_glucose_level  is_diabetic  \n",
       "age                           0.101328             0.110631     0.257933  \n",
       "hypertension                  0.080939             0.084429     0.197823  \n",
       "heart_disease                 0.067589             0.070066     0.171727  \n",
       "bmi_score                     0.082997             0.091261     0.214357  \n",
       "haemoglobin_level             1.000000             0.166733     0.400660  \n",
       "blood_glucose_level           0.166733             1.000000     0.419558  \n",
       "is_diabetic                   0.400660             0.419558     1.000000  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "ef6bc472",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yl/blp7v0ys1lzdfkls51wcv3l40000gn/T/ipykernel_90388/160651264.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr_matrix = data.corr()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = data.corr()\n",
    "sns.heatmap(corr_matrix, annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24d62a4",
   "metadata": {},
   "source": [
    "Observation: Feature haemoglobin_level, blood_glucose_level has some correlation with target (is_diabetic). \n",
    "    Whereas age, hypertension, heart_disease, bmi_score has almost no correlation with target (is_diabetic).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "fa61eac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting hypertension and heart_disease into object datatype. \n",
    "\n",
    "data.hypertension = np.where((data.hypertension == 1), \"Yes\", \"No\")\n",
    "data.heart_disease = np.where((data.heart_disease == 1), \"Yes\", \"No\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "23dc74ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   gender               100000 non-null  object \n",
      " 1   age                  100000 non-null  int64  \n",
      " 2   hypertension         100000 non-null  object \n",
      " 3   heart_disease        100000 non-null  object \n",
      " 4   smoking_history      100000 non-null  object \n",
      " 5   bmi_score            100000 non-null  float64\n",
      " 6   haemoglobin_level    100000 non-null  float64\n",
      " 7   blood_glucose_level  100000 non-null  int64  \n",
      " 8   is_diabetic          100000 non-null  int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "6bb5857c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting cleaned data into .csv file\n",
    "\n",
    "data.to_csv(\"data/diabetes_clean.csv\", index = False)"
   ]
  }
 ],
 "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
}