Diabetes_Risk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Early Stage Diabetes Risk in Indivisuals using Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Scources\n",
    "* ### [https://archive.ics.uci.edu/ml/datasets/Early+stage+diabetes+risk+prediction+dataset]()\n",
    "* ### [https://archive.ics.uci.edu/ml/machine-learning-databases/00529/]()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Preprocessing and Visualizing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem at Hand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importng Machine Learning libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# For Metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Gender Polyuria Polydipsia sudden weight loss weakness Polyphagia  \\\n",
       "0   40   Male       No        Yes                 No      Yes         No   \n",
       "1   58   Male       No         No                 No      Yes         No   \n",
       "2   41   Male      Yes         No                 No      Yes        Yes   \n",
       "3   45   Male       No         No                Yes      Yes        Yes   \n",
       "4   60   Male      Yes        Yes                Yes      Yes        Yes   \n",
       "\n",
       "  Genital thrush visual blurring Itching Irritability delayed healing  \\\n",
       "0             No              No     Yes           No             Yes   \n",
       "1             No             Yes      No           No              No   \n",
       "2             No              No     Yes           No             Yes   \n",
       "3            Yes              No     Yes           No             Yes   \n",
       "4             No             Yes     Yes          Yes             Yes   \n",
       "\n",
       "  partial paresis muscle stiffness Alopecia Obesity     class  \n",
       "0              No              Yes      Yes     Yes  Positive  \n",
       "1             Yes               No      Yes      No  Positive  \n",
       "2              No              Yes      Yes      No  Positive  \n",
       "3              No               No       No      No  Positive  \n",
       "4             Yes              Yes      Yes     Yes  Positive  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"./data/diabetes_data_upload.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                   0\n",
       "Gender                0\n",
       "Polyuria              0\n",
       "Polydipsia            0\n",
       "sudden weight loss    0\n",
       "weakness              0\n",
       "Polyphagia            0\n",
       "Genital thrush        0\n",
       "visual blurring       0\n",
       "Itching               0\n",
       "Irritability          0\n",
       "delayed healing       0\n",
       "partial paresis       0\n",
       "muscle stiffness      0\n",
       "Alopecia              0\n",
       "Obesity               0\n",
       "class                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Type of data in the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                    int64\n",
       "Gender                object\n",
       "Polyuria              object\n",
       "Polydipsia            object\n",
       "sudden weight loss    object\n",
       "weakness              object\n",
       "Polyphagia            object\n",
       "Genital thrush        object\n",
       "visual blurring       object\n",
       "Itching               object\n",
       "Irritability          object\n",
       "delayed healing       object\n",
       "partial paresis       object\n",
       "muscle stiffness      object\n",
       "Alopecia              object\n",
       "Obesity               object\n",
       "class                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'polyuria', 'polydipsia', 'sudden weight loss',\n",
       "       'weakness', 'polyphagia', 'genital thrush', 'visual blurring',\n",
       "       'itching', 'irritability', 'delayed healing', 'partial paresis',\n",
       "       'muscle stiffness', 'alopecia', 'obesity', 'class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=df.columns.str.lower().str.replace(\" \",\"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'polyuria', 'polydipsia', 'sudden_weight_loss',\n",
       "       'weakness', 'polyphagia', 'genital_thrush', 'visual_blurring',\n",
       "       'itching', 'irritability', 'delayed_healing', 'partial_paresis',\n",
       "       'muscle_stiffness', 'alopecia', 'obesity', 'class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['polyuria',\n",
       " 'polydipsia',\n",
       " 'sudden_weight_loss',\n",
       " 'weakness',\n",
       " 'polyphagia',\n",
       " 'genital_thrush',\n",
       " 'visual_blurring',\n",
       " 'itching',\n",
       " 'irritability',\n",
       " 'delayed_healing',\n",
       " 'partial_paresis',\n",
       " 'muscle_stiffness',\n",
       " 'alopecia',\n",
       " 'obesity']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_enc=list((df.columns)[2:-1])\n",
    "label_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in label_enc:\n",
    "    df[col]=le.fit_transform(df[col].astype(\"str\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encoded(li):\n",
    "    for i in range(len(li)):\n",
    "        print(f\"{li[i]} = {i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the overall dataset\n",
      "No = 0\n",
      "Yes = 1\n"
     ]
    }
   ],
   "source": [
    "print(\"For the overall dataset\")\n",
    "encoded(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "le_gender=LabelEncoder()\n",
    "le_class=LabelEncoder()\n",
    "df['gender']=le_gender.fit_transform(df[\"gender\"].astype(\"str\"))\n",
    "df['class']=le_class.fit_transform(df[\"class\"].astype(\"str\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Diabetes test : \n",
      "Negative = 0\n",
      "Positive = 1\n"
     ]
    }
   ],
   "source": [
    "print(\"For Diabetes test : \")\n",
    "encoded(le_class.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the Gender : \n",
      "Female = 0\n",
      "Male = 1\n"
     ]
    }
   ],
   "source": [
    "print(\"For the Gender : \")\n",
    "encoded(le_gender.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                   int64\n",
       "gender                int32\n",
       "polyuria              int32\n",
       "polydipsia            int32\n",
       "sudden_weight_loss    int32\n",
       "weakness              int32\n",
       "polyphagia            int32\n",
       "genital_thrush        int32\n",
       "visual_blurring       int32\n",
       "itching               int32\n",
       "irritability          int32\n",
       "delayed_healing       int32\n",
       "partial_paresis       int32\n",
       "muscle_stiffness      int32\n",
       "alopecia              int32\n",
       "obesity               int32\n",
       "class                 int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "## Analyzing and Plotting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  polyuria  polydipsia  sudden_weight_loss  weakness  \\\n",
       "0   40       1         0           1                   0         1   \n",
       "1   58       1         0           0                   0         1   \n",
       "2   41       1         1           0                   0         1   \n",
       "3   45       1         0           0                   1         1   \n",
       "4   60       1         1           1                   1         1   \n",
       "\n",
       "   polyphagia  genital_thrush  visual_blurring  itching  irritability  \\\n",
       "0           0               0                0        1             0   \n",
       "1           0               0                1        0             0   \n",
       "2           1               0                0        1             0   \n",
       "3           1               1                0        1             0   \n",
       "4           1               0                1        1             1   \n",
       "\n",
       "   delayed_healing  partial_paresis  muscle_stiffness  alopecia  obesity  \\\n",
       "0                1                0                 1         1        1   \n",
       "1                0                1                 0         1        0   \n",
       "2                1                0                 1         1        0   \n",
       "3                1                0                 0         0        0   \n",
       "4                1                1                 1         1        1   \n",
       "\n",
       "   class  \n",
       "0      1  \n",
       "1      1  \n",
       "2      1  \n",
       "3      1  \n",
       "4      1  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.028846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.151466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age\n",
       "count  520.000000\n",
       "mean    48.028846\n",
       "std     12.151466\n",
       "min     16.000000\n",
       "25%     39.000000\n",
       "50%     47.500000\n",
       "75%     57.000000\n",
       "max     90.000000"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().iloc[:,0].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>gender</th>\n",
       "      <th>polyuria</th>\n",
       "      <th>polydipsia</th>\n",
       "      <th>sudden_weight_loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>polyphagia</th>\n",
       "      <th>genital_thrush</th>\n",
       "      <th>visual_blurring</th>\n",
       "      <th>itching</th>\n",
       "      <th>irritability</th>\n",
       "      <th>delayed_healing</th>\n",
       "      <th>partial_paresis</th>\n",
       "      <th>muscle_stiffness</th>\n",
       "      <th>alopecia</th>\n",
       "      <th>obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>40</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>45</td>\n",
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       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  polyuria  polydipsia  sudden_weight_loss  weakness  \\\n",
       "0   40       1         0           1                   0         1   \n",
       "1   58       1         0           0                   0         1   \n",
       "2   41       1         1           0                   0         1   \n",
       "3   45       1         0           0                   1         1   \n",
       "4   60       1         1           1                   1         1   \n",
       "\n",
       "   polyphagia  genital_thrush  visual_blurring  itching  irritability  \\\n",
       "0           0               0                0        1             0   \n",
       "1           0               0                1        0             0   \n",
       "2           1               0                0        1             0   \n",
       "3           1               1                0        1             0   \n",
       "4           1               0                1        1             1   \n",
       "\n",
       "   delayed_healing  partial_paresis  muscle_stiffness  alopecia  obesity  \\\n",
       "0                1                0                 1         1        1   \n",
       "1                0                1                 0         1        0   \n",
       "2                1                0                 1         1        0   \n",
       "3                1                0                 0         0        0   \n",
       "4                1                1                 1         1        1   \n",
       "\n",
       "   class  \n",
       "0      1  \n",
       "1      1  \n",
       "2      1  \n",
       "3      1  \n",
       "4      1  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative = 0\n",
      "Positive = 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df[\"class\"])\n",
    "plt.title(\"Distribution of data as per class\")\n",
    "plt.tight_layout()\n",
    "encoded(le_class.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female = 0\n",
      "Male = 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df['gender'])\n",
    "plt.title(\"Ratio of Gender in Dataset\")\n",
    "plt.tight_layout()\n",
    "encoded(le_gender.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max 90\n",
      "Min 16\n"
     ]
    }
   ],
   "source": [
    "#Finding the min and max age for Diabetes.\n",
    "print(\"Max\",df['age'].max())\n",
    "print(\"Min\",df['age'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=[\"less than 10\",\"10-20\",\"20-30\",\"30-40\",\"40-50\",\"50-60\",\"60-70\",\"70-80\",\"80 and more\"]\n",
    "bins=[0,10,20,30,40,50,60,70,80,90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "frq_df=df.groupby([pd.cut(df['age'],bins=bins,labels=labels)]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "less than 10      0\n",
       "10-20             1\n",
       "20-30            44\n",
       "30-40           123\n",
       "40-50           145\n",
       "50-60           127\n",
       "60-70            66\n",
       "70-80            10\n",
       "80 and more       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frq_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "frq_df=frq_df.reset_index(name=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>less than 10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20-30</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30-40</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40-50</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  count\n",
       "0  less than 10      0\n",
       "1         10-20      1\n",
       "2         20-30     44\n",
       "3         30-40    123\n",
       "4         40-50    145"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frq_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(data=frq_df,x=\"age\",y=\"count\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Frequency Distribtion of age')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frq_df.plot(kind=\"line\",figsize=(10,5))\n",
    "plt.title(\"Frequency Distribtion of age\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d9add0e0c8>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(df[\"age\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relation of various features with Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d9afc497c8>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(df.corr(),annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix=df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d9b0247c48>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "highest_corr=corr_matrix[corr_matrix>=0.2]\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(highest_corr,cmap=\"Greens\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List of Features with highest correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_features_per_corr=corr_matrix.abs().unstack().sort_values(kind=\"quicksort\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "obesity             itching               0.001894\n",
      "itching             obesity               0.001894\n",
      "                    sudden_weight_loss    0.004516\n",
      "sudden_weight_loss  itching               0.004516\n",
      "obesity             gender                0.005396\n",
      "                                            ...   \n",
      "polydipsia          polydipsia            1.000000\n",
      "polyuria            polyuria              1.000000\n",
      "gender              gender                1.000000\n",
      "obesity             obesity               1.000000\n",
      "class               class                 1.000000\n",
      "Length: 289, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(top_features_per_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering and Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest,chi2,RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'polyuria', 'polydipsia', 'sudden_weight_loss',\n",
       "       'weakness', 'polyphagia', 'genital_thrush', 'visual_blurring',\n",
       "       'itching', 'irritability', 'delayed_healing', 'partial_paresis',\n",
       "       'muscle_stiffness', 'alopecia', 'obesity', 'class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,:-1].values\n",
    "y=df.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,:-1]\n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "skb=SelectKBest(score_func=chi2,k=10)\n",
    "best_feature_fit=skb.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectKBest(score_func=<function chi2 at 0x000001D9AF7E5798>)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_feature_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_score=pd.DataFrame(best_feature_fit.scores_,columns=[\"Feature_Scores\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>Feature_Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.845767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.747637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.184593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120.785515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57.749309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.724262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33.198418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.914009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.124571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.047826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.334127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.620188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>55.314286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24.402793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.250284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature_Scores\n",
       "0        18.845767\n",
       "1        38.747637\n",
       "2       116.184593\n",
       "3       120.785515\n",
       "4        57.749309\n",
       "5        12.724262\n",
       "6        33.198418\n",
       "7         4.914009\n",
       "8        18.124571\n",
       "9         0.047826\n",
       "10       35.334127\n",
       "11        0.620188\n",
       "12       55.314286\n",
       "13        4.875000\n",
       "14       24.402793\n",
       "15        2.250284"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_col_name=pd.DataFrame(x.columns,columns=[\"Feature_name\"])\n",
    "best_feat_df=pd.concat([feature_score,feature_col_name],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature_Scores</th>\n",
       "      <th>Feature_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.845767</td>\n",
       "      <td>age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.747637</td>\n",
       "      <td>gender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.184593</td>\n",
       "      <td>polyuria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120.785515</td>\n",
       "      <td>polydipsia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57.749309</td>\n",
       "      <td>sudden_weight_loss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.724262</td>\n",
       "      <td>weakness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33.198418</td>\n",
       "      <td>polyphagia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.914009</td>\n",
       "      <td>genital_thrush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.124571</td>\n",
       "      <td>visual_blurring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.047826</td>\n",
       "      <td>itching</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.334127</td>\n",
       "      <td>irritability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.620188</td>\n",
       "      <td>delayed_healing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>55.314286</td>\n",
       "      <td>partial_paresis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.875000</td>\n",
       "      <td>muscle_stiffness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24.402793</td>\n",
       "      <td>alopecia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.250284</td>\n",
       "      <td>obesity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature_Scores        Feature_name\n",
       "0        18.845767                 age\n",
       "1        38.747637              gender\n",
       "2       116.184593            polyuria\n",
       "3       120.785515          polydipsia\n",
       "4        57.749309  sudden_weight_loss\n",
       "5        12.724262            weakness\n",
       "6        33.198418          polyphagia\n",
       "7         4.914009      genital_thrush\n",
       "8        18.124571     visual_blurring\n",
       "9         0.047826             itching\n",
       "10       35.334127        irritability\n",
       "11        0.620188     delayed_healing\n",
       "12       55.314286     partial_paresis\n",
       "13        4.875000    muscle_stiffness\n",
       "14       24.402793            alopecia\n",
       "15        2.250284             obesity"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# higher the better\n",
    "best_feat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['polydipsia', 'polyuria', 'sudden_weight_loss', 'partial_paresis',\n",
       "       'gender', 'irritability', 'polyphagia', 'alopecia', 'age',\n",
       "       'visual_blurring', 'weakness', 'genital_thrush'], dtype=object)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_feat_df.nlargest(12,\"Feature_Scores\")[\"Feature_name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "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>Feature_Scores</th>\n",
       "      <th>Feature_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120.785515</td>\n",
       "      <td>polydipsia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.184593</td>\n",
       "      <td>polyuria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57.749309</td>\n",
       "      <td>sudden_weight_loss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>55.314286</td>\n",
       "      <td>partial_paresis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.747637</td>\n",
       "      <td>gender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.334127</td>\n",
       "      <td>irritability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33.198418</td>\n",
       "      <td>polyphagia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24.402793</td>\n",
       "      <td>alopecia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.845767</td>\n",
       "      <td>age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.124571</td>\n",
       "      <td>visual_blurring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.724262</td>\n",
       "      <td>weakness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.914009</td>\n",
       "      <td>genital_thrush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.875000</td>\n",
       "      <td>muscle_stiffness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.250284</td>\n",
       "      <td>obesity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.620188</td>\n",
       "      <td>delayed_healing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.047826</td>\n",
       "      <td>itching</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature_Scores        Feature_name\n",
       "3       120.785515          polydipsia\n",
       "2       116.184593            polyuria\n",
       "4        57.749309  sudden_weight_loss\n",
       "12       55.314286     partial_paresis\n",
       "1        38.747637              gender\n",
       "10       35.334127        irritability\n",
       "6        33.198418          polyphagia\n",
       "14       24.402793            alopecia\n",
       "0        18.845767                 age\n",
       "8        18.124571     visual_blurring\n",
       "5        12.724262            weakness\n",
       "7         4.914009      genital_thrush\n",
       "13        4.875000    muscle_stiffness\n",
       "15        2.250284             obesity\n",
       "11        0.620188     delayed_healing\n",
       "9         0.047826             itching"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_feat_df.sort_values(by=\"Feature_Scores\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'gender', 'polyuria', 'polydipsia', 'sudden_weight_loss',\n",
      "       'weakness', 'polyphagia', 'genital_thrush', 'visual_blurring',\n",
      "       'itching', 'irritability', 'delayed_healing', 'partial_paresis',\n",
      "       'muscle_stiffness', 'alopecia', 'obesity'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(x.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the best suitable model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x.values,y.values,test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model=LogisticRegression()\n",
    "lr_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8910256410256411"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is : 0.8910256410256411\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy of the Logistic model is : \"+str(accuracy_score(y_pred,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_model=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree Classifier model is : 0.9487179487179487\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy of the Decision Tree Classifier model is : \"+str(dt_model.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using RandomForest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree Classifier model is : 0.9551282051282052\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy of the Decision Tree Classifier model is : \"+str(rf_model.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So the best model turn out to be Random Forest Classifier with an accuracy of 0.96"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_file=open(\"./model/Logistic_reg_diabetes.pkl\",\"wb\")\n",
    "pickle.dump(lr_model,model_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_file=open(\"./model/Decision_tree_diabetes.pkl\",\"wb\")\n",
    "pickle.dump(dt_model,model_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_file=open(\"./model/RandomForest_diabetes.pkl\",\"wb\")\n",
    "pickle.dump(rf_model,model_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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