Diabetes_Disease_Prediction.ipynb

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    "# <center>Diabetes Disease Detection</center>\n",
    "\n",
    "## Overview:\n",
    "Diabetes is a disease that occurs when your blood glucose, also called blood sugar, is too high. Glucose is your body’s main source of energy. Your body can make glucose, but glucose also comes from the food you eat. Insulin is a hormone made by the pancreas that helps glucose get into your cells to be used for energy. If you have diabetes, your body doesn’t make enough—or any—insulin, or doesn’t use insulin properly. Glucose then stays in your blood and doesn’t reach your cells. Diabetes raises the risk for damage to the eyes, kidneys, nerves, and heart. Diabetes is also linked to some types of cancer.\n",
    "\n",
    "## Dataset Information:\n",
    "PIMA Indian Dataset: [Kaggle - Diabetes Disease Detection](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database)\n",
    "\n",
    "### Independent Feature:\n",
    "* Outcome\n",
    "\n",
    "### Dependent Feature:\n",
    "* Pregnancies\t\n",
    "* Glucose\t\n",
    "* BloodPressure\t\n",
    "* SkinThickness\t\n",
    "* Insulin\t\n",
    "* BMI\t\n",
    "* DiabetesPedigreeFunction\t\n",
    "* Age\n",
    "\n",
    "### Training : Testing :: 800 : 200 :: 80 : 20\n",
    "\n",
    "### Evaluation Metrics Considered:\n",
    "* Accuracy, Precision, Recision, F1 Score, ROC AUC Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing required libraries\n",
    "* NumPy: For numerical computations and array manipulation.\n",
    "* Pandas: For data manipulation and analysis.\n",
    "* Seaborn: For statistical data visualization.\n",
    "* Matplotlib: For creating plots and visualizations.\n",
    "* Scikit-learn: For machine learning algorithms and evaluation metrics.\n",
    "* XGBoost: For Extreme gradient boosting machine learning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "2c411f76441a20c7d33b83484ba7bd5b85666e88"
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   "outputs": [],
   "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, train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, roc_auc_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\n",
    "\n",
    "from evidently import ColumnMapping\n",
    "from evidently.report import Report\n",
    "from evidently.metric_preset import DataDriftPreset, TargetDriftPreset, ClassificationPreset, DataQualityPreset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Ingestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "2d34ee41f879077adc2a8f2298e9bbb63f9d1cf3"
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   "outputs": [
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       "  <thead>\n",
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       "      <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",
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       "     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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data/diabetes.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "   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  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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
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       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
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       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
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       "      <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",
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       "    <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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    500\n",
       "1    268\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Data Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 9)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(268, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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>604</th>\n",
       "      <td>4</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.212</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>6</td>\n",
       "      <td>147</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.178</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>7</td>\n",
       "      <td>142</td>\n",
       "      <td>90</td>\n",
       "      <td>24</td>\n",
       "      <td>480</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.128</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>3</td>\n",
       "      <td>158</td>\n",
       "      <td>70</td>\n",
       "      <td>30</td>\n",
       "      <td>328</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.344</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>66</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.196</td>\n",
       "      <td>22</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",
       "604            4      183              0              0        0  28.4   \n",
       "642            6      147             80              0        0  29.5   \n",
       "695            7      142             90             24      480  30.4   \n",
       "480            3      158             70             30      328  35.5   \n",
       "397            0      131             66             40        0  34.3   \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",
       "604                     0.212   36        1  \n",
       "642                     0.178   50        1  \n",
       "695                     0.128   43        1  \n",
       "480                     0.344   35        1  \n",
       "397                     0.196   22        1  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_n = class_1.sample(500, replace=True)\n",
    "df = pd.concat([class_0, df_n], axis=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 9)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Feature Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pregnancies', 'Outcome']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discrete_features=[feature for feature in numerical_columns if len(df[feature].unique())<25]\n",
    "discrete_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Glucose',\n",
       " 'BloodPressure',\n",
       " 'SkinThickness',\n",
       " 'Insulin',\n",
       " 'BMI',\n",
       " 'DiabetesPedigreeFunction',\n",
       " 'Age']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous_features=[feature for feature in numerical_columns if feature not in discrete_features]\n",
    "continuous_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Training and Testing Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x =  df.drop(columns=['Outcome'],axis=1)\n",
    "y = df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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",
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       "      <th>Age</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",
       "    </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>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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>4</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.212</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>6</td>\n",
       "      <td>147</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.178</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>7</td>\n",
       "      <td>142</td>\n",
       "      <td>90</td>\n",
       "      <td>24</td>\n",
       "      <td>480</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.128</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>3</td>\n",
       "      <td>158</td>\n",
       "      <td>70</td>\n",
       "      <td>30</td>\n",
       "      <td>328</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.344</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>66</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.196</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 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",
       "604            4      183              0              0        0  28.4   \n",
       "642            6      147             80              0        0  29.5   \n",
       "695            7      142             90             24      480  30.4   \n",
       "480            3      158             70             30      328  35.5   \n",
       "397            0      131             66             40        0  34.3   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "1                       0.351   31  \n",
       "3                       0.167   21  \n",
       "5                       0.201   30  \n",
       "7                       0.134   29  \n",
       "10                      0.191   30  \n",
       "..                        ...  ...  \n",
       "604                     0.212   36  \n",
       "642                     0.178   50  \n",
       "695                     0.128   43  \n",
       "480                     0.344   35  \n",
       "397                     0.196   22  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "x = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# 5. Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic Regression\n",
      "Training Accuracy: 0.7425\n",
      "Testing Accuracy: 0.73\n",
      "Precision: 0.7169811320754716\n",
      "Recall: 0.76\n",
      "F1 Score: 0.7378640776699029\n",
      "Specificity: 0.7\n",
      "ROC AUC Score: 0.73\n",
      "==================================================\n",
      "Model: Naive Bayes\n",
      "Training Accuracy: 0.72875\n",
      "Testing Accuracy: 0.74\n",
      "Precision: 0.74\n",
      "Recall: 0.74\n",
      "F1 Score: 0.74\n",
      "Specificity: 0.74\n",
      "ROC AUC Score: 0.74\n",
      "==================================================\n",
      "Model: Random Forest Classifier\n",
      "Training Accuracy: 0.9925\n",
      "Testing Accuracy: 0.825\n",
      "Precision: 0.782608695652174\n",
      "Recall: 0.9\n",
      "F1 Score: 0.8372093023255814\n",
      "Specificity: 0.75\n",
      "ROC AUC Score: 0.825\n",
      "==================================================\n",
      "Model: XG Boost\n",
      "Training Accuracy: 0.83625\n",
      "Testing Accuracy: 0.76\n",
      "Precision: 0.7096774193548387\n",
      "Recall: 0.88\n",
      "F1 Score: 0.7857142857142857\n",
      "Specificity: 0.64\n",
      "ROC AUC Score: 0.76\n",
      "==================================================\n",
      "Model: K Nearest Neighbors\n",
      "Training Accuracy: 0.7625\n",
      "Testing Accuracy: 0.71\n",
      "Precision: 0.71\n",
      "Recall: 0.71\n",
      "F1 Score: 0.71\n",
      "Specificity: 0.71\n",
      "ROC AUC Score: 0.71\n",
      "==================================================\n",
      "Model: Decision Tree\n",
      "Training Accuracy: 0.86\n",
      "Testing Accuracy: 0.815\n",
      "Precision: 0.7692307692307693\n",
      "Recall: 0.9\n",
      "F1 Score: 0.8294930875576036\n",
      "Specificity: 0.73\n",
      "ROC AUC Score: 0.8150000000000001\n",
      "==================================================\n",
      "Model: Support Vector Machine\n",
      "Training Accuracy: 0.855\n",
      "Testing Accuracy: 0.795\n",
      "Precision: 0.7565217391304347\n",
      "Recall: 0.87\n",
      "F1 Score: 0.8093023255813954\n",
      "Specificity: 0.72\n",
      "ROC AUC Score: 0.795\n",
      "==================================================\n",
      "Model: AdaBoost Classifier\n",
      "Training Accuracy: 0.84\n",
      "Testing Accuracy: 0.765\n",
      "Precision: 0.7304347826086957\n",
      "Recall: 0.84\n",
      "F1 Score: 0.7813953488372093\n",
      "Specificity: 0.69\n",
      "ROC AUC Score: 0.7649999999999999\n",
      "==================================================\n",
      "Model: Gradient Boosting Classifier\n",
      "Training Accuracy: 0.935\n",
      "Testing Accuracy: 0.785\n",
      "Precision: 0.7478260869565218\n",
      "Recall: 0.86\n",
      "F1 Score: 0.8\n",
      "Specificity: 0.71\n",
      "ROC AUC Score: 0.7849999999999999\n",
      "==================================================\n",
      "Model: Extra Trees Classifier\n",
      "Training Accuracy: 1.0\n",
      "Testing Accuracy: 0.86\n",
      "Precision: 0.8529411764705882\n",
      "Recall: 0.87\n",
      "F1 Score: 0.8613861386138614\n",
      "Specificity: 0.85\n",
      "ROC AUC Score: 0.8600000000000001\n",
      "==================================================\n",
      "\n",
      "The best model based on accuracy is Extra Trees Classifier with Testing Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "model_list = []\n",
    "train_accuracy_list = []\n",
    "test_accuracy_list = []\n",
    "precision_list = []\n",
    "recall_list = []\n",
    "f1_list = []\n",
    "specificity_list = []\n",
    "roc_auc_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",
    "    train_accuracy = accuracy_score(y_train, y_pred_train)\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",
    "    tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "    specificity = tn / (tn + fp)\n",
    "    roc_auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "    print(f\"Model: {model_name}\")\n",
    "    print(f\"Training Accuracy: {train_accuracy}\")\n",
    "    print(f\"Testing Accuracy: {test_accuracy}\")\n",
    "    print(f\"Precision: {precision}\")\n",
    "    print(f\"Recall: {recall}\")\n",
    "    print(f\"F1 Score: {f1}\")\n",
    "    print(f\"Specificity: {specificity}\")\n",
    "    print(f\"ROC AUC Score: {roc_auc}\")\n",
    "\n",
    "    model_list.append(model_name)\n",
    "    train_accuracy_list.append(train_accuracy)\n",
    "    test_accuracy_list.append(test_accuracy)\n",
    "    precision_list.append(precision)\n",
    "    recall_list.append(recall)\n",
    "    f1_list.append(f1)\n",
    "    specificity_list.append(specificity)\n",
    "    roc_auc_list.append(roc_auc)\n",
    "\n",
    "    print(\"=\" * 50)\n",
    "\n",
    "max_accuracy_index = test_accuracy_list.index(max(test_accuracy_list))\n",
    "print(f\"\\nThe best model based on accuracy is {model_list[max_accuracy_index]} with Testing Accuracy: {test_accuracy_list[max_accuracy_index]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall (Sensitivity)</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Extra Trees Classifier</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.852941</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.861386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.99250</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.86000</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.829493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.85500</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.756522</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.809302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>0.93500</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.747826</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>0.84000</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.730435</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.781395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XG Boost</td>\n",
       "      <td>0.83625</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.709677</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.72875</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.74250</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.716981</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.737864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K Nearest Neighbors</td>\n",
       "      <td>0.76250</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model  Train Accuracy  Test Accuracy  Precision  \\\n",
       "9        Extra Trees Classifier         1.00000          0.860   0.852941   \n",
       "2      Random Forest Classifier         0.99250          0.825   0.782609   \n",
       "5                 Decision Tree         0.86000          0.815   0.769231   \n",
       "6        Support Vector Machine         0.85500          0.795   0.756522   \n",
       "8  Gradient Boosting Classifier         0.93500          0.785   0.747826   \n",
       "7           AdaBoost Classifier         0.84000          0.765   0.730435   \n",
       "3                      XG Boost         0.83625          0.760   0.709677   \n",
       "1                   Naive Bayes         0.72875          0.740   0.740000   \n",
       "0           Logistic Regression         0.74250          0.730   0.716981   \n",
       "4           K Nearest Neighbors         0.76250          0.710   0.710000   \n",
       "\n",
       "   Recall (Sensitivity)  F1 Score  \n",
       "9                  0.87  0.861386  \n",
       "2                  0.90  0.837209  \n",
       "5                  0.90  0.829493  \n",
       "6                  0.87  0.809302  \n",
       "8                  0.86  0.800000  \n",
       "7                  0.84  0.781395  \n",
       "3                  0.88  0.785714  \n",
       "1                  0.74  0.740000  \n",
       "0                  0.76  0.737864  \n",
       "4                  0.71  0.710000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = {\n",
    "    'Model': model_list,\n",
    "    'Train Accuracy': train_accuracy_list,\n",
    "    'Test Accuracy': test_accuracy_list,\n",
    "    'Precision': precision_list,\n",
    "    'Recall (Sensitivity)': recall_list,\n",
    "    'F1 Score': f1_list\n",
    "}\n",
    "\n",
    "results = pd.DataFrame(results).sort_values(by=['Test Accuracy', 'Precision', 'Recall (Sensitivity)'], ascending=False)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Hyperparamter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'Logistic Regression': {'C': [0.1, 1, 10]},\n",
    "    'Random Forest Classifier': {'n_estimators': [50, 100], 'max_depth': [5, 10]},\n",
    "    'XG Boost': {'learning_rate': [0.01, 0.1], 'n_estimators': [50, 100], 'max_depth': [10, 15]},\n",
    "    'K Nearest Neighbors': {'n_neighbors': [5, 10, 15]},\n",
    "    'Decision Tree': {'max_depth': [5, 10]},\n",
    "    'Support Vector Machine': {'C': [1, 10]},\n",
    "    'AdaBoost Classifier': {'n_estimators': [50, 100]},\n",
    "    'Gradient Boosting Classifier': {'n_estimators': [50, 100]},\n",
    "    'Extra Trees Classifier': {'n_estimators': [50, 100]}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best parameters for Logistic Regression: {'C': 0.1}\n",
      "Train Accuracy for Logistic Regression: 0.7425\n",
      "Test Accuracy for Logistic Regression: 0.73\n",
      "Precision for Logistic Regression: 0.7169811320754716\n",
      "Recall for Logistic Regression: 0.76\n",
      "F1 Score for Logistic Regression: 0.7378640776699029\n",
      "Specificity for Logistic Regression: 0.7\n",
      "ROC AUC Score for Logistic Regression: 0.73\n",
      "==================================================\n",
      "\n",
      "Best parameters for Random Forest Classifier: {'max_depth': 10, 'n_estimators': 50}\n",
      "Train Accuracy for Random Forest Classifier: 0.9975\n",
      "Test Accuracy for Random Forest Classifier: 0.845\n",
      "Precision for Random Forest Classifier: 0.8\n",
      "Recall for Random Forest Classifier: 0.92\n",
      "F1 Score for Random Forest Classifier: 0.8558139534883721\n",
      "Specificity for Random Forest Classifier: 0.77\n",
      "ROC AUC Score for Random Forest Classifier: 0.845\n",
      "==================================================\n",
      "\n",
      "Best parameters for XG Boost: {'learning_rate': 0.1, 'max_depth': 10, 'n_estimators': 100}\n",
      "Train Accuracy for XG Boost: 0.9225\n",
      "Test Accuracy for XG Boost: 0.795\n",
      "Precision for XG Boost: 0.7478991596638656\n",
      "Recall for XG Boost: 0.89\n",
      "F1 Score for XG Boost: 0.8127853881278538\n",
      "Specificity for XG Boost: 0.7\n",
      "ROC AUC Score for XG Boost: 0.7949999999999999\n",
      "==================================================\n",
      "\n",
      "Best parameters for K Nearest Neighbors: {'n_neighbors': 5}\n",
      "Train Accuracy for K Nearest Neighbors: 0.85125\n",
      "Test Accuracy for K Nearest Neighbors: 0.76\n",
      "Precision for K Nearest Neighbors: 0.7241379310344828\n",
      "Recall for K Nearest Neighbors: 0.84\n",
      "F1 Score for K Nearest Neighbors: 0.7777777777777778\n",
      "Specificity for K Nearest Neighbors: 0.68\n",
      "ROC AUC Score for K Nearest Neighbors: 0.7599999999999999\n",
      "==================================================\n",
      "\n",
      "Best parameters for Decision Tree: {'max_depth': 10}\n",
      "Train Accuracy for Decision Tree: 0.955\n",
      "Test Accuracy for Decision Tree: 0.785\n",
      "Precision for Decision Tree: 0.7522123893805309\n",
      "Recall for Decision Tree: 0.85\n",
      "F1 Score for Decision Tree: 0.7981220657276995\n",
      "Specificity for Decision Tree: 0.72\n",
      "ROC AUC Score for Decision Tree: 0.785\n",
      "==================================================\n",
      "\n",
      "Best parameters for Support Vector Machine: {'C': 10}\n",
      "Train Accuracy for Support Vector Machine: 0.89375\n",
      "Test Accuracy for Support Vector Machine: 0.82\n",
      "Precision for Support Vector Machine: 0.7758620689655172\n",
      "Recall for Support Vector Machine: 0.9\n",
      "F1 Score for Support Vector Machine: 0.8333333333333334\n",
      "Specificity for Support Vector Machine: 0.74\n",
      "ROC AUC Score for Support Vector Machine: 0.82\n",
      "==================================================\n",
      "\n",
      "Best parameters for AdaBoost Classifier: {'n_estimators': 50}\n",
      "Train Accuracy for AdaBoost Classifier: 0.84\n",
      "Test Accuracy for AdaBoost Classifier: 0.765\n",
      "Precision for AdaBoost Classifier: 0.7304347826086957\n",
      "Recall for AdaBoost Classifier: 0.84\n",
      "F1 Score for AdaBoost Classifier: 0.7813953488372093\n",
      "Specificity for AdaBoost Classifier: 0.69\n",
      "ROC AUC Score for AdaBoost Classifier: 0.7649999999999999\n",
      "==================================================\n",
      "\n",
      "Best parameters for Gradient Boosting Classifier: {'n_estimators': 100}\n",
      "Train Accuracy for Gradient Boosting Classifier: 0.935\n",
      "Test Accuracy for Gradient Boosting Classifier: 0.785\n",
      "Precision for Gradient Boosting Classifier: 0.7478260869565218\n",
      "Recall for Gradient Boosting Classifier: 0.86\n",
      "F1 Score for Gradient Boosting Classifier: 0.8\n",
      "Specificity for Gradient Boosting Classifier: 0.71\n",
      "ROC AUC Score for Gradient Boosting Classifier: 0.7849999999999999\n",
      "==================================================\n",
      "\n",
      "Best parameters for Extra Trees Classifier: {'n_estimators': 50}\n",
      "Train Accuracy for Extra Trees Classifier: 1.0\n",
      "Test Accuracy for Extra Trees Classifier: 0.845\n",
      "Precision for Extra Trees Classifier: 0.8165137614678899\n",
      "Recall for Extra Trees Classifier: 0.89\n",
      "F1 Score for Extra Trees Classifier: 0.8516746411483254\n",
      "Specificity for Extra Trees Classifier: 0.8\n",
      "ROC AUC Score for Extra Trees Classifier: 0.8450000000000001\n",
      "==================================================\n",
      "We got Random Forest Classifier as the model with the highest test accuracy of 0.845\n"
     ]
    }
   ],
   "source": [
    "modelss = []\n",
    "train_accuracies = []\n",
    "test_accuracies = []\n",
    "precisions = []\n",
    "recalls = []\n",
    "f1_scores = []\n",
    "specificities = []\n",
    "roc_auc_scores = []\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    if model_name in param_grid:\n",
    "        grid_search = GridSearchCV(model, param_grid[model_name], cv=5, n_jobs=-1)\n",
    "\n",
    "        grid_search.fit(x_train, y_train)\n",
    "        best_model_cv = grid_search.best_estimator_\n",
    "        best_params = grid_search.best_params_\n",
    "        print(f\"\\nBest parameters for {model_name}: {best_params}\")\n",
    "\n",
    "        y_train_pred = best_model_cv.predict(x_train)\n",
    "        y_pred = best_model_cv.predict(x_test)\n",
    "\n",
    "        train_accuracy = accuracy_score(y_train, y_train_pred)\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",
    "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "        specificity = tn / (tn + fp)\n",
    "        roc_auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "        modelss.append(model_name)\n",
    "        train_accuracies.append(train_accuracy)\n",
    "        test_accuracies.append(test_accuracy)\n",
    "        precisions.append(precision)\n",
    "        recalls.append(recall)\n",
    "        f1_scores.append(f1)\n",
    "        specificities.append(specificity)\n",
    "        roc_auc_scores.append(roc_auc)\n",
    "\n",
    "        print(f\"Train Accuracy for {model_name}: {train_accuracy}\")\n",
    "        print(f\"Test Accuracy for {model_name}: {test_accuracy}\")\n",
    "        print(f\"Precision for {model_name}: {precision}\")\n",
    "        print(f\"Recall for {model_name}: {recall}\")\n",
    "        print(f\"F1 Score for {model_name}: {f1}\")\n",
    "        print(f\"Specificity for {model_name}: {specificity}\")\n",
    "        print(f\"ROC AUC Score for {model_name}: {roc_auc}\")\n",
    "        print(\"=\" * 50)\n",
    "\n",
    "best_test_accuracy_index = test_accuracies.index(max(test_accuracies))\n",
    "print(f\"We got {modelss[best_test_accuracy_index]} as the model with the highest test accuracy of {test_accuracies[best_test_accuracy_index]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall (Sensitivity)</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>ROC AUC Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Extra Trees Classifier</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.816514</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.851675</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.99750</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.855814</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.89375</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.775862</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XG Boost</td>\n",
       "      <td>0.92250</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.747899</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.812785</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.95500</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.752212</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.798122</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>0.93500</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.747826</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost Classifier</td>\n",
       "      <td>0.84000</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.730435</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.781395</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K Nearest Neighbors</td>\n",
       "      <td>0.85125</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.724138</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.74250</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.716981</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.737864</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model  Train Accuracy  Test Accuracy  Precision  \\\n",
       "8        Extra Trees Classifier         1.00000          0.845   0.816514   \n",
       "1      Random Forest Classifier         0.99750          0.845   0.800000   \n",
       "5        Support Vector Machine         0.89375          0.820   0.775862   \n",
       "2                      XG Boost         0.92250          0.795   0.747899   \n",
       "4                 Decision Tree         0.95500          0.785   0.752212   \n",
       "7  Gradient Boosting Classifier         0.93500          0.785   0.747826   \n",
       "6           AdaBoost Classifier         0.84000          0.765   0.730435   \n",
       "3           K Nearest Neighbors         0.85125          0.760   0.724138   \n",
       "0           Logistic Regression         0.74250          0.730   0.716981   \n",
       "\n",
       "   Recall (Sensitivity)  F1 Score  Specificity  ROC AUC Score  \n",
       "8                  0.89  0.851675         0.80          0.845  \n",
       "1                  0.92  0.855814         0.77          0.845  \n",
       "5                  0.90  0.833333         0.74          0.820  \n",
       "2                  0.89  0.812785         0.70          0.795  \n",
       "4                  0.85  0.798122         0.72          0.785  \n",
       "7                  0.86  0.800000         0.71          0.785  \n",
       "6                  0.84  0.781395         0.69          0.765  \n",
       "3                  0.84  0.777778         0.68          0.760  \n",
       "0                  0.76  0.737864         0.70          0.730  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results1 = {\n",
    "    'Model': modelss,\n",
    "    'Train Accuracy': train_accuracies,\n",
    "    'Test Accuracy': test_accuracies,\n",
    "    'Precision': precisions,\n",
    "    'Recall (Sensitivity)': recalls,\n",
    "    'F1 Score': f1_scores,\n",
    "    'Specificity': specificities,\n",
    "    'ROC AUC Score': roc_auc_scores\n",
    "}\n",
    "results1 = pd.DataFrame(results1).sort_values(by=['Test Accuracy', 'Precision', 'Recall (Sensitivity)'], ascending=False)\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "results1.to_csv('Diabetes_Evaluation_Metrics.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC AUC Curve Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve(ax, y_true, y_score, model_name):\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_score)\n",
    "    roc_auc = roc_auc_score(y_true, y_score)\n",
    "    ax.plot(fpr, tpr, lw=2, label='%s (area = %0.2f)' % (model_name, roc_auc))\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    model.fit(x_train, y_train)\n",
    "    y_score = model.predict_proba(x_test)[:, 1]\n",
    "    plot_roc_curve(ax, y_test, y_score, model_name)\n",
    "\n",
    "\n",
    "ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "ax.set_xlim([0.0, 1.0])\n",
    "ax.set_ylim([0.0, 1.05])\n",
    "ax.set_xlabel('False Positive Rate')\n",
    "ax.set_ylabel('True Positive Rate')\n",
    "ax.set_title('Receiver Operating Characteristic')\n",
    "ax.legend(loc=\"lower right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesClassifier(n_estimators=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ExtraTreesClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html\">?<span>Documentation for ExtraTreesClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ExtraTreesClassifier(n_estimators=50)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesClassifier(n_estimators=50)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = grid_search.best_estimator_\n",
    "best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'monotonic_cst': None,\n",
       " 'n_estimators': 50,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(best_model,open('Diabetes_Model.pkl','wb'))\n",
    "pickle.dump(scaler,open('Diabetes_Preprocessor.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Model File Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "model = pickle.load(open(\"../Artifacts/Diabetes_Disease/Diabetes_Model.pkl\", \"rb\"))\n",
    "processor = pickle.load(open(\"../Artifacts/Diabetes_Disease/Diabetes_Preprocessor.pkl\", \"rb\"))\n",
    "\n",
    "data = processor.transform([x_train[0]])\n",
    "predictions = model.predict(data)\n",
    "\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Model Monitoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1 Monitoring Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Outcome'\n",
    "prediction = 'prediction'\n",
    "numerical_features = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction',\n",
    "                      'Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_mapping = ColumnMapping()\n",
    "column_mapping.target = target\n",
    "column_mapping.prediction = prediction\n",
    "column_mapping.numerical_features = numerical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_rows = int(len(df) * 0.70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "reference = df[:num_rows]\n",
    "current = df[num_rows:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ExtraTreesClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html\">?<span>Documentation for ExtraTreesClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ExtraTreesClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = ExtraTreesClassifier(n_estimators= 100)\n",
    "classifier.fit(reference[numerical_features],reference[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_prediction = classifier.predict(reference[numerical_features])\n",
    "cur_prediction = classifier.predict(current[numerical_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "reference['prediction'] = ref_prediction\n",
    "current['prediction'] = cur_prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2 Model Performance Monitoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_per = Report(metrics=[ClassificationPreset()])\n",
    "classifier_per.run(current_data=reference, reference_data=None, column_mapping=column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/icon?family=Material+Icons\"/>\n",
       "<style>\n",
       ".reset-this-parent {\n",
       "  all: initial;\n",
       "}\n",
       ".reset-this-parent h5 {\n",
       "  all: initial;\n",
       "  font: initial;\n",
       "}\n",
       "\n",
       "svg {\n",
       "  height: intrinsic !important;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "    var evidently_dashboard_7d76b5110c8b4385a39c5ed9dbea09a1 = {\"name\": \"Report\", \"widgets\": [{\"type\": \"counter\", \"title\": \"\", \"size\": 2, \"id\": \"ClassificationQualityMetric-0\", \"details\": \"\", \"alertsPosition\": null, \"alertStats\": null, \"params\": {\"counters\": [{\"value\": \"\", \"label\": \"Classification Model Performance. 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       "    var additional_graphs_evidently_dashboard_7d76b5110c8b4385a39c5ed9dbea09a1 = {};\n",
       "</script>\n",
       "<script>\n",
       "function domReady(fn) {\n",
       "  // If we're early to the party\n",
       "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "  // If late; I mean on time.\n",
       "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
       "    fn();\n",
       "  }\n",
       "}\n",
       "\n",
       "domReady(function () {\n",
       "    requirejs([\"evidently\"], function(ev) {\n",
       "        drawDashboard(evidently_dashboard_7d76b5110c8b4385a39c5ed9dbea09a1,\n",
       "        new Map(Object.entries(additional_graphs_evidently_dashboard_7d76b5110c8b4385a39c5ed9dbea09a1)),\n",
       "        \"root_evidently_dashboard_7d76b5110c8b4385a39c5ed9dbea09a1\");\n",
       "    },\n",
       "    function(err) {\n",
       "        $(\"#root_evidently_dashboard_7d76b5110c8b4385a39c5ed9dbea09a1\").innerHTML = \"Failed to load\";\n",
       "    })\n",
       "});\n",
       "</script>\n",
       "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_7d76b5110c8b4385a39c5ed9dbea09a1\">Loading...</div>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_per.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_per.save(\"Model_Performance_Metrics.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.3 Data Drift Monitoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_drift_report = Report(metrics=[DataDriftPreset()])\n",
    "data_drift_report.run(reference_data=reference, current_data=current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/icon?family=Material+Icons\"/>\n",
       "<style>\n",
       ".reset-this-parent {\n",
       "  all: initial;\n",
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       ".reset-this-parent h5 {\n",
       "  all: initial;\n",
       "  font: initial;\n",
       "}\n",
       "\n",
       "svg {\n",
       "  height: intrinsic !important;\n",
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       "</style>\n",
       "<script>\n",
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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"automargin\": true, \"zerolinewidth\": 2}, \"yaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"automargin\": true, \"zerolinewidth\": 2}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\", \"gridwidth\": 2}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\", \"gridwidth\": 2}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\", \"gridwidth\": 2}}, \"shapedefaults\": {\"line\": {\"color\": 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\"widgets\": [], \"pageSize\": 5}};\n",
       "</script>\n",
       "<script>\n",
       "function domReady(fn) {\n",
       "  // If we're early to the party\n",
       "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "  // If late; I mean on time.\n",
       "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
       "    fn();\n",
       "  }\n",
       "}\n",
       "\n",
       "domReady(function () {\n",
       "    requirejs([\"evidently\"], function(ev) {\n",
       "        drawDashboard(evidently_dashboard_1dc06c5ed8c84c2682286e69e86ede69,\n",
       "        new Map(Object.entries(additional_graphs_evidently_dashboard_1dc06c5ed8c84c2682286e69e86ede69)),\n",
       "        \"root_evidently_dashboard_1dc06c5ed8c84c2682286e69e86ede69\");\n",
       "    },\n",
       "    function(err) {\n",
       "        $(\"#root_evidently_dashboard_1dc06c5ed8c84c2682286e69e86ede69\").innerHTML = \"Failed to load\";\n",
       "    })\n",
       "});\n",
       "</script>\n",
       "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_1dc06c5ed8c84c2682286e69e86ede69\">Loading...</div>\n",
       "\n"
      ],
      "text/plain": [
       "<evidently.report.report.Report at 0x16472669c10>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_drift_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_drift_report.save(\"Data_Drift_Metrics.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.4 Data Quality Monitoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_quality_report = Report(metrics=[DataQualityPreset()])\n",
    "data_quality_report.run(reference_data=reference, current_data=current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/icon?family=Material+Icons\"/>\n",
       "<style>\n",
       ".reset-this-parent {\n",
       "  all: initial;\n",
       "}\n",
       ".reset-this-parent h5 {\n",
       "  all: initial;\n",
       "  font: initial;\n",
       "}\n",
       "\n",
       "svg {\n",
       "  height: intrinsic !important;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
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[0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}], \"surface\": [{\"type\": \"surface\", \"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}], \"mesh3d\": [{\"type\": \"mesh3d\", \"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}], \"scatter\": [{\"fillpattern\": {\"fillmode\": \"overlay\", \"size\": 10, \"solidity\": 0.2}, \"type\": \"scatter\"}], \"parcoords\": [{\"type\": \"parcoords\", \"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}}], \"scatterpolargl\": [{\"type\": \"scatterpolargl\", \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}}], \"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}, \"pattern\": {\"fillmode\": \"overlay\", \"size\": 10, \"solidity\": 0.2}}, \"type\": \"bar\"}], \"scattergeo\": [{\"type\": \"scattergeo\", \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}}], \"scatterpolar\": [{\"type\": \"scatterpolar\", \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}}], \"histogram\": [{\"marker\": {\"pattern\": {\"fillmode\": \"overlay\", \"size\": 10, \"solidity\": 0.2}}, \"type\": \"histogram\"}], \"scattergl\": [{\"type\": \"scattergl\", \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}}], \"scatter3d\": [{\"type\": \"scatter3d\", \"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}}], \"scattermapbox\": [{\"type\": \"scattermapbox\", \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}}], \"scatterternary\": [{\"type\": \"scatterternary\", \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}}], \"scattercarpet\": [{\"type\": \"scattercarpet\", \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}, \"pattern\": {\"fillmode\": \"overlay\", \"size\": 10, \"solidity\": 0.2}}, \"type\": \"barpolar\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}]}, \"layout\": {\"autotypenumbers\": \"strict\", \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"hovermode\": \"closest\", \"hoverlabel\": {\"align\": \"left\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"bgcolor\": \"#E5ECF6\", \"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"ternary\": {\"bgcolor\": \"#E5ECF6\", \"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]]}, \"xaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"automargin\": true, \"zerolinewidth\": 2}, \"yaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"automargin\": true, \"zerolinewidth\": 2}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\", \"gridwidth\": 2}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\", \"gridwidth\": 2}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\", \"gridwidth\": 2}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"geo\": {\"bgcolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"subunitcolor\": \"white\", \"showland\": true, \"showlakes\": true, \"lakecolor\": \"white\"}, \"title\": {\"x\": 0.05}, \"mapbox\": {\"style\": \"light\"}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45], \"tickangle\": -45}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"type\": \"category\"}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.55, 1.0], \"tickangle\": -45}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 1.0], \"matches\": \"y\", \"showticklabels\": false, \"type\": \"category\"}, \"annotations\": [{\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Current\", \"x\": 0.225, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Reference\", \"x\": 0.775, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}], \"coloraxis\": {\"colorscale\": [[0.0, \"rgb(5,48,97)\"], [0.1, \"rgb(33,102,172)\"], [0.2, \"rgb(67,147,195)\"], [0.3, \"rgb(146,197,222)\"], [0.4, \"rgb(209,229,240)\"], [0.5, \"rgb(247,247,247)\"], [0.6, \"rgb(253,219,199)\"], [0.7, \"rgb(244,165,130)\"], [0.8, \"rgb(214,96,77)\"], [0.9, \"rgb(178,24,43)\"], [1.0, \"rgb(103,0,31)\"]]}}}, \"insights\": [], \"additionalGraphs\": [], \"alerts\": [], \"tabs\": [], \"widgets\": [], \"pageSize\": 5}}], \"widgets\": [], \"pageSize\": 5}]};\n",
       "    var additional_graphs_evidently_dashboard_4e738521f949405ba7f634a376d2d3d3 = {};\n",
       "</script>\n",
       "<script>\n",
       "function domReady(fn) {\n",
       "  // If we're early to the party\n",
       "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "  // If late; I mean on time.\n",
       "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
       "    fn();\n",
       "  }\n",
       "}\n",
       "\n",
       "domReady(function () {\n",
       "    requirejs([\"evidently\"], function(ev) {\n",
       "        drawDashboard(evidently_dashboard_4e738521f949405ba7f634a376d2d3d3,\n",
       "        new Map(Object.entries(additional_graphs_evidently_dashboard_4e738521f949405ba7f634a376d2d3d3)),\n",
       "        \"root_evidently_dashboard_4e738521f949405ba7f634a376d2d3d3\");\n",
       "    },\n",
       "    function(err) {\n",
       "        $(\"#root_evidently_dashboard_4e738521f949405ba7f634a376d2d3d3\").innerHTML = \"Failed to load\";\n",
       "    })\n",
       "});\n",
       "</script>\n",
       "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_4e738521f949405ba7f634a376d2d3d3\">Loading...</div>\n",
       "\n"
      ],
      "text/plain": [
       "<evidently.report.report.Report at 0x16472b08590>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_quality_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_quality_report.save(\"Data_Quality_Metrics.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.5 Target Drift Monitoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_target_drift_report = Report(metrics=[TargetDriftPreset()])\n",
    "num_target_drift_report.run(reference_data=reference, current_data=current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/icon?family=Material+Icons\"/>\n",
       "<style>\n",
       ".reset-this-parent {\n",
       "  all: initial;\n",
       "}\n",
       ".reset-this-parent h5 {\n",
       "  all: initial;\n",
       "  font: initial;\n",
       "}\n",
       "\n",
       "svg {\n",
       "  height: intrinsic !important;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "    var evidently_dashboard_69c22cad31c84ed7a970e586a8bc0a90 = {\"name\": \"Report\", \"widgets\": [{\"type\": \"counter\", \"title\": \"\", \"size\": 2, \"id\": \"ColumnDriftMetric-0\", \"details\": \"\", \"alertsPosition\": null, \"alertStats\": null, \"params\": {\"counters\": [{\"value\": \"Drift in column 'prediction'\", \"label\": \"Data drift detected. Drift detection method: Z-test p_value. 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       "    var additional_graphs_evidently_dashboard_69c22cad31c84ed7a970e586a8bc0a90 = {};\n",
       "</script>\n",
       "<script>\n",
       "function domReady(fn) {\n",
       "  // If we're early to the party\n",
       "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "  // If late; I mean on time.\n",
       "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
       "    fn();\n",
       "  }\n",
       "}\n",
       "\n",
       "domReady(function () {\n",
       "    requirejs([\"evidently\"], function(ev) {\n",
       "        drawDashboard(evidently_dashboard_69c22cad31c84ed7a970e586a8bc0a90,\n",
       "        new Map(Object.entries(additional_graphs_evidently_dashboard_69c22cad31c84ed7a970e586a8bc0a90)),\n",
       "        \"root_evidently_dashboard_69c22cad31c84ed7a970e586a8bc0a90\");\n",
       "    },\n",
       "    function(err) {\n",
       "        $(\"#root_evidently_dashboard_69c22cad31c84ed7a970e586a8bc0a90\").innerHTML = \"Failed to load\";\n",
       "    })\n",
       "});\n",
       "</script>\n",
       "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_69c22cad31c84ed7a970e586a8bc0a90\">Loading...</div>\n",
       "\n"
      ],
      "text/plain": [
       "<evidently.report.report.Report at 0x16472c90a50>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_target_drift_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_target_drift_report.save(\"Target_Drift_Metrics.json\")"
   ]
  }
 ],
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