Diabetes Prediction using Classification Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1  =pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Pregnancies: Number of times pregnant\n",
    "- Glucose: Plasma glucose concentration over 2 hours in an oral glucose tolerance test\n",
    "- BloodPressure: Diastolic blood pressure (mm Hg)\n",
    "- SkinThickness: Triceps skin fold thickness (mm)\n",
    "- Insulin: 2-Hour serum insulin (mu U/ml)\n",
    "- BMI: Body mass index (weight in kg/(height in m)2)\n",
    "- DiabetesPedigreeFunction: Diabetes pedigree function (a function which scores likelihood of diabetes based on family history)\n",
    "- Age: Age (years)\n",
    "- Outcome: Class variable (0 if non-diabetic, 1 if diabetic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
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       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
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       "      <td>70</td>\n",
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       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    768.000000\n",
       "mean      20.536458\n",
       "std       15.952218\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%       23.000000\n",
       "75%       32.000000\n",
       "max       99.000000\n",
       "Name: SkinThickness, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['SkinThickness'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['SkinThickness'] = data1['SkinThickness'].replace(0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    768.000000\n",
       "mean      22.014323\n",
       "std       14.103401\n",
       "min        5.000000\n",
       "25%        5.000000\n",
       "50%       23.000000\n",
       "75%       32.000000\n",
       "max       99.000000\n",
       "Name: SkinThickness, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['SkinThickness'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f715f5d3ef0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data1.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>index</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>24</td>\n",
       "      <td>11</td>\n",
       "      <td>143</td>\n",
       "      <td>94</td>\n",
       "      <td>33</td>\n",
       "      <td>146</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>145</td>\n",
       "      <td>82</td>\n",
       "      <td>19</td>\n",
       "      <td>110</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>138</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>0.420</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>13</td>\n",
       "      <td>126</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>43.4</td>\n",
       "      <td>0.583</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>86</td>\n",
       "      <td>13</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.178</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>88</td>\n",
       "      <td>15</td>\n",
       "      <td>136</td>\n",
       "      <td>70</td>\n",
       "      <td>32</td>\n",
       "      <td>110</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0.153</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>159</td>\n",
       "      <td>17</td>\n",
       "      <td>163</td>\n",
       "      <td>72</td>\n",
       "      <td>41</td>\n",
       "      <td>114</td>\n",
       "      <td>40.9</td>\n",
       "      <td>0.817</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>193</td>\n",
       "      <td>11</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>52.3</td>\n",
       "      <td>0.578</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>215</td>\n",
       "      <td>12</td>\n",
       "      <td>151</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>271</td>\n",
       "      <td>41.8</td>\n",
       "      <td>0.742</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>254</td>\n",
       "      <td>12</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "      <td>258</td>\n",
       "      <td>27.6</td>\n",
       "      <td>0.926</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>259</td>\n",
       "      <td>11</td>\n",
       "      <td>155</td>\n",
       "      <td>76</td>\n",
       "      <td>28</td>\n",
       "      <td>150</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1.353</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>274</td>\n",
       "      <td>13</td>\n",
       "      <td>106</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.251</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>298</td>\n",
       "      <td>14</td>\n",
       "      <td>100</td>\n",
       "      <td>78</td>\n",
       "      <td>25</td>\n",
       "      <td>184</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.412</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>323</td>\n",
       "      <td>13</td>\n",
       "      <td>152</td>\n",
       "      <td>90</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>26.8</td>\n",
       "      <td>0.731</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>333</td>\n",
       "      <td>12</td>\n",
       "      <td>106</td>\n",
       "      <td>80</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>0.137</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>357</td>\n",
       "      <td>13</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>39.9</td>\n",
       "      <td>0.569</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>358</td>\n",
       "      <td>12</td>\n",
       "      <td>88</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>54</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.378</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>375</td>\n",
       "      <td>12</td>\n",
       "      <td>140</td>\n",
       "      <td>82</td>\n",
       "      <td>43</td>\n",
       "      <td>325</td>\n",
       "      <td>39.2</td>\n",
       "      <td>0.528</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>436</td>\n",
       "      <td>12</td>\n",
       "      <td>140</td>\n",
       "      <td>85</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>37.4</td>\n",
       "      <td>0.244</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>455</td>\n",
       "      <td>14</td>\n",
       "      <td>175</td>\n",
       "      <td>62</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.212</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>510</td>\n",
       "      <td>12</td>\n",
       "      <td>84</td>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.297</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>518</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>0.180</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>558</td>\n",
       "      <td>11</td>\n",
       "      <td>103</td>\n",
       "      <td>68</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>46.2</td>\n",
       "      <td>0.126</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>559</td>\n",
       "      <td>11</td>\n",
       "      <td>85</td>\n",
       "      <td>74</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.300</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>582</td>\n",
       "      <td>12</td>\n",
       "      <td>121</td>\n",
       "      <td>78</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0.259</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>590</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "      <td>84</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>46.8</td>\n",
       "      <td>0.925</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>614</td>\n",
       "      <td>11</td>\n",
       "      <td>138</td>\n",
       "      <td>74</td>\n",
       "      <td>26</td>\n",
       "      <td>144</td>\n",
       "      <td>36.1</td>\n",
       "      <td>0.557</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>635</td>\n",
       "      <td>13</td>\n",
       "      <td>104</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0.465</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>648</td>\n",
       "      <td>11</td>\n",
       "      <td>136</td>\n",
       "      <td>84</td>\n",
       "      <td>35</td>\n",
       "      <td>130</td>\n",
       "      <td>28.3</td>\n",
       "      <td>0.260</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>658</td>\n",
       "      <td>11</td>\n",
       "      <td>127</td>\n",
       "      <td>106</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.190</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>691</td>\n",
       "      <td>13</td>\n",
       "      <td>158</td>\n",
       "      <td>114</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>0.257</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>740</td>\n",
       "      <td>11</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>37</td>\n",
       "      <td>150</td>\n",
       "      <td>42.3</td>\n",
       "      <td>0.785</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>744</td>\n",
       "      <td>13</td>\n",
       "      <td>153</td>\n",
       "      <td>88</td>\n",
       "      <td>37</td>\n",
       "      <td>140</td>\n",
       "      <td>40.6</td>\n",
       "      <td>1.174</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>745</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>84</td>\n",
       "      <td>33</td>\n",
       "      <td>105</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.488</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0      24           11      143             94             33      146  36.6   \n",
       "1      28           13      145             82             19      110  22.2   \n",
       "2      36           11      138             76              5        0  33.2   \n",
       "3      72           13      126             90              5        0  43.4   \n",
       "4      86           13      106             72             54        0  36.6   \n",
       "5      88           15      136             70             32      110  37.1   \n",
       "6     159           17      163             72             41      114  40.9   \n",
       "7     193           11      135              0              5        0  52.3   \n",
       "8     215           12      151             70             40      271  41.8   \n",
       "9     254           12       92             62              7      258  27.6   \n",
       "10    259           11      155             76             28      150  33.3   \n",
       "11    274           13      106             70              5        0  34.2   \n",
       "12    298           14      100             78             25      184  36.6   \n",
       "13    323           13      152             90             33       29  26.8   \n",
       "14    333           12      106             80              5        0  23.6   \n",
       "15    357           13      129              0             30        0  39.9   \n",
       "16    358           12       88             74             40       54  35.3   \n",
       "17    375           12      140             82             43      325  39.2   \n",
       "18    436           12      140             85             33        0  37.4   \n",
       "19    455           14      175             62             30        0  33.6   \n",
       "20    510           12       84             72             31        0  29.7   \n",
       "21    518           13       76             60              5        0  32.8   \n",
       "22    558           11      103             68             40        0  46.2   \n",
       "23    559           11       85             74              5        0  30.1   \n",
       "24    582           12      121             78             17        0  26.5   \n",
       "25    590           11      111             84             40        0  46.8   \n",
       "26    614           11      138             74             26      144  36.1   \n",
       "27    635           13      104             72              5        0  31.2   \n",
       "28    648           11      136             84             35      130  28.3   \n",
       "29    658           11      127            106              5        0  39.0   \n",
       "30    691           13      158            114              5        0  42.3   \n",
       "31    740           11      120             80             37      150  42.3   \n",
       "32    744           13      153             88             37      140  40.6   \n",
       "33    745           12      100             84             33      105  30.0   \n",
       "\n",
       "    DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                      0.254   51        1  \n",
       "1                      0.245   57        0  \n",
       "2                      0.420   35        0  \n",
       "3                      0.583   42        1  \n",
       "4                      0.178   45        0  \n",
       "5                      0.153   43        1  \n",
       "6                      0.817   47        1  \n",
       "7                      0.578   40        1  \n",
       "8                      0.742   38        1  \n",
       "9                      0.926   44        1  \n",
       "10                     1.353   51        1  \n",
       "11                     0.251   52        0  \n",
       "12                     0.412   46        1  \n",
       "13                     0.731   43        1  \n",
       "14                     0.137   44        0  \n",
       "15                     0.569   44        1  \n",
       "16                     0.378   48        0  \n",
       "17                     0.528   58        1  \n",
       "18                     0.244   41        0  \n",
       "19                     0.212   38        1  \n",
       "20                     0.297   46        1  \n",
       "21                     0.180   41        0  \n",
       "22                     0.126   42        0  \n",
       "23                     0.300   35        0  \n",
       "24                     0.259   62        0  \n",
       "25                     0.925   45        1  \n",
       "26                     0.557   50        1  \n",
       "27                     0.465   38        1  \n",
       "28                     0.260   42        1  \n",
       "29                     0.190   51        0  \n",
       "30                     0.257   44        1  \n",
       "31                     0.785   48        1  \n",
       "32                     1.174   39        0  \n",
       "33                     0.488   46        0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# changing all the pregnancy values more than *10 to 10*\n",
    "p1 = data1[data1['Pregnancies']>10]\n",
    "p1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lokesh/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "p1['Pregnancies'] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.925</td>\n",
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       "      <td>0.465</td>\n",
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "24            10      143             94             33      146  36.6   \n",
       "28            10      145             82             19      110  22.2   \n",
       "36            10      138             76              5        0  33.2   \n",
       "72            10      126             90              5        0  43.4   \n",
       "86            10      106             72             54        0  36.6   \n",
       "88            10      136             70             32      110  37.1   \n",
       "159           10      163             72             41      114  40.9   \n",
       "193           10      135              0              5        0  52.3   \n",
       "215           10      151             70             40      271  41.8   \n",
       "254           10       92             62              7      258  27.6   \n",
       "259           10      155             76             28      150  33.3   \n",
       "274           10      106             70              5        0  34.2   \n",
       "298           10      100             78             25      184  36.6   \n",
       "323           10      152             90             33       29  26.8   \n",
       "333           10      106             80              5        0  23.6   \n",
       "357           10      129              0             30        0  39.9   \n",
       "358           10       88             74             40       54  35.3   \n",
       "375           10      140             82             43      325  39.2   \n",
       "436           10      140             85             33        0  37.4   \n",
       "455           10      175             62             30        0  33.6   \n",
       "510           10       84             72             31        0  29.7   \n",
       "518           10       76             60              5        0  32.8   \n",
       "558           10      103             68             40        0  46.2   \n",
       "559           10       85             74              5        0  30.1   \n",
       "582           10      121             78             17        0  26.5   \n",
       "590           10      111             84             40        0  46.8   \n",
       "614           10      138             74             26      144  36.1   \n",
       "635           10      104             72              5        0  31.2   \n",
       "648           10      136             84             35      130  28.3   \n",
       "658           10      127            106              5        0  39.0   \n",
       "691           10      158            114              5        0  42.3   \n",
       "740           10      120             80             37      150  42.3   \n",
       "744           10      153             88             37      140  40.6   \n",
       "745           10      100             84             33      105  30.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "24                      0.254   51        1  \n",
       "28                      0.245   57        0  \n",
       "36                      0.420   35        0  \n",
       "72                      0.583   42        1  \n",
       "86                      0.178   45        0  \n",
       "88                      0.153   43        1  \n",
       "159                     0.817   47        1  \n",
       "193                     0.578   40        1  \n",
       "215                     0.742   38        1  \n",
       "254                     0.926   44        1  \n",
       "259                     1.353   51        1  \n",
       "274                     0.251   52        0  \n",
       "298                     0.412   46        1  \n",
       "323                     0.731   43        1  \n",
       "333                     0.137   44        0  \n",
       "357                     0.569   44        1  \n",
       "358                     0.378   48        0  \n",
       "375                     0.528   58        1  \n",
       "436                     0.244   41        0  \n",
       "455                     0.212   38        1  \n",
       "510                     0.297   46        1  \n",
       "518                     0.180   41        0  \n",
       "558                     0.126   42        0  \n",
       "559                     0.300   35        0  \n",
       "582                     0.259   62        0  \n",
       "590                     0.925   45        1  \n",
       "614                     0.557   50        1  \n",
       "635                     0.465   38        1  \n",
       "648                     0.260   42        1  \n",
       "658                     0.190   51        0  \n",
       "691                     0.257   44        1  \n",
       "740                     0.785   48        1  \n",
       "744                     1.174   39        0  \n",
       "745                     0.488   46        0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data1[data1['Pregnancies']<=10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <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>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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       "      <th>763</th>\n",
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       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
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       "</table>\n",
       "<p>734 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              5        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              5        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[734 rows x 9 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([p1, data2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "24            10      143             94             33      146  36.6   \n",
       "28            10      145             82             19      110  22.2   \n",
       "36            10      138             76              5        0  33.2   \n",
       "72            10      126             90              5        0  43.4   \n",
       "86            10      106             72             54        0  36.6   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
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       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "24                      0.254   51        1  \n",
       "28                      0.245   57        0  \n",
       "36                      0.420   35        0  \n",
       "72                      0.583   42        1  \n",
       "86                      0.178   45        0  \n",
       "..                        ...  ...      ...  \n",
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       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['index'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0             10      143             94             33      146  36.6   \n",
       "1             10      145             82             19      110  22.2   \n",
       "2             10      138             76              5        0  33.2   \n",
       "3             10      126             90              5        0  43.4   \n",
       "4             10      106             72             54        0  36.6   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              5        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.254   51        1  \n",
       "1                       0.245   57        0  \n",
       "2                       0.420   35        0  \n",
       "3                       0.583   42        1  \n",
       "4                       0.178   45        0  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['Glucose']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    763.000000\n",
       "mean     121.686763\n",
       "std       30.535641\n",
       "min       44.000000\n",
       "25%       99.000000\n",
       "50%      117.000000\n",
       "75%      141.000000\n",
       "max      199.000000\n",
       "Name: Glucose, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Glucose'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    763.000000\n",
       "mean      69.115334\n",
       "std       19.399240\n",
       "min        0.000000\n",
       "25%       62.000000\n",
       "50%       72.000000\n",
       "75%       80.000000\n",
       "max      122.000000\n",
       "Name: BloodPressure, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['BloodPressure'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "    .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>7</th>\n",
       "      <td>10</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>52.3</td>\n",
       "      <td>0.578</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>39.9</td>\n",
       "      <td>0.569</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>7</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.484</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>7</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.305</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.304</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>0.270</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0.773</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>7</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>25.2</td>\n",
       "      <td>0.209</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>3</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.761</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.933</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>2</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.240</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>0</td>\n",
       "      <td>167</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>0.839</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>1</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.282</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0.932</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>3</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.187</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.256</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.108</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>42.4</td>\n",
       "      <td>0.205</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>2</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>19.6</td>\n",
       "      <td>0.832</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>8</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.183</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>0.630</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.174</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>6</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.189</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>6</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>0.501</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>4</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.302</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.342</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>6</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.190</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>4</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.212</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.141</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>4</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.610</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.253</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0.304</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.261</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "7             10      135              0              5        0  52.3   \n",
       "15            10      129              0             30        0  39.9   \n",
       "41            10      115              0              5        0  35.3   \n",
       "49             7      100              0              5        0  30.0   \n",
       "80             7      105              0              5        0   0.0   \n",
       "91             2       84              0              5        0   0.0   \n",
       "108            0      131              0              5        0  43.2   \n",
       "111            2       74              0              5        0   0.0   \n",
       "199            2       87              0             23        0  28.9   \n",
       "247            7      119              0              5        0  25.2   \n",
       "284            3      141              0              5        0  30.0   \n",
       "289            0      138              0              5        0  36.3   \n",
       "292            2      146              0              5        0  27.5   \n",
       "321            0      167              0              5        0  32.3   \n",
       "352            1      180              0              5        0  43.3   \n",
       "355            0      117              0              5        0  33.8   \n",
       "366            3      116              0              5        0  23.5   \n",
       "442            0       94              0              5        0   0.0   \n",
       "446            2       99              0              5        0  22.2   \n",
       "451            0      141              0              5        0  42.4   \n",
       "468            2      119              0              5        0  19.6   \n",
       "482            8      120              0              5        0  30.0   \n",
       "498            0      145              0              5        0  44.2   \n",
       "508            3       80              0              5        0   0.0   \n",
       "534            6      114              0              5        0   0.0   \n",
       "545            6       91              0              5        0  29.8   \n",
       "547            4      132              0              5        0  32.9   \n",
       "598            0       73              0              5        0  21.1   \n",
       "609            6       96              0              5        0  23.7   \n",
       "612            4      183              0              5        0  28.4   \n",
       "626            0      119              0              5        0  32.4   \n",
       "649            4       90              0              5        0  28.0   \n",
       "700            0       99              0              5        0  25.0   \n",
       "706            2      129              0              5        0  38.5   \n",
       "709           10      115              0              5        0   0.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "7                       0.578   40        1  \n",
       "15                      0.569   44        1  \n",
       "41                      0.134   29        0  \n",
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       "498                     0.630   31        1  \n",
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data[(data['BloodPressure']==0) & (data['Insulin']==0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    763.000000\n",
       "mean      31.986763\n",
       "std        7.893221\n",
       "min        0.000000\n",
       "25%       27.300000\n",
       "50%       32.000000\n",
       "75%       36.550000\n",
       "max       67.100000\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['BMI'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = data[data['BMI']==0]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin  BMI  \\\n",
       "43             8      125             96              5        0  0.0   \n",
       "80             7      105              0              5        0  0.0   \n",
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       "\n",
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       "43                      0.232   54        1  \n",
       "80                      0.305   24        0  \n",
       "91                      0.304   21        0  \n",
       "111                     0.102   22        0  \n",
       "173                     0.572   21        0  \n",
       "388                     1.731   21        0  \n",
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lokesh/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
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   ],
   "source": [
    "p1['BMI']=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['BMI']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.concat([data,p1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0             10      143             94             33      146  36.6   \n",
       "1             10      145             82             19      110  22.2   \n",
       "2             10      138             76              5        0  33.2   \n",
       "3             10      126             90              5        0  43.4   \n",
       "4             10      106             72             54        0  36.6   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "442            0       94              0              5        0  10.0   \n",
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       "688            5      136             82              5        0  10.0   \n",
       "709           10      115              0              5        0  10.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.254   51        1  \n",
       "1                       0.245   57        0  \n",
       "2                       0.420   35        0  \n",
       "3                       0.583   42        1  \n",
       "4                       0.178   45        0  \n",
       "..                        ...  ...      ...  \n",
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       "688                     0.640   69        0  \n",
       "709                     0.261   30        1  \n",
       "\n",
       "[763 rows x 9 columns]"
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     "execution_count": 30,
     "metadata": {},
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    "data"
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  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    763.000000\n",
       "mean       0.472477\n",
       "std        0.331963\n",
       "min        0.078000\n",
       "25%        0.243500\n",
       "50%        0.374000\n",
       "75%        0.626500\n",
       "max        2.420000\n",
       "Name: DiabetesPedigreeFunction, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['DiabetesPedigreeFunction'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    763.000000\n",
       "mean      80.292267\n",
       "std      115.457448\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%       36.000000\n",
       "75%      128.500000\n",
       "max      846.000000\n",
       "Name: Insulin, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Insulin'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[data['Insulin']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>126</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>43.4</td>\n",
       "      <td>0.583</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>52.3</td>\n",
       "      <td>0.578</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>39.9</td>\n",
       "      <td>0.569</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10</td>\n",
       "      <td>175</td>\n",
       "      <td>62</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.212</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>84</td>\n",
       "      <td>72</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.297</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>6</td>\n",
       "      <td>190</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.278</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9</td>\n",
       "      <td>170</td>\n",
       "      <td>74</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
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       "      <th>766</th>\n",
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       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.232</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>0.261</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "3             10      126             90              5        0  43.4   \n",
       "7             10      135              0              5        0  52.3   \n",
       "15            10      129              0             30        0  39.9   \n",
       "19            10      175             62             30        0  33.6   \n",
       "20            10       84             72             31        0  29.7   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "759            6      190             92              5        0  35.5   \n",
       "761            9      170             74             31        0  44.0   \n",
       "766            1      126             60              5        0  30.1   \n",
       "43             8      125             96              5        0  10.0   \n",
       "709           10      115              0              5        0  10.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "3                       0.583   42        1  \n",
       "7                       0.578   40        1  \n",
       "15                      0.569   44        1  \n",
       "19                      0.212   38        1  \n",
       "20                      0.297   46        1  \n",
       "..                        ...  ...      ...  \n",
       "759                     0.278   66        1  \n",
       "761                     0.403   43        1  \n",
       "766                     0.349   47        1  \n",
       "43                      0.232   54        1  \n",
       "709                     0.261   30        1  \n",
       "\n",
       "[136 rows x 9 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['Insulin']==0) & (data['Outcome']==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0             10      143             94             33      146  36.6   \n",
       "1             10      145             82             19      110  22.2   \n",
       "2             10      138             76              5        0  33.2   \n",
       "3             10      126             90              5        0  43.4   \n",
       "4             10      106             72             54        0  36.6   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
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       "709           10      115              0              5        0  10.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.254   51        1  \n",
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       "4                       0.178   45        0  \n",
       "..                        ...  ...      ...  \n",
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       "508                     0.174   22        0  \n",
       "534                     0.189   26        0  \n",
       "688                     0.640   69        0  \n",
       "709                     0.261   30        1  \n",
       "\n",
       "[763 rows x 9 columns]"
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0             10      143             94             33      146  36.6   \n",
       "1             10      145             82             19      110  22.2   \n",
       "2             10      138             76              5        0  33.2   \n",
       "3             10      126             90              5        0  43.4   \n",
       "4             10      106             72             54        0  36.6   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "442            0       94              0              5        0  10.0   \n",
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       "688            5      136             82              5        0  10.0   \n",
       "709           10      115              0              5        0  10.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                       0.254   51  \n",
       "1                       0.245   57  \n",
       "2                       0.420   35  \n",
       "3                       0.583   42  \n",
       "4                       0.178   45  \n",
       "..                        ...  ...  \n",
       "442                     0.256   25  \n",
       "508                     0.174   22  \n",
       "534                     0.189   26  \n",
       "688                     0.640   69  \n",
       "709                     0.261   30  \n",
       "\n",
       "[763 rows x 8 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.iloc[:,:-1]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=100,test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(534, 8)\n",
      "(534,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "Dc = DecisionTreeClassifier()\n",
    "Dc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = Dc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7205240174672489"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "score = accuracy_score(y_test,y_pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[115,  24],\n",
       "       [ 40,  50]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score1 = confusion_matrix(y_test,y_pred)\n",
    "score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lokesh/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "Rf =RandomForestClassifier()\n",
    "Rf.fit(X_train,y_train)\n",
    "y_pred = Rf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7554585152838428"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = accuracy_score(y_test,y_pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>10</td>\n",
       "      <td>143</td>\n",
       "      <td>94</td>\n",
       "      <td>33</td>\n",
       "      <td>146</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>145</td>\n",
       "      <td>82</td>\n",
       "      <td>19</td>\n",
       "      <td>110</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>138</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>0.420</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>126</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>43.4</td>\n",
       "      <td>0.583</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.178</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.256</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.174</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>6</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.189</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>82</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.640</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.261</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>763 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0             10      143             94             33      146  36.6   \n",
       "1             10      145             82             19      110  22.2   \n",
       "2             10      138             76              5        0  33.2   \n",
       "3             10      126             90              5        0  43.4   \n",
       "4             10      106             72             54        0  36.6   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "442            0       94              0              5        0  10.0   \n",
       "508            3       80              0              5        0  10.0   \n",
       "534            6      114              0              5        0  10.0   \n",
       "688            5      136             82              5        0  10.0   \n",
       "709           10      115              0              5        0  10.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.254   51        1  \n",
       "1                       0.245   57        0  \n",
       "2                       0.420   35        0  \n",
       "3                       0.583   42        1  \n",
       "4                       0.178   45        0  \n",
       "..                        ...  ...      ...  \n",
       "442                     0.256   25        0  \n",
       "508                     0.174   22        0  \n",
       "534                     0.189   26        0  \n",
       "688                     0.640   69        0  \n",
       "709                     0.261   30        1  \n",
       "\n",
       "[763 rows x 9 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lokesh/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "L = LogisticRegression()\n",
    "L.fit(X_train,y_train)\n",
    "y_pred = L.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.777292576419214"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = accuracy_score(y_test,y_pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "Gr = GradientBoostingClassifier()\n",
    "Gr.fit(X_train,y_train)\n",
    "y_pred = Gr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7860262008733624"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = accuracy_score(y_test,y_pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inp(Preg,Gl,BP,SkinThickness,Insulin,BMI,Diab,Age):\n",
    "    x = np.zeros(len(X.columns))\n",
    "    x[0]=Preg\n",
    "    x[1]=Gl\n",
    "    x[2]=BP\n",
    "    x[3]=SkinThickness\n",
    "    x[4]=Insulin\n",
    "    x[5]=BMI\n",
    "    x[6]=Diab\n",
    "    x[7]=Age\n",
    "    out = Gr.predict([x])[0]\n",
    "    return out\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp(10,115,0,5,0,50,0.261,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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  "language_info": {
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    "name": "ipython",
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