Notebook_Experiments/Diabetes_Disease_Prediction.ipynb

{
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier"
   ]
  },
  {
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   "execution_count": 2,
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       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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      "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",
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       "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]"
      ]
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     "execution_count": 2,
     "metadata": {},
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   "source": [
    "df = pd.read_csv('Data/diabetes.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Outcome\"].value_counts()\n",
    "\n",
    "class_0 = df[df[\"Outcome\"]==0]\n",
    "class_1 = df[df[\"Outcome\"]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "1              1       85             66             29        0  26.6   \n",
       "3              1       89             66             23       94  28.1   \n",
       "5              5      116             74              0        0  25.6   \n",
       "7             10      115              0              0        0  35.3   \n",
       "10             4      110             92              0        0  37.6   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "762            9       89             62              0        0  22.5   \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",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "1                       0.351   31        0  \n",
       "3                       0.167   21        0  \n",
       "5                       0.201   30        0  \n",
       "7                       0.134   29        0  \n",
       "10                      0.191   30        0  \n",
       "..                        ...  ...      ...  \n",
       "762                     0.142   33        0  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[500 rows x 9 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
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    "class_0"
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       "      <td>39</td>\n",
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       "      <td>1.057</td>\n",
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       "      <th>759</th>\n",
       "      <td>6</td>\n",
       "      <td>190</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.278</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>9</td>\n",
       "      <td>170</td>\n",
       "      <td>74</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>268 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "4              0      137             40             35      168  43.1   \n",
       "6              3       78             50             32       88  31.0   \n",
       "8              2      197             70             45      543  30.5   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "755            1      128             88             39      110  36.5   \n",
       "757            0      123             72              0        0  36.3   \n",
       "759            6      190             92              0        0  35.5   \n",
       "761            9      170             74             31        0  44.0   \n",
       "766            1      126             60              0        0  30.1   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "2                       0.672   32        1  \n",
       "4                       2.288   33        1  \n",
       "6                       0.248   26        1  \n",
       "8                       0.158   53        1  \n",
       "..                        ...  ...      ...  \n",
       "755                     1.057   37        1  \n",
       "757                     0.258   52        1  \n",
       "759                     0.278   66        1  \n",
       "761                     0.403   43        1  \n",
       "766                     0.349   47        1  \n",
       "\n",
       "[268 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>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>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>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>0.191</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>13</td>\n",
       "      <td>126</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43.4</td>\n",
       "      <td>0.583</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>110</td>\n",
       "      <td>46</td>\n",
       "      <td>130</td>\n",
       "      <td>67.1</td>\n",
       "      <td>0.319</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>14</td>\n",
       "      <td>175</td>\n",
       "      <td>62</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.212</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>0.630</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>167</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.962</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "1              1       85             66             29        0  26.6   \n",
       "3              1       89             66             23       94  28.1   \n",
       "5              5      116             74              0        0  25.6   \n",
       "7             10      115              0              0        0  35.3   \n",
       "10             4      110             92              0        0  37.6   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "72            13      126             90              0        0  43.4   \n",
       "177            0      129            110             46      130  67.1   \n",
       "455           14      175             62             30        0  33.6   \n",
       "484            0      145              0              0        0  44.2   \n",
       "356            1      125             50             40      167  33.3   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "1                       0.351   31        0  \n",
       "3                       0.167   21        0  \n",
       "5                       0.201   30        0  \n",
       "7                       0.134   29        0  \n",
       "10                      0.191   30        0  \n",
       "..                        ...  ...      ...  \n",
       "72                      0.583   42        1  \n",
       "177                     0.319   26        1  \n",
       "455                     0.212   38        1  \n",
       "484                     0.630   31        1  \n",
       "356                     0.962   28        1  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_n = class_1.sample(500, replace=True)\n",
    "\n",
    "df = pd.concat([class_0, df_n], axis=0)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "287399dd48440ea7218b37d4ba2879f7d26913c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1000 entries, 1 to 356\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               1000 non-null   int64  \n",
      " 1   Glucose                   1000 non-null   int64  \n",
      " 2   BloodPressure             1000 non-null   int64  \n",
      " 3   SkinThickness             1000 non-null   int64  \n",
      " 4   Insulin                   1000 non-null   int64  \n",
      " 5   BMI                       1000 non-null   float64\n",
      " 6   DiabetesPedigreeFunction  1000 non-null   float64\n",
      " 7   Age                       1000 non-null   int64  \n",
      " 8   Outcome                   1000 non-null   int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 78.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df.columns\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "e982f132558616b264da875afdc7b018e757e676"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.099000</td>\n",
       "      <td>127.114000</td>\n",
       "      <td>69.473000</td>\n",
       "      <td>20.69800</td>\n",
       "      <td>85.087</td>\n",
       "      <td>32.509000</td>\n",
       "      <td>0.488880</td>\n",
       "      <td>34.604000</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.466297</td>\n",
       "      <td>33.358954</td>\n",
       "      <td>19.494083</td>\n",
       "      <td>16.57237</td>\n",
       "      <td>124.321</td>\n",
       "      <td>7.879823</td>\n",
       "      <td>0.349517</td>\n",
       "      <td>11.952659</td>\n",
       "      <td>0.50025</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>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>0.247000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>122.500000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>14.000</td>\n",
       "      <td>32.750000</td>\n",
       "      <td>0.391000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>33.00000</td>\n",
       "      <td>140.000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.647000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.00000</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>99.00000</td>\n",
       "      <td>846.000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies      Glucose  BloodPressure  SkinThickness   Insulin  \\\n",
       "count  1000.000000  1000.000000    1000.000000     1000.00000  1000.000   \n",
       "mean      4.099000   127.114000      69.473000       20.69800    85.087   \n",
       "std       3.466297    33.358954      19.494083       16.57237   124.321   \n",
       "min       0.000000     0.000000       0.000000        0.00000     0.000   \n",
       "25%       1.000000   102.000000      64.000000        0.00000     0.000   \n",
       "50%       3.000000   122.500000      72.000000       23.00000    14.000   \n",
       "75%       7.000000   151.000000      80.000000       33.00000   140.000   \n",
       "max      17.000000   199.000000     122.000000       99.00000   846.000   \n",
       "\n",
       "               BMI  DiabetesPedigreeFunction          Age     Outcome  \n",
       "count  1000.000000               1000.000000  1000.000000  1000.00000  \n",
       "mean     32.509000                  0.488880    34.604000     0.50000  \n",
       "std       7.879823                  0.349517    11.952659     0.50025  \n",
       "min       0.000000                  0.078000    21.000000     0.00000  \n",
       "25%      27.800000                  0.247000    25.000000     0.00000  \n",
       "50%      32.750000                  0.391000    31.000000     0.50000  \n",
       "75%      36.600000                  0.647000    42.000000     1.00000  \n",
       "max      67.100000                  2.420000    81.000000     1.00000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = []\n",
    "numerical_columns = []\n",
    "\n",
    "for col in df.columns:\n",
    "    if df[col].dtype == 'object':\n",
    "        categorical_columns.append(col)\n",
    "    else:\n",
    "        numerical_columns.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Features:  ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome']\n",
      "\n",
      "Categorical Features:  []\n"
     ]
    }
   ],
   "source": [
    "print(\"Numerical Features: \",numerical_columns)\n",
    "\n",
    "print(\"\\nCategorical Features: \",categorical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "discrete_features=[feature for feature in numerical_columns if len(df[feature].unique())<25]\n",
    "continuous_features=[feature for feature in numerical_columns if feature not in discrete_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3,3, figsize=(20, 15))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, col in enumerate(df.columns):\n",
    "    ax = axes[i]\n",
    "    df.boxplot(column=col, ax=ax)\n",
    "    ax.set_ylabel(col)\n",
    "\n",
    "for j in range(len(df.columns), 3,3):\n",
    "    fig.delaxes(axes[j])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in continuous_features:\n",
    "    IQR = df[feature].quantile(0.75) - df[feature].quantile(0.25)\n",
    "    lower_limit = df[feature].quantile(0.25) - (IQR * 3)\n",
    "    upper_limit = df[feature].quantile(0.75) + (IQR * 3)\n",
    "    df.loc[df[feature] >= upper_limit, feature] = upper_limit\n",
    "    df.loc[df[feature] < lower_limit, feature] = lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x =  df.drop(columns=['Outcome'],axis=1)\n",
    "y = df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "x = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test =train_test_split(x,y,test_size=0.20,random_state=40,stratify=df['Outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    'Logistic Regression': LogisticRegression(),\n",
    "    'Naive Bayes': GaussianNB(),\n",
    "    'Random Forest Classifier': RandomForestClassifier(n_estimators=20, max_depth=10),\n",
    "    'XG Boost': XGBClassifier(learning_rate=0.01, n_estimators=25, max_depth=15, gamma=0.6, subsample=0.52, colsample_bytree=0.6, seed=27,\n",
    "                              reg_lambda=2, booster='dart', colsample_bylevel=0.6, colsample_bynode=0.5),\n",
    "    'K Nearest Neighbors': KNeighborsClassifier(n_neighbors=10),\n",
    "    'Decision Tree': DecisionTreeClassifier(criterion='entropy', random_state=0, max_depth=6),\n",
    "    'Support Vector Machine': SVC(kernel='rbf', C=2, probability=True),\n",
    "    'AdaBoost Classifier': AdaBoostClassifier(),\n",
    "    'Gradient Boosting Classifier': GradientBoostingClassifier(),\n",
    "    'Extra Trees Classifier': ExtraTreesClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic Regression\n",
      "Testing Accuracy:  0.685\n",
      "Precision:  0.6907216494845361\n",
      "Recall:  0.67\n",
      "F1 Score:  0.6802030456852791\n",
      "===================================\n",
      "Model: Naive Bayes\n",
      "Testing Accuracy:  0.715\n",
      "Precision:  0.7311827956989247\n",
      "Recall:  0.68\n",
      "F1 Score:  0.7046632124352332\n",
      "===================================\n",
      "Model: Random Forest Classifier\n",
      "Testing Accuracy:  0.85\n",
      "Precision:  0.7916666666666666\n",
      "Recall:  0.95\n",
      "F1 Score:  0.8636363636363635\n",
      "===================================\n",
      "Model: XG Boost\n",
      "Testing Accuracy:  0.755\n",
      "Precision:  0.7256637168141593\n",
      "Recall:  0.82\n",
      "F1 Score:  0.7699530516431924\n",
      "===================================\n",
      "Model: K Nearest Neighbors\n",
      "Testing Accuracy:  0.71\n",
      "Precision:  0.7142857142857143\n",
      "Recall:  0.7\n",
      "F1 Score:  0.7070707070707072\n",
      "===================================\n",
      "Model: Decision Tree\n",
      "Testing Accuracy:  0.76\n",
      "Precision:  0.7131147540983607\n",
      "Recall:  0.87\n",
      "F1 Score:  0.7837837837837837\n",
      "===================================\n",
      "Model: Support Vector Machine\n",
      "Testing Accuracy:  0.76\n",
      "Precision:  0.7363636363636363\n",
      "Recall:  0.81\n",
      "F1 Score:  0.7714285714285715\n",
      "===================================\n",
      "Model: AdaBoost Classifier\n",
      "Testing Accuracy:  0.775\n",
      "Precision:  0.7433628318584071\n",
      "Recall:  0.84\n",
      "F1 Score:  0.7887323943661971\n",
      "===================================\n",
      "Model: Gradient Boosting Classifier\n",
      "Testing Accuracy:  0.79\n",
      "Precision:  0.7589285714285714\n",
      "Recall:  0.85\n",
      "F1 Score:  0.8018867924528302\n",
      "===================================\n",
      "Model: Extra Trees Classifier\n",
      "Testing Accuracy:  0.875\n",
      "Precision:  0.8440366972477065\n",
      "Recall:  0.92\n",
      "F1 Score:  0.8803827751196172\n",
      "===================================\n",
      "The best model based on accuracy is Extra Trees Classifier with Testing Accuracy: 0.875\n"
     ]
    }
   ],
   "source": [
    "model_list = []\n",
    "accuracy_list = []\n",
    "precision_list = []\n",
    "recall_list = []\n",
    "f1_list = []\n",
    "\n",
    "for model_name, model in models.items():\n",
    "\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred_train = model.predict(x_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "\n",
    "    test_accuracy = accuracy_score(y_test, y_pred)\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(f\"Model: {model_name}\")\n",
    "    print(\"Testing Accuracy: \", test_accuracy)\n",
    "    print(\"Precision: \",precision)\n",
    "    print(\"Recall: \",recall)\n",
    "    print(\"F1 Score: \",f1)\n",
    "\n",
    "\n",
    "    model_list.append(model_name)\n",
    "    accuracy_list.append(test_accuracy)\n",
    "    precision_list.append(precision)\n",
    "    recall_list.append(recall)\n",
    "    f1_list.append(f1)\n",
    "\n",
    "    print(\"=\" * 35)\n",
    "\n",
    "max_accuracy_index = accuracy_list.index(max(accuracy_list))\n",
    "print(f\"The best model based on accuracy is {model_list[max_accuracy_index]} with Testing Accuracy: {accuracy_list[max_accuracy_index]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = models['Extra Trees Classifier']\n",
    "pickle.dump(best_model,open('Diabetes_Model.pkl','wb'))\n",
    "pickle.dump(scaler,open('Diabetes_Preprocessor.pkl','wb'))"
   ]
  }
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