diabetics_check.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1787f644",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import norm\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f1212f84",
   "metadata": {
    "scrolled": true
   },
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>56</td>\n",
       "      <td>89</td>\n",
       "      <td>533</td>\n",
       "      <td>26.637558</td>\n",
       "      <td>2.465202</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>115</td>\n",
       "      <td>79</td>\n",
       "      <td>82</td>\n",
       "      <td>348</td>\n",
       "      <td>27.301961</td>\n",
       "      <td>1.695262</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>92</td>\n",
       "      <td>106</td>\n",
       "      <td>79</td>\n",
       "      <td>397</td>\n",
       "      <td>21.483862</td>\n",
       "      <td>1.561779</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>115</td>\n",
       "      <td>58</td>\n",
       "      <td>72</td>\n",
       "      <td>274</td>\n",
       "      <td>15.451017</td>\n",
       "      <td>0.663310</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>179</td>\n",
       "      <td>72</td>\n",
       "      <td>61</td>\n",
       "      <td>725</td>\n",
       "      <td>48.797996</td>\n",
       "      <td>0.252216</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>11</td>\n",
       "      <td>154</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>667</td>\n",
       "      <td>31.545104</td>\n",
       "      <td>0.901156</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>6</td>\n",
       "      <td>179</td>\n",
       "      <td>50</td>\n",
       "      <td>61</td>\n",
       "      <td>512</td>\n",
       "      <td>17.638450</td>\n",
       "      <td>1.424150</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>5</td>\n",
       "      <td>114</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>572</td>\n",
       "      <td>34.228839</td>\n",
       "      <td>1.232788</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>15</td>\n",
       "      <td>118</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>570</td>\n",
       "      <td>40.568408</td>\n",
       "      <td>0.626608</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>55</td>\n",
       "      <td>29.887446</td>\n",
       "      <td>0.572953</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin        BMI   \n",
       "0               1      189             56             89      533  26.637558  \\\n",
       "1               9      115             79             82      348  27.301961   \n",
       "2              15       92            106             79      397  21.483862   \n",
       "3              11      115             58             72      274  15.451017   \n",
       "4              14      179             72             61      725  48.797996   \n",
       "...           ...      ...            ...            ...      ...        ...   \n",
       "4995           11      154             89             23      667  31.545104   \n",
       "4996            6      179             50             61      512  17.638450   \n",
       "4997            5      114             60             27      572  34.228839   \n",
       "4998           15      118             79             28      570  40.568408   \n",
       "4999            1      132             52             42       55  29.887446   \n",
       "\n",
       "      DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     2.465202   33        1  \n",
       "1                     1.695262   47        0  \n",
       "2                     1.561779   38        0  \n",
       "3                     0.663310   45        0  \n",
       "4                     0.252216   73        1  \n",
       "...                        ...  ...      ...  \n",
       "4995                  0.901156   59        1  \n",
       "4996                  1.424150   37        1  \n",
       "4997                  1.232788   36        0  \n",
       "4998                  0.626608   38        0  \n",
       "4999                  0.572953   27        0  \n",
       "\n",
       "[5000 rows x 9 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"Final_Diabetes.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c3b180fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f11e77e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=['Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']\n",
    "for col in x:\n",
    "    data[col]=data[col].replace(0,np.NaN)\n",
    "    mean=int(data[col].mean(skipna=True))\n",
    "    data[col]=data[col].replace(np.NaN,mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "d996b979",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.00000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.475000</td>\n",
       "      <td>134.98040</td>\n",
       "      <td>80.130400</td>\n",
       "      <td>54.372200</td>\n",
       "      <td>425.384800</td>\n",
       "      <td>32.742047</td>\n",
       "      <td>1.284356</td>\n",
       "      <td>50.869800</td>\n",
       "      <td>0.618200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.180149</td>\n",
       "      <td>37.91731</td>\n",
       "      <td>17.584085</td>\n",
       "      <td>25.935331</td>\n",
       "      <td>242.965412</td>\n",
       "      <td>10.111788</td>\n",
       "      <td>0.693494</td>\n",
       "      <td>17.666728</td>\n",
       "      <td>0.485877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.008868</td>\n",
       "      <td>0.080308</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>102.00000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>23.864798</td>\n",
       "      <td>0.692852</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>136.00000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>429.000000</td>\n",
       "      <td>33.125206</td>\n",
       "      <td>1.283382</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>168.00000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>636.000000</td>\n",
       "      <td>41.443057</td>\n",
       "      <td>1.872322</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>200.00000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>49.974408</td>\n",
       "      <td>2.499902</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness      Insulin   \n",
       "count  5000.000000  5000.00000    5000.000000    5000.000000  5000.000000  \\\n",
       "mean      8.475000   134.98040      80.130400      54.372200   425.384800   \n",
       "std       5.180149    37.91731      17.584085      25.935331   242.965412   \n",
       "min       0.000000    70.00000      50.000000      10.000000     1.000000   \n",
       "25%       4.000000   102.00000      65.000000      32.000000   215.000000   \n",
       "50%       8.000000   136.00000      80.000000      55.000000   429.000000   \n",
       "75%      13.000000   168.00000      96.000000      77.000000   636.000000   \n",
       "max      17.000000   200.00000     110.000000      99.000000   846.000000   \n",
       "\n",
       "               BMI  DiabetesPedigreeFunction          Age      Outcome  \n",
       "count  5000.000000               5000.000000  5000.000000  5000.000000  \n",
       "mean     32.742047                  1.284356    50.869800     0.618200  \n",
       "std      10.111788                  0.693494    17.666728     0.485877  \n",
       "min      15.008868                  0.080308    21.000000     0.000000  \n",
       "25%      23.864798                  0.692852    36.000000     0.000000  \n",
       "50%      33.125206                  1.283382    51.000000     1.000000  \n",
       "75%      41.443057                  1.872322    66.000000     1.000000  \n",
       "max      49.974408                  2.499902    81.000000     1.000000  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "08f38765",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>56</td>\n",
       "      <td>89</td>\n",
       "      <td>533.0</td>\n",
       "      <td>26.637558</td>\n",
       "      <td>2.465202</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>115</td>\n",
       "      <td>79</td>\n",
       "      <td>82</td>\n",
       "      <td>348.0</td>\n",
       "      <td>27.301961</td>\n",
       "      <td>1.695262</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>92</td>\n",
       "      <td>106</td>\n",
       "      <td>79</td>\n",
       "      <td>397.0</td>\n",
       "      <td>21.483862</td>\n",
       "      <td>1.561779</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>115</td>\n",
       "      <td>58</td>\n",
       "      <td>72</td>\n",
       "      <td>274.0</td>\n",
       "      <td>15.451017</td>\n",
       "      <td>0.663310</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>179</td>\n",
       "      <td>72</td>\n",
       "      <td>61</td>\n",
       "      <td>725.0</td>\n",
       "      <td>48.797996</td>\n",
       "      <td>0.252216</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin        BMI   \n",
       "0            1      189             56             89    533.0  26.637558  \\\n",
       "1            9      115             79             82    348.0  27.301961   \n",
       "2           15       92            106             79    397.0  21.483862   \n",
       "3           11      115             58             72    274.0  15.451017   \n",
       "4           14      179             72             61    725.0  48.797996   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                  2.465202   33        1  \n",
       "1                  1.695262   47        0  \n",
       "2                  1.561779   38        0  \n",
       "3                  0.663310   45        0  \n",
       "4                  0.252216   73        1  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "00491a0e",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>11</td>\n",
       "      <td>154</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>667.0</td>\n",
       "      <td>31.545104</td>\n",
       "      <td>0.901156</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>6</td>\n",
       "      <td>179</td>\n",
       "      <td>50</td>\n",
       "      <td>61</td>\n",
       "      <td>512.0</td>\n",
       "      <td>17.638450</td>\n",
       "      <td>1.424150</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>5</td>\n",
       "      <td>114</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>572.0</td>\n",
       "      <td>34.228839</td>\n",
       "      <td>1.232788</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>15</td>\n",
       "      <td>118</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>570.0</td>\n",
       "      <td>40.568408</td>\n",
       "      <td>0.626608</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>55.0</td>\n",
       "      <td>29.887446</td>\n",
       "      <td>0.572953</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin        BMI   \n",
       "4995           11      154             89             23    667.0  31.545104  \\\n",
       "4996            6      179             50             61    512.0  17.638450   \n",
       "4997            5      114             60             27    572.0  34.228839   \n",
       "4998           15      118             79             28    570.0  40.568408   \n",
       "4999            1      132             52             42     55.0  29.887446   \n",
       "\n",
       "      DiabetesPedigreeFunction  Age  Outcome  \n",
       "4995                  0.901156   59        1  \n",
       "4996                  1.424150   37        1  \n",
       "4997                  1.232788   36        0  \n",
       "4998                  0.626608   38        0  \n",
       "4999                  0.572953   27        0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "af0c114b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outcome\n",
      "1    3091\n",
      "0    1909\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "count=data['Outcome'].value_counts()\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "2653b96f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count.plot(kind='bar',color=['green','blue'])\n",
    "plt.title(\"Diabetic and non diabetic\")\n",
    "plt.xlabel(\"Diabetic status\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xticks(rotation=0)\n",
    "for i, c in enumerate(count):\n",
    "    plt.text(i, c + 5, str(c), ha='center', va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "4f0f6e3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average age of individuals is: 50.8698\n",
      "Median age of individuals is: 51.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Average age of individuals is: \",end='')\n",
    "avg_age=data['Age'].mean()\n",
    "print(avg_age)\n",
    "print(\"Median age of individuals is: \",end='')\n",
    "med_age=data['Age'].median()\n",
    "print(med_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8801e50b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pregnancies', ylabel='Glucose'>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter',x='Pregnancies',y='Glucose',c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "1ebc203c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Blood Pressure')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data['BloodPressure'])\n",
    "plt.xlabel(\"Blood Pressure\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc55b794",
   "metadata": {},
   "source": [
    "There are no outliers in blood pressure as there are no outliers in the boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "bb6be8b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.742046537128275\n",
      "10.111788110518422\n"
     ]
    }
   ],
   "source": [
    "mean=data['BMI'].mean()\n",
    "std=np.sqrt(data['BMI'].var())\n",
    "print(mean)\n",
    "print(std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "1204c632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa9b20bcee0>]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=norm.pdf(data['BMI'],mean,std)\n",
    "plt.plot(data['BMI'],f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "5c715cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([313., 346., 330., 310., 339., 295., 334., 312., 357., 343., 359.,\n",
       "        336., 349., 326., 351.]),\n",
       " array([15.00886786, 17.33990385, 19.67093984, 22.00197583, 24.33301182,\n",
       "        26.66404781, 28.9950838 , 31.32611979, 33.65715578, 35.98819177,\n",
       "        38.31922776, 40.65026375, 42.98129974, 45.31233573, 47.64337172,\n",
       "        49.97440771]),\n",
       " <BarContainer object of 15 artists>)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['BMI'],color=\"skyblue\",edgecolor='white',bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "04388322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BMI', ylabel='SkinThickness'>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter',x='BMI',y='SkinThickness',c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "015de280",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between BMI and SkinThickness is Negative\n"
     ]
    }
   ],
   "source": [
    "print(\"Correlation between BMI and SkinThickness is \",end='')\n",
    "correlation=data['BMI'].corr(data['SkinThickness'])\n",
    "if correlation>0.5:\n",
    "    print(\"Positive\")\n",
    "else:\n",
    "    print(\"Negative\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "2faef4f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do people with more pregnancies tend to be older?\n",
      "Yes\n"
     ]
    }
   ],
   "source": [
    "print(\"Do people with more pregnancies tend to be older?\")\n",
    "correlation=data['Pregnancies'].corr(data['Age'])\n",
    "if(correlation>0):\n",
    "    print(\"Yes\")\n",
    "else:\n",
    "    print(\"No\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "455921f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pregnancies', ylabel='Age'>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter',x='Pregnancies',y='Age',c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "7b3d12fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Outcome', ylabel='Glucose'>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Outcome',y='Glucose',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f78ae246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Outcome', ylabel='Glucose'>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZPElEQVR4nO3de7ScdX3v8feHhIRrSCAbxCSysSZqAAtxF2PxnCrQEFktQWtdcEpBzDKt0IvoaovWVU7pYi1rta7SC21Y0ICLg3I7GloqsICa0zTB7lDuiNkGhB3QbC5JuEhC4Hv+eJ5shjB79vwm88zsye/zWmuvPPOZZ2Z+D4H94bnM71FEYGZmBrBXtwdgZmYTh0vBzMxGuRTMzGyUS8HMzEa5FMzMbNTkbg9gd8ycOTP6+/u7PQwzs56ybt26ZyKir95zPV0K/f39DA4OdnsYZmY9RdJPxnrOh4/MzGyUS8HMzEa5FMzMbJRLwczMRrkUzMxsVGWlIGmOpLskPSzpIUl/WOYHS7pd0vryzxllLkmXShqSdL+kBVWNjdWr4aKLij/NzHqN9MZPm1W5p7AD+EJEzAcWAudLmg9cCNwREXOBO8rHAB8F5pY/y4DLKhnVokXwoQ/BxRcXf55ySiUfY2ZWiV2LoM3FUFkpRMTTEXFPufwC8AgwC1gCXFWudhVwerm8BLg6CmuB6ZIOb+ugVq+G229/c3bbbd5jMLPeMFYBtLEYOnJOQVI/cBxwN3BYRDxdPvVT4LByeRbwZM3Lhsts1/daJmlQ0uDIyEjaQG67LS03M8tM5aUg6QDgRuBzEbG19rko7vCTdJefiFgeEQMRMdDXV/db2mObNy8tNzPLTKWlIGlvikK4JiJuKuOf7TwsVP65qcw3AnNqXj67zNrnuefScjOzzFR59ZGAK4BHIuKva55aCZxTLp8DfLcmP7u8CmkhsKXmMFN7HHZYWm5mlpkqJ8Q7Afht4AFJ95bZl4CvANdJWgr8BPhk+dwtwKnAEPAycG7bR3TMMWm5mVlmKiuFiPgPYKxT4ifVWT+A86saDwBPPjl2/t73VvrRZma9wN9oNjOzUXmVwnHHwaRJb84mTSpyMzPLrBQA9tqr8WMzs4zl9Rvx8cdhv/3enO27b5GbmVlmpdDfD1u3vjl74YUiNzOzzErhRz+C2OUL1BFFbmZmmZWC5z4yM2sor1Lw3EdmZg3lVQqe+8jMrKG8SsFzH5mZNZRXKXjuIzOzhvIqhUZzH5mZWWalYGZmDeVVCmPdvjP1tp5mZnuovErhrrvScjOzzORVCm9/e1puZpaZvEphw4a03MwsM3mVwvvfn5abmWUmr1KYOzctNzPLTF6lcMstabmZWWbyKoUZM9JyM7PMVFYKkq6UtEnSgzXZsZLWSrpX0qCk48tcki6VNCTpfkkLKhnU/PlpuZlZZqrcU1gBLN4l+yrw5xFxLPBn5WOAjwJzy59lwGWVjGj16rTczCwzlZVCRKwCdp2TOoBp5fJBwFPl8hLg6iisBaZLOrztg/LhIzOzhiZ3+PM+B9wq6WsUhfTLZT4LqJ2VbrjMnm7rpx90UFpuZpaZTp9o/ixwQUTMAS4Arkh9A0nLyvMRgyOpcxbdfHNabmaWmU6XwjnATeXy9cDx5fJGYE7NerPL7C0iYnlEDETEQF9fX9qnz5yZlpuZZabTpfAU8Cvl8onA+nJ5JXB2eRXSQmBLRLT30BHA0qVpuZlZZio7pyDpWuDDwExJw8BFwGeAv5E0GXiF4kojgFuAU4Eh4GXg3EoGtX59Wm5mlpnKSiEizhzjqbdMNBQRAZxf1VhGrVuXlpuZZSavbzQfeGBabmaWmbxKYdWqtNzMLDN5lcK0aWm5mVlm8iqFj30sLTczy0xepbBpU1puZpaZvErhPe9Jy83MMpNXKbztbWm5mVlm8iqFG29My83MMpNXKZiZWUN5lcI++6TlZmaZyasUbrghLTczy0xepbB9e1puZpaZvErhXe9Ky83MMpNXKQwPp+VmZpnJqxSOPDItNzPLTF6lsGhRWm5mlpm8SuGaa9JyM7PM5FUKmzen5WZmmcmrFObPT8vNzDKTVym8+GJabmaWmbxK4Ygj0nIzs8zkVQqPPJKWm5llprJSkHSlpE2SHtwl/31JP5T0kKSv1uRflDQk6VFJp1QyqClT0nIzs8xUuaewAlhcG0j6CLAE+MWIOAr4WpnPB84Ajipf8w+SJrV9ROedl5abmWWmslKIiFXAc7vEnwW+EhHbynV23hx5CfCtiNgWEY8BQ8DxbR/U3nun5WZmmen0OYV5wP+QdLek70v6pTKfBTxZs95wmb2FpGWSBiUNjoyMpH36D3+YlpuZZabTpTAZOBhYCPwRcJ0kpbxBRCyPiIGIGOjr60v79GeeScvNzDLT6VIYBm6Kwg+A14GZwEZgTs16s8usvXyTHTOzhjpdCt8BPgIgaR4wBXgGWAmcIWmqpCOBucAP2v7pO3ak5WZmmanyktRrgTXAuyUNS1oKXAm8s7xM9VvAOeVew0PAdcDDwPeA8yPitbYPat68tNzMLDOTq3rjiDhzjKfOGmP9S4BLqhoPAJs2peVmZpnJ6xvNBx6YlpuZZSavUjjqqLTczCwzeZXC2rVpuZlZZvIqhWnT0nIzs8zkVQpz5qTlZmaZyasU1q1Ly83MMpNXKbztbWm5mVlm8iqFY49Ny83MMpNXKXzve2m5mVlm8iqF18aYOWOs3MwsM3mVwoc+lJabmWUmr1IYGkrLzcwyk1cpvPBCWm5mlpm8SmHhwrTczCwzeZXCnXem5WZmmWm6FCQdIenkcnlfSb0337QPH5mZNdRUKUj6DHAD8E9lNJvi1pq9xRPimZk11OyewvnACcBWgIhYDxxa1aAq89JLabmZWWaaLYVtEbF95wNJk4GoZkgVmjQpLTczy0yzpfB9SV8C9pX0q8D1wM3VDasixx+flpuZZabZUrgQGAEeAH4HuAX4clWDqsyGDWm5mVlmmiqFiHg9Ii6PiN8ElgF3R0TDw0eSrpS0SdKDdZ77gqSQNLN8LEmXShqSdL+kBa1szLhefjktNzPLTLNXH/27pGmSDgbWAZdL+sY4L1sBLK7zXnOARcATNfFHgbnlzzLgsmbGlWzGjLTczCwzzR4+OigitgIfB66OiA8AJzV6QUSsAp6r89Q3gD/mzSeql5TvGxGxFpgu6fAmx9a8xx5Ly83MMtNsKUwuf0l/EviXVj9M0hJgY0Tct8tTs4Anax4Pl1m991gmaVDS4MjISKtDMTOzOpothYuBW4EfR8R/SXonsD7lgyTtB3wJ+LO0Ib5ZRCyPiIGIGOjr60t78f77p+VmZpmZ3MxKEXE9xWWoOx9vAH4j8bN+ATgSuE8SFN+KvkfS8cBGYE7NurPLrL18otnMrKFmTzTPlvR/y6uJNkm6UdLslA+KiAci4tCI6I+IfopDRAsi4qfASuDs8iqkhcCWiHg6dWPGdeAY0zWNlZuZZabZw0f/TPGL++3lz81lNiZJ1wJrgHdLGpa0tMHqtwAbgCHgcuC8JseV5pVX0nIzs8w0dfgI6IuI2hJYIelzjV4QEWeO83x/zXJQzK9ULU9zYWbWULN7Cs9KOkvSpPLnLODZKgdWibFOTKeesDYz20M1Wwqfprgc9afA08AngHOrGlRlhofTcjOzzDR79dFPgNMqHkv19tsPXnyxfm5mZk1ffXSVpOk1j2dIurKyUVVlzpy03MxsIpk6NS1vQbOHj94XEZt3PoiI54Hj2jaKTnn00bTczGwi2bYtLW9Bs6Wwl6TRWePKifGavXJp4ii+NNd8bmaWmWZ/sX8dWCPpekAUJ5ovqWxUVTnwQNi8uX5uZjbRTZ4MO3bUz9v1Ec2sFBFXSxoETiyjj0fEw20bRafUK4RGuZnZRFKvEBrlLWiqFCS9A3iR4lvNo1lEPDH2qyagKVNg+/b6uZmZNX346F954/4H+1JMbPcocFQVg6rMrFn1750wq+4s3WZm2Wn28NExtY/L22VWMz9RlXyTHTOzhpq9+uhNIuIe4ANtHouZmXVZs+cUPl/zcC9gAfBUJSOq0syZ8Mwz9XMzM2v6nELtNZs7KM4x3Nj+4VSsXiE0ys3MMtPsOYU/r3ogZmbWfQ1LQdLNvHHV0VtERG9Nkjd1av2vg7dx3hAzs8pMmgSvvVY/b5Px9hS+VifbWRK9NzdEB+YNMTOrTL1CaJS3YLxSmA7Mjoi/B5D0A6CPohj+pG2jMDOzCWG8S1L/mJpvMQNTgAHgw8DvVjSm6uw1xuaOlZuZTSRjzXHUwbmPpkTEkzWP/yMinqW4Pef+bRtFp7z+elpuZjaRdGDuo/H+F3lG7YOI+L2ah76xsZnZHma8Urhb0md2DSX9DvCDaoZUobFuu+nbcZpZL5g2LS1vwXiHjy4AviPpfwH3lNn7ganA6Y1eWN6u89eATRFxdJn9FfDrwHbgx8C5O+/oJumLwFLgNeAPIuLWFransZdfTsvNzCaSrVvT8hY03FOIiE0R8cvAXwCPlz8XR8QHI+Jn47z3CmDxLtntwNER8T7gR8AXASTNB86gmHV1MfAPktp34a2ZmTWl2W803wncmfLGEbFKUv8u2W01D9dS3MENYAnwrYjYBjwmaQg4HliT8pnj2ndf+PnP6+dmZhPd3nvDq6/Wz9ukm9difhr4t3J5FlB7ldNwmb2FpGWSBiUNjoyMpH1ivUJolJuZTST1CqFR3oKulIKkP6WYWO+a1NdGxPKIGIiIgb4+XwBlZtZO7fvGQ5MkfYriBPRJEbFzyoyNwJya1WaXmZmZ7bTPPvDKK/XzNunonoKkxRTfkj4tImov+VkJnCFpqqQjgbn04iWvZmZVqlcIjfIWVLanIOlaiukwZkoaBi6iuNpoKnC7JIC1EfG7EfGQpOuAhykOK50fEe2b4cnMzJpSWSlExJl14isarH8JcElV4wFgyhTYvr1+bmZmXb36qPPqFUKj3MwsM3mVgpmZNeRSMDPrFR2Y/t+lYGbWKzow/b9LwcysV4x1UUwbL5ZxKZiZ9YoOXCzjUjAzs1F5lcJYs6F6llQzMyC3UvAsqWZmDeVVCh24nMvMrJfl9dvQpWBmvawDh8Dz+m24Y0dabmY2kXTgEHhepWBmZg3lVQpj3ce0jfc3NTOrzAEHpOUtyKsUOnB/UzOzyrz4YlregrxKwczMGsqrFIq7vTWfm5lNJNOmpeUtyKsUItJyM7OJZOvWtLwFeZWCmZk15FIwM+sV+++flrfApWBm1iteeiktb0FlpSDpSkmbJD1Ykx0s6XZJ68s/Z5S5JF0qaUjS/ZIWVDUuMzMbW5V7CiuAxbtkFwJ3RMRc4I7yMcBHgbnlzzLgsgrHZWbWm3p57qOIWAU8t0u8BLiqXL4KOL0mvzoKa4Hpkg6vamxmZj1p27a0vAWdPqdwWEQ8XS7/FDisXJ4FPFmz3nCZvYWkZZIGJQ2OjIxUN1Izs4lm0qS0vAVdO9EcEQEkf0EgIpZHxEBEDPT19aW9uAP/QM3MKnPooWl5CzpdCj/beVio/HNTmW8E5tSsN7vM2uu119JyM7OJZOMYvxbHylvQ6VJYCZxTLp8DfLcmP7u8CmkhsKXmMJOZmUFHpuqp8pLUa4E1wLslDUtaCnwF+FVJ64GTy8cAtwAbgCHgcuC8qsZlZtaz+vvT8hZMbts77SIizhzjqZPqrBvA+VWNxcxsj/DUU2l5C/yNZjMzG+VSMDPrFTNnpuUtcCmYmfWKPfDqIzMzm8BcCmZmvWL69LS8BS4FM7NesXlzWt4Cl4KZmY1yKZiZ9Yo9cO4jMzNr1Y4daXkLXApmZr3ikEPS8ha4FMzMesUTT6TlLXApmJnZKJeCmVmvOOqotLwFLgUzs17x6KNpeQtcCmZmvWL79rS8BS4FM7NeMWNGWt4Cl4KZWa8YGUnLW+BSMDPrFVOnpuUtcCmYmfWKU05Jy1vgUjAz6xVr1qTlLXApmJn1iuefT8tb4FIwM+sV/f1peQu6UgqSLpD0kKQHJV0raR9JR0q6W9KQpG9LmtKNsZmZTVjPPpuWt6DjpSBpFvAHwEBEHA1MAs4A/hL4RkS8C3geWNrpsZmZTWiTJ6flLejW4aPJwL6SJgP7AU8DJwI3lM9fBZzenaGZmU1QCxak5S3oeClExEbga8ATFGWwBVgHbI6InXeKGAZm1Xu9pGWSBiUNjrTxCxtmZhPevfem5S3oxuGjGcAS4Ejg7cD+wOJmXx8RyyNiICIG+vr6KhqlmdkEFJGWt6Abh49OBh6LiJGIeBW4CTgBmF4eTgKYDWzswtjMzCauhQvT8hZ0oxSeABZK2k+SgJOAh4G7gE+U65wDfLcLYzMzm7jWr0/LW9CNcwp3U5xQvgd4oBzDcuBPgM9LGgIOAa7o9NjMzCa0gw5Ky1vQvuuYEkTERcBFu8QbgOO7MBwzs94wb179KS3mzWvbR/gbzWZmveLmm9PyFrgUzMx6xbZtaXkLXApmZr3iAx9Iy1vgUjAz6xWbNqXlLXApmJn1iu3b0/IWuBTMzHrFccel5S1wKZiZ9YoHH0zLW+BSMDPrFdOmpeUtcCmYmfWK009Py1vgUjAz6xUvvZSWtyCvUujArpeZWWUWLUrLW5BXKWzZkpabmU0kJ5zw1gJYtKjI26QrE+J1VUQxo+DWrcUeggvBzHrJrbfC6tVw221tLwTIsRTARWBmve2EE9peBjvldfjIzMwacimYmdkol4KZmY1yKZiZ2SiXgpmZjVJEdHsMLZM0AvykxZfPBJ5p43B6gbc5D97mPOzONh8REX31nujpUtgdkgYjYqDb4+gkb3MevM15qGqbffjIzMxGuRTMzGxUzqWwvNsD6AJvcx68zXmoZJuzPadgZmZvlfOegpmZ7cKlYGZmo/b4UpC0WNKjkoYkXVjn+amSvl0+f7ek/i4Ms62a2ObPS3pY0v2S7pB0RDfG2U7jbXPNer8hKST1/OWLzWyzpE+Wf9cPSfo/nR5juzXx7/Y7JN0l6b/Lf79P7cY420XSlZI2SXpwjOcl6dLyn8f9khbs9odGxB77A0wCfgy8E5gC3AfM32Wd84B/LJfPAL7d7XF3YJs/AuxXLn82h20u1zsQWAWsBQa6Pe4O/D3PBf4bmFE+PrTb4+7ANi8HPlsuzwce7/a4d3Ob/yewAHhwjOdPBf4NELAQuHt3P3NP31M4HhiKiA0RsR34FrBkl3WWAFeVyzcAJ0lSB8fYbuNuc0TcFREvlw/XArM7PMZ2a+bvGeAvgL8EXunk4CrSzDZ/Bvj7iHgeICI2dXiM7dbMNgew8/66BwFPdXB8bRcRq4DnGqyyBLg6CmuB6ZIO353P3NNLYRbwZM3j4TKru05E7AC2AId0ZHTVaGabay2l+D+NXjbuNpe71XMi4l87ObAKNfP3PA+YJ2m1pLWSFndsdNVoZpv/N3CWpGHgFuD3OzO0rkn9731ced55zQCQdBYwAPxKt8dSJUl7AX8NfKrLQ+m0yRSHkD5MsTe4StIxEbG5m4Oq2JnAioj4uqQPAt+UdHREvN7tgfWKPX1PYSMwp+bx7DKru46kyRS7nM92ZHTVaGabkXQy8KfAaRGxrUNjq8p423wgcDTw75Iepzj2urLHTzY38/c8DKyMiFcj4jHgRxQl0aua2ealwHUAEbEG2Idi4rg9VVP/vafY00vhv4C5ko6UNIXiRPLKXdZZCZxTLn8CuDPKMzg9atxtlnQc8E8UhdDrx5lhnG2OiC0RMTMi+iOin+I8ymkRMdid4bZFM/9uf4diLwFJMykOJ23o4BjbrZltfgI4CUDSeylKYaSjo+yslcDZ5VVIC4EtEfH07rzhHn34KCJ2SPo94FaKKxeujIiHJF0MDEbESuAKil3MIYoTOmd0b8S7r8lt/ivgAOD68pz6ExFxWtcGvZua3OY9SpPbfCuwSNLDwGvAH0VEz+4FN7nNXwAul3QBxUnnT/Xy/+RJupai2GeW50kuAvYGiIh/pDhvciowBLwMnLvbn9nD/7zMzKzN9vTDR2ZmlsClYGZmo1wKZmY2yqVgZmajXApmZjbKpWDZkzRb0nclrZf0Y0l/U14H3+g1X+rU+Mw6yaVgWSsnP7wJ+E5EzKX4gtcBwCXjvNSlYHskl4Ll7kTglYj4Z4CIeA24APi0pPMk/d3OFSX9i6QPS/oKsK+keyVdUz53djmf/X2Svllm/ZLurLlvxTvKfIWky8pJ6jaU73mlpEckraj5vEWS1ki6R9L1kg7o2D8Vy5ZLwXJ3FLCuNoiIrRTTJdT9xn9EXAj8PCKOjYjfknQU8GXgxIj4ReAPy1X/FrgqIt4HXANcWvM2M4APUhTQSuAb5ViOkXRsOS3Fl4GTI2IBMAh8vh0bbNbIHj3NhVmHnAhcHxHPAETEzvnvPwh8vFz+JvDVmtfcHBEh6QHgZxHxAICkh4B+ionN5gOry6lIpgBrKt4OM5eCZe9hiokQR0maBrwD2Myb96b3aePn7pyZ9vWa5Z2PJ1PMVXR7RJzZxs80G5cPH1nu7gD2k3Q2gKRJwNeBFRQzih4raS9Jcyju/LXTq5L2LpfvBH5T0iHlexxc5v/JGxMs/hbw/xLGtRY4QdK7yvfcX9K81I0zS+VSsKyVM2h+jOKX+nqKew68QnF10WrgMYq9iUuBe2peuhy4X9I1EfEQxdVK35d0H8UNfaC469e5ku4Hfps3zjU0M64RipsCXVu+fg3wnla306xZniXVzMxGeU/BzMxGuRTMzGyUS8HMzEa5FMzMbJRLwczMRrkUzMxslEvBzMxG/X/EpT4+lYRMfgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter',x='Outcome',y='Glucose',c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "5b655b1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean DiabetesPedigreeFunction of diabetic women is:1.3806148171989994\n",
      "Mean DiabetesPedigreeFunction of non-diabetic women is:1.1284969665989129\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean DiabetesPedigreeFunction of diabetic women is:\",end='')\n",
    "diab_mean=data[data['Outcome']==1]['DiabetesPedigreeFunction'].mean()\n",
    "print(diab_mean)\n",
    "print(\"Mean DiabetesPedigreeFunction of non-diabetic women is:\",end='')\n",
    "diab_mean=data[data['Outcome']==0]['DiabetesPedigreeFunction'].mean()\n",
    "print(diab_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "8b6e258f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean age of diabetic women is:54.53898414752507\n",
      "Mean age of non-diabetic women is:44.92875851231011\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean age of diabetic women is:\",end='')\n",
    "age_mean=data[data['Outcome']==1]['Age'].mean()\n",
    "print(age_mean)\n",
    "print(\"Mean age of non-diabetic women is:\",end='')\n",
    "age_mean=data[data['Outcome']==0]['Age'].mean()\n",
    "print(age_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f51b67db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1196\n",
      "1846\n"
     ]
    }
   ],
   "source": [
    "print((data[(data['Age'] < 50) & (data['Outcome'] == 0)]).shape[0])\n",
    "print((data[(data['Age'] > 50) & (data['Outcome'] == 1)]).shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a898109f",
   "metadata": {},
   "source": [
    "Mean of diabetic with age>50 is more compared to that of <50, hence we can say that elder women have high chances of getting diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "9ab9a8bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, '0'), Text(1, 0, '1')])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Outcome', y='Age', data=data)\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "8ffe17a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Insulin', y='Age', data=data)\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "bd8e1098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([568., 558., 529., 289., 576., 550., 262., 579., 540., 549.]),\n",
       " array([ 0. ,  1.7,  3.4,  5.1,  6.8,  8.5, 10.2, 11.9, 13.6, 15.3, 17. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['Pregnancies'],edgecolor='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "72202bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BloodPressure', ylabel='Age'>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter',x='BloodPressure',y='Age',c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "91a6449c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between BMI and SkinThickness is 0.11069543037148165\n"
     ]
    }
   ],
   "source": [
    "data[col]=data['Outcome']\n",
    "print(\"Correlation between BMI and SkinThickness is \",end='')\n",
    "correlation=data['Pregnancies'].corr(data['Outcome'])\n",
    "print(correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "ee5e454a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.drop(\"Outcome\",axis=1)\n",
    "y=data['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "de85383c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "d1f677b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier()\n",
    "rf_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "df04a72b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "fc544ea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[381   0]\n",
      " [  0 619]]\n"
     ]
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print('Confusion Matrix:\\n', conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "261367f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       381\n",
      "           1       1.00      1.00      1.00       619\n",
      "\n",
      "    accuracy                           1.00      1000\n",
      "   macro avg       1.00      1.00      1.00      1000\n",
      "weighted avg       1.00      1.00      1.00      1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_report = classification_report(y_test, y_pred)\n",
    "print('Classification Report:\\n', class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "7a94d553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 100.00%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy * 100:.2f}%') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "775d4c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the entire dataset is: 100.00%\n"
     ]
    }
   ],
   "source": [
    "y_pred_full = rf_model.predict(x)\n",
    "accuracy_full = accuracy_score(y, y_pred_full)\n",
    "print(f'Accuracy on the entire dataset is: {accuracy_full * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3194de0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67b6b3e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}