Diabetes_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d4f16ca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00cb8f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Diabetes.csv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "469e07fd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
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       "      <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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       "      <th>1</th>\n",
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       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
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       "      <td>26.6</td>\n",
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       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
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       "      <th>3</th>\n",
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       "      <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",
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       "      <th>763</th>\n",
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       "      <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",
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       "      <th>765</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>766</th>\n",
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       "      <td>60</td>\n",
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       "      <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": {},
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    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
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   "id": "c61ed64d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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       "    <tr>\n",
       "      <th>764</th>\n",
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       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
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       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
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       "      <th>765</th>\n",
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       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
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       "    <tr>\n",
       "      <th>766</th>\n",
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       "      <td>126</td>\n",
       "      <td>60</td>\n",
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       "      <td>30.1</td>\n",
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       "      <th>767</th>\n",
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       "      <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",
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       "  </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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb49f26d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os.stat_result(st_mode=33206, st_ino=70087269201056319, st_dev=5120994, st_nlink=1, st_uid=0, st_gid=0, st_size=23873, st_atime=1685821434, st_mtime=1679478374, st_ctime=1679478374)\n",
      "\n",
      "File Size in MegaBytes is 0.022767066955566406\n"
     ]
    }
   ],
   "source": [
    "#check the file size\n",
    "import os\n",
    "file_name = r\"C:\\Users\\IDEH\\Desktop\\datasets\\Diabetes.csv.csv\"\n",
    "file_stats = os.stat(file_name)\n",
    "print(file_stats)\n",
    "#\n",
    "print(f'\\nFile Size in MegaBytes is {file_stats.st_size / (1024 * 1024)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44916c4b",
   "metadata": {},
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df.groupby('Pregnancies').size().plot(kind='pie', autopct='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2864ff8",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "560eda55",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6eb688e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03d89245",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "726f01b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(df,hue='Outcome')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f1af4a",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b2ac9d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(['Outcome'],axis=1)\n",
    "#x=df[['Glucose']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d900146",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Outcome']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c2bf9c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y,\n",
    "                                                test_size=0.3, random_state=132)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6dec5d",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e78b2d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "abdaa72c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eec38710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d80a241f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "981a3ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "009d3c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = r2_score(ytest,y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c9c39d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24543744140460078"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "646020af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#there is no linear correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "982e45cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using nbg model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "375c2c62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7dd6bee6",
   "metadata": {},
   "source": [
    "# Naive Bayes Guassian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d22226be",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ec859fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1a868123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussian.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "15ee6530",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes score:  0.7229437229437229\n"
     ]
    }
   ],
   "source": [
    "print(\"Naive Bayes score: \",gaussian.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea15b803",
   "metadata": {},
   "source": [
    "# Decision Tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4d555a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "feaf7f3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=40, random_state=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=DecisionTreeClassifier(criterion='gini',max_depth=40,random_state=0)\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cd93d8d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_model_pred=model.predict(xtest)\n",
    "y_pred_train_model=model.predict(xtrain)\n",
    "y_pred_train_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "177cad10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model accuracy score with criterion gini Index : 0.7013\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('model accuracy score with criterion gini Index : {0:0.4f}'.format(accuracy_score(ytest,y_model_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "12861fec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 1.0000\n",
      "Test set score: 0.7013\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(model.score(xtrain, ytrain)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(model.score(xtest, ytest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c81e92ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training-set accuracy score: 1.0000\n"
     ]
    }
   ],
   "source": [
    "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(ytrain, y_pred_train_model)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591afc6b",
   "metadata": {},
   "source": [
    "# Random Forrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "25115e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score with 10 decision trees :0.7446\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(random_state=0)\n",
    "model.fit(xtrain,ytrain)\n",
    "y_pred=model.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Model accuracy score with 10 decision trees :{0:0.4f}'.format(accuracy_score(ytest,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b04df865",
   "metadata": {},
   "outputs": [],
   "source": [
    "#second approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ee858e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=RandomForestClassifier(n_estimators=100,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2d0e13bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "218046e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score with 100 decision trees :0.7446\n"
     ]
    }
   ],
   "source": [
    "y_pred_100=model2.predict(xtest)\n",
    "print('Model accuracy score with 100 decision trees :{0:0.4f}'.format(accuracy_score(ytest,y_pred_100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "389e0877",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11b385ad",
   "metadata": {},
   "source": [
    "# PCA + Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "19090ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1beca4ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "xtrain = pd.DataFrame(scaler.fit_transform(xtrain), columns = x.columns)\n",
    "\n",
    "xtest = pd.DataFrame(scaler.transform(xtest), columns = x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "99d7e672",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(xtrain, ytrain)\n",
    "y_pred = logreg.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f0dabe3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression accuracy score with all the features:\n",
      " 0.7489\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression accuracy score with all the features:\\n {0:0.4f}'. format(accuracy_score(ytest, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1658df7",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b351b9f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cc4db53c",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e7eab0e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3f2292b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "predknn = knn.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "17a45c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "88b3ca5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[122  28]\n",
      " [ 43  38]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(ytest,predknn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "93f98a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.81      0.77       150\n",
      "           1       0.58      0.47      0.52        81\n",
      "\n",
      "    accuracy                           0.69       231\n",
      "   macro avg       0.66      0.64      0.65       231\n",
      "weighted avg       0.68      0.69      0.68       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,predknn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fbd2917c",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "# Will take some time\n",
    "for i in range(1,40):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(xtrain,ytrain)\n",
    "    pred_i = knn.predict(xtest)\n",
    "    error_rate.append(np.mean(pred_i != ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "78da9687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d934edc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITH K=36\n",
      "\n",
      "\n",
      "[[145   5]\n",
      " [ 49  32]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.97      0.84       150\n",
      "           1       0.86      0.40      0.54        81\n",
      "\n",
      "    accuracy                           0.77       231\n",
      "   macro avg       0.81      0.68      0.69       231\n",
      "weighted avg       0.79      0.77      0.74       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# NOW WITH K=36\n",
    "knn = KNeighborsClassifier(n_neighbors=36)\n",
    "\n",
    "knn.fit(xtrain,ytrain)\n",
    "predknn2 = knn.predict(xtest)\n",
    "\n",
    "print('WITH K=36')\n",
    "print('\\n')\n",
    "print(confusion_matrix(ytest,predknn2))\n",
    "print('\\n')\n",
    "print(classification_report(ytest,predknn2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad123a2",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c56f911b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_profiling\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "289ab7d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e00d0bdf6de47bb9724182a03789a35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(HTML(value='<div id=\"overview-content\" class=\"row variable spacing\">\\n    <div class=\"row\">\\n   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Report generated with <a href=\"https://github.com/pandas-profiling/pandas-profiling\">pandas-profiling</a>."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile = data.profile_report(title='Diabetes Profiling Report')\n",
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c1978dc5",
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.iloc[:, :8]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "473c92fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data[\"Outcome\"]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "df4818df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(576, 8)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, random_state=0)\n",
    "    \n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "41951e3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(576,)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bb19ec81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192, 8)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "85fa52df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192,)\n"
     ]
    }
   ],
   "source": [
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e8705ff8",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>9</td>\n",
       "      <td>89</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.142</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>58</td>\n",
       "      <td>36</td>\n",
       "      <td>94</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.261</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.601</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>9</td>\n",
       "      <td>145</td>\n",
       "      <td>80</td>\n",
       "      <td>46</td>\n",
       "      <td>130</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.637</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "762            9       89             62              0        0  22.5   \n",
       "127            1      118             58             36       94  33.3   \n",
       "564            0       91             80              0        0  32.4   \n",
       "375           12      140             82             43      325  39.2   \n",
       "663            9      145             80             46      130  37.9   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "762                     0.142   33  \n",
       "127                     0.261   23  \n",
       "564                     0.601   27  \n",
       "375                     0.528   58  \n",
       "663                     0.637   40  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "78f40660",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize Features\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5220f320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.50755225, -1.01521454, -0.40451932, -1.31921491, -0.71823753,\n",
       "        -1.22070104, -0.98325882, -0.04863985],\n",
       "       [-0.82986389, -0.09964691, -0.61509602,  0.9287299 ,  0.08374747,\n",
       "         0.13719053, -0.62493647, -0.88246592],\n",
       "       [-1.12204091, -0.95207195,  0.54307587, -1.31921491, -0.71823753,\n",
       "         0.0240329 ,  0.39884168, -0.5489355 ],\n",
       "       [ 2.38408331,  0.59492164,  0.64836422,  1.36583027,  2.05458297,\n",
       "         0.87900167,  0.17903049,  2.03592532],\n",
       "       [ 1.50755225,  0.75277813,  0.54307587,  1.55315901,  0.39089067,\n",
       "         0.71555175,  0.50724171,  0.53503839]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:5, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e6f7fdb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear\n",
      "0.7638888888888888\n",
      "poly\n",
      "0.7934027777777778\n",
      "rbf\n",
      "0.8246527777777778\n",
      "sigmoid\n",
      "0.6510416666666666\n"
     ]
    }
   ],
   "source": [
    "for k in ('linear', 'poly', 'rbf', 'sigmoid'):\n",
    "    model = svm.SVC(kernel=k)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_train)\n",
    "    print(k)\n",
    "    print(accuracy_score(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a5f9f450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the best model\n",
    "model = svm.SVC(kernel='rbf')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b1d3f52e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient = np.array([[ 1., 150., 70., 45., 0., 40., 1.5, 25]])\n",
    "    \n",
    "# Normalize the data with the values used in the training set\n",
    "patient = scaler.transform(patient)\n",
    "    \n",
    "predbest=model.predict(patient)\n",
    "predbest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d08cbac2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.7760416666666666\n"
     ]
    }
   ],
   "source": [
    "# Accuracy on Testing Set\n",
    "X_test = scaler.transform(X_test)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "325b77d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6770833333333334\n"
     ]
    }
   ],
   "source": [
    "y_zero = np.zeros(y_test.shape)\n",
    "print(accuracy_score(y_test, y_zero))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3deb51ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision is 0.7111111111111111\n",
      "Recall is 0.5161290322580645\n",
      "F1 score is 0.5981308411214953\n"
     ]
    }
   ],
   "source": [
    "# Compute precision, recall and f1 score\n",
    "from sklearn.metrics import recall_score, precision_score, f1_score\n",
    "    \n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "    \n",
    "print(\"Precision is\", precision)\n",
    "print(\"Recall is\", recall)\n",
    "print(\"F1 score is\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "03713e4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.90      0.84       130\n",
      "           1       0.71      0.52      0.60        62\n",
      "\n",
      "    accuracy                           0.78       192\n",
      "   macro avg       0.75      0.71      0.72       192\n",
      "weighted avg       0.77      0.78      0.77       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "844826b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "073f8b03",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b04c12a3",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# NEW DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ed2b05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "newdata=pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ed2389c",
   "metadata": {},
   "outputs": [],
   "source": [
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01ca9931",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf=pd.DataFrame(newdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b0745a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# f = plt.figure(figsize=(19, 15))\n",
    "# plt.matshow(ndf.corr(), fignum=f.number)\n",
    "# plt.xticks(range(ndf.select_dtypes(['number']).shape[1]), ndf.select_dtypes(['number']).columns, fontsize=14, rotation=45)\n",
    "# plt.yticks(range(ndf.select_dtypes(['number']).shape[1]), ndf.select_dtypes(['number']).columns, fontsize=14)\n",
    "# cb = plt.colorbar()\n",
    "# cb.ax.tick_params(labelsize=14)\n",
    "# plt.title('Correlation Matrix', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa3c6e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3670cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x2=ndf.Code\n",
    "y2=ndf.Value\n",
    "x2.values.reshape(-1,1)\n",
    "y2.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2767113f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain2,ytrain2,xtest2,ytest2 = train_test_split(x2,y2,test_size=.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed8edaa0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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