ML(KNN by Hirad and Parisan) logreg =1,  knn=0, naivebayes=0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "c30d198d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "da892512",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <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>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",
       "      <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>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\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",
       "      <td>1</td>\n",
       "      <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",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <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": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"diabetes.csv\")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "afefa132",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "d0a59b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "caee044a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "6c6c573a",
   "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",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "f645e703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "8b916717",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Glucose\"]=data[\"Glucose\"].replace(0,np.nan)\n",
    "data[\"BloodPressure\"]=data[\"BloodPressure\"].replace(0,np.nan)\n",
    "data[\"SkinThickness\"]=data[\"SkinThickness\"].replace(0,np.nan)\n",
    "data[\"Insulin\"]=data[\"Insulin\"].replace(0,np.nan)\n",
    "data[\"BMI\"]=data[\"BMI\"].replace(0,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "900c9afa",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</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",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0      NaN  33.6   \n",
       "1            1     85.0           66.0           29.0      NaN  26.6   \n",
       "2            8    183.0           64.0            NaN      NaN  23.3   \n",
       "3            1     89.0           66.0           23.0     94.0  28.1   \n",
       "4            0    137.0           40.0           35.0    168.0  43.1   \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  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "b0f3cad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   0\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     374\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "d3e220cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.0000\n",
       "Glucose                     117.0000\n",
       "BloodPressure                72.0000\n",
       "SkinThickness                29.0000\n",
       "Insulin                     125.0000\n",
       "BMI                          32.3000\n",
       "DiabetesPedigreeFunction      0.3725\n",
       "Age                          29.0000\n",
       "Outcome                       0.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "64099686",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna(data.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "c19f26bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
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       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
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       "      <th>3</th>\n",
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       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</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>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>32.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
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       "      <td>30.5</td>\n",
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       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>0.232</td>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0    125.0  33.6   \n",
       "1            1     85.0           66.0           29.0    125.0  26.6   \n",
       "2            8    183.0           64.0           29.0    125.0  23.3   \n",
       "3            1     89.0           66.0           23.0     94.0  28.1   \n",
       "4            0    137.0           40.0           35.0    168.0  43.1   \n",
       "5            5    116.0           74.0           29.0    125.0  25.6   \n",
       "6            3     78.0           50.0           32.0     88.0  31.0   \n",
       "7           10    115.0           72.0           29.0    125.0  35.3   \n",
       "8            2    197.0           70.0           45.0    543.0  30.5   \n",
       "9            8    125.0           96.0           29.0    125.0  32.3   \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",
       "5                     0.201   30        0  \n",
       "6                     0.248   26        1  \n",
       "7                     0.134   29        0  \n",
       "8                     0.158   53        1  \n",
       "9                     0.232   54        1  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "8be37646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "b4ec37fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "\n",
    "plt.subplot(4,4,1)\n",
    "sb.boxplot(data[\"Pregnancies\"])\n",
    "\n",
    "plt.subplot(4,4,2)\n",
    "sb.boxplot(data[\"Glucose\"])\n",
    "\n",
    "plt.subplot(4,4,3)\n",
    "sb.boxplot(data[\"BloodPressure\"])\n",
    "\n",
    "plt.subplot(4,4,4)\n",
    "sb.boxplot(data[\"SkinThickness\"])\n",
    "\n",
    "\n",
    "plt.subplot(4,4,5)\n",
    "sb.boxplot(data[\"Insulin\"])\n",
    "\n",
    "plt.subplot(4,4,6)\n",
    "sb.boxplot(data[\"BMI\"])\n",
    "\n",
    "plt.subplot(4,4,7)\n",
    "sb.boxplot(data[\"DiabetesPedigreeFunction\"])\n",
    "\n",
    "plt.subplot(4,4,8)\n",
    "sb.boxplot(data[\"Age\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "ebd024e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Pregnancies\"]=data[\"Pregnancies\"].clip(lower=data[\"Pregnancies\"].quantile(0.05),upper=data[\"Pregnancies\"].quantile(0.95))\n",
    "data[\"BloodPressure\"]=data[\"BloodPressure\"].clip(lower=data[\"BloodPressure\"].quantile(0.05),upper=data[\"BloodPressure\"].quantile(0.95))\n",
    "data[\"SkinThickness\"]=data[\"SkinThickness\"].clip(lower=data[\"SkinThickness\"].quantile(0.07),upper=data[\"SkinThickness\"].quantile(0.93))\n",
    "data[\"Insulin\"]=data[\"Insulin\"].clip(lower=data[\"Insulin\"].quantile(0.25),upper=data[\"Insulin\"].quantile(0.75))\n",
    "data[\"BMI\"]=data[\"BMI\"].clip(lower=data[\"BMI\"].quantile(0.05),upper=data[\"BMI\"].quantile(0.95))\n",
    "data[\"DiabetesPedigreeFunction\"]=data[\"DiabetesPedigreeFunction\"].clip(lower=data[\"DiabetesPedigreeFunction\"].quantile(0.05),upper=data[\"DiabetesPedigreeFunction\"].quantile(0.95))\n",
    "data[\"Age\"]=data[\"Age\"].clip(lower=data[\"Age\"].quantile(0.05),upper=data[\"Age\"].quantile(0.95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "c1a1c52c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "\n",
    "plt.subplot(4,4,1)\n",
    "sb.boxplot(data[\"Pregnancies\"])\n",
    "\n",
    "plt.subplot(4,4,2)\n",
    "sb.boxplot(data[\"Glucose\"])\n",
    "\n",
    "plt.subplot(4,4,3)\n",
    "sb.boxplot(data[\"BloodPressure\"])\n",
    "\n",
    "plt.subplot(4,4,4)\n",
    "sb.boxplot(data[\"SkinThickness\"])\n",
    "\n",
    "\n",
    "plt.subplot(4,4,5)\n",
    "sb.boxplot(data[\"Insulin\"])\n",
    "\n",
    "plt.subplot(4,4,6)\n",
    "sb.boxplot(data[\"BMI\"])\n",
    "\n",
    "plt.subplot(4,4,7)\n",
    "sb.boxplot(data[\"DiabetesPedigreeFunction\"])\n",
    "\n",
    "plt.subplot(4,4,8)\n",
    "sb.boxplot(data[\"Age\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "635e37e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "cd0c6f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.DataFrame(data,columns=[\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\",\"Age\"]).values\n",
    "y=data.Outcome.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "72c9adf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[148.   ,  72.   ,  35.   , ...,  33.6  ,   0.627,  50.   ],\n",
       "       [ 85.   ,  66.   ,  29.   , ...,  26.6  ,   0.351,  31.   ],\n",
       "       [183.   ,  64.   ,  29.   , ...,  23.3  ,   0.672,  32.   ],\n",
       "       ...,\n",
       "       [121.   ,  72.   ,  23.   , ...,  26.2  ,   0.245,  30.   ],\n",
       "       [126.   ,  60.   ,  29.   , ...,  30.1  ,   0.349,  47.   ],\n",
       "       [ 93.   ,  70.   ,  31.   , ...,  30.4  ,   0.315,  23.   ]])"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "e5121979",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "d9cc83fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:179: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "k=12\n",
    "\n",
    "clf=KNeighborsClassifier(k)\n",
    "\n",
    "clf.fit(x_train,y_train)\n",
    "\n",
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "039b3738",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is : 0.7748917748917749\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy is :\",metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "e2301a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:179: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "k=3\n",
    "\n",
    "clf=KNeighborsClassifier(k)\n",
    "\n",
    "clf.fit(x_train,y_train)\n",
    "\n",
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "8103d9a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is : 0.7056277056277056\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy is :\",metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "bdcd630b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Parisan\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:179: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "k=10\n",
    "\n",
    "clf=KNeighborsClassifier(k)\n",
    "\n",
    "clf.fit(x_train,y_train)\n",
    "\n",
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "62ffac5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is : 0.7575757575757576\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy is :\",metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3fd14c3",
   "metadata": {},
   "source": [
    "# Creat a for loop to avoid these process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "f1b17f95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.71861472, 0.70995671, 0.70562771, 0.72294372, 0.74458874,\n",
       "       0.73160173, 0.73593074, 0.74025974, 0.74458874, 0.75757576,\n",
       "       0.76190476, 0.77489177, 0.76190476, 0.76623377, 0.77056277,\n",
       "       0.77489177, 0.76623377, 0.75757576, 0.76623377, 0.77489177])"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=20\n",
    "\n",
    "acc=np.zeros((k))\n",
    "\n",
    "for i in range(1, k+1):\n",
    "    clf=KNeighborsClassifier(n_neighbors=i)\n",
    "    clf.fit(x_train,y_train.ravel())\n",
    "    y_pred=clf.predict(x_test)\n",
    "    acc[i-1]=metrics.accuracy_score(y_test,y_pred)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "f5fea603",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7748917748917749\n",
      "0.7056277056277056\n"
     ]
    }
   ],
   "source": [
    "print(np.max(acc))\n",
    "print(np.min(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "364f58c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is : 0.7748917748917749\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy is :\",metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eba600b8",
   "metadata": {},
   "source": [
    "# Another Way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "125f8080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_acc=[]\n",
    "test_acc=[]\n",
    "\n",
    "neighbors_setting=range(1,20)\n",
    "\n",
    "for n_neighbors in neighbors_setting:\n",
    "    knn=KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    knn.fit(x_train,y_train.ravel())\n",
    "    training_acc.append(knn.score(x_train,y_train))\n",
    "    test_acc.append(knn.score(x_test,y_test))\n",
    "    \n",
    "plt.figure(figsize=(7,4),dpi=100)\n",
    "\n",
    "plt.plot(neighbors_setting,training_acc,label=\"accuracy of the training set\")\n",
    "\n",
    "plt.plot(neighbors_setting,test_acc,label=\"accuracy of the test set\")\n",
    "\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "\n",
    "plt.ylabel(\"Accuracy\")\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3206486",
   "metadata": {},
   "source": [
    "# Improve Model :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "2179bc84",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "c3ed4a24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 49 candidates, totalling 245 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(n_neighbors=19), n_jobs=-1,\n",
       "             param_grid={'n_neighbors': range(1, 50)}, scoring='accuracy',\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters={\"n_neighbors\":range(1,50)}\n",
    "\n",
    "grid_knn=GridSearchCV(estimator=knn,\n",
    "                      param_grid=parameters,\n",
    "                      scoring=\"accuracy\",\n",
    "                      cv=5,verbose=1,\n",
    "                      n_jobs=-1)\n",
    "\n",
    "grid_knn.fit(x_train,y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "39144da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 10}"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_knn.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "4ab820e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.4, 0.6],\n",
       "       [0.9, 0.1],\n",
       "       [0.2, 0.8],\n",
       "       ...,\n",
       "       [0.8, 0.2],\n",
       "       [0.5, 0.5],\n",
       "       [1. , 0. ]])"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_knn.predict_proba(x)\n",
    "# 2 predict factor (proba, predict) proba means the probability of accuracy(x,y) y means accuracy\n",
    "# for example x=0.4 y=0.6 predict =1\n",
    "# x=0.9, y=0.1 predict = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "837e12e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_knn.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "d58c98d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7786458333333334"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_knn.score(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "4c2c4664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "grid_knn.classes_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "61ce89c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[433,  67],\n",
       "       [103, 165]], dtype=int64)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y, grid_knn.predict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "fd989e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y, grid_knn.predict(x))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(cm)\n",
    "ax.grid(False)\n",
    "ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\n",
    "ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\n",
    "ax.set_ylim(1.5, -0.5)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax.text(j, i, cm[i, j], ha='center', va='center', color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "c7e703bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.87      0.84       500\n",
      "           1       0.71      0.62      0.66       268\n",
      "\n",
      "    accuracy                           0.78       768\n",
      "   macro avg       0.76      0.74      0.75       768\n",
      "weighted avg       0.77      0.78      0.77       768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y, grid_knn.predict(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57beeb17",
   "metadata": {},
   "source": [
    "# Practice\n",
    "\n",
    "What are these?\n",
    "\n",
    "(C=10.0, class_weight=None, dual=False, fit_intercept=True,\n",
    "\n",
    "intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
    "\n",
    "multi_class='warn', n_jobs=None, penalty='l2',\n",
    "\n",
    "random_state=0, solver='liblinear', tol=0.0001, verbose=0,\n",
    "\n",
    "warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "c47139bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = pd.DataFrame(data , columns=['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age'])\n",
    "df2= pd.DataFrame({\"Pregnancies\":[0],\n",
    "                  \"Glucose\":[80],\n",
    "                  \"BloodPressure\":[72],\n",
    "                  \"SkinThickness\":[0],\n",
    "                  \"Insulin\":[0],\n",
    "                  \"BMI\":[23],\n",
    "                  \"DiabetesPedigreeFunction\":[0.5],\n",
    "                  \"Age\":[30],\n",
    "                  \"Outcome\":[0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "d550700c",
   "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>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.62700</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.35100</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.67200</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.16700</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>127.25</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.13285</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.34000</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.24500</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.34900</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.31500</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>769 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6    148.0           72.0           35.0   125.00  33.6   \n",
       "1              1     85.0           66.0           29.0   125.00  26.6   \n",
       "2              8    183.0           64.0           29.0   125.00  23.3   \n",
       "3              1     89.0           66.0           23.0   121.50  28.1   \n",
       "4              0    137.0           52.0           35.0   127.25  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "764            2    122.0           70.0           27.0   125.00  36.8   \n",
       "765            5    121.0           72.0           23.0   121.50  26.2   \n",
       "766            1    126.0           60.0           29.0   125.00  30.1   \n",
       "767            1     93.0           70.0           31.0   125.00  30.4   \n",
       "0              0     80.0           72.0            0.0     0.00  23.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.62700   50        1  \n",
       "1                     0.35100   31        0  \n",
       "2                     0.67200   32        1  \n",
       "3                     0.16700   21        0  \n",
       "4                     1.13285   33        1  \n",
       "..                        ...  ...      ...  \n",
       "764                   0.34000   27        0  \n",
       "765                   0.24500   30        0  \n",
       "766                   0.34900   47        1  \n",
       "767                   0.31500   23        0  \n",
       "0                     0.50000   30        0  \n",
       "\n",
       "[769 rows x 9 columns]"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data.append(df2)\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "4444d1fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data2[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']][:768]\n",
    "y_train = data2[[\"Outcome\"]][:768].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "744abc8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = data2[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']][768:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "45e5e3c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "clf=KNeighborsClassifier(k)\n",
    "\n",
    "clf.fit(X_train,y_train.ravel())\n",
    "\n",
    "y_pred=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "c3fece10",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "10fe8899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "020c9881",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9cdf00c6",
   "metadata": {},
   "source": [
    "# LogReg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "6089c01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.DataFrame(data,columns=[\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\",\"Age\"]).values\n",
    "y=data.Outcome.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "c0a811d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "9377f27e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "7232187e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "3e7a5fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(x_train,y_train.ravel())\n",
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "3f8ab813",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7965367965367965\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "ded10a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = pd.DataFrame(data , columns=['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age'])\n",
    "df3= pd.DataFrame({\"Pregnancies\":[0],\n",
    "                  \"Glucose\":[80],\n",
    "                  \"BloodPressure\":[72],\n",
    "                  \"SkinThickness\":[0],\n",
    "                  \"Insulin\":[0],\n",
    "                  \"BMI\":[23],\n",
    "                  \"DiabetesPedigreeFunction\":[0.5],\n",
    "                  \"Age\":[30],\n",
    "                  \"Outcome\":[0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "8454d668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.62700</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.35100</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.67200</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.16700</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>127.25</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.13285</td>\n",
       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.34000</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.24500</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.34900</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.31500</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>769 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6    148.0           72.0           35.0   125.00  33.6   \n",
       "1              1     85.0           66.0           29.0   125.00  26.6   \n",
       "2              8    183.0           64.0           29.0   125.00  23.3   \n",
       "3              1     89.0           66.0           23.0   121.50  28.1   \n",
       "4              0    137.0           52.0           35.0   127.25  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "764            2    122.0           70.0           27.0   125.00  36.8   \n",
       "765            5    121.0           72.0           23.0   121.50  26.2   \n",
       "766            1    126.0           60.0           29.0   125.00  30.1   \n",
       "767            1     93.0           70.0           31.0   125.00  30.4   \n",
       "0              0     80.0           72.0            0.0     0.00  23.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.62700   50        1  \n",
       "1                     0.35100   31        0  \n",
       "2                     0.67200   32        1  \n",
       "3                     0.16700   21        0  \n",
       "4                     1.13285   33        1  \n",
       "..                        ...  ...      ...  \n",
       "764                   0.34000   27        0  \n",
       "765                   0.24500   30        0  \n",
       "766                   0.34900   47        1  \n",
       "767                   0.31500   23        0  \n",
       "0                     0.50000   30        0  \n",
       "\n",
       "[769 rows x 9 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 = data.append(df3)\n",
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "fa392983",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data3[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']][:768]\n",
    "y_train = data3[[\"Outcome\"]][:768].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "cc0783e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = data3[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']][768:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "da93fee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10, random_state=0, solver='liblinear')"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver='liblinear', C=10, random_state=0)\n",
    "model.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "e5dfb152",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "c978db65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78454780",
   "metadata": {},
   "source": [
    "# Gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dff0a317",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "2f2a22d6",
   "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>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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>33.6</td>\n",
       "      <td>0.62700</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.35100</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.67200</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.16700</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>127.25</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.13285</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>101.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>127.25</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.17100</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.34000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.24500</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.34900</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.31500</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0      148.0           72.0           35.0   125.00  33.6   \n",
       "1       85.0           66.0           29.0   125.00  26.6   \n",
       "2      183.0           64.0           29.0   125.00  23.3   \n",
       "3       89.0           66.0           23.0   121.50  28.1   \n",
       "4      137.0           52.0           35.0   127.25  43.1   \n",
       "..       ...            ...            ...      ...   ...   \n",
       "763    101.0           76.0           42.0   127.25  32.9   \n",
       "764    122.0           70.0           27.0   125.00  36.8   \n",
       "765    121.0           72.0           23.0   121.50  26.2   \n",
       "766    126.0           60.0           29.0   125.00  30.1   \n",
       "767     93.0           70.0           31.0   125.00  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                     0.62700   50  \n",
       "1                     0.35100   31  \n",
       "2                     0.67200   32  \n",
       "3                     0.16700   21  \n",
       "4                     1.13285   33  \n",
       "..                        ...  ...  \n",
       "763                   0.17100   58  \n",
       "764                   0.34000   27  \n",
       "765                   0.24500   30  \n",
       "766                   0.34900   47  \n",
       "767                   0.31500   23  \n",
       "\n",
       "[768 rows x 7 columns]"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(data , columns=['Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age'])\n",
    "y = data.Outcome.values.reshape(-1,1) # Target variable\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "018f34d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "3e738f9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "38c94dda",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf2 = GaussianNB()\n",
    "clf2.fit(x_train,y_train.ravel())\n",
    "y_pred = clf2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "fb8aa6a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8008658008658008\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "a9f2e050",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = pd.DataFrame(data , columns=['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age'])\n",
    "df4= pd.DataFrame({\"Pregnancies\":[0],\n",
    "                  \"Glucose\":[80],\n",
    "                  \"BloodPressure\":[72],\n",
    "                  \"SkinThickness\":[0],\n",
    "                  \"Insulin\":[0],\n",
    "                  \"BMI\":[23],\n",
    "                  \"DiabetesPedigreeFunction\":[0.5],\n",
    "                  \"Age\":[30],\n",
    "                  \"Outcome\":[0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "49a32e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.62700</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.35100</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.67200</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.16700</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>127.25</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.13285</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.34000</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.24500</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.34900</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.31500</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>769 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6    148.0           72.0           35.0   125.00  33.6   \n",
       "1              1     85.0           66.0           29.0   125.00  26.6   \n",
       "2              8    183.0           64.0           29.0   125.00  23.3   \n",
       "3              1     89.0           66.0           23.0   121.50  28.1   \n",
       "4              0    137.0           52.0           35.0   127.25  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "764            2    122.0           70.0           27.0   125.00  36.8   \n",
       "765            5    121.0           72.0           23.0   121.50  26.2   \n",
       "766            1    126.0           60.0           29.0   125.00  30.1   \n",
       "767            1     93.0           70.0           31.0   125.00  30.4   \n",
       "0              0     80.0           72.0            0.0     0.00  23.0   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.62700   50        1  \n",
       "1                     0.35100   31        0  \n",
       "2                     0.67200   32        1  \n",
       "3                     0.16700   21        0  \n",
       "4                     1.13285   33        1  \n",
       "..                        ...  ...      ...  \n",
       "764                   0.34000   27        0  \n",
       "765                   0.24500   30        0  \n",
       "766                   0.34900   47        1  \n",
       "767                   0.31500   23        0  \n",
       "0                     0.50000   30        0  \n",
       "\n",
       "[769 rows x 9 columns]"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4 = data.append(df4)\n",
    "data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "a96acbc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data4[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']][:768]\n",
    "y_train = data4[[\"Outcome\"]][:768].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "53553ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = data4[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']][768:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "ea87944b",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf2 = GaussianNB()\n",
    "clf2.fit(X_train,y_train.ravel())\n",
    "y_pred = clf2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "271a10be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7ef12c0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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