{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec7469d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ac7b41ee",
   "metadata": {},
   "source": [
    "# Intro\n",
    "## General\n",
    "Machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. \n",
    "In most of the situations we want to have a machine learning system to make **predictions**, so we have several categories of machine learning tasks depending on the type of prediction needed: **Classification, Regression, Clustering, Generation**, etc.\n",
    "\n",
    "**Classification** is the task whose goal is the prediction of the label of the class to which the input belongs (e.g., Classification of images in two classes: cats and dogs).\n",
    "**Regression** is the task whose goal is the prediction of numerical value(s) related to the input (e.g., House rent prediction, Estimated time of arrival ).\n",
    "**Generation** is the task whose goal is the creation of something new related to the input (e.g., Text translation, Audio beat generation, Image denoising ). **Clustering** is the task of grouping a set of objects in such a way that objects in the same group (called a **cluster**) are more similar (in some sense) to each other than to those in other **clusters** (e.g., Clients clutering).\n",
    "\n",
    "In machine learning, there are learning paradigms that relate to one aspect of the dataset: **the presence of the label to be predicted**. **Supervised Learning** is the paradigm of learning that is applied when the dataset has the label variables to be predicted, known as ` y variables`. **Unsupervised Learning** is the paradigm of learning that is applied when the dataset has not the label variables to be predicted. **Self-supervised Learning** is the paradigm of learning that is applied when part of the X dataset is considere as the label to be predicted (e.g., the Dataset is made of texts and the model try to predict the next word of each sentence).\n",
    "\n",
    "## Notebook overview\n",
    "\n",
    "This notebook is a guide to start practicing Machine Learning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced98da1",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a75f15",
   "metadata": {},
   "source": [
    "## Installation\n",
    "Here is the section to install all the packages/libraries that will be needed to tackle the challlenge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "536ea537",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install  optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "4745fad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Data Handling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "##Visualization Libraries \n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns \n",
    "%matplotlib inline\n",
    "import plotly.express as ex\n",
    "import plotly.express as px\n",
    "import plotly.express as px\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "init_notebook_mode(connected=True)  # Initialize Plotly for offline mode\n",
    "\n",
    "# Feature Processing (Scikit-learn processing, etc. )\n",
    "import phik\n",
    "\n",
    "# Machine Learning (Scikit-learn Estimators, Catboost, LightGBM, etc. )\n",
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.ensemble import BaggingClassifier,AdaBoostClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.compose import ColumnTransformer \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import classification_report,ConfusionMatrixDisplay\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Hyperparameters Fine-tuning (Scikit-learn hp search, cross-validation, etc. )\n",
    "import optuna\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score,f1_score\n",
    "\n",
    "# Other packages\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "# Suppress all warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7951bfb3",
   "metadata": {},
   "source": [
    "# II. Data Loading\n",
    "Data Loading\n",
    "Here is the section to load the datasets (train, eval, test) and the additional files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb10dcc",
   "metadata": {},
   "source": [
    "#### 2.1 train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "5bf01a21",
   "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",
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       "    }\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>ID</th>\n",
       "      <th>Plasma_glucose</th>\n",
       "      <th>Blood_Work_R1</th>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <th>Blood_Work_R2</th>\n",
       "      <th>Blood_Work_R3</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood_Work_R4</th>\n",
       "      <th>Age</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>Sepsis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ICU200010</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU200011</td>\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",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICU200012</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICU200013</td>\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>1</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ICU200014</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Plasma_glucose  Blood_Work_R1  Blood_Pressure  Blood_Work_R2  \\\n",
       "0  ICU200010               6            148              72             35   \n",
       "1  ICU200011               1             85              66             29   \n",
       "2  ICU200012               8            183              64              0   \n",
       "3  ICU200013               1             89              66             23   \n",
       "4  ICU200014               0            137              40             35   \n",
       "\n",
       "   Blood_Work_R3   BMI  Blood_Work_R4  Age  Insurance    Sepsis  \n",
       "0              0  33.6          0.627   50          0  Positive  \n",
       "1              0  26.6          0.351   31          0  Negative  \n",
       "2              0  23.3          0.672   32          1  Positive  \n",
       "3             94  28.1          0.167   21          1  Negative  \n",
       "4            168  43.1          2.288   33          1  Positive  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=r\"C:\\Users\\Gregory Arthur\\Desktop\\Pandas\\Sepsis\\Paitients_Files_Train.csv\"\n",
    "df_train= pd.read_csv(path)\n",
    "df_train.rename(columns={'PRG':'Plasma_glucose','PL': 'Blood_Work_R1','PR': 'Blood_Pressure',\n",
    "                         'SK': 'Blood_Work_R2','TS': 'Blood_Work_R3','M11': 'BMI',\n",
    "                         'BD2': 'Blood_Work_R4',\n",
    "                         'Sepssis': 'Sepsis'}, inplace=True)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e4beb5",
   "metadata": {},
   "source": [
    "#### 2.2. test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "7db8d8fa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Plasma_glucose</th>\n",
       "      <th>Blood_Work_R1</th>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <th>Blood_Work_R2</th>\n",
       "      <th>Blood_Work_R3</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood_Work_R4</th>\n",
       "      <th>Age</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>Sepssis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ICU200010</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ICU200011</td>\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",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICU200012</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICU200013</td>\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>1</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ICU200014</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Plasma_glucose  Blood_Work_R1  Blood_Pressure  Blood_Work_R2  \\\n",
       "0  ICU200010               6            148              72             35   \n",
       "1  ICU200011               1             85              66             29   \n",
       "2  ICU200012               8            183              64              0   \n",
       "3  ICU200013               1             89              66             23   \n",
       "4  ICU200014               0            137              40             35   \n",
       "\n",
       "   Blood_Work_R3   BMI  Blood_Work_R4  Age  Insurance   Sepssis  \n",
       "0              0  33.6          0.627   50          0  Positive  \n",
       "1              0  26.6          0.351   31          0  Negative  \n",
       "2              0  23.3          0.672   32          1  Positive  \n",
       "3             94  28.1          0.167   21          1  Negative  \n",
       "4            168  43.1          2.288   33          1  Positive  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=r\"C:\\Users\\Gregory Arthur\\Desktop\\Pandas\\Sepsis\\Paitients_Files_Train.csv\"\n",
    "df_test= pd.read_csv(path)\n",
    "df_test.rename(columns={'PRG':'Plasma_glucose','PL': 'Blood_Work_R1','PR': 'Blood_Pressure',\n",
    "                         'SK': 'Blood_Work_R2','TS': 'Blood_Work_R3','M11': 'BMI',\n",
    "                         'BD2': 'Blood_Work_R4'}, inplace=True)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4a6f17",
   "metadata": {},
   "source": [
    "# III. Exploratory Data Analysis: EDA\n",
    "Here is the section to **inspect** the datasets in depth, **present** it, make **hypotheses** and **think** the *cleaning, processing and features creation*."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675c5c48",
   "metadata": {},
   "source": [
    "### 3.1 Dataset Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad24bd6",
   "metadata": {},
   "source": [
    "#### 3.1.1 Data info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "ce75a08b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 599 entries, 0 to 598\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   ID              599 non-null    object \n",
      " 1   Plasma_glucose  599 non-null    int64  \n",
      " 2   Blood_Work_R1   599 non-null    int64  \n",
      " 3   Blood_Pressure  599 non-null    int64  \n",
      " 4   Blood_Work_R2   599 non-null    int64  \n",
      " 5   Blood_Work_R3   599 non-null    int64  \n",
      " 6   BMI             599 non-null    float64\n",
      " 7   Blood_Work_R4   599 non-null    float64\n",
      " 8   Age             599 non-null    int64  \n",
      " 9   Insurance       599 non-null    int64  \n",
      " 10  Sepsis          599 non-null    object \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 51.6+ KB\n",
      "the info df_train dataset are: \n",
      "\n",
      " None \n",
      "---------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 599 entries, 0 to 598\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   ID              599 non-null    object \n",
      " 1   Plasma_glucose  599 non-null    int64  \n",
      " 2   Blood_Work_R1   599 non-null    int64  \n",
      " 3   Blood_Pressure  599 non-null    int64  \n",
      " 4   Blood_Work_R2   599 non-null    int64  \n",
      " 5   Blood_Work_R3   599 non-null    int64  \n",
      " 6   BMI             599 non-null    float64\n",
      " 7   Blood_Work_R4   599 non-null    float64\n",
      " 8   Age             599 non-null    int64  \n",
      " 9   Insurance       599 non-null    int64  \n",
      " 10  Sepssis         599 non-null    object \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 51.6+ KB\n",
      "the info df_test dataset are: \n",
      "\n",
      " None \n",
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# checking for info on the datasets\n",
    "data = {'df_train': df_train, 'df_test': df_test}\n",
    "for name, dataset in data.items():\n",
    "    print(f\"the info {name} dataset are: \\n\\n\",dataset.info(),\"\\n\" + \"---\" * 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a68eb7",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- the train dataset has 11 features with 599 entries.\n",
    "- the test dataset has 10 features with 169 entries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b1e695",
   "metadata": {},
   "source": [
    "#### 3.1.2 checking missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "f082ad5e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the info df_train dataset are: \n",
      "\n",
      " ID                0\n",
      "Plasma_glucose    0\n",
      "Blood_Work_R1     0\n",
      "Blood_Pressure    0\n",
      "Blood_Work_R2     0\n",
      "Blood_Work_R3     0\n",
      "BMI               0\n",
      "Blood_Work_R4     0\n",
      "Age               0\n",
      "Insurance         0\n",
      "Sepsis            0\n",
      "dtype: int64 \n",
      "---------------------------------------------\n",
      "the info df_test dataset are: \n",
      "\n",
      " ID                0\n",
      "Plasma_glucose    0\n",
      "Blood_Work_R1     0\n",
      "Blood_Pressure    0\n",
      "Blood_Work_R2     0\n",
      "Blood_Work_R3     0\n",
      "BMI               0\n",
      "Blood_Work_R4     0\n",
      "Age               0\n",
      "Insurance         0\n",
      "Sepssis           0\n",
      "dtype: int64 \n",
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# checking for missing values in the datasets\n",
    "data = {'df_train': df_train, 'df_test': df_test}\n",
    "for name, dataset in data.items():\n",
    "    print(f\"the info {name} dataset are: \\n\\n\",dataset.isna().sum(),\"\\n\" + \"---\" * 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfeb2702",
   "metadata": {},
   "source": [
    "- both datasets have no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083af0a9",
   "metadata": {},
   "source": [
    "#### 3.1.3 checking Data shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "7c0ca96e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(599, 11) (599, 11)\n"
     ]
    }
   ],
   "source": [
    "# checking for the shapes of the datasets\n",
    "print(df_train.shape, df_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5549ae",
   "metadata": {},
   "source": [
    "#### 3.1.4 Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "f1576871",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Plasma_glucose</th>\n",
       "      <td>599.0</td>\n",
       "      <td>3.824708</td>\n",
       "      <td>3.362839</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Work_R1</th>\n",
       "      <td>599.0</td>\n",
       "      <td>120.153589</td>\n",
       "      <td>32.682364</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.000</td>\n",
       "      <td>116.000</td>\n",
       "      <td>140.000</td>\n",
       "      <td>198.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <td>599.0</td>\n",
       "      <td>68.732888</td>\n",
       "      <td>19.335675</td>\n",
       "      <td>0.000</td>\n",
       "      <td>64.000</td>\n",
       "      <td>70.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Work_R2</th>\n",
       "      <td>599.0</td>\n",
       "      <td>20.562604</td>\n",
       "      <td>16.017622</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Work_R3</th>\n",
       "      <td>599.0</td>\n",
       "      <td>79.460768</td>\n",
       "      <td>116.576176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>36.000</td>\n",
       "      <td>123.500</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>599.0</td>\n",
       "      <td>31.920033</td>\n",
       "      <td>8.008227</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.100</td>\n",
       "      <td>32.000</td>\n",
       "      <td>36.550</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Work_R4</th>\n",
       "      <td>599.0</td>\n",
       "      <td>0.481187</td>\n",
       "      <td>0.337552</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.647</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>599.0</td>\n",
       "      <td>33.290484</td>\n",
       "      <td>11.828446</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.000</td>\n",
       "      <td>29.000</td>\n",
       "      <td>40.000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>599.0</td>\n",
       "      <td>0.686144</td>\n",
       "      <td>0.464447</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count        mean         std     min     25%      50%  \\\n",
       "Plasma_glucose  599.0    3.824708    3.362839   0.000   1.000    3.000   \n",
       "Blood_Work_R1   599.0  120.153589   32.682364   0.000  99.000  116.000   \n",
       "Blood_Pressure  599.0   68.732888   19.335675   0.000  64.000   70.000   \n",
       "Blood_Work_R2   599.0   20.562604   16.017622   0.000   0.000   23.000   \n",
       "Blood_Work_R3   599.0   79.460768  116.576176   0.000   0.000   36.000   \n",
       "BMI             599.0   31.920033    8.008227   0.000  27.100   32.000   \n",
       "Blood_Work_R4   599.0    0.481187    0.337552   0.078   0.248    0.383   \n",
       "Age             599.0   33.290484   11.828446  21.000  24.000   29.000   \n",
       "Insurance       599.0    0.686144    0.464447   0.000   0.000    1.000   \n",
       "\n",
       "                    75%     max  \n",
       "Plasma_glucose    6.000   17.00  \n",
       "Blood_Work_R1   140.000  198.00  \n",
       "Blood_Pressure   80.000  122.00  \n",
       "Blood_Work_R2    32.000   99.00  \n",
       "Blood_Work_R3   123.500  846.00  \n",
       "BMI              36.550   67.10  \n",
       "Blood_Work_R4     0.647    2.42  \n",
       "Age              40.000   81.00  \n",
       "Insurance         1.000    1.00  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics\n",
    "df_train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "d7f56a70",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Plasma_glucose</th>\n",
       "      <td>599.0</td>\n",
       "      <td>3.824708</td>\n",
       "      <td>3.362839</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Work_R1</th>\n",
       "      <td>599.0</td>\n",
       "      <td>120.153589</td>\n",
       "      <td>32.682364</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.000</td>\n",
       "      <td>116.000</td>\n",
       "      <td>140.000</td>\n",
       "      <td>198.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <td>599.0</td>\n",
       "      <td>68.732888</td>\n",
       "      <td>19.335675</td>\n",
       "      <td>0.000</td>\n",
       "      <td>64.000</td>\n",
       "      <td>70.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Work_R2</th>\n",
       "      <td>599.0</td>\n",
       "      <td>20.562604</td>\n",
       "      <td>16.017622</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Work_R3</th>\n",
       "      <td>599.0</td>\n",
       "      <td>79.460768</td>\n",
       "      <td>116.576176</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>36.000</td>\n",
       "      <td>123.500</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>599.0</td>\n",
       "      <td>31.920033</td>\n",
       "      <td>8.008227</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.100</td>\n",
       "      <td>32.000</td>\n",
       "      <td>36.550</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blood_Work_R4</th>\n",
       "      <td>599.0</td>\n",
       "      <td>0.481187</td>\n",
       "      <td>0.337552</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.647</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>599.0</td>\n",
       "      <td>33.290484</td>\n",
       "      <td>11.828446</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.000</td>\n",
       "      <td>29.000</td>\n",
       "      <td>40.000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insurance</th>\n",
       "      <td>599.0</td>\n",
       "      <td>0.686144</td>\n",
       "      <td>0.464447</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count        mean         std     min     25%      50%  \\\n",
       "Plasma_glucose  599.0    3.824708    3.362839   0.000   1.000    3.000   \n",
       "Blood_Work_R1   599.0  120.153589   32.682364   0.000  99.000  116.000   \n",
       "Blood_Pressure  599.0   68.732888   19.335675   0.000  64.000   70.000   \n",
       "Blood_Work_R2   599.0   20.562604   16.017622   0.000   0.000   23.000   \n",
       "Blood_Work_R3   599.0   79.460768  116.576176   0.000   0.000   36.000   \n",
       "BMI             599.0   31.920033    8.008227   0.000  27.100   32.000   \n",
       "Blood_Work_R4   599.0    0.481187    0.337552   0.078   0.248    0.383   \n",
       "Age             599.0   33.290484   11.828446  21.000  24.000   29.000   \n",
       "Insurance       599.0    0.686144    0.464447   0.000   0.000    1.000   \n",
       "\n",
       "                    75%     max  \n",
       "Plasma_glucose    6.000   17.00  \n",
       "Blood_Work_R1   140.000  198.00  \n",
       "Blood_Pressure   80.000  122.00  \n",
       "Blood_Work_R2    32.000   99.00  \n",
       "Blood_Work_R3   123.500  846.00  \n",
       "BMI              36.550   67.10  \n",
       "Blood_Work_R4     0.647    2.42  \n",
       "Age              40.000   81.00  \n",
       "Insurance         1.000    1.00  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "469f396c",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "\n",
    "- From the descriptive statistics of the datasets there are some features with minimum value of 0.thus in Plasma glucose, Blood Work Result-1, Blood Pressure,Blood Work Result-2, Blood Work Result-3, and Body mass index. \n",
    "From Domain Knowledge in biology ,these features should not have a value of 0. Therefore, it is reasonable to assume that any missing value was filled out with 0.\n",
    "\n",
    "\n",
    "- All features  have very high standard deviations, which means they are spreaded over a very wide range except for Blood Work Result-4.\n",
    "From the descriptive statistics of the train set, the mean and median of some columns are very different from each other, especially in the following features: Plasma glucose, Blood Work Result-2, Blood Work Result-3, Blood Work Result-4, Age. This indicates these columns have skewness in their distribution. Other columns also has some minor discrepancies between their means and medians, but not as extreme as those mentioned above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d686080",
   "metadata": {},
   "source": [
    "#### 3.1.5 finding count and percentage of missing values in all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "40198a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_missing_val(df, cols):\n",
    "    print('\\tMissing val Count\\tMissing val Percentage')\n",
    "    for col in cols:\n",
    "        missing_cnt = df[col].value_counts()[0] # frequency of zero entries in a particular column\n",
    "        missing_percentage = round((missing_cnt/len(df) * 100), 2)\n",
    "        print(str(col) + ': \\t\\t' + str(missing_cnt) + '\\t\\t\\t' + str(missing_percentage).zfill(5) + '\\t%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "d59ea54f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tMissing val Count\tMissing val Percentage\n",
      "Plasma_glucose: \t\t93\t\t\t15.53\t%\n",
      "Blood_Pressure: \t\t28\t\t\t04.67\t%\n",
      "Blood_Work_R2: \t\t175\t\t\t29.22\t%\n",
      "Blood_Work_R3: \t\t289\t\t\t48.25\t%\n",
      "BMI: \t\t9\t\t\t001.5\t%\n"
     ]
    }
   ],
   "source": [
    "# finding the missing values in the respective columns in the train set\n",
    "train_missing_col=['Plasma_glucose', 'Blood_Pressure', 'Blood_Work_R2', 'Blood_Work_R3', 'BMI']\n",
    "count_missing_val(df_train, train_missing_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "addf710b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tMissing val Count\tMissing val Percentage\n",
      "Plasma_glucose: \t\t93\t\t\t15.53\t%\n",
      "Blood_Pressure: \t\t28\t\t\t04.67\t%\n",
      "Blood_Work_R2: \t\t175\t\t\t29.22\t%\n",
      "Blood_Work_R3: \t\t289\t\t\t48.25\t%\n",
      "BMI: \t\t9\t\t\t001.5\t%\n"
     ]
    }
   ],
   "source": [
    "# finding the missing values in the respective columns in the test set\n",
    "test_missing_col=['Plasma_glucose', 'Blood_Pressure', 'Blood_Work_R2', 'Blood_Work_R3', 'BMI']\n",
    "count_missing_val(df_test,test_missing_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23005987",
   "metadata": {},
   "source": [
    "#### 3.1.6 checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "9d077984",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  0  duplicated rows for the training set\n",
      "There are  0  duplicated rows for the test set\n"
     ]
    }
   ],
   "source": [
    "#Check for duplicates\n",
    "duplicate_rows_train = df_train.duplicated().sum()\n",
    "duplicate_rows_test = df_test.duplicated().sum()\n",
    "print('There are ',duplicate_rows_train,' duplicated rows for the training set')\n",
    "print('There are ',duplicate_rows_test,' duplicated rows for the test set')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e90f05a",
   "metadata": {},
   "source": [
    "### 3.2 Descriptive Statistics & Central Tendencies\n",
    "\n",
    "#### 3.2.1 checking for skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "738d33d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Blood_Work_R3     2.401585\n",
       "Blood_Work_R4     1.989472\n",
       "Age               1.152353\n",
       "Plasma_glucose    0.914008\n",
       "Blood_Work_R2     0.164063\n",
       "Blood_Work_R1     0.116180\n",
       "BMI              -0.405255\n",
       "Insurance        -0.804257\n",
       "Blood_Pressure   -1.874662\n",
       "dtype: float64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for skewness\n",
    "df_train.skew(numeric_only=True).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fad7bd",
   "metadata": {},
   "source": [
    "from the above it can be observed that:\n",
    "- Positively-skewed: Blood Work Result-3, Blood Work Result-4, Age, Plasma glucose, Blood Work Result-1, Blood Work Result-2\n",
    "- Negatively-skewed: Blood Pressure, Insurance, Body mass index "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f62ec10",
   "metadata": {},
   "source": [
    "#### 3.2.2 visualization of skewness for the train set\n",
    "- Histograms of these features will be plotted to visualize their distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "932bcf83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a copy\n",
    "train= df_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "bea221c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist_plot(df, avoid, name_for_title):\n",
    "    df_copy = df.copy(deep=True) # Copy the original dataframe\n",
    "    df_copy.drop(labels=avoid, axis='columns',inplace=True) # Drop avoid columns\n",
    "    \n",
    "    # Set up subplots in 3x3 grid\n",
    "    fig, axes = plt.subplots(3, 3, constrained_layout=True, figsize=(10, 8))\n",
    "    plt.suptitle('Distribution of Features in ' + name_for_title + ' Set', fontsize=20)\n",
    "    \n",
    "    r = 0 # current row\n",
    "    c = 0 # current column\n",
    "    \n",
    "    for col in df_copy.columns:\n",
    "        # Plot histogram of each column\n",
    "        sns.histplot(data=df, x=col, kde=True, ax=axes[r, c])\n",
    "        axes[r, c].title.set_text('Histogram of ' + str(col))\n",
    "         # Update position for next subplot\n",
    "        if (c == 2):\n",
    "            c = 0\n",
    "            r += 1\n",
    "        else: \n",
    "            c += 1\n",
    "            \n",
    "    fig.delaxes(axes[2, 2]) # Delete unused subplot\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "78ef0857",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_plot(df=train, avoid=['ID', 'Insurance', 'Sepsis'], name_for_title='Train')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0399fbe6",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- Right-skewed: Plasma glucose, Blood Work Result-2, Blood Work Result-3 , Blood Work Result-4, Age.\n",
    "- Normally-distributed: Blood Work Result-1 , Blood Pressure, Body mass index.\n",
    "- The skewness will be dealt with in the later section by normalizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "68b379af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Blood_Work_R3     2.401585\n",
       "Blood_Work_R4     1.989472\n",
       "Age               1.152353\n",
       "Plasma_glucose    0.914008\n",
       "Blood_Work_R2     0.164063\n",
       "Blood_Work_R1     0.116180\n",
       "BMI              -0.405255\n",
       "Insurance        -0.804257\n",
       "Blood_Pressure   -1.874662\n",
       "dtype: float64"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similarly, checking for skewness for  the test dataset\n",
    "df_test.skew(numeric_only=True).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a38a35b",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- Similar to the train set, the columns in the test set have very large standard deviation as well.\n",
    "- Positively-skewed: Blood Work Result-3, Blood Work Result-4, Age, Plasma glucose, Blood Work Result-1\n",
    "- Negatively-skewed: Blood Pressure, Insurance, Body mass index, Blood Work Result-2 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01de8ae0",
   "metadata": {},
   "source": [
    "#### 3.2.3 visualization of skewness for the test set\n",
    "- The histograms of these features will be plotted to visualize their distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "25388dcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a copy\n",
    "test=df_test.copy()\n",
    "dist_plot(df=test, avoid=['ID', 'Insurance'], name_for_title='Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23fbd6b7",
   "metadata": {},
   "source": [
    "the distribution of the features are:\n",
    "- Right-skewed: Plasma glucose, Blood Work Result-2, Blood Work Result-3, Blood Work Result-4, Age.\n",
    "- Normally-distributed: Blood Work Result-1, Blood Pressure, Body mass index."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8b83cc",
   "metadata": {},
   "source": [
    "#### 3.2.4 checking for outliers of each numerical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "8e38f9ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to see the outlier of each numerical column\n",
    "sns.boxplot(data=train, orient=\"h\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "894a853f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to see the outlier of each numerical column\n",
    "sns.boxplot(data=test, orient=\"h\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56990c31",
   "metadata": {},
   "source": [
    "- the output indicates strongly that there are outliers in all the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd513513",
   "metadata": {},
   "source": [
    "# IV. Data Cleaning and Pre-processing\n",
    "\n",
    "#### 4.1 Handling of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "8319a544",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to check for outliers in the dataframe\n",
    "def handle_outliers(data_frame, column_name, method='clip', threshold=1.5):\n",
    "    \"\"\"\n",
    "    Handle outliers in a specified column of a DataFrame using the IQR method.\n",
    "\n",
    "    Parameters:\n",
    "        data_frame (pd.DataFrame): The DataFrame containing the data.\n",
    "        column_name (str): The name of the column to handle outliers for.\n",
    "        method (str, optional): The method to handle outliers. Options are 'clip' (default) or 'remove'.\n",
    "        threshold (float, optional): The threshold to determine outliers in terms of IQR. Default is 1.5.\n",
    "\n",
    "    Returns:\n",
    "        pd.Series: The updated column with outliers handled.\n",
    "    \"\"\"\n",
    "    column = data_frame[column_name]\n",
    "    q1 = column.quantile(0.25)\n",
    "    q3 = column.quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    lower_bound = q1 - threshold * iqr\n",
    "    upper_bound = q3 + threshold * iqr\n",
    "\n",
    "    if method == 'clip':\n",
    "        updated_column = column.clip(lower_bound, upper_bound)\n",
    "    elif method == 'remove':\n",
    "        updated_column = column[(column >= lower_bound) & (column <= upper_bound)]\n",
    "    else:\n",
    "        raise ValueError(\"Invalid method. Choose 'clip' or 'remove'.\")\n",
    "\n",
    "    return updated_column\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ce7d2f",
   "metadata": {},
   "source": [
    "#### 4.2.1 Handling of outliers in the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "53af9ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# handling outliers in the train dataset\n",
    "train['Plasma_glucose'] = handle_outliers(train, 'Plasma_glucose', method='clip')\n",
    "train['Blood_Work_R1']= handle_outliers(train, 'Blood_Work_R1', method='clip')\n",
    "train['Blood_Pressure']= handle_outliers(train, 'Blood_Pressure', method='clip')\n",
    "train['Blood_Work_R2']= handle_outliers(train, 'Blood_Work_R2', method='clip')\n",
    "train['Blood_Work_R3']= handle_outliers(train, 'Blood_Work_R3', method='clip')\n",
    "train['BMI']= handle_outliers(train, 'BMI', method='clip')\n",
    "train['Age']= handle_outliers(train, 'Age', method='clip')\n",
    "train['Insurance']= handle_outliers(train, 'Insurance', method='clip')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "b06917ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# handling outliers in the test dataset\n",
    "test['Plasma_glucose'] = handle_outliers(test, 'Plasma_glucose', method='clip')\n",
    "test['Blood_Work_R1']= handle_outliers(test, 'Blood_Work_R1', method='clip')\n",
    "test['Blood_Pressure']= handle_outliers(test, 'Blood_Pressure', method='clip')\n",
    "test['Blood_Work_R2']= handle_outliers(test, 'Blood_Work_R2', method='clip')\n",
    "test['Blood_Work_R3']= handle_outliers(test, 'Blood_Work_R3', method='clip')\n",
    "test['BMI']= handle_outliers(test, 'BMI', method='clip')\n",
    "test['Age']= handle_outliers(test, 'Age', method='clip')\n",
    "test['Insurance']= handle_outliers(test, 'Insurance', method='clip')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9f7e92",
   "metadata": {},
   "source": [
    "#### 4.2.2  visualization of outliers in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "e36026fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for  outlier of each numerical column age\n",
    "sns.boxplot(data=train, orient=\"h\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "2f9a6631",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for  outlier of each numerical column age\n",
    "sns.boxplot(data=test, orient=\"h\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc436f6d",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "there are no outliers now "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6549d2fd",
   "metadata": {},
   "source": [
    "# V. Univariate Analysis\n",
    "Here is the section to explore, analyze, visualize each variable independently of the others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62cb305e",
   "metadata": {},
   "source": [
    "#### 5.1 visualization of the percentage unique values of our target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "cb3878dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "Sepsis=%{label}<extra></extra>",
         "labels": [
          "Positive",
          "Negative",
          "Positive",
          "Negative",
          "Positive",
          "Negative",
          "Positive",
          "Negative",
          "Positive",
          "Positive",
          "Negative",
          "Positive",
          "Negative",
          "Positive",
          "Positive",
          "Positive",
          "Positive",
          "Positive",
          "Negative",
          "Positive",
          "Negative",
          "Negative",
          "Positive",
          "Positive",
          "Positive",
          "Positive",
          "Positive",
          "Negative",
          "Negative",
          "Negative",
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   "source": [
    "# visualizing the percentage unique values of the target  features.\n",
    "import plotly.express as px\n",
    "fig_1 = px.pie(train, names='Sepsis', title='Plot of Ratio of the Label Variables (Sepsis)')\n",
    "iplot(fig_1)"
   ]
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    "✍ summary:\n",
    "- from the visual, Label feature, We can observe that there is an imbalance in our dataset; therefore we will have to deal with that later.\n",
    "- 65.3% of the patients are negative.\n",
    "- 34.7% of the patients are positive."
   ]
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    "#### 5.2 visualization of the features against  our target \n",
    "- At this stage we are going to compare all other features to the target or label. Since all features are continuous, box plot is used because it can clearly show the distribution, mean, and median of the features."
   ]
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   "execution_count": 284,
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    "# function to compare the features against the target\n",
    "def compare_plot(df, col):\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.title('Comparison of ' + str(col) + ' between sepsis-positive and sepsis-negative patients', \n",
    "              fontsize=20)\n",
    "    \n",
    "    # Property for the mean marker\n",
    "    meanline = {'marker':'o', 'markersize':'10'}\n",
    "    \n",
    "    # Box plot of each column separated by Positive and Negative with sepsis\n",
    "    # Show mean on box plot\n",
    "    sns.boxplot(data=df, x='Sepsis', y=col, showmeans=True, meanprops=meanline)\n",
    "    \n",
    "    plt.xlabel('Sepsis', size=18)\n",
    "    plt.ylabel(col, size=18)"
   ]
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n0+mPHTtWL774oh577DEdP35cMTExeuSRRzR8+HCrzNChQ3X+/Hn169dPZ86cUdOmTbVo0SKFhIT4NBYAAAAA8MZhjDF2BwEAKLzi4+MVFRWluLg4RUZG5rieixcvqm3btpKks/V6SP6BvgoRcJWarIit0yVJCxcuzNCrEYVL+raD9WkfXx0LAABXL27xAwAAAAAAgK24xQ8A8kBSUpKOHj2qoKCgDG/EO3funEaMGKHFixfLz89Pd999t5577jl+9QcgY4wSEhLsDqNQSb+8WHY5ExISIofDYXcYAICrHAkqAMgDn3zyiR5//HH16tVLkydPdhnWrl07rVmzRs47rH/55RetXr1ay5cv5wIBuMolJCRYt6sh+5wvgUD2cGskAKAg4BY/AMgDsbGxkqRu3bq5fD9v3jytXr1aDodD3bt310MPPaTAwECtXr1a06dPtyNUAAAAALAdPagAIA/89ttvkqSbb77Z5fsZM2bI4XDomWee0auvvipJqlevnh577DHNmDFDPXv2zPdYARRM45qeUrA/77LJjDFSUtql/wf5SXREzZrEVIcGrom2OwwAACwkqAAgD5w4cUJFihRRsWLFXL5fvny5JOmhhx6yvuvRo4cee+wx/fzzz/kaI4CCLdjfKNjf7igKhxC7AyiUSH4CAAoWbvEDgDxw/vx5+fm5NrH79u3TiRMnVKFCBVWpUsX6PiwsTEWLFtWpU6fyO0wAAAAAKBBIUAFAHoiOjta5c+d05swZ67tly5ZJkho3bpyhfEpKisLDw/MrPAAAAAAoUEhQAUAeqFevniRp0qRJkqS0tDRNmjRJDodDt99+u0vZEydO6Ny5cypTpky+xwkAAAAABQEJKgDIA7169ZIxRs8++6zatm2rW265RevWrVN4eLi6dOniUnb16tWSpBo1atgRKgAAAADYjgQVAOSB++67T71791ZqaqpiY2O1detWhYSEaOLEiSpatKhL2VmzZrntWQUAAAAAVwve4gcAeWTy5Mnq27evfvzxRxUtWlR33nmnrrnmGpcySUlJioqKUs+ePfWvf/3LpkgBAAAAwF4kqAAgDzVp0kRNmjTxODwoKEgfffRRPkYEAAAAAAUPt/gBAAAAAADAVvSgAoA89ssvvyg2NlZ///23Ll68aL3ZT5KSk5N14sQJORwOlS1b1sYoAQAAAMA+JKgAII/ExcWpT58+mjt3riTJGCOHw5EhQVW7dm2dPn1aP//8s2rWrGlTtAAAAABgH27xA4A8kJycrLZt22ru3LkqUqSI2rVrp5CQkAzlihQpogcffFBpaWn6+uuvbYgUAAAAAOxHggoA8sCkSZO0fv16XXPNNdq1a5fmzZunqKgot2U7d+4sSVq1alV+hggAAAAABQYJKgDIAzNnzpTD4dC7776rmJgYr2Xr1q0rPz8//f777/kUHQAAAAAULCSoACAPbN++XQ6HQ61atcq0bFBQkKKionTy5Ml8iAwAAAAACh4SVACQBy5cuKCIiAgFBQVlqXxycrICAnhvBQAAAICrEwkqAMgDJUqUUHx8vM6dO5dp2b179+rcuXOZ3goIAAAAAFcqElQAkAcaNmwoSVqwYEGmZceOHStJuu222/I0JgAAAAAoqLifBADyQJ8+fTRnzhy9+OKLuu222zz2jvrwww/13nvvyeFwqF+/fvkcZcFijPnfH6nJ9gWCK1+67ctluwMAAIBtSFABQB5o166dOnfurG+++Ub169dXt27ddPHiRUnSRx99pL///lvfffedfv31Vxlj9PDDD1u9rq5WiYmJ1v8jfv7CxkgKFyMjE2xkAo0cyQ45Eh1yyGF3WIVGYmKiihQpYncYAAAAVz0SVACQR6ZPn66QkBB9/vnnevfdd63v+/fvL+l/PTf69Omj8ePH2xIjCq+0oDQlXpeohBoJSotMs773i/dTyG8hCv4jWH5J3MkPAACAwoEEFQDkkZCQEE2fPl2PPPKIPvnkE/344486fPiwUlNTVaZMGTVp0kT9+vVTs2bN7A61QAgODrb+f7b2/ZJ/oI3RFGxpEfuUUmmB5JfxVsi0iDRduOWCLtRPVsDf7eR3tnL+B1jQpSZbvfTSb3cAAACwDwkqAMhjTZs2VdOmTe0Oo8BzONLdluYfSILKg7TwvUqp9K0kI7d38jm/80tWSpVvFfB3Z/mdq5KPERYuLtsdAAAAbEPffwDIA35+fgoICNAff/xhdyi4ghi/BKVU8JKcSs8hSUYpFb6V8UvI++AAAACAXCBBBQB5IDQ0VOHh4bruuuvsDgVXkLRiOy7d1pfVTj8OSX7JSiu6Iy/DAgAAAHKNBBUA5IHy5csrOTnj84GAnDIySo3emqNxU4tvlZHxcUQAAACA75CgAoA80K5dOyUkJGjlypV2h4Irhf9FKfhM1ntPOTl0aTx/bvMDAABAwUWCCgDywLBhw1SyZEn1799fR44csTscXAncvLEve+Mn+SYOAAAAIA/wFj8AyAO//fabXn31VQ0aNEg33HCDevTooSZNmqhUqVLy9/f3OF6zZs3yMUoUKmm5fKthWpBv4gAAAADyAAkqAMgDLVq0cHl9/fjx4zV+/Hiv4zgcDqWkpOR1aCisUkOlxKJS0Jns3eZnJCUVlVJD8iQsAAAAwBe4xQ8A8ogxJluftLQ0u0NGAeaQQ/6n6uVoXP+T9eTI9sOrAAAAgPxDggoA8kBaWlqOPoA3fqdrXrrVL6sv5DMOKS1Qfmdq5mlcAAAAQG6RoAIAoJBwpIUo4EAHSY7Mk1T/f3jAgQ5ypHF7HwAAAAo2nkEFAD52/Phxbd68WfHx8YqOjlaDBg1UrFgxu8PCFcLvXBUF/N1ZKRW+/d+b/dLfvedMXKUFKuBAB/mdq5LfIQIAAADZRoIKAHwkLi5O/fr10zfffCNj/te9xc/PT71799b777+v0NBQGyPElcLvXBUF7npUaUV3KLX4Vin4zP8GJhWV/8l68jtzoxxpwbbFCAAAAGQHCSoA8IGUlBTddddd2rJli0tySpJSU1M1efJkHThwQIsWLbIpQlxpHGkh8j91s/xO1ZP8EyS/JCktSEoN4YHoAAAAKHR4BhUA+MCnn36qzZs3yxij2267TR9++KEWLFig8ePHq3bt2jLGaPHixZo/f77doeIK45BDjtRQOZKjLv1LcgoAAACFED2oAMAHvvzySzkcDnXr1k3Tp093GdavXz+1bdtWS5cu1ddff6327dvbFCUAXHmMpHg/KcEhhRgpMk2kaQEAKIToQQUAPvDLL79IkkaNGpVhmL+/v1555RUZY6xy+aVy5cpyOBwZPgMGDJAkJSQkaMCAASpevLjCw8PVuXNnHTt2LF9jBICcOOeQvo2QHo6RulWQ+pS/9O/DMZe+P0eWCgCAQoUEFQD4wKlTpxQWFqZKlSq5HV6rVi2rXH7atGmTjhw5Yn0WL14sSerSpYskadCgQZo/f76++uorrVy5UocPH9a9996brzECQHZtCZF6lZc+LiYdvex+gKMBl77vVf5SOQAAUDhwix8A+EBycrKKFy/ucXhYWJhVLj+VLFnS5e///ve/uvbaa9W8eXPFxcVp0qRJmjFjhu644w5J0pQpU1SjRg2tX79et956a77GCkAuL1lITLUxkALsp1Dp1VKXbu0zbnpJOb9LlDSilPT8UanuxfyMsHBIv31d/nIPAADsQIIKAK4SSUlJ+uyzzzR48GA5HA5t2bJFycnJatmypVWmevXqqlixotatW+cxQZWYmKjExETr7/j4+DyPHbhapN+3Bq7xnPS+WqUFpel019OXslOZ3AdgHJJJk0aWkIp9WUx+Sdw44EliYqKKFClidxgAgKscCSoA8JHU1FQdOHDA6y/RmZWpWLFiXoWnuXPn6syZM+rdu7ck6ejRowoKClLRokVdypUuXVpHjx71WM/o0aP18ssv51mcAOBJ4nWJl85es/p8Kb9LZROvTVTob6F5GBkAAMgtElQA4CP//POPKleu7HG4w+HwWsbhcCglJSVvgpM0adIktW3bVjExMbmqZ9iwYRo8eLD1d3x8vCpUqJDb8ABICg4Otv4/rulJBfvbGEwBYyQNqCBdyOZ4DkmRN1/Q+FIXeLtfOomp/+ull367AwDALiSoAMBHCvIzPP7++28tWbJEs2fPtr4rU6aMkpKSdObMGZdeVMeOHVOZMmU81hUcHMzFDJBHHI7/pVCC/UWCKp04P+loYPbHM45L4yUFSpFpvo/rSpB+uwMAwC4kqADAB6ZMmWJ3CF5NmTJFpUqVUrt27azvbr75ZgUGBmrp0qXq3LmzJGnXrl3av3+/GjVqZFeoAOBWQi5zKBcdUqRvQgEAAHmABBUA+ECvXr3sDsGjtLQ0TZkyRb169VJAwP+a/aioKPXt21eDBw9WdHS0IiMj9fjjj6tRo0a8wQ9AgROSy06qoQW3kysAABAJKgAo0L766itdvHhRPXv2zHEdS5Ys0f79+9WnT58Mw9599135+fmpc+fOSkxMVOvWrTVhwoTchAwAeSIyTSqbLB0NuHTbXlY5jFQmRYrg9j4AAAo0ElQAUIA98cQTOnHiRK4SVK1atfL4fKyQkBCNHz9e48ePz3H9AJAfHJLan5U+Lpb9ce85m/UX/wEAAHv42R0AAMC7gvzwdQDIT3eek4LNpV5RWeEwl8rfcS5v4wIAALlHggoAAACFQriRnjtxqTdUZkkqh7lU7vkTl8YDAAAFGwkqAAAAFBo3J0gjjv+vJ9XliSrnd8FGevm4VC/BnjgBAED28AwqAAAAFCo3J0ifHpSWhUvzIqQjgf8bVibl0jOn7jwnhdFzCgCAQoMEFQAAAAqdcHMpEdX+rHTWT7rokELNpbf18UB0AAAKHxJUAAAAKLQckiLTpEi7AwEAALnCM6gAAAAAAABgKxJUAAAAAAAAsBUJKgAAAAAAANiKBBUAAAAAAABsRYIKAAAAAAAAtuItfgBQgHXt2lXx8fF2hwEAAAAAeYoEFQAUYO+9957dIQA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      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EpE7Jzs4mKyvrtvutW7fO8j0gIACAwYMH4+rqSmlpKRMmTLhlc3OTyURBQUG122fPnl3l+IIFCyguLsZoNBIZGWkZv379+i3jtbe3B+CRR2r296ym693JmiIiIiLWQP9cREREpE757bff8PHxoW/fviQlJVXqK1VaWsqRI0cYPnw4CxcuBCrewtetWzegosn5hx9+CMDGjRvp27cvBw8exGQyARUJquPHj7NgwQLatWvH9u3bq4yhQYMGrFu3jvHjx3P+/HmgoqJq3rx5ljcJjhkzBnd3d8uchIQE+vTpw/r168nNzbWMX79+nU2bNvH+++8D0Ldv3xpdh40bN9K1a1dWrlzJyZMnLePl5eXs2bOHKVOmABAcHEzDhg1rtKaIiIiINTDWdgAiIiIid6J+/fqYTCZ27tzJzp07gYq+TU5OTly6dKlStVRAQABffvllpcqimJgYiouLGT9+PLt27WLXrl3Y2tri5OTE5cuXK72Rz2AwVBlDx44dCQoKIjExkY8++ghXV1cuX75saeAeFhbG/PnzK80xmUzs3r3b0uzd3t4ee3v7SjH7+PhYkmy3Yzab+eGHH/jhhx8ALOdw6dIlS/LN3d2dTz75pEbriYiIiFgLJatERESkTgkPD+fEiRPs3LmTAwcO8Ouvv5Kbm0tBQQEODg64u7vj7+/PoEGDGDx4cJWPwMXFxdG7d2+WLl1KSkoK2dnZFBQU4OLiQsuWLQkODiYiIoIePXpUG0dCQgIBAQEsXbqUjIwMbGxs8Pb2Zvjw4YwZM4Z69epV2j82NhYPDw/27dtHRkYG+fn5FBYW0rBhQ9q1a0dkZCSjRo3Czs6uRtchIiKCpKQk9u3bR3p6Ovn5+Vy8eBFnZ2e8vb3p378/Y8eOxdXV9Y6ur4iIiEhtM5hv1axBRERERCxmzJjBzJkzCQ0N5dtvv63tcEREREQeSOpZJSIiIiIiIiIiVkPJKhERERERERERsRpKVomIiIiIiIiIiNVQskpERERERERERKyGGqyLiIiIiIiIiIjVUGWViIiIiIiIiIhYDSWrRERERERERETEaihZJSIiIiIiIiIiVkPJKhERERERERERsRpKVomIiIiIiIiIiNVQskpERERERERERKyGklUiIiIiIiIiImI1lKwSERERERERERGr8T9fvZz3tNnL8gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calling  the compare plot function in the train set\n",
    "cols = ['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2', \n",
    "        'Blood_Work_R3', 'BMI', 'Blood_Work_R4']\n",
    "for col in cols:\n",
    "    compare_plot(train, col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a00bcd1",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- It can be easily observed that patients who are positive with sepsis have higher readings in all of their health features comparing to the negative group. Hence, it can be reasonably inferred that patients with higher level of plasma glucose, Body mass index , and for blood work results are more likely to develop sepsis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bfe3167",
   "metadata": {},
   "source": [
    "#### 5.2.1 visualization of  count of  age feature \n",
    "Here we would like know the frequency of the age in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "f4e319a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# determining age count\n",
    "Age_counts = train.groupby('Age').size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "5c9dfaca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of age count\n",
    "fig_2 = ex.scatter(Age_counts, x='Age', y='count', size='count', color= 'Age', hover_name='Age',log_y=False, size_max=60)\n",
    "fig_2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa3ae6e",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- from the visual the age with the highest frequency are ages 21 and 22 whiles age 61 has the lowest frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35bc5df8",
   "metadata": {},
   "source": [
    "#### 5.2.2 visualization of age group against the target \n",
    "- Here, we are going to create age group to aids in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "90aeb03c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the bin edges for the age groups\n",
    "bins = list(range(20, 81, 10))\n",
    "\n",
    "# Create the bin labels for the age groups (7 labels for 8 bins)\n",
    "basket = ['{0}-{1}'.format(i, i + 9) for i in range(20, 81, 10)[:-1]]\n",
    "\n",
    "# Use pd.cut() to assign age groups to each 'Age' value\n",
    "train['Age_group'] = pd.cut(train['Age'], bins=bins, labels=basket, right=False)\n",
    "\n",
    "# Plot the count of each age group colored by 'Sepsis'\n",
    "sns.countplot(data=train, x='Age_group', hue='Sepsis');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d370ba",
   "metadata": {},
   "source": [
    "✍ summary:\n",
    "- from the visual, comparatively from 21-29 age group are less susceptive to sepsis than from 30-59 while patients at 60-69 are more susceptible sepsis, which i believe is because to weak immune system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514295f6",
   "metadata": {},
   "source": [
    "#### 5.2.3 visualization of count of other features against sepsis\n",
    "- Here, we want varify how other features varies in terms of count with respect to the target feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "3461e69f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
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   "source": [
    "def create_histograms(data_frame, column_name):\n",
    "    fig_3 = px.histogram(\n",
    "        data_frame=data_frame,\n",
    "        x=column_name,\n",
    "        color=\"Sepsis\",\n",
    "        facet_col=\"Sepsis\",\n",
    "        nbins=50,\n",
    "        title=f\"{column_name} with Respect to Sepsis\"\n",
    "    )\n",
    "    fig_3.show()\n",
    "\n",
    "# Assuming your DataFrame is named 'train'\n",
    "excluded_columns = [\"Sepsis\", \"ID\"]  # List of columns to exclude\n",
    "\n",
    "for column in train.columns:\n",
    "    if column not in excluded_columns:\n",
    "        create_histograms(train, column)"
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  },
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   "source": [
    "✍ summary:\n",
    "- Plasma glucose with Sepsis: from the visual it is evident that positive sepsis tends to fall as count of plasma glucose increases.Similarly, for the negative sepsis tends to also gradually diminish as the count of the plasma glucose increases. thus higher plasma glucose, the higher Plasma exchange, the higher the potential to improve survival in sepsis by removing inflammatory cytokines and restoring deficient plasma proteins.\n",
    "\n",
    "- blood work result-1 and Sepsis: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   },
   "source": [
    "## Bivariate Analysis"
   ]
  },
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   "cell_type": "code",
   "execution_count": 290,
   "id": "c64022f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure',\n",
       "       'Blood_Work_R2', 'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age',\n",
       "       'Insurance', 'Sepsis', 'Age_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2002ced",
   "metadata": {},
   "source": [
    "- Relationship Between Blood Pressure and Body mass index with Respect To Sepsis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "c87d6168",
   "metadata": {},
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   "source": [
    "fig_3=px.scatter(train, x=\"BMI\", y= \"Blood_Pressure\",color= \"Sepsis\", \n",
    "                 title= \"Relationship Between Blood Pressure and Body mass index with Respect To Sepsis\")\n",
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    "- Relationship Between Plasma glucose and Body mass index with Respect To Sepsis"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_4=px.scatter(train, x=\"Plasma_glucose\", y= \"BMI\",color= \"Sepsis\", \n",
    "                 title= \"Relationship Between Plasma glucose and Body mass index with Respect To Sepsis\")\n",
    "\n",
    "iplot(fig_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39a67ba",
   "metadata": {},
   "source": [
    "### Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "448dfd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "##sns.heatmap(train.corr(), annot= True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4da87e21",
   "metadata": {},
   "source": [
    "✍ Observation\n",
    "- the correlation between features are weak, except for the plasma glucose and age which is 0.54. this also is weak. Hence all features will be kept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "6735d13e",
   "metadata": {},
   "outputs": [],
   "source": [
    "##sns.heatmap(test.corr(), annot= True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ccb37b",
   "metadata": {},
   "source": [
    "✍ Observation\n",
    "- similarly, the correlation between features are weak, except for the plasma glucose and age with a correlation coefficient of 0.54. this also is weak. Hence all features will be kept."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e7a597",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff0e13c",
   "metadata": {},
   "source": [
    "- in this section we will be selecting the best features to feed our models with. We will be using the Phi-Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "5a75d558",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval columns not set, guessing: ['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2', 'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age', 'Insurance']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID                1.000000\n",
       "Plasma_glucose    0.282180\n",
       "Blood_Work_R1     0.611669\n",
       "Blood_Pressure    0.205354\n",
       "Blood_Work_R2     0.229503\n",
       "Blood_Work_R3     0.404616\n",
       "BMI               0.429088\n",
       "Blood_Work_R4     0.231272\n",
       "Age               0.404641\n",
       "Insurance         0.066436\n",
       "Sepsis            1.000000\n",
       "Age_group         0.393934\n",
       "Name: Sepsis, dtype: float64"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import phik\n",
    "#the correlation of other features with churn\n",
    "sepsis_corr= train.phik_matrix().loc[\"Sepsis\"]\n",
    "#sorting the values \n",
    "sepsis_cor=sepsis_corr.sort_values()\n",
    "sepsis_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "83f28e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Phik Correlation Matrix for all  Features')"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting the phi-k correlation mattress\n",
    "plt.figure(figsize= (8,4));\n",
    "sns.heatmap(sepsis_corr.to_frame(), annot= True, cmap= \"coolwarm\")\n",
    "plt.title(\"Phik Correlation Matrix for all  Features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e20266e",
   "metadata": {},
   "source": [
    "- from the phik correlation matrix, we will be dropping age_group since age and age_group are almost the same and we also drop insurance and ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "4762bbc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop some columns\n",
    "train.drop(columns=['ID', 'Insurance','Age_group'], axis=1, inplace=True)\n",
    "test.drop(columns=['ID','Insurance'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "6b01556f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Positive', 'Negative'], dtype=object)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique values of the target\n",
    "train['Sepsis'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "948df680",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace Positive with 1 and Negative with 0 in target column\n",
    "train['Sepsis'].replace(to_replace='Positive', value='1', inplace=True)\n",
    "train['Sepsis'].replace(to_replace='Negative', value='0', inplace=True)\n",
    "# changing sepsis object type to integer\n",
    "train['Sepsis'] = train['Sepsis'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "b978396d",
   "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>Plasma_glucose</th>\n",
       "      <th>Blood_Work_R1</th>\n",
       "      <th>Blood_Pressure</th>\n",
       "      <th>Blood_Work_R2</th>\n",
       "      <th>Blood_Work_R3</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood_Work_R4</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sepsis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plasma_glucose  Blood_Work_R1  Blood_Pressure  Blood_Work_R2  \\\n",
       "0             6.0          148.0              72             35   \n",
       "1             1.0           85.0              66             29   \n",
       "2             8.0          183.0              64              0   \n",
       "3             1.0           89.0              66             23   \n",
       "4             0.0          137.0              40             35   \n",
       "\n",
       "   Blood_Work_R3   BMI  Blood_Work_R4  Age  Sepsis  \n",
       "0            0.0  33.6          0.627   50       1  \n",
       "1            0.0  26.6          0.351   31       0  \n",
       "2            0.0  23.3          0.672   32       1  \n",
       "3           94.0  28.1          0.167   21       0  \n",
       "4          168.0  43.1          2.288   33       1  "
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "9a2b287d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 599 entries, 0 to 598\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Plasma_glucose  599 non-null    float64\n",
      " 1   Blood_Work_R1   599 non-null    float64\n",
      " 2   Blood_Pressure  599 non-null    int64  \n",
      " 3   Blood_Work_R2   599 non-null    int64  \n",
      " 4   Blood_Work_R3   599 non-null    float64\n",
      " 5   BMI             599 non-null    float64\n",
      " 6   Blood_Work_R4   599 non-null    float64\n",
      " 7   Age             599 non-null    int64  \n",
      " 8   Sepsis          599 non-null    int32  \n",
      "dtypes: float64(5), int32(1), int64(3)\n",
      "memory usage: 39.9 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "4fb69019",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 599 entries, 0 to 598\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Plasma_glucose  599 non-null    float64\n",
      " 1   Blood_Work_R1   599 non-null    float64\n",
      " 2   Blood_Pressure  599 non-null    int64  \n",
      " 3   Blood_Work_R2   599 non-null    int64  \n",
      " 4   Blood_Work_R3   599 non-null    float64\n",
      " 5   BMI             599 non-null    float64\n",
      " 6   Blood_Work_R4   599 non-null    float64\n",
      " 7   Age             599 non-null    int64  \n",
      " 8   Sepssis         599 non-null    object \n",
      "dtypes: float64(5), int64(3), object(1)\n",
      "memory usage: 42.2+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "d105fcd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sepsis\n",
       "0    391\n",
       "1    208\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Sepsis.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2012db0e",
   "metadata": {},
   "source": [
    "### handling imbalance target class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "f1140242",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "# Separate majority and minority classes\n",
    "train_majority = train[train.Sepsis==0]\n",
    "train_minority = train[train.Sepsis==1]\n",
    " \n",
    "# Upsample minority class\n",
    "train_minority_upsampled = resample(train_minority, \n",
    "                                 replace=True,     # sample with replacement\n",
    "                                 n_samples=391,    # to match majority class\n",
    "                                 random_state=123) # reproducible results\n",
    " \n",
    "train_upsampled = pd.concat([train_majority, train_minority_upsampled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "d4f71912",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sepsis\n",
       "0    391\n",
       "1    391\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display new class counts\n",
    "train_upsampled['Sepsis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "409facc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age', 'Sepsis'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_upsampled.columns"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f4f5eee0",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "82b7ff81",
   "metadata": {},
   "source": [
    "### Splitting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "915da509",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_upsampled.drop('Sepsis', axis=1)\n",
    "y = train_upsampled.Sepsis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "f049b868",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size = .2, random_state=42, stratify= y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "b9d8a664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((625, 8), (157, 8), (625,), (157,))"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "e7b5b689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plasma_glucose    0\n",
       "Blood_Work_R1     0\n",
       "Blood_Pressure    0\n",
       "Blood_Work_R2     0\n",
       "Blood_Work_R3     0\n",
       "BMI               0\n",
       "Blood_Work_R4     0\n",
       "Age               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "X_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "71bb3594",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plasma_glucose    0\n",
       "Blood_Work_R1     0\n",
       "Blood_Pressure    0\n",
       "Blood_Work_R2     0\n",
       "Blood_Work_R3     0\n",
       "BMI               0\n",
       "Blood_Work_R4     0\n",
       "Age               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking for missing values\n",
    "X_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "3f3eab91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# checking for datatypes\n",
    "def check_dtypes(data_frame):\n",
    "    dtypes_dict = data_frame.dtypes.to_dict()\n",
    "    return dtypes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "caf6fd7f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Plasma_glucose': dtype('float64'),\n",
       " 'Blood_Work_R1': dtype('float64'),\n",
       " 'Blood_Pressure': dtype('int64'),\n",
       " 'Blood_Work_R2': dtype('int64'),\n",
       " 'Blood_Work_R3': dtype('float64'),\n",
       " 'BMI': dtype('float64'),\n",
       " 'Blood_Work_R4': dtype('float64'),\n",
       " 'Age': dtype('int64')}"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_dtypes(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece93dbf",
   "metadata": {},
   "source": [
    "✍ Observation\n",
    "- there are no categorical features in the X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "d8a64e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_attr=X_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a445b3",
   "metadata": {},
   "source": [
    "### Creating Pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "fabd2785",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating pipelines\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "num_pipeline= Pipeline([('imputer', SimpleImputer()),('scaler', StandardScaler())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "0e41d6d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "full_pipeline=ColumnTransformer([('num_pipe',num_pipeline,num_attr)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc7f3fc",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a9abbbd",
   "metadata": {},
   "source": [
    "creating a pipeline for each ml model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "29c32256",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained= [] # empty list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b5a885c",
   "metadata": {},
   "source": [
    "DecisionTree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "074f4eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiating the model\n",
    "DTC=DecisionTreeClassifier() \n",
    "\n",
    "from imblearn.pipeline import Pipeline\n",
    "\n",
    "DTC = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", BaggingClassifier(base_estimator=DecisionTreeClassifier(random_state=42, max_depth=6, min_samples_leaf=8)))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "4f793c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=6,\n",
       "                                                                         min_samples_leaf=8,\n",
       "                                                                         random_state=42)))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=6,\n",
       "                                                                         min_samples_leaf=8,\n",
       "                                                                         random_state=42)))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-75\" type=\"checkbox\" ><label for=\"sk-estimator-id-75\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-76\" type=\"checkbox\" ><label for=\"sk-estimator-id-76\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-77\" type=\"checkbox\" ><label for=\"sk-estimator-id-77\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-78\" type=\"checkbox\" ><label for=\"sk-estimator-id-78\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-79\" type=\"checkbox\" ><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=6,\n",
       "                                                        min_samples_leaf=8,\n",
       "                                                        random_state=42))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=6, min_samples_leaf=8, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=6, min_samples_leaf=8, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model',\n",
       "                 BaggingClassifier(base_estimator=DecisionTreeClassifier(max_depth=6,\n",
       "                                                                         min_samples_leaf=8,\n",
       "                                                                         random_state=42)))])"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTC.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "167dd8ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1= DTC.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "0aedac8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.84      0.80        73\n",
      "           1       0.85      0.79      0.81        84\n",
      "\n",
      "    accuracy                           0.81       157\n",
      "   macro avg       0.81      0.81      0.81       157\n",
      "weighted avg       0.81      0.81      0.81       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(classification_report(model_1,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "b9f788f1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_1,y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "8fbc5362",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(DTC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40958092",
   "metadata": {},
   "source": [
    "Logistic Regressor Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "83299d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "LRP=LogisticRegression()\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LRP = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", BaggingClassifier(base_estimator=LogisticRegression(random_state=42)))\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "91ab5cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassifier(base_estimator=LogisticRegression(random_state=42)))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassifier(base_estimator=LogisticRegression(random_state=42)))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=LogisticRegression(random_state=42))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model',\n",
       "                 BaggingClassifier(base_estimator=LogisticRegression(random_state=42)))])"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LRP.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "9753041e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2= LRP.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "4a17b7b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.71      0.72        82\n",
      "           1       0.69      0.72      0.71        75\n",
      "\n",
      "    accuracy                           0.71       157\n",
      "   macro avg       0.71      0.71      0.71       157\n",
      "weighted avg       0.71      0.71      0.71       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_2, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "c2d695a5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_2, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "95b78a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(LRP)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f1bc1f",
   "metadata": {},
   "source": [
    "Random forest pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "7709c4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "940fff08",
   "metadata": {},
   "outputs": [],
   "source": [
    "#instantiating\n",
    "RFC=RandomForestClassifier()\n",
    "\n",
    "RFC= Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", BaggingClassifier(base_estimator=RandomForestClassifier(random_state=42, max_depth=6, min_samples_leaf=8)))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "01f9430e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassifier(base_estimator=RandomForestClassifier(max_depth=6,\n",
       "                                                                         min_samples_leaf=8,\n",
       "                                                                         random_state=42)))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassifier(base_estimator=RandomForestClassifier(max_depth=6,\n",
       "                                                                         min_samples_leaf=8,\n",
       "                                                                         random_state=42)))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-97\" type=\"checkbox\" ><label for=\"sk-estimator-id-97\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=RandomForestClassifier(max_depth=6,\n",
       "                                                        min_samples_leaf=8,\n",
       "                                                        random_state=42))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-98\" type=\"checkbox\" ><label for=\"sk-estimator-id-98\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=6, min_samples_leaf=8, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-99\" type=\"checkbox\" ><label for=\"sk-estimator-id-99\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=6, min_samples_leaf=8, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model',\n",
       "                 BaggingClassifier(base_estimator=RandomForestClassifier(max_depth=6,\n",
       "                                                                         min_samples_leaf=8,\n",
       "                                                                         random_state=42)))])"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "ba743642",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3= RFC.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "bc2226d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.80      0.76        71\n",
      "           1       0.82      0.74      0.78        86\n",
      "\n",
      "    accuracy                           0.77       157\n",
      "   macro avg       0.77      0.77      0.77       157\n",
      "weighted avg       0.78      0.77      0.77       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_3,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "8d4b019d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_3,y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "791bfe76",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(RFC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f4ee92f",
   "metadata": {},
   "source": [
    "SVM Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "3784d3a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiating\n",
    "SVM=SVC()\n",
    "\n",
    "SVM= Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", BaggingClassifier(base_estimator=SVC(gamma='auto')))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "7b5fad8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, BaggingClassifier(base_estimator=SVC(gamma=&#x27;auto&#x27;)))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-100\" type=\"checkbox\" ><label for=\"sk-estimator-id-100\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, BaggingClassifier(base_estimator=SVC(gamma=&#x27;auto&#x27;)))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-101\" type=\"checkbox\" ><label for=\"sk-estimator-id-101\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-102\" type=\"checkbox\" ><label for=\"sk-estimator-id-102\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-103\" type=\"checkbox\" ><label for=\"sk-estimator-id-103\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-104\" type=\"checkbox\" ><label for=\"sk-estimator-id-104\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-105\" type=\"checkbox\" ><label for=\"sk-estimator-id-105\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-106\" type=\"checkbox\" ><label for=\"sk-estimator-id-106\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=SVC(gamma=&#x27;auto&#x27;))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-107\" type=\"checkbox\" ><label for=\"sk-estimator-id-107\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=&#x27;auto&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-108\" type=\"checkbox\" ><label for=\"sk-estimator-id-108\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=&#x27;auto&#x27;)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model', BaggingClassifier(base_estimator=SVC(gamma='auto')))])"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVM.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "7ec17203",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_4= SVM.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "38805867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.75      0.74        77\n",
      "           1       0.76      0.74      0.75        80\n",
      "\n",
      "    accuracy                           0.75       157\n",
      "   macro avg       0.75      0.75      0.75       157\n",
      "weighted avg       0.75      0.75      0.75       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_4, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "dde6eadb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AEKMiBwCYQlutyEnkAABTMAyLDC+SsTfH+hOtdQAAghgVOQDAFHgfOQAAQaytjpHTWgcAIIhRkQMATKGtTnYjkQMATKGtttZJ5AAAU2irFTlj5AAABDEqcgCAKRhettYDtSInkQMATMGQZBjeHR+IaK0DABDESOQAAFM492Q3b5bmWrx4sSwWix544AHnuqFDh8pisbgskydP9vjctNYBAKbQWrPWP/jgA61atUoDBw5ssm3SpElauHCh83NUVJTH56ciBwDAA1VVVS5LXV3dBfetrq7W+PHjtXr1anXq1KnJ9qioKCUmJjoXq9XqcTwkcgCAKZx7IIw3iyTZbDbFxsY6l5ycnAteMzs7Wz/+8Y81fPjw825fv369OnfurP79+2vOnDk6c+aMx9+L1joAwBQMw8tZ698eW1JS4lI5R0REnHf/DRs2aP/+/frggw/Ou/2OO+5QamqqkpOTdeDAAc2aNUuFhYXauHGjR3GRyAEA8IDVav3BFnhJSYmmTZumbdu2KTIy8rz73HPPPc5/DxgwQElJSRo2bJiKi4vVo0cPt+OhtQ4AMIVzk928WdyVn5+vEydO6IorrlBYWJjCwsKUl5enZcuWKSwsTHa7vckx6enpkqSioiKPvhcVOQDAFFpy1vqwYcN08OBBl3UTJkxQ7969NWvWLIWGhjY5pqCgQJKUlJTkUVwkcgCAKTgMiywt9PazmJgY9e/f32Vdhw4dFB8fr/79+6u4uFi5ubkaNWqU4uPjdeDAAU2fPl1Dhgw5721q34dEDgBACwsPD9f27du1dOlS1dTUyGazKTMzUw899JDH5yKRAwBMwVez1ptr586dzn/bbDbl5eV5d8JvkcgBAKbQmMi9GSP3YTA+xKx1AACCGBU5AMAUWutZ6/5GIgcAmIIh794pHqCddVrrAAAEMypyAIAp0FoHACCYtdHeOokcAGAOXlbkCtCKnDFyAACCGBU5AMAUWvvJbv5CIgcAmEJbnexGax0AgCBGRQ4AMAfD4t2EtQCtyEnkAABTaKtj5LTWAQAIYlTkAABz4IEwAAAEr7Y6a92tRP6nP/3J7RPeeuutzQ4GAAB4xq1EPmbMGLdOZrFYZLfbvYkHAAD/CdD2uDfcSuQOh8PfcQAA4FdttbXu1az12tpaX8UBAIB/GT5YApDHidxut+uRRx7RxRdfrOjoaH3xxReSpLlz52rNmjU+DxAAAFyYx4l80aJFWrdunR5//HGFh4c71/fv31/PPfecT4MDAMB3LD5YAo/HifzFF1/UH/7wB40fP16hoaHO9YMGDdKnn37q0+AAAPAZWuuNjh8/rp49ezZZ73A41NDQ4JOgAACAezxO5H379tU777zTZP1rr72myy+/3CdBAQDgc220Ivf4yW4PP/ywsrKydPz4cTkcDm3cuFGFhYV68cUXtWXLFn/ECACA99ro2888rshvu+02bd68Wdu3b1eHDh308MMP69ChQ9q8ebNuuukmf8QIAAAuoFnPWr/uuuu0bds2X8cCAIDftNXXmDb7pSn79u3ToUOHJDWOmw8ePNhnQQEA4HO8/azRsWPHNG7cOL377rvq2LGjJOnUqVP6t3/7N23YsEFdu3b1dYwAAOACPB4jv/vuu9XQ0KBDhw7p5MmTOnnypA4dOiSHw6G7777bHzECAOC9c5PdvFkCkMcVeV5ent577z2lpaU516Wlpen3v/+9rrvuOp8GBwCAr1iMxsWb4wORx4ncZrOd98EvdrtdycnJPgkKAACfa6Nj5B631p944glNnTpV+/btc67bt2+fpk2bpt/97nc+DQ4AAHw/txJ5p06dFBcXp7i4OE2YMEEFBQVKT09XRESEIiIilJ6erv379+uXv/ylv+MFAKB5WnGMfPHixbJYLHrggQec62pra5Wdna34+HhFR0crMzNTFRUVHp/brdb60qVLPT4xAAABpZVa6x988IFWrVqlgQMHuqyfPn26/vznP+vVV19VbGyspkyZorFjx+rdd9/16PxuJfKsrCyPTgoAAKTq6mqNHz9eq1ev1qOPPupcX1lZqTVr1ig3N1c33nijJGnt2rXq06eP9u7dq2uuucbta3g8Rv6vamtrVVVV5bIAABCQfPTSlO/mvbq6ugteMjs7Wz/+8Y81fPhwl/X5+flqaGhwWd+7d2+lpKRoz549Hn0tjxN5TU2NpkyZoi5duqhDhw7q1KmTywIAQEDyUSK32WyKjY11Ljk5Oee93IYNG7R///7zbi8vL1d4eLjzwWrnJCQkqLy83KOv5fHtZ7/97W/19ttva8WKFbrzzju1fPlyHT9+XKtWrdLixYs9PR0AAEGlpKREVqvV+TkiIuK8+0ybNk3btm1TZGSkX+PxOJFv3rxZL774ooYOHaoJEybouuuuU8+ePZWamqr169dr/Pjx/ogTAADv+Og1plar1SWRn09+fr5OnDihK664wrnObrdr165deuaZZ7R161bV19fr1KlTLlV5RUWFEhMTPQrL49b6yZMn1b17d0mNX+bkyZOSpB/96EfatWuXp6cDAKBFnHuymzeLu4YNG6aDBw+qoKDAuVx55ZUaP36889/t2rXTjh07nMcUFhbq6NGjysjI8Oh7eVyRd+/eXYcPH1ZKSop69+6tV155RVdffbU2b97cpNcPAIAZxcTEqH///i7rOnTooPj4eOf6iRMnasaMGYqLi5PVatXUqVOVkZHh0Yx1qRmJfMKECfroo490/fXXa/bs2Ro9erSeeeYZNTQ06KmnnvL0dAAAtIwAe0TrkiVLFBISoszMTNXV1WnEiBF69tlnPT6PxTC8e1X6l19+qfz8fPXs2bPJze7+VlVVpdjYWB35NEnWGK/upAMC1u1dPWuzAcHkrNGgnXpTlZWVPzju3FznckXKfz+qkPbNn3jm+KZWR2c95NdYm8Pjivy7UlNTlZqa6otYAADwG4u8fPuZzyLxLbcS+bJly9w+4f3339/sYAAAgGfcSuRLlixx62QWi6VVEvnEjJsVZglv8esCLWFr6dutHQLgN1WnHerUq4Uu5qPbzwKNW4n88OHD/o4DAAD/CrDJbr7CDDEAAIKY15PdAAAICm20IieRAwBMwdOns53v+EBEax0AgCBGRQ4AMIc22lpvVkX+zjvv6Oc//7kyMjJ0/PhxSdJLL72k3bt3+zQ4AAB8xkfvIw80Hify119/XSNGjFD79u314Ycfqq6uTpJUWVmpxx57zOcBAgCAC/M4kT/66KNauXKlVq9erXbt2jnXX3vttdq/f79PgwMAwFda8jWmLcnjMfLCwkINGTKkyfrY2FidOnXKFzEBAOB7bfTJbh5X5ImJiSoqKmqyfvfu3erevbtPggIAwOcYI280adIkTZs2TX//+99lsVhUWlqq9evXa+bMmbr33nv9ESMAALgAj1vrs2fPlsPh0LBhw3TmzBkNGTJEERERmjlzpqZOneqPGAEA8FpbfSCMx4ncYrHowQcf1G9+8xsVFRWpurpaffv2VXR0tD/iAwDAN9rofeTNfiBMeHi4+vbt68tYAACAhzxO5DfccIMslgvP3Pvb3/7mVUAAAPiFt7eQtZWK/LLLLnP53NDQoIKCAn388cfKysryVVwAAPgWrfVGS5YsOe/6+fPnq7q62uuAAACA+3z29rOf//znev755311OgAAfKuN3kfus7ef7dmzR5GRkb46HQAAPsXtZ98aO3asy2fDMFRWVqZ9+/Zp7ty5PgsMAAD8MI8TeWxsrMvnkJAQpaWlaeHChbr55pt9FhgAAPhhHiVyu92uCRMmaMCAAerUqZO/YgIAwPfa6Kx1jya7hYaG6uabb+YtZwCAoNNWX2Pq8az1/v3764svvvBHLAAAwEMeJ/JHH31UM2fO1JYtW1RWVqaqqiqXBQCAgNXGbj2TPBgjX7hwoX79619r1KhRkqRbb73V5VGthmHIYrHIbrf7PkoAALzVRsfI3U7kCxYs0OTJk/X222/7Mx4AAOABtxO5YTT+KXL99df7LRgAAPyFB8JI3/vWMwAAAprZW+uS1KtXrx9M5idPnvQqIAAA4D6PEvmCBQuaPNkNAIBgQGtd0n/+53+qS5cu/ooFAAD/aeHW+ooVK7RixQodOXJEktSvXz89/PDDGjlypCRp6NChysvLcznmV7/6lVauXOnRddxO5IyPAwDgvq5du2rx4sW69NJLZRiGXnjhBd1222368MMP1a9fP0nSpEmTtHDhQucxUVFRHl/H41nrAAAEpRauyEePHu3yedGiRVqxYoX27t3rTORRUVFKTEz0IigPnuzmcDhoqwMAgpavnrX+3Sea1tXV/eC17Xa7NmzYoJqaGmVkZDjXr1+/Xp07d1b//v01Z84cnTlzxuPv5fFrTAEACEo+qshtNpvL6nnz5mn+/PnnPeTgwYPKyMhQbW2toqOjtWnTJvXt21eSdMcddyg1NVXJyck6cOCAZs2apcLCQm3cuNGjsEjkAAB4oKSkRFar1fk5IiLigvumpaWpoKBAlZWVeu2115SVlaW8vDz17dtX99xzj3O/AQMGKCkpScOGDVNxcbF69OjhdjwkcgCAOfioIrdarS6J/PuEh4erZ8+ekqTBgwfrgw8+0NNPP61Vq1Y12Tc9PV2SVFRURCIHAOC7AuE+cofDccEx9YKCAklSUlKSR+ckkQMA4Adz5szRyJEjlZKSotOnTys3N1c7d+7U1q1bVVxcrNzcXI0aNUrx8fE6cOCApk+friFDhmjgwIEeXYdEDgAwhxa+/ezEiRP6xS9+obKyMsXGxmrgwIHaunWrbrrpJpWUlGj79u1aunSpampqZLPZlJmZqYceesjjsEjkAABTaOnW+po1ay64zWazNXmqW3O5fR85AAAIPFTkAABz4DWmAAAEsTaayGmtAwAQxKjIAQCmYPl28eb4QEQiBwCYQxttrZPIAQCmEAhPdvMHxsgBAAhiVOQAAHOgtQ4AQJAL0GTsDVrrAAAEMSpyAIAptNXJbiRyAIA5tNExclrrAAAEMSpyAIAp0FoHACCY0VoHAACBhoocAGAKtNYBAAhmbbS1TiIHAJhDG03kjJEDABDEqMgBAKbAGDkAAMGM1joAAAg0VOQAAFOwGIYsRvPLam+O9ScSOQDAHGitAwCAQENFDgAwBWatAwAQzGitAwCAQENFDgAwBVrrAAAEszbaWieRAwBMoa1W5IyRAwAQxKjIAQDm0EZb61TkAADTONdeb87iqRUrVmjgwIGyWq2yWq3KyMjQX//6V+f22tpaZWdnKz4+XtHR0crMzFRFRYXH1yGRAwDgB127dtXixYuVn5+vffv26cYbb9Rtt92mTz75RJI0ffp0bd68Wa+++qry8vJUWlqqsWPHenwdWusAAHMwjMbFm+M9MHr0aJfPixYt0ooVK7R371517dpVa9asUW5urm688UZJ0tq1a9WnTx/t3btX11xzjdvXoSIHAJiCN231f22vV1VVuSx1dXU/eG273a4NGzaopqZGGRkZys/PV0NDg4YPH+7cp3fv3kpJSdGePXs8+l4kcgAAPGCz2RQbG+tccnJyLrjvwYMHFR0drYiICE2ePFmbNm1S3759VV5ervDwcHXs2NFl/4SEBJWXl3sUD611AIA5+GjWeklJiaxWq3N1RETEBQ9JS0tTQUGBKisr9dprrykrK0t5eXleBNEUiRwAYAoWR+PizfGSnLPQ3REeHq6ePXtKkgYPHqwPPvhATz/9tH72s5+pvr5ep06dcqnKKyoqlJiY6FFctNYBAGghDodDdXV1Gjx4sNq1a6cdO3Y4txUWFuro0aPKyMjw6JxU5Gji9ru/1L/d9LW6djuj+toQHSqw6vmneuj4kSjnPrf8R6mGjqpQz77Vioq26z+uuVY1p9u1YtSA+176XaL+5ynXqqdrj1qteedTSVLpkXCtXpisT96PVkO9RYNvqFL2o8fV6aKzrREufKWFHwgzZ84cjRw5UikpKTp9+rRyc3O1c+dObd26VbGxsZo4caJmzJihuLg4Wa1WTZ06VRkZGR7NWJdI5DiP/led0pY/Juuzg1aFhhnKmvaFFq3+SL+69WrVfRMqSYqItCv/3TjlvxunCdMPt3LEgOdS077R4peLnZ9DQxt/S9eeCdF/jeuh7n2/0X+/WiRJeuHxJD2c1U1Pb/lcIfQxg1ZLP2v9xIkT+sUvfqGysjLFxsZq4MCB2rp1q2666SZJ0pIlSxQSEqLMzEzV1dVpxIgRevbZZz2Oq1UT+a5du/TEE08oPz9fZWVl2rRpk8aMGdOaIUHSw78a5PL5qQd7a8Pu93Rp39P6OL+jJOnNl2ySpAFX/bOlwwN8IjRUiuvStML+5P0OqigJ1/L/V6gOMY2Dor95+ktl9hmggt3RumJIdUuHCl9p4fvI16xZ873bIyMjtXz5ci1fvrz5MamVx8hramo0aNAgr78E/KtDTOMvu9OVNHDQdhw/HK5xl/dT1jV9tDg7RSeONQ4NNdRbJIvULvz/fmm3izBkCZE+eT+6tcIFLqhVfzOPHDlSI0eOdHv/uro6lxvvq6qq/BEW/oXFYuhXs4r0yX6rvizilxjaht5X1Gjm0m/UtUedTp5op/95MlG//umlWvX2p+o9uEaRUQ6tWZSsCbNLJVm0ZlGSHHaLTp7gj9lgxmtMA0BOTo7LTfg2m621Q2rz7nvoc6VeWqPFM/u2diiAz1x142kNGV2p7n1rdeXQ03r0f75QdVWodv2pozrG2/XQqiP6+zarxlw6UD9NG6CaqlD1HHBGlqD6jYkmDB8sASio/rycM2eOZsyY4fxcVVVFMvejex/8TFdf/w/9Nusy/aMisrXDAfwmOtaurt3rVHqk8cEeg4ee1ro9h1T5j1CFhjVu/89B/ZSU8sOP4gRaWlAl8oiIiO99gg58xdC9D36ujGFfa/Zdl6niePvWDgjwq29qQlT6ZbiGZTa4rI+Nt0uSCnZH69TXYbrmZobzgllbba0HVSJHy7hv7ucaOqpCC6cO0DdnQtWpc2MVUnM6TPV1jbefdepcp06d65Wc8o0k6ZJLa/TNmVCdKItUdSX3kyOw/WFBsq65uVJdujboH+Vheul3SQoNkYb+tPEujK0b4pRyaa1i48/qUH4HrXj4Yv30nq9k60lFHtRaeNZ6SyGRo4mf/GepJOnxFwpc1j/1YJq2v5EkSRp1e6nGZ3/p3PbESwVN9gEC1ddl7ZRz3yU6/c9QxcafVb+rarR0y2fq+G0Ffqw4QmtzknT6VKgSbPUad3+Fxt7zVStHDZxfqyby6upqFRUVOT8fPnxYBQUFiouLU0pKSitGZm6j+g39wX3WP9tN65/t5v9gAD/4r5Vffu/2iQ+WaeKDZS0UDVoKrXU/2Ldvn2644Qbn53MT2bKysrRu3bpWigoA0Ca18CNaW0qrJvKhQ4fKCNAxBwAAggFj5AAAU6C1DgBAMHMYjYs3xwcgEjkAwBza6Bg5DxwEACCIUZEDAEzBIi/HyH0WiW+RyAEA5tBGn+xGax0AgCBGRQ4AMAVuPwMAIJgxax0AAAQaKnIAgClYDEMWLyaseXOsP5HIAQDm4Ph28eb4AERrHQCAIEZFDgAwBVrrAAAEszY6a51EDgAwB57sBgAAAg0VOQDAFHiyGwAAwYzWOgAACDRU5AAAU7A4Ghdvjg9EJHIAgDnQWgcAAIGGihwAYA48EAYAgODVVh/RSmsdAIAgRiIHAJjDuclu3iweyMnJ0VVXXaWYmBh16dJFY8aMUWFhocs+Q4cOlcVicVkmT57s0XVI5AAAczD0f+8kb87iYWc9Ly9P2dnZ2rt3r7Zt26aGhgbdfPPNqqmpcdlv0qRJKisrcy6PP/64R9dhjBwAYAq+GiOvqqpyWR8REaGIiIgm+7/11lsun9etW6cuXbooPz9fQ4YMca6PiopSYmJis+OiIgcAwAM2m02xsbHOJScnx63jKisrJUlxcXEu69evX6/OnTurf//+mjNnjs6cOeNRPFTkAABzMOTlA2Ea/1NSUiKr1epcfb5q/LscDoceeOABXXvtterfv79z/R133KHU1FQlJyfrwIEDmjVrlgoLC7Vx40a3wyKRAwDMwUdPdrNarS6J3B3Z2dn6+OOPtXv3bpf199xzj/PfAwYMUFJSkoYNG6bi4mL16NHDrXPTWgcAwI+mTJmiLVu26O2331bXrl2/d9/09HRJUlFRkdvnpyIHAJiDQ5LFy+M9YBiGpk6dqk2bNmnnzp3q1q3bDx5TUFAgSUpKSnL7OiRyAIAptPST3bKzs5Wbm6s333xTMTExKi8vlyTFxsaqffv2Ki4uVm5urkaNGqX4+HgdOHBA06dP15AhQzRw4EC3r0MiBwDAD1asWCGp8aEv/2rt2rW66667FB4eru3bt2vp0qWqqamRzWZTZmamHnroIY+uQyIHAJhDC7/G1PiB/W02m/Ly8pofz7dI5AAAc+B95AAAINBQkQMAzKGNVuQkcgCAObTw7WcthUQOADCFlr79rKUwRg4AQBCjIgcAmANj5AAABDGHIVm8SMaOwEzktNYBAAhiVOQAAHOgtQ4AQDDzMpErMBM5rXUAAIIYFTkAwBxorQMAEMQchrxqjzNrHQAA+BoVOQDAHAxH4+LN8QGIRA4AMAfGyAEACGKMkQMAgEBDRQ4AMAda6wAABDFDXiZyn0XiU7TWAQAIYlTkAABzoLUOAEAQczgkeXEvuCMw7yOntQ4AQBCjIgcAmAOtdQAAglgbTeS01gEACGJU5AAAc2ijj2glkQMATMEwHDK8eIOZN8f6E4kcAGAOhuFdVc0YOQAA8DUqcgCAORhejpEHaEVOIgcAmIPDIVm8GOcO0DFyWusAAAQxEjkAwBzOPRDGm8UDOTk5uuqqqxQTE6MuXbpozJgxKiwsdNmntrZW2dnZio+PV3R0tDIzM1VRUeHRdUjkAABTMBwOrxdP5OXlKTs7W3v37tW2bdvU0NCgm2++WTU1Nc59pk+frs2bN+vVV19VXl6eSktLNXbsWI+uwxg5AAAeqKqqcvkcERGhiIiIJvu99dZbLp/XrVunLl26KD8/X0OGDFFlZaXWrFmj3Nxc3XjjjZKktWvXqk+fPtq7d6+uueYat+KhIgcAmIOPWus2m02xsbHOJScnx63LV1ZWSpLi4uIkSfn5+WpoaNDw4cOd+/Tu3VspKSnas2eP21+LihwAYA4OQ7J4f/tZSUmJrFarc/X5qvEml3Y49MADD+jaa69V//79JUnl5eUKDw9Xx44dXfZNSEhQeXm522GRyAEA8IDVanVJ5O7Izs7Wxx9/rN27d/s8HhI5AMAcDEOSN/eRN6+anzJlirZs2aJdu3apa9euzvWJiYmqr6/XqVOnXKryiooKJSYmun1+xsgBAKZgOAyvF4+uZxiaMmWKNm3apL/97W/q1q2by/bBgwerXbt22rFjh3NdYWGhjh49qoyMDLevQ0UOADAHwyHvKnLPjs3OzlZubq7efPNNxcTEOMe9Y2Nj1b59e8XGxmrixImaMWOG4uLiZLVaNXXqVGVkZLg9Y10ikQMA4BcrVqyQJA0dOtRl/dq1a3XXXXdJkpYsWaKQkBBlZmaqrq5OI0aM0LPPPuvRdUjkAABTMByGDC9mrRsejpG7s39kZKSWL1+u5cuXNzcsEjkAwCRauLXeUoI6kZ/7a+esUd/KkQD+U3U6MH95AL5QVd348+1ptdscZ9Xg1VtMz6rBd8H4UFAn8tOnT0uS8k79sZUjAfynU6/WjgDwv9OnTys2NtYv5w4PD1diYqJ2l//F63MlJiYqPDzcB1H5jsVoiT+D/MThcKi0tFQxMTGyWCytHY4pVFVVyWazNXmyEdAW8PPd8gzD0OnTp5WcnKyQEP/dEV1bW6v6eu+7t+Hh4YqMjPRBRL4T1BV5SEiIy831aDnNebIRECz4+W5Z/qrE/1VkZGTAJWBf4YEwAAAEMRI5AABBjEQOj0RERGjevHluve0HCDb8fCMYBfVkNwAAzI6KHACAIEYiBwAgiJHIAQAIYiRyAACCGIkcblu+fLkuueQSRUZGKj09Xe+//35rhwT4xK5duzR69GglJyfLYrHojTfeaO2QALeRyOGWl19+WTNmzNC8efO0f/9+DRo0SCNGjNCJEydaOzTAazU1NRo0aJBXr5IEWgu3n8Et6enpuuqqq/TMM89IanzOvc1m09SpUzV79uxWjg7wHYvFok2bNmnMmDGtHQrgFipy/KD6+nrl5+dr+PDhznUhISEaPny49uzZ04qRAQBI5PhBX3/9tex2uxISElzWJyQkqLy8vJWiAgBIJHIAAIIaiRw/qHPnzgoNDVVFRYXL+oqKCiUmJrZSVAAAiUQON4SHh2vw4MHasWOHc53D4dCOHTuUkZHRipEBAMJaOwAEhxkzZigrK0tXXnmlrr76ai1dulQ1NTWaMGFCa4cGeK26ulpFRUXOz4cPH1ZBQYHi4uKUkpLSipEBP4zbz+C2Z555Rk888YTKy8t12WWXadmyZUpPT2/tsACv7dy5UzfccEOT9VlZWVq3bl3LBwR4gEQOAEAQY4wcAIAgRiIHACCIkcgBAAhiJHIAAIIYiRwAgCBGIgcAIIiRyAEACGIkcgAAghiJHPDSXXfdpTFjxjg/Dx06VA888ECLx7Fz505ZLBadOnXqgvtYLBa98cYbbp9z/vz5uuyyy7yK68iRI7JYLCooKPDqPADOj0SONumuu+6SxWKRxWJReHi4evbsqYULF+rs2bN+v/bGjRv1yCOPuLWvO8kXAL4PL01Bm3XLLbdo7dq1qqur01/+8hdlZ2erXbt2mjNnTpN96+vrFR4e7pPrxsXF+eQ8AOAOKnK0WREREUpMTFRqaqruvfdeDR8+XH/6058k/V87fNGiRUpOTlZaWpokqaSkRLfffrs6duyouLg43XbbbTpy5IjznHa7XTNmzFDHjh0VHx+v3/72t/ru6wq+21qvq6vTrFmzZLPZFBERoZ49e2rNmjU6cuSI80UdnTp1ksVi0V133SWp8TWxOTk56tatm9q3b69Bgwbptddec7nOX/7yF/Xq1Uvt27fXDTfc4BKnu2bNmqVevXopKipK3bt319y5c9XQ0NBkv1WrVslmsykqKkq33367KisrXbY/99xz6tOnjyIjI9W7d289++yzHscCoHlI5DCN9u3bq76+3vl5x44dKiws1LZt27RlyxY1NDRoxIgRiomJ0TvvvKN3331X0dHRuuWWW5zHPfnkk1q3bp2ef/557d69WydPntSmTZu+97q/+MUv9Mc//lHLli3ToUOHtGrVKkVHR8tms+n111+XJBUWFqqsrExPP/20JCknJ0cvvviiVq5cqU8++UTTp0/Xz3/+c+Xl5Ulq/INj7NixGj16tAoKCnT33Xdr9uzZHv9vEhMTo3Xr1ul///d/9fTTT2v16tVasmSJyz5FRUV65ZVXtHnzZr311lv68MMPdd999zm3r1+/Xg8//LAWLVqkQ4cO6bHHHtPcuXP1wgsveBwPgGYwgDYoKyvLuO222wzDMAyHw2Fs27bNiIiIMGbOnOncnpCQYNTV1TmPeemll4y0tDTD4XA419XV1Rnt27c3tm7dahiGYSQlJRmPP/64c3tDQ4PRtWtX57UMwzCuv/56Y9q0aYZhGEZhYaEhydi2bdt543z77bcNScY///lP57ra2lojKirKeO+991z2nThxojFu3DjDMAxjzpw5Rt++fV22z5o1q8m5vkuSsWnTpgtuf+KJJ4zBgwc7P8+bN88IDQ01jh075lz317/+1QgJCTHKysoMwzCMHj16GLm5uS7neeSRR4yMjAzDMAzj8OHDhiTjww8/vOB1ATQfY+Ros7Zs2aLo6Gg1NDTI4XDojjvu0Pz5853bBwwY4DIu/tFHH6moqEgxMTEu56mtrVVxcbEqKytVVlbm8g72sLAwXXnllU3a6+cUFBQoNDRU119/vdtxFxUV6cyZM7rppptc1tfX1+vyyy+XJB06dKjJu+AzMjLcvsY5L7/8spYtW6bi4mJVV1fr7NmzslqtLvukpKTo4osvdrmOw+FQYWGhYmJiVFxcrIkTJ2rSpEnOfc6ePavY2FiP4wHgORI52qwbbrhBK1asUHh4uJKTkxUW5vrj3qFDB5fP1dXVGjx4sNavX9/kXBdddFGzYmjfvr3Hx1RXV0uS/vznP7skUKlx3N9X9uzZo/Hjx2vBggUaMWKEYmNjtWHDBj355JMex7p69eomf1iEhob6LFYAF0YiR5vVoUMH9ezZ0+39r7jiCr388svq0qVLk6r0nKSkJP3973/XkCFDJDVWnvn5+briiivOu/+AAQPkcDiUl5en4cOHN9l+riNgt9ud6/r27auIiAgdPXr0gpV8nz59nBP3ztm7d+8Pf8l/8d577yk1NVUPPvigc92XX37ZZL+jR4+qtLRUycnJzuuEhIQoLS1NCQkJSk5O1hdffKHx48d7dH0AvsFkN+Bb48ePV+fOnXXbbbfpnXfe0eHDh7Vz507df//9OnbsmCRp2rRpWrx4sd544w19+umnuu+++773HvBLLrlEWVlZ+uUvf6k33njDec5XXnlFkpSamiqLxaItW7boq6++UnV1tWJiYjRz5kxNnz5dL7zwgoqLi7V//379/ve/d04gmzx5sj7//HP95je/UWFhoXJzc7Vu3TqPvu+ll16qo0ePasOGDSouLtayZcvOO3EvMjJSWVlZ+uijj/TOO+/o/vvv1+23367ExERJ0oIFC5STk6Nly5bps88+08GDB7V27Vo99dRTHsUDoHlI5MC3oqKitGvXLqWkpGjs2LHq06ePJk6cqNraWmeF/utf/1p33nmnsrKylJGRoZiYGP30pz/93vOuWLFC//7v/6777rtPvXv31qRJk1RTUyNJuvjii7VgwQLNnj1bCQkJmjJliiTpkUce0dy5c5WTk6M+ffrolltu0Z///Gd169ZNUuO49euvv6433nhDgwYN0sqVK/XYY4959H1vvfVWTZ8+XVOmTNFll12m9957T3Pnzm2yX8+ePTV27FiNGjVKN998swYOHOhye9ndd9+t5557TmvXrtWAAQN0/fXXa926dc5YAfiXxbjQLB0AABDwqMgBAAhiJHIAAIIYiRwAgCBGIgcAIIiRyAEACGIkcgAAghiJHACAIEYiBwAgiJHIAQAIYiRyAACCGIkcAIAg9v8B3CAi87FnWj0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_4, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "4b0d2b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfdd7505",
   "metadata": {},
   "source": [
    "XGBoost Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "11e7ac35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiating the model\n",
    "XGB=XGBClassifier()\n",
    "\n",
    "XGB= Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", BaggingClassifier(base_estimator=XGBClassifier(random_state=42)))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "3a40405c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassi...\n",
       "                                                                gpu_id=None,\n",
       "                                                                grow_policy=None,\n",
       "                                                                importance_type=None,\n",
       "                                                                interaction_constraints=None,\n",
       "                                                                learning_rate=None,\n",
       "                                                                max_bin=None,\n",
       "                                                                max_cat_threshold=None,\n",
       "                                                                max_cat_to_onehot=None,\n",
       "                                                                max_delta_step=None,\n",
       "                                                                max_depth=None,\n",
       "                                                                max_leaves=None,\n",
       "                                                                min_child_weight=None,\n",
       "                                                                missing=nan,\n",
       "                                                                monotone_constraints=None,\n",
       "                                                                n_estimators=100,\n",
       "                                                                n_jobs=None,\n",
       "                                                                num_parallel_tree=None,\n",
       "                                                                predictor=None,\n",
       "                                                                random_state=42, ...)))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-109\" type=\"checkbox\" ><label for=\"sk-estimator-id-109\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassi...\n",
       "                                                                gpu_id=None,\n",
       "                                                                grow_policy=None,\n",
       "                                                                importance_type=None,\n",
       "                                                                interaction_constraints=None,\n",
       "                                                                learning_rate=None,\n",
       "                                                                max_bin=None,\n",
       "                                                                max_cat_threshold=None,\n",
       "                                                                max_cat_to_onehot=None,\n",
       "                                                                max_delta_step=None,\n",
       "                                                                max_depth=None,\n",
       "                                                                max_leaves=None,\n",
       "                                                                min_child_weight=None,\n",
       "                                                                missing=nan,\n",
       "                                                                monotone_constraints=None,\n",
       "                                                                n_estimators=100,\n",
       "                                                                n_jobs=None,\n",
       "                                                                num_parallel_tree=None,\n",
       "                                                                predictor=None,\n",
       "                                                                random_state=42, ...)))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-110\" type=\"checkbox\" ><label for=\"sk-estimator-id-110\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-111\" type=\"checkbox\" ><label for=\"sk-estimator-id-111\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-112\" type=\"checkbox\" ><label for=\"sk-estimator-id-112\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-113\" type=\"checkbox\" ><label for=\"sk-estimator-id-113\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-114\" type=\"checkbox\" ><label for=\"sk-estimator-id-114\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-115\" type=\"checkbox\" ><label for=\"sk-estimator-id-115\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                               callbacks=None,\n",
       "                                               colsample_bylevel=None,\n",
       "                                               colsample_bynode=None,\n",
       "                                               colsample_bytree=None,\n",
       "                                               early_stopping_rounds=None,\n",
       "                                               enable_categorical=False,\n",
       "                                               eval_metric=None,\n",
       "                                               feature_types=None, gamma=None,\n",
       "                                               gpu_id=None, grow_policy=None,\n",
       "                                               importance_type=None,\n",
       "                                               interaction_constraints=None,\n",
       "                                               learning_rate=None, max_bin=None,\n",
       "                                               max_cat_threshold=None,\n",
       "                                               max_cat_to_onehot=None,\n",
       "                                               max_delta_step=None,\n",
       "                                               max_depth=None, max_leaves=None,\n",
       "                                               min_child_weight=None,\n",
       "                                               missing=nan,\n",
       "                                               monotone_constraints=None,\n",
       "                                               n_estimators=100, n_jobs=None,\n",
       "                                               num_parallel_tree=None,\n",
       "                                               predictor=None, random_state=42, ...))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-116\" type=\"checkbox\" ><label for=\"sk-estimator-id-116\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-117\" type=\"checkbox\" ><label for=\"sk-estimator-id-117\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model',\n",
       "                 BaggingClassi...\n",
       "                                                                gpu_id=None,\n",
       "                                                                grow_policy=None,\n",
       "                                                                importance_type=None,\n",
       "                                                                interaction_constraints=None,\n",
       "                                                                learning_rate=None,\n",
       "                                                                max_bin=None,\n",
       "                                                                max_cat_threshold=None,\n",
       "                                                                max_cat_to_onehot=None,\n",
       "                                                                max_delta_step=None,\n",
       "                                                                max_depth=None,\n",
       "                                                                max_leaves=None,\n",
       "                                                                min_child_weight=None,\n",
       "                                                                missing=nan,\n",
       "                                                                monotone_constraints=None,\n",
       "                                                                n_estimators=100,\n",
       "                                                                n_jobs=None,\n",
       "                                                                num_parallel_tree=None,\n",
       "                                                                predictor=None,\n",
       "                                                                random_state=42, ...)))])"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XGB.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "a38f8c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_5=XGB.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "0a40bf55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.86      0.83        73\n",
      "           1       0.87      0.81      0.84        84\n",
      "\n",
      "    accuracy                           0.83       157\n",
      "   macro avg       0.83      0.84      0.83       157\n",
      "weighted avg       0.84      0.83      0.83       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_5, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "d4b32cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_5, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "7d3c7772",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(XGB)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d766a9",
   "metadata": {},
   "source": [
    "AdaBoostClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "1c56a493",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ada=AdaBoostClassifier()\n",
    "\n",
    "Ada = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", BaggingClassifier(base_estimator=AdaBoostClassifier(random_state=42)))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "fdb852a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassifier(base_estimator=AdaBoostClassifier(random_state=42)))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-118\" type=\"checkbox\" ><label for=\"sk-estimator-id-118\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassifier(base_estimator=AdaBoostClassifier(random_state=42)))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-119\" type=\"checkbox\" ><label for=\"sk-estimator-id-119\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-120\" type=\"checkbox\" ><label for=\"sk-estimator-id-120\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-121\" type=\"checkbox\" ><label for=\"sk-estimator-id-121\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-122\" type=\"checkbox\" ><label for=\"sk-estimator-id-122\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-123\" type=\"checkbox\" ><label for=\"sk-estimator-id-123\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-124\" type=\"checkbox\" ><label for=\"sk-estimator-id-124\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=AdaBoostClassifier(random_state=42))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-125\" type=\"checkbox\" ><label for=\"sk-estimator-id-125\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-126\" type=\"checkbox\" ><label for=\"sk-estimator-id-126\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model',\n",
       "                 BaggingClassifier(base_estimator=AdaBoostClassifier(random_state=42)))])"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ada.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "2a482df1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_6 = Ada.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "19d6ee18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.79      0.77        76\n",
      "           1       0.79      0.77      0.78        81\n",
      "\n",
      "    accuracy                           0.78       157\n",
      "   macro avg       0.78      0.78      0.78       157\n",
      "weighted avg       0.78      0.78      0.78       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_6, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "ec1e67fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_6, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "6b7bd1e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_trained.append(Ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6efa9978",
   "metadata": {},
   "source": [
    "Naive Bayes pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "78d18c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "NBP=GaussianNB()\n",
    "\n",
    "NBP= Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", BaggingClassifier(base_estimator=GaussianNB()))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "1138de0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, BaggingClassifier(base_estimator=GaussianNB()))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-127\" type=\"checkbox\" ><label for=\"sk-estimator-id-127\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;, BaggingClassifier(base_estimator=GaussianNB()))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-128\" type=\"checkbox\" ><label for=\"sk-estimator-id-128\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-129\" type=\"checkbox\" ><label for=\"sk-estimator-id-129\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-130\" type=\"checkbox\" ><label for=\"sk-estimator-id-130\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-131\" type=\"checkbox\" ><label for=\"sk-estimator-id-131\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-132\" type=\"checkbox\" ><label for=\"sk-estimator-id-132\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-133\" type=\"checkbox\" ><label for=\"sk-estimator-id-133\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=GaussianNB())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-134\" type=\"checkbox\" ><label for=\"sk-estimator-id-134\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-135\" type=\"checkbox\" ><label for=\"sk-estimator-id-135\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model', BaggingClassifier(base_estimator=GaussianNB()))])"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NBP.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "01196a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_7= NBP.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "4b37e461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.69      0.70        81\n",
      "           1       0.68      0.70      0.69        76\n",
      "\n",
      "    accuracy                           0.69       157\n",
      "   macro avg       0.69      0.69      0.69       157\n",
      "weighted avg       0.69      0.69      0.69       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(model_7, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "7f280339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(model_7,y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7222c00",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "f344a007",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "71354812",
   "metadata": {
    "scrolled": true
   },
   "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>metric(accuracy_score)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.834395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.777070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.770701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.764331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.732484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.732484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.681529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    model  metric(accuracy_score)\n",
       "5           XGBClassifier                0.834395\n",
       "4      AdaBoostClassifier                0.777070\n",
       "6  RandomForestClassifier                0.770701\n",
       "0  DecisionTreeClassifier                0.764331\n",
       "1                     SVC                0.732484\n",
       "2      LogisticRegression                0.732484\n",
       "3              GaussianNB                0.681529"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipelines = [DTC, SVM, LRP, NBP, Ada, XGB, RFC]\n",
    "res = []\n",
    "\n",
    "for pipe in pipelines:\n",
    "    pipe.fit(X_train, y_train)\n",
    "    model_name = pipe.named_steps['model'].base_estimator.__class__.__name__\n",
    "    accuracy_score_value = accuracy_score(y_test, pipe.predict(X_test))\n",
    "    result = {'model': model_name, 'metric(accuracy_score)': accuracy_score_value}\n",
    "    res.append(result)\n",
    "results = pd.DataFrame(res)\n",
    "results = results.sort_values(by='metric(accuracy_score)', ascending=False)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d886778",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "c1ce86f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'model__base_estimator__learning_rate': 0.1, 'model__base_estimator__max_depth': 10, 'model__base_estimator__n_estimators': 100}\n",
      "Accuracy: 0.8280254777070064\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "#from sklearn.ensemble import BaggingClassifier\n",
    "#from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Define your best model pipeline\n",
    "XGB = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", BaggingClassifier(base_estimator=XGBClassifier(random_state=42)))\n",
    "])\n",
    "\n",
    "# Define the hyperparameters grid for XGBoost\n",
    "param_grid_xgb = {\n",
    "    \"model__base_estimator__learning_rate\": [0.1, 0.01],\n",
    "    \"model__base_estimator__max_depth\": [5,10,20],\n",
    "    \"model__base_estimator__n_estimators\": [50,100, 200]\n",
    "}\n",
    "\n",
    "# Perform grid search cross-validation to find the best hyperparameters\n",
    "grid_search_xgb = GridSearchCV(estimator=XGB, param_grid=param_grid_xgb, scoring='accuracy', cv=5)\n",
    "grid_search_xgb.fit(X_train, y_train)\n",
    "\n",
    "# Get the best hyperparameters and the corresponding model\n",
    "best_params_xgb = grid_search_xgb.best_params_\n",
    "best_model_xgb = grid_search_xgb.best_estimator_\n",
    "\n",
    "# Predict on the test set using the best XGBoost model\n",
    "y_pred = best_model_xgb.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Best Parameters: {best_params_xgb}\")\n",
    "print(f\"Accuracy: {accuracy}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "c7063200",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2023-08-16 04:37:20,508] A new study created in memory with name: no-name-958800a8-5a95-4a48-a755-b4ae3019c7c1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2023-08-16 04:37:23,738] Trial 0 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.0520795042519819, 'max_depth': 10, 'n_estimators': 290}. Best is trial 0 with value: 0.8471337579617835.\n",
      "[I 2023-08-16 04:37:26,326] Trial 1 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.035931748082217316, 'max_depth': 8, 'n_estimators': 206}. Best is trial 1 with value: 0.8535031847133758.\n",
      "[I 2023-08-16 04:37:28,770] Trial 2 finished with value: 0.7961783439490446 and parameters: {'learning_rate': 0.009073370471955857, 'max_depth': 4, 'n_estimators': 246}. Best is trial 1 with value: 0.8535031847133758.\n",
      "[I 2023-08-16 04:37:31,164] Trial 3 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.0579110194890994, 'max_depth': 6, 'n_estimators': 240}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:33,456] Trial 4 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.0713637041284674, 'max_depth': 10, 'n_estimators': 207}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:36,574] Trial 5 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.02568057937038189, 'max_depth': 8, 'n_estimators': 254}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:40,274] Trial 6 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.023978792741253446, 'max_depth': 8, 'n_estimators': 267}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:42,570] Trial 7 finished with value: 0.821656050955414 and parameters: {'learning_rate': 0.012778048921081302, 'max_depth': 5, 'n_estimators': 239}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:43,468] Trial 8 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.09612891233296086, 'max_depth': 5, 'n_estimators': 94}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:45,168] Trial 9 finished with value: 0.7707006369426752 and parameters: {'learning_rate': 0.0028187319984132354, 'max_depth': 5, 'n_estimators': 171}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:46,104] Trial 10 finished with value: 0.7898089171974523 and parameters: {'learning_rate': 0.0564574893873288, 'max_depth': 3, 'n_estimators': 130}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:48,189] Trial 11 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.03925063778624174, 'max_depth': 7, 'n_estimators': 177}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:50,375] Trial 12 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.0403399200137452, 'max_depth': 7, 'n_estimators': 200}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:51,132] Trial 13 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.06438090348102447, 'max_depth': 6, 'n_estimators': 61}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:53,497] Trial 14 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.04244284470120159, 'max_depth': 7, 'n_estimators': 210}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:55,114] Trial 15 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.07433498546926925, 'max_depth': 6, 'n_estimators': 156}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:37:58,871] Trial 16 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.04967533146497527, 'max_depth': 9, 'n_estimators': 297}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:01,680] Trial 17 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.030158003402073624, 'max_depth': 6, 'n_estimators': 223}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:03,441] Trial 18 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.0457675914283466, 'max_depth': 7, 'n_estimators': 131}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:04,715] Trial 19 finished with value: 0.8089171974522293 and parameters: {'learning_rate': 0.06035162406508116, 'max_depth': 3, 'n_estimators': 187}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:06,085] Trial 20 finished with value: 0.7961783439490446 and parameters: {'learning_rate': 0.01927076660311625, 'max_depth': 4, 'n_estimators': 145}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:08,432] Trial 21 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.040186200625423475, 'max_depth': 7, 'n_estimators': 207}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:11,402] Trial 22 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.03161333444980395, 'max_depth': 7, 'n_estimators': 230}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:14,500] Trial 23 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.04494635479930401, 'max_depth': 9, 'n_estimators': 270}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:16,544] Trial 24 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.052335529233513395, 'max_depth': 6, 'n_estimators': 195}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:19,049] Trial 25 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.04091709492854585, 'max_depth': 8, 'n_estimators': 221}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:22,505] Trial 26 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.03300279514597589, 'max_depth': 9, 'n_estimators': 274}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:24,973] Trial 27 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.04597662244168654, 'max_depth': 7, 'n_estimators': 174}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:27,780] Trial 28 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.056820577347, 'max_depth': 5, 'n_estimators': 254}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:30,911] Trial 29 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.05010394035986082, 'max_depth': 6, 'n_estimators': 282}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:32,939] Trial 30 finished with value: 0.8280254777070064 and parameters: {'learning_rate': 0.036768219940068, 'max_depth': 4, 'n_estimators': 224}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:34,674] Trial 31 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.07164545355886145, 'max_depth': 6, 'n_estimators': 157}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:36,457] Trial 32 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.064903774637731, 'max_depth': 7, 'n_estimators': 161}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:39,122] Trial 33 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.07757870415992338, 'max_depth': 6, 'n_estimators': 195}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:41,014] Trial 34 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.054166373068663176, 'max_depth': 8, 'n_estimators': 124}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:42,168] Trial 35 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.08293710193988568, 'max_depth': 5, 'n_estimators': 106}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:45,387] Trial 36 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.06152500542080194, 'max_depth': 8, 'n_estimators': 209}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:48,017] Trial 37 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.06912372717779612, 'max_depth': 7, 'n_estimators': 248}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:49,846] Trial 38 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.057552399681063786, 'max_depth': 6, 'n_estimators': 147}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:52,094] Trial 39 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.051614102479588045, 'max_depth': 5, 'n_estimators': 234}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:55,063] Trial 40 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.025753638423113498, 'max_depth': 10, 'n_estimators': 188}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:38:57,940] Trial 41 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.04429973020050706, 'max_depth': 7, 'n_estimators': 211}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:00,707] Trial 42 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.037832563978956374, 'max_depth': 8, 'n_estimators': 208}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:06,241] Trial 43 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.03912575932176018, 'max_depth': 7, 'n_estimators': 241}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:09,215] Trial 44 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.04810802374898164, 'max_depth': 6, 'n_estimators': 260}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:11,615] Trial 45 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.04230710249932261, 'max_depth': 8, 'n_estimators': 194}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:13,761] Trial 46 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.03292170231016474, 'max_depth': 7, 'n_estimators': 180}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:15,655] Trial 47 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.03579831274605314, 'max_depth': 6, 'n_estimators': 162}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:18,046] Trial 48 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.04892065597885101, 'max_depth': 5, 'n_estimators': 219}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:21,079] Trial 49 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.04330909566412975, 'max_depth': 7, 'n_estimators': 201}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:21,925] Trial 50 finished with value: 0.7898089171974523 and parameters: {'learning_rate': 0.027900160960136082, 'max_depth': 4, 'n_estimators': 73}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:24,680] Trial 51 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.052513157004534905, 'max_depth': 6, 'n_estimators': 181}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:27,627] Trial 52 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.054066939776803054, 'max_depth': 6, 'n_estimators': 196}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:30,907] Trial 53 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.041915082535092955, 'max_depth': 7, 'n_estimators': 216}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:34,324] Trial 54 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.04689336829219141, 'max_depth': 6, 'n_estimators': 232}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:38,378] Trial 55 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.03511830252902967, 'max_depth': 5, 'n_estimators': 205}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:40,805] Trial 56 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.05939622272529863, 'max_depth': 7, 'n_estimators': 165}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:42,668] Trial 57 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.05298486115597513, 'max_depth': 6, 'n_estimators': 149}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:46,452] Trial 58 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.04041239263528627, 'max_depth': 8, 'n_estimators': 186}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:49,372] Trial 59 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.04605182364559595, 'max_depth': 7, 'n_estimators': 138}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:52,534] Trial 60 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.02249643396708523, 'max_depth': 6, 'n_estimators': 245}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:55,579] Trial 61 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.03962842121882983, 'max_depth': 8, 'n_estimators': 228}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:39:58,735] Trial 62 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.05034947044390224, 'max_depth': 9, 'n_estimators': 220}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:01,124] Trial 63 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.03142587904272401, 'max_depth': 7, 'n_estimators': 171}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:04,205] Trial 64 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.04287852028056826, 'max_depth': 8, 'n_estimators': 201}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:06,993] Trial 65 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.05593767652802316, 'max_depth': 8, 'n_estimators': 214}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:10,887] Trial 66 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.03619202506551033, 'max_depth': 5, 'n_estimators': 257}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:14,468] Trial 67 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.04835933079681406, 'max_depth': 9, 'n_estimators': 237}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:17,002] Trial 68 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.04006669670199703, 'max_depth': 7, 'n_estimators': 189}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:19,743] Trial 69 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.04501671408388091, 'max_depth': 6, 'n_estimators': 229}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:23,763] Trial 70 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.02908237014243601, 'max_depth': 9, 'n_estimators': 224}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:26,633] Trial 71 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.06324414043497681, 'max_depth': 5, 'n_estimators': 278}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:29,448] Trial 72 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.05769315079170576, 'max_depth': 5, 'n_estimators': 266}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:31,779] Trial 73 finished with value: 0.8280254777070064 and parameters: {'learning_rate': 0.0513082432526349, 'max_depth': 4, 'n_estimators': 249}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:34,100] Trial 74 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.057896754994181995, 'max_depth': 6, 'n_estimators': 200}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:37,585] Trial 75 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.055902099176191516, 'max_depth': 7, 'n_estimators': 291}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:39,699] Trial 76 finished with value: 0.8089171974522293 and parameters: {'learning_rate': 0.047343952115583954, 'max_depth': 3, 'n_estimators': 252}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:43,256] Trial 77 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.06161110588875032, 'max_depth': 6, 'n_estimators': 240}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:45,489] Trial 78 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.0669517738345679, 'max_depth': 5, 'n_estimators': 211}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:48,820] Trial 79 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.037600287019006785, 'max_depth': 7, 'n_estimators': 263}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:50,593] Trial 80 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.04952644098216695, 'max_depth': 6, 'n_estimators': 156}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:53,597] Trial 81 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.05462210076415881, 'max_depth': 6, 'n_estimators': 276}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:40:57,462] Trial 82 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.043685583624009865, 'max_depth': 6, 'n_estimators': 298}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:00,746] Trial 83 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.05189850279589834, 'max_depth': 7, 'n_estimators': 286}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:03,108] Trial 84 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.03416278297737751, 'max_depth': 5, 'n_estimators': 205}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:05,448] Trial 85 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.059866309098581684, 'max_depth': 6, 'n_estimators': 192}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:07,642] Trial 86 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.046258965662074936, 'max_depth': 7, 'n_estimators': 170}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:09,371] Trial 87 finished with value: 0.8343949044585988 and parameters: {'learning_rate': 0.04136524726586448, 'max_depth': 7, 'n_estimators': 116}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:12,126] Trial 88 finished with value: 0.8535031847133758 and parameters: {'learning_rate': 0.048757721769424577, 'max_depth': 8, 'n_estimators': 182}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:15,314] Trial 89 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.0536672036923929, 'max_depth': 6, 'n_estimators': 219}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:18,429] Trial 90 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.03796193607558221, 'max_depth': 6, 'n_estimators': 227}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:20,489] Trial 91 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.07542621190629262, 'max_depth': 6, 'n_estimators': 140}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:22,135] Trial 92 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.069894147157859, 'max_depth': 5, 'n_estimators': 153}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:24,911] Trial 93 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.050412883822362385, 'max_depth': 10, 'n_estimators': 198}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:27,433] Trial 94 finished with value: 0.8471337579617835 and parameters: {'learning_rate': 0.044763746949653994, 'max_depth': 6, 'n_estimators': 178}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:30,170] Trial 95 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.06361963428356003, 'max_depth': 4, 'n_estimators': 285}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:32,990] Trial 96 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.07248772005784801, 'max_depth': 7, 'n_estimators': 243}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:34,972] Trial 97 finished with value: 0.8407643312101911 and parameters: {'learning_rate': 0.04162102270814467, 'max_depth': 6, 'n_estimators': 166}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:38,642] Trial 98 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.05572736600175226, 'max_depth': 8, 'n_estimators': 271}. Best is trial 3 with value: 0.8598726114649682.\n",
      "[I 2023-08-16 04:41:41,237] Trial 99 finished with value: 0.8598726114649682 and parameters: {'learning_rate': 0.06732446458719978, 'max_depth': 6, 'n_estimators': 235}. Best is trial 3 with value: 0.8598726114649682.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of finished trials:  100\n",
      "Best trial:\n",
      "Value:  0.8598726114649682\n",
      "Params: \n",
      "    learning_rate: 0.0579110194890994\n",
      "    max_depth: 6\n",
      "    n_estimators: 240\n"
     ]
    }
   ],
   "source": [
    "import optuna\n",
    "from sklearn.datasets import load_iris\n",
    "XGB = XGBClassifier()\n",
    "\n",
    "# Define the objective function for Optuna\n",
    "def objective(trial):\n",
    "    learning_rate = trial.suggest_float('learning_rate', 0.001, 0.1)\n",
    "    max_depth = trial.suggest_int('max_depth', 3, 10)\n",
    "    n_estimators = trial.suggest_int('n_estimators', 50, 300)\n",
    "\n",
    "    model = Pipeline([\n",
    "        (\"col_trans\", full_pipeline),\n",
    "        (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "        (\"model\", BaggingClassifier(base_estimator=XGBClassifier(learning_rate=learning_rate, max_depth=max_depth, n_estimators=n_estimators, random_state=42), random_state=42))\n",
    "    ])\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    return accuracy\n",
    "\n",
    "# Create a study object and optimize\n",
    "study = optuna.create_study(direction='maximize')  # 'maximize' for accuracy\n",
    "study.optimize(objective, n_trials=100)  # You can adjust the number of trials\n",
    "\n",
    "# Print the optimization results\n",
    "print('Number of finished trials: ', len(study.trials))\n",
    "print('Best trial:')\n",
    "trial = study.best_trial\n",
    "\n",
    "print('Value: ', trial.value)\n",
    "print('Params: ')\n",
    "for key, value in trial.params.items():\n",
    "    print(f'    {key}: {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "7cc5f785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'XGBClassifier__learning_rate': 0.0579110194890994,\n",
       " 'XGBClassifier__max_depth': 6,\n",
       " 'XGBClassifier__n_estimators': 240}"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the result\n",
    "def get_params(input_study) :\n",
    "    params = {k: v for k, v in input_study.best_params.items() if k not in ('dim_red', 'scalers')}\n",
    "    change = []\n",
    "    for k,v in dict(params).items():\n",
    "        tmp_name = k\n",
    "        if 'XGBClassifier' not in tmp_name :\n",
    "            res = f\"XGBClassifier__{tmp_name}\"\n",
    "            params[res] = params.pop(tmp_name)\n",
    "            change.append(res)\n",
    "    return params\n",
    "\n",
    "params = get_params(study)\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "f5e8f0a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#hy_XGB=XGB.set_params(**params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "26ee47a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "XGB_hy = Pipeline([\n",
    "    (\"col_trans\", full_pipeline),\n",
    "    (\"feature_selection\", SelectKBest(score_func=f_classif, k='all')),\n",
    "    (\"model\", BaggingClassifier(base_estimator=XGBClassifier(\n",
    "        learning_rate=0.06800670161468735,\n",
    "        max_depth=5,\n",
    "        n_estimators=249,\n",
    "        random_state=42\n",
    "    )))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "41123426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassi...\n",
       "                                                                grow_policy=None,\n",
       "                                                                importance_type=None,\n",
       "                                                                interaction_constraints=None,\n",
       "                                                                learning_rate=0.06800670161468735,\n",
       "                                                                max_bin=None,\n",
       "                                                                max_cat_threshold=None,\n",
       "                                                                max_cat_to_onehot=None,\n",
       "                                                                max_delta_step=None,\n",
       "                                                                max_depth=5,\n",
       "                                                                max_leaves=None,\n",
       "                                                                min_child_weight=None,\n",
       "                                                                missing=nan,\n",
       "                                                                monotone_constraints=None,\n",
       "                                                                n_estimators=249,\n",
       "                                                                n_jobs=None,\n",
       "                                                                num_parallel_tree=None,\n",
       "                                                                predictor=None,\n",
       "                                                                random_state=42, ...)))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-136\" type=\"checkbox\" ><label for=\"sk-estimator-id-136\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;col_trans&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;feature_selection&#x27;, SelectKBest(k=&#x27;all&#x27;)),\n",
       "                (&#x27;model&#x27;,\n",
       "                 BaggingClassi...\n",
       "                                                                grow_policy=None,\n",
       "                                                                importance_type=None,\n",
       "                                                                interaction_constraints=None,\n",
       "                                                                learning_rate=0.06800670161468735,\n",
       "                                                                max_bin=None,\n",
       "                                                                max_cat_threshold=None,\n",
       "                                                                max_cat_to_onehot=None,\n",
       "                                                                max_delta_step=None,\n",
       "                                                                max_depth=5,\n",
       "                                                                max_leaves=None,\n",
       "                                                                min_child_weight=None,\n",
       "                                                                missing=nan,\n",
       "                                                                monotone_constraints=None,\n",
       "                                                                n_estimators=249,\n",
       "                                                                n_jobs=None,\n",
       "                                                                num_parallel_tree=None,\n",
       "                                                                predictor=None,\n",
       "                                                                random_state=42, ...)))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-137\" type=\"checkbox\" ><label for=\"sk-estimator-id-137\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_trans: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num_pipe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-138\" type=\"checkbox\" ><label for=\"sk-estimator-id-138\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_pipe</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Plasma_glucose&#x27;, &#x27;Blood_Work_R1&#x27;, &#x27;Blood_Pressure&#x27;, &#x27;Blood_Work_R2&#x27;,\n",
       "       &#x27;Blood_Work_R3&#x27;, &#x27;BMI&#x27;, &#x27;Blood_Work_R4&#x27;, &#x27;Age&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-139\" type=\"checkbox\" ><label for=\"sk-estimator-id-139\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-140\" type=\"checkbox\" ><label for=\"sk-estimator-id-140\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-141\" type=\"checkbox\" ><label for=\"sk-estimator-id-141\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;)</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-142\" type=\"checkbox\" ><label for=\"sk-estimator-id-142\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">model: BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                               callbacks=None,\n",
       "                                               colsample_bylevel=None,\n",
       "                                               colsample_bynode=None,\n",
       "                                               colsample_bytree=None,\n",
       "                                               early_stopping_rounds=None,\n",
       "                                               enable_categorical=False,\n",
       "                                               eval_metric=None,\n",
       "                                               feature_types=None, gamma=None,\n",
       "                                               gpu_id=None, grow_policy=None,\n",
       "                                               importance_type=None,\n",
       "                                               interaction_constraints=None,\n",
       "                                               learning_rate=0.06800670161468735,\n",
       "                                               max_bin=None,\n",
       "                                               max_cat_threshold=None,\n",
       "                                               max_cat_to_onehot=None,\n",
       "                                               max_delta_step=None, max_depth=5,\n",
       "                                               max_leaves=None,\n",
       "                                               min_child_weight=None,\n",
       "                                               missing=nan,\n",
       "                                               monotone_constraints=None,\n",
       "                                               n_estimators=249, n_jobs=None,\n",
       "                                               num_parallel_tree=None,\n",
       "                                               predictor=None, random_state=42, ...))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-143\" type=\"checkbox\" ><label for=\"sk-estimator-id-143\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.06800670161468735,\n",
       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=249, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-144\" type=\"checkbox\" ><label for=\"sk-estimator-id-144\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.06800670161468735,\n",
       "              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=249, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('col_trans',\n",
       "                 ColumnTransformer(transformers=[('num_pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age'],\n",
       "      dtype='object'))])),\n",
       "                ('feature_selection', SelectKBest(k='all')),\n",
       "                ('model',\n",
       "                 BaggingClassi...\n",
       "                                                                grow_policy=None,\n",
       "                                                                importance_type=None,\n",
       "                                                                interaction_constraints=None,\n",
       "                                                                learning_rate=0.06800670161468735,\n",
       "                                                                max_bin=None,\n",
       "                                                                max_cat_threshold=None,\n",
       "                                                                max_cat_to_onehot=None,\n",
       "                                                                max_delta_step=None,\n",
       "                                                                max_depth=5,\n",
       "                                                                max_leaves=None,\n",
       "                                                                min_child_weight=None,\n",
       "                                                                missing=nan,\n",
       "                                                                monotone_constraints=None,\n",
       "                                                                n_estimators=249,\n",
       "                                                                n_jobs=None,\n",
       "                                                                num_parallel_tree=None,\n",
       "                                                                predictor=None,\n",
       "                                                                random_state=42, ...)))])"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XGB_hy.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "24c36c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model= XGB_hy.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "3f33690b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.86      0.83        73\n",
      "           1       0.87      0.81      0.84        84\n",
      "\n",
      "    accuracy                           0.83       157\n",
      "   macro avg       0.83      0.84      0.83       157\n",
      "weighted avg       0.84      0.83      0.83       157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(final_model, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "97c1d418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(final_model,y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "049f347e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "8d6d671f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:\\\\Users\\\\Gregory Arthur\\\\Desktop\\\\models\\\\xgb.joblib']"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(XGB_hy, r\"C:\\Users\\Gregory Arthur\\Desktop\\models\\xgb.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "4e43091d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Plasma_glucose', 'Blood_Work_R1', 'Blood_Pressure', 'Blood_Work_R2',\n",
       "       'Blood_Work_R3', 'BMI', 'Blood_Work_R4', 'Age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "0247e3f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 625 entries, 547 to 566\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Plasma_glucose  625 non-null    float64\n",
      " 1   Blood_Work_R1   625 non-null    float64\n",
      " 2   Blood_Pressure  625 non-null    int64  \n",
      " 3   Blood_Work_R2   625 non-null    int64  \n",
      " 4   Blood_Work_R3   625 non-null    float64\n",
      " 5   BMI             625 non-null    float64\n",
      " 6   Blood_Work_R4   625 non-null    float64\n",
      " 7   Age             625 non-null    int64  \n",
      "dtypes: float64(5), int64(3)\n",
      "memory usage: 43.9 KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "538f0974",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "340a8ca0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27b0272b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}