{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: openpyxl in c:\\usman\\assignment\\ml_env\\lib\\site-packages (3.1.5)\n",
      "Requirement already satisfied: et-xmlfile in c:\\usman\\assignment\\ml_env\\lib\\site-packages (from openpyxl) (2.0.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install openpyxl\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import train_test_split, LeaveOneOut, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load & Analyze the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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>Patient No.</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Family history</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Obese/non obese</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Triglycerides level</th>\n",
       "      <th>HDL level</th>\n",
       "      <th>LDL level</th>\n",
       "      <th>VLDL level</th>\n",
       "      <th>Health_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>No</td>\n",
       "      <td>1.64</td>\n",
       "      <td>64</td>\n",
       "      <td>23.80</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>145</td>\n",
       "      <td>119</td>\n",
       "      <td>60</td>\n",
       "      <td>66.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.70</td>\n",
       "      <td>70</td>\n",
       "      <td>24.22</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>220</td>\n",
       "      <td>107</td>\n",
       "      <td>69</td>\n",
       "      <td>134.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>1.67</td>\n",
       "      <td>63</td>\n",
       "      <td>22.59</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>190</td>\n",
       "      <td>251</td>\n",
       "      <td>42</td>\n",
       "      <td>108.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>48</td>\n",
       "      <td>No</td>\n",
       "      <td>1.61</td>\n",
       "      <td>79</td>\n",
       "      <td>30.48</td>\n",
       "      <td>Obese</td>\n",
       "      <td>228</td>\n",
       "      <td>185</td>\n",
       "      <td>65</td>\n",
       "      <td>134.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>74</td>\n",
       "      <td>No</td>\n",
       "      <td>1.76</td>\n",
       "      <td>83</td>\n",
       "      <td>26.79</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>157</td>\n",
       "      <td>113</td>\n",
       "      <td>49</td>\n",
       "      <td>90.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Patient No.  Gender  Age Family history  Height  Weight    BMI  \\\n",
       "0            1  Female   65             No    1.64      64  23.80   \n",
       "1            2  Female   50            Yes    1.70      70  24.22   \n",
       "2            3  Female   45             No    1.67      63  22.59   \n",
       "3            4  Female   48             No    1.61      79  30.48   \n",
       "4            5    Male   74             No    1.76      83  26.79   \n",
       "\n",
       "  Obese/non obese  Cholesterol  Triglycerides level  HDL level  LDL level  \\\n",
       "0       Non-obese          145                  119         60       66.0   \n",
       "1       Non-obese          220                  107         69      134.0   \n",
       "2       Non-obese          190                  251         42      108.0   \n",
       "3           Obese          228                  185         65      134.0   \n",
       "4       Non-obese          157                  113         49       90.0   \n",
       "\n",
       "   VLDL level Health_status  \n",
       "0        19.0       healthy  \n",
       "1        17.0       healthy  \n",
       "2        40.0       healthy  \n",
       "3        29.0       healthy  \n",
       "4        18.0       healthy  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"colelithiasis_dataset.xlsx\"  # Update with your file path\n",
    "data = pd.read_excel(file_path)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Family history</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Obese/non obese</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Triglycerides level</th>\n",
       "      <th>HDL level</th>\n",
       "      <th>LDL level</th>\n",
       "      <th>VLDL level</th>\n",
       "      <th>Health_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>No</td>\n",
       "      <td>1.64</td>\n",
       "      <td>64</td>\n",
       "      <td>23.80</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>145</td>\n",
       "      <td>119</td>\n",
       "      <td>60</td>\n",
       "      <td>66.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.70</td>\n",
       "      <td>70</td>\n",
       "      <td>24.22</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>220</td>\n",
       "      <td>107</td>\n",
       "      <td>69</td>\n",
       "      <td>134.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>1.67</td>\n",
       "      <td>63</td>\n",
       "      <td>22.59</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>190</td>\n",
       "      <td>251</td>\n",
       "      <td>42</td>\n",
       "      <td>108.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>48</td>\n",
       "      <td>No</td>\n",
       "      <td>1.61</td>\n",
       "      <td>79</td>\n",
       "      <td>30.48</td>\n",
       "      <td>Obese</td>\n",
       "      <td>228</td>\n",
       "      <td>185</td>\n",
       "      <td>65</td>\n",
       "      <td>134.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>74</td>\n",
       "      <td>No</td>\n",
       "      <td>1.76</td>\n",
       "      <td>83</td>\n",
       "      <td>26.79</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>157</td>\n",
       "      <td>113</td>\n",
       "      <td>49</td>\n",
       "      <td>90.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age Family history  Height  Weight    BMI Obese/non obese  \\\n",
       "0  Female   65             No    1.64      64  23.80       Non-obese   \n",
       "1  Female   50            Yes    1.70      70  24.22       Non-obese   \n",
       "2  Female   45             No    1.67      63  22.59       Non-obese   \n",
       "3  Female   48             No    1.61      79  30.48           Obese   \n",
       "4    Male   74             No    1.76      83  26.79       Non-obese   \n",
       "\n",
       "   Cholesterol  Triglycerides level  HDL level  LDL level  VLDL level  \\\n",
       "0          145                  119         60       66.0        19.0   \n",
       "1          220                  107         69      134.0        17.0   \n",
       "2          190                  251         42      108.0        40.0   \n",
       "3          228                  185         65      134.0        29.0   \n",
       "4          157                  113         49       90.0        18.0   \n",
       "\n",
       "  Health_status  \n",
       "0       healthy  \n",
       "1       healthy  \n",
       "2       healthy  \n",
       "3       healthy  \n",
       "4       healthy  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop unnecessary columns (e.g., Patient No.)\n",
    "data = data.drop(columns=['Patient No.'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Gender               100 non-null    object \n",
      " 1   Age                  100 non-null    int64  \n",
      " 2   Family history       100 non-null    object \n",
      " 3   Height               100 non-null    float64\n",
      " 4   Weight               100 non-null    int64  \n",
      " 5   BMI                  100 non-null    float64\n",
      " 6   Obese/non obese      100 non-null    object \n",
      " 7   Cholesterol          100 non-null    int64  \n",
      " 8   Triglycerides level  100 non-null    int64  \n",
      " 9   HDL level            100 non-null    int64  \n",
      " 10  LDL level            100 non-null    float64\n",
      " 11  VLDL level           100 non-null    float64\n",
      " 12  Health_status        100 non-null    object \n",
      "dtypes: float64(4), int64(5), object(4)\n",
      "memory usage: 10.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Family history</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Obese/non obese</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Triglycerides level</th>\n",
       "      <th>HDL level</th>\n",
       "      <th>LDL level</th>\n",
       "      <th>VLDL level</th>\n",
       "      <th>Health_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>No</td>\n",
       "      <td>1.64</td>\n",
       "      <td>64</td>\n",
       "      <td>23.80</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>145</td>\n",
       "      <td>119</td>\n",
       "      <td>60</td>\n",
       "      <td>66.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.70</td>\n",
       "      <td>70</td>\n",
       "      <td>24.22</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>220</td>\n",
       "      <td>107</td>\n",
       "      <td>69</td>\n",
       "      <td>134.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>1.67</td>\n",
       "      <td>63</td>\n",
       "      <td>22.59</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>190</td>\n",
       "      <td>251</td>\n",
       "      <td>42</td>\n",
       "      <td>108.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>48</td>\n",
       "      <td>No</td>\n",
       "      <td>1.61</td>\n",
       "      <td>79</td>\n",
       "      <td>30.48</td>\n",
       "      <td>Obese</td>\n",
       "      <td>228</td>\n",
       "      <td>185</td>\n",
       "      <td>65</td>\n",
       "      <td>134.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>74</td>\n",
       "      <td>No</td>\n",
       "      <td>1.76</td>\n",
       "      <td>83</td>\n",
       "      <td>26.79</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>157</td>\n",
       "      <td>113</td>\n",
       "      <td>49</td>\n",
       "      <td>90.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age Family history  Height  Weight    BMI Obese/non obese  \\\n",
       "0  Female   65             No    1.64      64  23.80       Non-obese   \n",
       "1  Female   50            Yes    1.70      70  24.22       Non-obese   \n",
       "2  Female   45             No    1.67      63  22.59       Non-obese   \n",
       "3  Female   48             No    1.61      79  30.48           Obese   \n",
       "4    Male   74             No    1.76      83  26.79       Non-obese   \n",
       "\n",
       "   Cholesterol  Triglycerides level  HDL level  LDL level  VLDL level  \\\n",
       "0          145                  119         60       66.0        19.0   \n",
       "1          220                  107         69      134.0        17.0   \n",
       "2          190                  251         42      108.0        40.0   \n",
       "3          228                  185         65      134.0        29.0   \n",
       "4          157                  113         49       90.0        18.0   \n",
       "\n",
       "   Health_status  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode that target \n",
    "data['Health_status'].replace({'healthy': 0, 'patient': 1}, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gender', 'Age', 'Family history', 'Height', 'Weight', 'BMI', 'Obese/non obese', 'Cholesterol', 'Triglycerides level', 'HDL level', 'LDL level', 'VLDL level', 'Health_status']\n"
     ]
    }
   ],
   "source": [
    "print(data.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Family history</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Obese/non obese</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Triglycerides level</th>\n",
       "      <th>HDL level</th>\n",
       "      <th>LDL level</th>\n",
       "      <th>VLDL level</th>\n",
       "      <th>Health_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.64</td>\n",
       "      <td>64</td>\n",
       "      <td>23.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145</td>\n",
       "      <td>119</td>\n",
       "      <td>60</td>\n",
       "      <td>66.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>70</td>\n",
       "      <td>24.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>220</td>\n",
       "      <td>107</td>\n",
       "      <td>69</td>\n",
       "      <td>134.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.67</td>\n",
       "      <td>63</td>\n",
       "      <td>22.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190</td>\n",
       "      <td>251</td>\n",
       "      <td>42</td>\n",
       "      <td>108.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>79</td>\n",
       "      <td>30.48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>228</td>\n",
       "      <td>185</td>\n",
       "      <td>65</td>\n",
       "      <td>134.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.76</td>\n",
       "      <td>83</td>\n",
       "      <td>26.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>113</td>\n",
       "      <td>49</td>\n",
       "      <td>90.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Family history  Height  Weight    BMI  Obese/non obese  \\\n",
       "0     0.0   65             0.0    1.64      64  23.80              0.0   \n",
       "1     0.0   50             1.0    1.70      70  24.22              0.0   \n",
       "2     0.0   45             0.0    1.67      63  22.59              0.0   \n",
       "3     0.0   48             0.0    1.61      79  30.48              1.0   \n",
       "4     1.0   74             0.0    1.76      83  26.79              0.0   \n",
       "\n",
       "   Cholesterol  Triglycerides level  HDL level  LDL level  VLDL level  \\\n",
       "0          145                  119         60       66.0        19.0   \n",
       "1          220                  107         69      134.0        17.0   \n",
       "2          190                  251         42      108.0        40.0   \n",
       "3          228                  185         65      134.0        29.0   \n",
       "4          157                  113         49       90.0        18.0   \n",
       "\n",
       "   Health_status  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "\n",
    "categorical_columns = ['Gender','Family history','Obese/non obese']\n",
    "numerical_columns = [col_name for col_name in data.columns if col_name not in categorical_columns]\n",
    "\n",
    "encoder = OrdinalEncoder()\n",
    "\n",
    "data[categorical_columns] = encoder.fit_transform(data[categorical_columns])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display the heatmap of correlation matrix of the dataset\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(data.corr(numeric_only=False), annot=True, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Height', 'Weight', 'BMI', 'Cholesterol', 'Triglycerides level', 'LDL level', 'VLDL level']\n"
     ]
    }
   ],
   "source": [
    "# Features and target\n",
    "X = data.drop(columns=['Health_status','Age','HDL level'])\n",
    "y = data['Health_status']\n",
    "\n",
    "numerical_columns = [col_name for col_name in X.columns if col_name not in categorical_columns and col_name not in ['Health_status','Age','HDL level']]\n",
    "print(numerical_columns)\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train[numerical_columns] = scaler.fit_transform(X_train[numerical_columns])\n",
    "X_test[numerical_columns] = scaler.transform(X_test[numerical_columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Classifier with Linear Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        12\n",
      "           1       0.60      1.00      0.75        18\n",
      "\n",
      "    accuracy                           0.60        30\n",
      "   macro avg       0.30      0.50      0.38        30\n",
      "weighted avg       0.36      0.60      0.45        30\n",
      "\n",
      "SVM Accuracy: 0.6\n"
     ]
    }
   ],
   "source": [
    "svm_model_linear = SVC(kernel='linear', random_state=42, C= 0.002, class_weight='balanced')\n",
    "svm_model_linear.fit(X_train, y_train)\n",
    "svm_linear_preds = svm_model_linear.predict(X_test)\n",
    "\n",
    "svm_linear_cm = confusion_matrix(y_test, svm_linear_preds)\n",
    "plt.figure()\n",
    "sns.heatmap(svm_linear_cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('SVM Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, svm_linear_preds))\n",
    "print(f'SVM Accuracy: {accuracy_score(y_test, svm_linear_preds)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Classifier with polynomial Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3HHsbAAQJAAD85thbkbA3IgEAAN9RkQAAwG+Ove/bCRIAAPjNobUBAACQCRUJAAD85tj7vp0gAQCA3xxaGwAAAJlQkQAAwG+Ove/bCRIAAPjNsTdI2PvKAACA76hIAADgN4fJlgAA4GxaG44HWy4tW7ZMOnToIJUrVxbHcWTOnDkh9wcCARkyZIjExcVJdHS0tGnTRjZt2pSrYxAkAADIi4qE48GWS0lJSdKwYUOZMGFClvePHj1axo0bJ5MmTZJvvvlGYmJipG3btnL8+PEcH4PWBgAAlmrXrp3ZsqLViLFjx8qTTz4pHTt2NPvefPNNqVixoqlc3HHHHTk6BhUJAAAKSGsjOTlZjhw5ErLpPje2bdsme/bsMe2MoNjYWGnatKl8/fXXOX4eggQAAAWktZGQkGBO9uk33eeGhgilFYj09HbwvpygtQEAQAERHx8v/fv3D9kXFRUl4USQAADAZ45Hyz81NHgVHCpVqmS+7t2716zaCNLbl156aY6fh9YGAAB5ECQcDzYv1axZ04SJRYsWpe3TORe6eqNZs2Y5fh4qEgAAWCoxMVE2b94cMsFy3bp1UqZMGalWrZr07dtXhg8fLnXq1DHBYvDgweaaE506dcrxMQgSAAD4zQnPYVevXi2tW7dOux2cX9G1a1eZOnWqPP744+ZaEw888IAcOnRIWrRoIfPnz5eiRYvm+BhOQBeSWqZmv4/DPQQgX/pxTPtwDwHId4rmwVvq4p2nevI8iTO7SX7DHAkAAOAarQ0AAHzmWPyhXQQJAAB85hAkAACAW47FQYI5EgAAwDUqEgAA+M0RaxEkAADwmUNrAwAAIDMqEgAA+MyxuCJBkAAAwGeOxUGC1gYAAHCNigQAAD5zLK5IECQAAPCbI9aitQEAAFyjIgEAgM8cWhsAAMAthyABAADcciwOEsyRAAAArlGRAADAb45YiyABAIDPHFobAAAAmVGRAADAZ47FFQmCBAAAPnMsDhK0NgAAgGtUJAAA8JljcUWCIAEAgN8csRatDQAA4BoVCQAAfObQ2gAAAG45BAkAAOCWY3GQYI4EAABwjYoEAAB+c8RaBAkAAHzm0NoAAADIjIoEfBETFSn929WVtpdUlLLFo+SH347I07N/kO92HA730ICwmfnOdJn57gzZ9dtv5nat2nXkwZ4PS4urW4V7aPCZQ0UCyJ1Rf2sgLeqWk/5vr5cbxyyTLzbul2k9m0rF2KhwDw0ImwoVK0mffgNkxnsfyPSZs+SKpldKn0d6yebNm8I9NORBkHA82HLr6NGj0rdvX6levbpER0dL8+bNZdWqVZ6+NoIEPBdVOEJubFBJRn34k6zc+of8euCYvLhgk/l6d/Pq4R4eEDbXtL5Wrm7ZSqpXryE1atSUR/v0k2LFisl369eFe2iw1P333y8LFy6UadOmyYYNG+SGG26QNm3ayG//VxUr8K2NAwcOyL///W/5+uuvZc+ePWZfpUqVTGLq1q2blC9fPpzDg0uFIhwpFBkhySdTQvYfP5kiTc4vE7ZxAflJSkqKfLpgvvz55zFp2LBRuIcDC1sbf/75p8yaNUv+85//SMuWLc2+YcOGyYcffigTJ06U4cOHF+wgoaWVtm3bmjSu6eiCCy4w+/fu3Svjxo2TUaNGyYIFC6RJkybhGiJcSkpOkTXbDsqjN9SRzXsT5cDRZLn5sipyWY3S8uuBpHAPDwirTT9vlHvuukNOnEg2//v3wrgJUqt27XAPC35z8v6Qp06dMoG1aNGiIfu1xbF8+XLPjuMEAoGAhMGVV14pDRs2lEmTJmVKajqkhx56SL777jtTrchOcnKy2dJr8M/PxSlU2JdxI2eqlS0mo+9oIE1rl5VTKanyw84jsm1/klxcNVauH7U03MM7Z/04pn24h3DOO3nihOzevVsSE4/Kwk8XyOxZ78nrU98iTIRR0Tx4S12z38eePM9Po9pkOudFRUWZLSta4S9SpIhMnz5dKlasKDNmzJCuXbtK7dq1ZePGjQV7jsT69eulX79+WZZ7dJ/et27dmfuGCQkJEhsbG7IdWjXTp1Ejp7b/fkzumLBC6g+aL82f/lw6jf1SCkU6Zj9wLitcpIhUq15d6l90sfTp95hcULeevP3Wm+EeFgrIZMuELM55uu90dG6EvjmvUqWKCRta8b/zzjslIsK703/YgoTOhVi5cuVp79f7ND2dSXx8vBw+fDhkK3V5Z49HC7f+PJEi+48kS8noQtKyXnn57Pv/zYUB8D+pqammSgG7OR4FiazOebrvdGrVqiVLly6VxMRE2bFjhzm3njx5Us4//3zPXlvY5kgMGDBAHnjgAVmzZo1cd911aaFB50gsWrRIJk+eLM8999wZnyerkg5tjfBrWbec/pcjW/clSo1yMRJ/cz3ZsjdR3vtmZ7iHBoTNiy88Ly2ubimV4uLkWFKSfPLxR7J61UqZ+Orr4R4afOZ4NEciuzZGdmJiYsx28OBBM/9w9OjRBT9I9OrVS8qVKycvvPCCvPzyy2ZCiIqMjJTGjRvL1KlTpXNnKgsFVYnowjKwfV2pVKqoHD52Uuav3yPPfbJRTqWGZUoOkC/88cfv8mT8INm/f58UL1FCLrigrgkRzZpfFe6hwVILFiwwrY26devK5s2bZeDAgVKvXj3p3r17wZ9smZ6WWXQpqNJwUbhw4XwxqQWwDZMtgfBMtqwzcL4nz7NpzI25evzMmTNN62Pnzp1SpkwZufXWW2XEiBFmboVVl8jW4BAXFxfuYQAA4AsnTFfI1sq+39V9rmwJAAAKdkUCAACbORZ/aBdBAgAAnzn25ghaGwAAwD0qEgAA+Cwiwt6SBEECAACfOfbmCFobAADAPSoSAAD4zLG4JEGQAADAZ469OYIgAQCA3xyLkwRzJAAAgGtUJAAA8JljcUWCIAEAgM8ce3MErQ0AAOAeFQkAAHzmWFySIEgAAOAzx94cQWsDAAC4R0UCAACfORaXJAgSAAD4zLE3R9DaAAAA7lGRAADAZ47FJQmCBAAAPnPszREECQAA/OZYnCSYIwEAAFyjIgEAgM8cewsSBAkAAPzmWJwkaG0AAADXqEgAAOAzx96CBEECAAC/ORYnCVobAADANSoSAAD4zLG3IEGQAADAb47FSYLWBgAAcI2KBAAAPnMsrkgQJAAA8Jljb46gtQEAQF5UJBwPttxISUmRwYMHS82aNSU6Olpq1aolzzzzjAQCAU9fGxUJAAAs9Oyzz8rEiRPljTfekIsuukhWr14t3bt3l9jYWOndu7dnxyFIAABgYWvjq6++ko4dO0r79u3N7Ro1asiMGTNk5cqVnh6H1gYAABa2Npo3by6LFi2Sn3/+2dxev369LF++XNq1a+fpa6MiAQBAAZGcnGy29KKiosyW0RNPPCFHjhyRevXqSWRkpJkzMWLECOnSpYunY6IiAQCAzxzHmy0hIcHMcUi/6b6szJw5U95++22ZPn26rF271syVeO6558xXT19bwOvpm/lAzX4fh3sIQL7045j/9UoB/H9F86A2f/1LKzx5no/+3ijHFYmqVauaqkSvXr3S9g0fPlzeeust+emnn8QrtDYAACggok4TGrJy7NgxiYgIbTxoiyM1NdXTMREkAACwcNVGhw4dzJyIatWqmeWf3377rfzrX/+SHj16eHocggQAABZeInv8+PHmglQPP/yw7Nu3TypXriwPPvigDBkyxNPjECQAAPBZRBgqEiVKlJCxY8eazU+s2gAAAK5RkQAAwGeOxZ/aRZAAAMBnjr05gtYGAABwj4oEAAA+c8TekgRBAgAAC1dt5BVaGwAAwDUqEgAA+MyxeLYlQQIAAJ859uYIWhsAAMA9KhIAAPgswuKSBEECAACfOfbmCIIEAAB+cyxOEsyRAAAArlGRAADAZ469BQmCBAAAfouwOEnQ2gAAAK5RkQAAwGeO2IsgAQCAzxxaGwAAAJlRkQAAwGcRzjkeJObOnZvjJ7z55pvPZjwAAFjHsbi1kaMg0alTpxz/olJSUs52TAAAwKYgkZqa6v9IAACwlGNvQYI5EgAA+M2xOEm4ChJJSUmydOlS2b59u5w4cSLkvt69e3s1NgAArBBhb47IfZD49ttv5aabbpJjx46ZQFGmTBk5cOCAFCtWTCpUqECQAADgHJLr60j069dPOnToIAcPHpTo6GhZsWKF/Prrr9K4cWN57rnn/BklAAAFvLXheLBZESTWrVsnjz32mEREREhkZKQkJydL1apVZfTo0fKPf/zDn1ECAFCAOR5tVgSJwoULmxChtJWh8yRUbGys7Nixw/sRAgAAe+ZINGrUSFatWiV16tSRVq1ayZAhQ8wciWnTpsnFF1/szygBACjAIvJpWyIsFYmRI0dKXFyc+X7EiBFSunRp6dmzp+zfv19effVVP8YIAECB5jjebFZUJJo0aZL2vbY25s+f7/WYAABAAcEFqQAA8JmTX8sJ4QgSNWvWzPYXsnXr1rMdEwAAVnHszRG5DxJ9+/YNuX3y5ElzkSptcQwcONDLsQEAANuCRJ8+fbLcP2HCBFm9erUXYwIAwCoRYShJ1KhRw1wwMqOHH37YnLPDtmrjdNq1ayezZs3y6ukAALCGE4ZVG3qpht27d6dtCxcuNPtvv/32/DnZ8v333zefuwEAAMI/2bJ8+fIht0eNGiW1atUy14AK+wWp0v9CAoGA7Nmzx1xH4uWXX/Z0cAAA4P/Tj6XQLb2oqCizZUc/qfutt96S/v37ex5qch0kOnbsGDIIvVy2pp5rrrlG6tWrJ/nBniXzwj0EIF967MPzwz0EIN+ZcMuFvh8jwqPnSUhIkKeeeipk39ChQ2XYsGHZ/tycOXPk0KFD0q1bN/FaroPEmQYLAABCeVUFiI+PN1WF9M5UjVCvv/66mctYuXJlCXuQ0E/81EkbelXL9H7//XezLyUlxcvxAQCAXLQxMtKVG5999pl88MEH4odcBwmdE5EV7dkUKVLEizEBAGCViDBekGrKlCnmjX779u3DGyTGjRuXVp557bXXpHjx4mn3aRVi2bJl+WaOBAAA+UlEmIJEamqqCRJdu3aVQoX8+VSMHD/rCy+8kFaRmDRpkmlxBGklQi98ofsBAED+oC2N7du3S48ePXw7Ro6DxLZt28zX1q1bmz6Lfnw4AADIvx/adcMNN5x2SoJXcl3nWLx4sT8jAQDAUhEWf2hXrpe23nrrrfLss89m2j969GjPL7sJAAAsCxI6qfKmm27KtF/Xp+p9AAAg/J+1kVdy3dpITEzMcpln4cKF5ciRI16NCwAAa0Tk1xQQjorEJZdcIu+++26m/e+8847Ur1/fq3EBAGDVyTbCg82KisTgwYPlr3/9q2zZskWuvfZas2/RokUyffp08wmgAADg3JHrINGhQwfz4R8jR440wSE6OloaNmwon3/+OR8jDgBAFizubOQ+SCi9zGbwUps6L2LGjBkyYMAAWbNmDZ+1AQBABsyRyIKu0NBLbuoniT3//POmzbFixQpvRwcAAOypSOzZs0emTp1qPo5UKxGdO3c2H9alrQ4mWgIAkDWLCxI5r0jo3Ii6devKd999J2PHjpVdu3bJ+PHj/R0dAACWXNkywoOtQFck5s2bJ71795aePXtKnTp1/B0VAACwqyKxfPlyOXr0qDRu3FiaNm0qL730khw4cMDf0QEAYMlkywgPtgIdJK688kqZPHmy7N69Wx588EFzASqdaKmfdb5w4UITMgAAwLl1iexcr9qIiYkxn2uuFYoNGzbIY489JqNGjZIKFSrIzTff7M8oAQBAvnRWV9zUyZf6qZ87d+4015IAAACZMdnyDCIjI6VTp05mAwAAoRzJpykgvwQJAABwevm1muCF/PphYgAAoACgIgEAgM8iLK5IECQAAPCZk1/XbnqA1gYAAHCNigQAAD6LsLcgQZAAAMBvjsVBgtYGAABwjYoEAAA+i7C4JEGQAADAZxH25ghaGwAAwD0qEgAA+MyxuCJBkAAAwGcRfGgXAABwy7E3RzBHAgAAuEdFAgAAn0VYXJEgSAAA4LMIi3sbtDYAAIBrBAkAAHzmON5sufXbb7/J3XffLWXLlpXo6Gi55JJLZPXq1Z6+NlobAABY2No4ePCgXHXVVdK6dWuZN2+elC9fXjZt2iSlS5f29DgECQAALPTss89K1apVZcqUKWn7atas6flxaG0AAFBAWhvJycly5MiRkE33ZWXu3LnSpEkTuf3226VChQrSqFEjmTx5suevjSABAIDPIjzaEhISJDY2NmTTfVnZunWrTJw4UerUqSMLFiyQnj17Su/eveWNN97w9LXR2gAAoICIj4+X/v37h+yLiorK8rGpqammIjFy5EhzWysS33//vUyaNEm6du3q2ZgIEgAA+MzxaLKlhobTBYeM4uLipH79+iH7LrzwQpk1a5Z4iSABAIDPnDAcU1dsbNy4MWTfzz//LNWrV/f0OAQJAAAsXP7Zr18/ad68uWltdO7cWVauXCmvvvqq2bzEZEsAACx0+eWXy+zZs2XGjBly8cUXyzPPPCNjx46VLl26eHocKhIAAPjMCdNx//KXv5jNTwQJAAB85tj7mV20NgAAgHtUJAAAKCDLP/MjggQAAD6LEHvZ/NoAAIDPqEgAAOAzh9YGAABwyxF70doAAACuUZEAAMBnDq0NAADgVoTYiyABAIDPHIsrEjaHJAAA4DMqEgAA+MwRexEkAADwmWNxkqC1AQAAXKMiAQCAzyIsbm4QJAAA8Jljb46gtQEAANyjIgEAgM8cWhsAAMAtx94cQWsDAAC4R0UCAACfRdDaAAAAbjn25giCBAAAfnMsDhLMkQAAAK5RkQAAwGcOcyQAAIBbEfbmCFobAADAPSoSAAD4zKG1AQAA3HLszRG0NgAAgHtUJAAA8JlDawMAALgVYW+OoLUBAADcoyKBs3bVZbWk371t5LL61SSufKx07veqfLjku7T7O17bUO6/rYU0urCalC0VI03/liDf/fxbWMcMhMPTN9SSsjFFMu1fuvUPmbl+b1jGhLzhWNzaoCKBsxYTHSUbfv5N+ia8m+X9xaKLyFfrtsiT4+bk+diA/GT0kl8k/pOf07Zxy381+7/97Wi4h4Y8WLXheLDlxrBhw8RxnJCtXr16nr82KhI4a59++V+znc6Mj1eZr9XiyuThqID8J/FESsjt6yuVkP2JJ2TTgWNhGxPyhhOm41500UXy2Wefpd0uVMj70z5BAgDCINIRuaJqSfl88x/hHgosVqhQIalUqdK529rYsWOH9OjRI9vHJCcny5EjR0K2QGpo6geA/KZh5RISXThSVmw/HO6hIA9EOI4nW1bnPN13Ops2bZLKlSvL+eefL126dJHt27d7/9okH/vjjz/kjTfeyPYxCQkJEhsbG7Kd2rsmz8YIAG40q15K/rs3UQ4fPxXuoSCPWhuOB1tW5zzdl5WmTZvK1KlTZf78+TJx4kTZtm2bXH311XL06FF7Whtz587N9v6tW7ee8Tni4+Olf//+IfsqXD3orMcGAH4pE11I6lWIkcnf7Az3UFDAxGdxzouKisryse3atUv7vkGDBiZYVK9eXWbOnCn33XefHUGiU6dOZhZpIBA47WP0/uzoLzDjL9GJiPRsjADgtSurl5KjySny/Z7EcA8FBWy2ZVQW57ycKlWqlFxwwQWyefNmsaa1ERcXJx988IGkpqZmua1duzacw0MOxUQXkQYXVDGbqlGlrPm+aqXS5nbpksXM7Qtr/W/CzwU1KprbFcuWCOu4gXCdT7St8c32Q5J6+vdQsPA6Eo4H/3c2EhMTZcuWLebc66WwViQaN24sa9askY4dO2Z5/5mqFcgfLqtfXT59rU/a7dEDbjVfp81dIQ8MfUvat7pEJj99T9r905793wTa4ZM+kRGvfBKGEQPhU7dCjJQpVli+/pVJlvDXgAEDpEOHDqadsWvXLhk6dKhERkbKnXfeaU+QGDhwoCQlJZ32/tq1a8vixYvzdEzIvS/WbJLoRo+c9v63PvzGbABEftqXJL1m/xjuYeAc+BjxnTt3mtDw+++/S/ny5aVFixayYsUK8701QUJnj2YnJiZGWrVqlWfjAQDAlgtSvfPOO3lynHy9/BMAAORvXNkSAAC/OWItggQAAD5zLE4SBAkAACycbJlXmCMBAABcoyIBAIDPHLEXQQIAAL85Yi1aGwAAwDUqEgAA+MyxuCRBkAAAwGeOvTmC1gYAAHCPigQAAD5zxF4ECQAA/OaItWhtAAAA16hIAADgM8fikgRBAgAAnzn25giCBAAAfnPEXsyRAAAArlGRAADAb45YiyABAIDPHIuTBK0NAADgGhUJAAB85thbkCBIAADgN0fsRWsDAAC4RkUCAAC/OWItggQAAD5zLE4StDYAAIBrVCQAAPCZY29BgiABAIDfHLEXQQIAAL85Yi3mSAAAANeoSAAA4DPH4pIEQQIAAJ859uYIWhsAAMA9KhIAAPjMEXtRkQAAIC+ShOPBdhZGjRoljuNI3759xUsECQAALLdq1Sp55ZVXpEGDBp4/N0ECAIA8WLXhePB/biQmJkqXLl1k8uTJUrp0ac9fG0ECAIA8WLXheLC50atXL2nfvr20adNG/MBkSwAACojk5GSzpRcVFWW2rLzzzjuydu1a09rwCxUJAAAKyFzLhIQEiY2NDdl0X1Z27Nghffr0kbfffluKFi3q32sLBAIBsUx0o0fCPQQgX+oxpFe4hwDkOxNuudD3Y/zy+3FPnieuuJPjisScOXPklltukcjIyLR9KSkpZuVGRESEeZ7097lFawMAgAJyieyobNoYGV133XWyYcOGkH3du3eXevXqyaBBgzwJEYogAQCAhUqUKCEXX3xxyL6YmBgpW7Zspv1ngyABAIDPHIsvbUmQAADAZ47kD0uWLPH8OVm1AQAAXKMiAQCAz5z8UpLwAUECAADfOWIrWhsAAMA1KhIAAPjMsbcgQZAAAMBvjtiL1gYAAHCNigQAAD5zLC5JECQAACggn7WRHxEkAADwmyPWYo4EAABwjYoEAAA+c8ReBAkAAHzmWJwkaG0AAADXqEgAAOAzx+LmBkECAAC/OWItWhsAAMA1KhIAAPjMEXsRJAAA8JljcZKgtQEAAFyjIgEAgM8ci5sbBAkAAHzm2JsjaG0AAAD3CBIAAMA1WhsAAPjMsbi1QZAAAMBnjsWTLWltAAAA16hIAADgM8feggRBAgAAvzliL1obAADANSoSAAD4zRFrESQAAPCZY3GSoLUBAABcoyIBAIDPHHsLEgQJAAD85oi9aG0AAJAXScLxYMuFiRMnSoMGDaRkyZJma9asmcybN8/zl0aQAADAQuedd56MGjVK1qxZI6tXr5Zrr71WOnbsKD/88IOnx6G1AQCAhas2OnToEHJ7xIgRpkqxYsUKueiiizw7DkECAADLJ1umpKTIe++9J0lJSabF4SWCBAAABURycrLZ0ouKijJbVjZs2GCCw/Hjx6V48eIye/ZsqV+/vqdjcgKBQMDTZwT+j/5jT0hIkPj4+NP+IwfORfy3AbeGDRsmTz31VMi+oUOHmv1ZOXHihGzfvl0OHz4s77//vrz22muydOlST8MEQQK+OXLkiMTGxpp/wDpjGMD/8N8G8qoikVGbNm2kVq1a8sorr4hXaG0AAFBAROUiNGQlNTU1UxA5WwQJAAAsFB8fL+3atZNq1arJ0aNHZfr06bJkyRJZsGCBp8chSAAAYKF9+/bJvffeK7t37zatNL04lYaI66+/3tPjECTgGy2/6SQgJpMBofhvA3nh9ddfz5PjMNkSAAC4xiWyAQCAawQJAADgGkECAAC4RpAAAACuESTgmwkTJkiNGjWkaNGi0rRpU1m5cmW4hwSE1bJly8wnMlauXFkcx5E5c+aEe0jAWSNIwBfvvvuu9O/f3yxxW7t2rTRs2FDatm1r1jUD5yr95EX9b0FDNmALln/CF1qBuPzyy+Wll15Kuyxr1apV5dFHH5Unnngi3MMDwk4rEvpJjJ06dQr3UICzQkUCntNPm1uzZo35cJigiIgIc/vrr78O69gAAN4iSMBzBw4ckJSUFKlYsWLIfr29Z8+esI0LAOA9ggQAAHCNIAHPlStXTiIjI2Xv3r0h+/V2pUqVwjYuAID3CBLwXJEiRaRx48ayaNGitH062VJvN2vWLKxjAwB4i0//hC906WfXrl2lSZMmcsUVV8jYsWPN0rfu3buHe2hA2CQmJsrmzZvTbm/btk3WrVsnZcqUkWrVqoV1bIBbLP+Eb3Tp55gxY8wEy0svvVTGjRtnloUC56olS5ZI69atM+3X0D116tSwjAk4WwQJAADgGnMkAACAawQJAADgGkECAAC4RpAAAACuESQAAIBrBAkAAOAaQQIAALhGkAAs1K1bN+nUqVPa7WuuuUb69u0blgswOY4jhw4dyvNjA8gbBAkgj0/wemLVTT+TpHbt2vL000/LqVOnfD3uBx98IM8880yOHsvJH0Bu8FkbQB678cYbZcqUKZKcnCyffPKJ9OrVSwoXLizx8fEhjztx4oQJG17Qz3IAAD9QkQDyWFRUlPk49erVq0vPnj2lTZs2Mnfu3LR2xIgRI6Ry5cpSt25d8/gdO3ZI586dpVSpUiYQdOzYUX755Ze050tJSTEfkqb3ly1bVh5//HHJeOX7jK0NDTGDBg2SqlWrmvFoZeT11183zxv8LIjSpUubyoSOK/gJrgkJCVKzZk2Jjo6Whg0byvvvvx9yHA1GF1xwgblfnyf9OAHYiSABhJmedLX6oPSj1jdu3CgLFy6Ujz76SE6ePClt27aVEiVKyBdffCFffvmlFC9e3FQ1gj/z/PPPmw98+ve//y3Lly+XP/74Q2bPnp3tMe+9916ZMWOG+SC1H3/8UV555RXzvBosZs2aZR6j49i9e7e8+OKL5raGiDfffFMmTZokP/zwg/Tr10/uvvtuWbp0aVrg+etf/yodOnQwn2h5//33yxNPPOHzbw9A2OmHdgHIG127dg107NjRfJ+amhpYuHBhICoqKjBgwABzX8WKFQPJyclpj582bVqgbt265rFBen90dHRgwYIF5nZcXFxg9OjRafefPHkycN5556UdR7Vq1SrQp08f8/3GjRu1XGGOnZXFixeb+w8ePJi27/jx44FixYoFvvrqq5DH3nfffYE777zTfB8fHx+oX79+yP2DBg3K9FwA7MIcCSCPaaVB3/1rtUHbBXfddZcMGzbMzJW45JJLQuZFrF+/XjZv3mwqEukdP35ctmzZIocPHzZVg/Qfz16oUCFp0qRJpvZGkFYLIiMjpVWrVjkes47h2LFjcv3114fs16pIo0aNzPda2cj4MfHNmjXL8TEAFEwECSCP6dyBiRMnmsCgcyH0xB8UExMT8tjExERp3LixvP3225mep3z58q5bKbml41Aff/yxVKlSJeQ+nWMB4NxFkADymIYFndyYE5dddpm8++67UqFCBSlZsmSWj4mLi5NvvvlGWrZsaW7rUtI1a9aYn82KVj20EqJzG3SiZ0bBiohO4gyqX7++CQzbt28/bSXjwgsvNJNG01uxYkWOXieAgovJlkA+1qVLFylXrpxZqaGTLbdt22au89C7d2/ZuXOneUyfPn1k1KhRMmfOHPnpp5/k4YcfzvYaEDVq1JCuXbtKjx49zM8En3PmzJnmfl1Noqs1tAWzf/9+U43Q1sqAAQPMBMs33njDtFXWrl0r48ePN7fVQw89JJs2bZKBAweaiZrTp083k0AB2I0gAeRjxYoVk2XLlkm1atXMigh913/fffeZORLBCsVjjz0m99xzjwkHOidBT/q33HJLts+rrZXbbrvNhI569erJ3//+d0lKSjL3aeviqaeeMisuKlasKI888ojZrxe0Gjx4sFm9oePQlSPa6tDloErHqCs+NJzo0lBd3TFy5Ejff0cAwsvRGZdhHgMAACigqEgAAADXCBIAAMA1ggQAAHCNIAEAAFwjSAAAANcIEgAAwDWCBAAAcI0gAQAAXCNIAAAA1wgSAADANYIEAABwjSABAADErf8H8RInjzTfFc4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.45      0.75      0.56        12\n",
      "           1       0.70      0.39      0.50        18\n",
      "\n",
      "    accuracy                           0.53        30\n",
      "   macro avg       0.57      0.57      0.53        30\n",
      "weighted avg       0.60      0.53      0.53        30\n",
      "\n",
      "SVM Accuracy: 0.5333333333333333\n"
     ]
    }
   ],
   "source": [
    "svm_model_poly = SVC(kernel='poly', random_state=42, C= 1, class_weight='balanced')\n",
    "svm_model_poly.fit(X_train, y_train)\n",
    "svm_poly_preds = svm_model_poly.predict(X_test)\n",
    "\n",
    "svm_poly_cm = confusion_matrix(y_test, svm_poly_preds)\n",
    "plt.figure()\n",
    "sns.heatmap(svm_poly_cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('SVM Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, svm_poly_preds))\n",
    "print(f'SVM Accuracy: {accuracy_score(y_test, svm_poly_preds)}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Classifier with RBF Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.67      0.64        12\n",
      "           1       0.76      0.72      0.74        18\n",
      "\n",
      "    accuracy                           0.70        30\n",
      "   macro avg       0.69      0.69      0.69        30\n",
      "weighted avg       0.70      0.70      0.70        30\n",
      "\n",
      "SVM Accuracy: 0.7\n"
     ]
    }
   ],
   "source": [
    "svm_model_rbf = SVC(kernel='rbf', random_state=42, C= 0.3, class_weight='balanced')\n",
    "svm_model_rbf.fit(X_train, y_train)\n",
    "svm_rbf_preds = svm_model_rbf.predict(X_test)\n",
    "\n",
    "svm_rbf_cm = confusion_matrix(y_test, svm_rbf_preds)\n",
    "plt.figure()\n",
    "sns.heatmap(svm_rbf_cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('SVM Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, svm_rbf_preds))\n",
    "print(f'SVM Accuracy: {accuracy_score(y_test, svm_rbf_preds)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.67      0.67        12\n",
      "           1       0.78      0.78      0.78        18\n",
      "\n",
      "    accuracy                           0.73        30\n",
      "   macro avg       0.72      0.72      0.72        30\n",
      "weighted avg       0.73      0.73      0.73        30\n",
      "\n",
      "Random Forest Accuracy: 0.7333333333333333\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators=200, random_state=42, class_weight='balanced')\n",
    "rf_model.fit(X_train, y_train)\n",
    "rf_preds = rf_model.predict(X_test)\n",
    "\n",
    "rf_cm = confusion_matrix(y_test, rf_preds)\n",
    "plt.figure()\n",
    "sns.heatmap(rf_cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Random Forest Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, rf_preds))\n",
    "print(f'Random Forest Accuracy: {accuracy_score(y_test, rf_preds)}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RF Boosted version using Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.58      0.61        12\n",
      "           1       0.74      0.78      0.76        18\n",
      "\n",
      "    accuracy                           0.70        30\n",
      "   macro avg       0.69      0.68      0.68        30\n",
      "weighted avg       0.70      0.70      0.70        30\n",
      "\n",
      "Random Forest Boosted Accuracy: 0.7\n"
     ]
    }
   ],
   "source": [
    "rf_boosted = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\n",
    "rf_boosted.fit(X_train, y_train)\n",
    "y_pred_rf_boosted = rf_boosted.predict(X_test)\n",
    "\n",
    "\n",
    "rf_boosted_cm = confusion_matrix(y_test, y_pred_rf_boosted)\n",
    "plt.figure()\n",
    "sns.heatmap(rf_boosted_cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Random Forest Boosted Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, y_pred_rf_boosted))\n",
    "print(f'Random Forest Boosted Accuracy: {accuracy_score(y_test, y_pred_rf_boosted)}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Discriminant Analysis (GDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.50      0.55        12\n",
      "           1       0.70      0.78      0.74        18\n",
      "\n",
      "    accuracy                           0.67        30\n",
      "   macro avg       0.65      0.64      0.64        30\n",
      "weighted avg       0.66      0.67      0.66        30\n",
      "\n",
      "GDA Accuracy: 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "gda = QuadraticDiscriminantAnalysis()\n",
    "gda.fit(X_train, y_train)\n",
    "y_pred_gda = gda.predict(X_test)\n",
    "\n",
    "gda_cm = confusion_matrix(y_test, y_pred_gda)\n",
    "plt.figure()\n",
    "sns.heatmap(gda_cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('GDA Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, y_pred_gda))\n",
    "print(f'GDA Accuracy: {accuracy_score(y_test, y_pred_gda)}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.67      0.57        12\n",
      "           1       0.71      0.56      0.62        18\n",
      "\n",
      "    accuracy                           0.60        30\n",
      "   macro avg       0.61      0.61      0.60        30\n",
      "weighted avg       0.63      0.60      0.60        30\n",
      "\n",
      "Logistic Regression Accuracy: 0.6\n"
     ]
    }
   ],
   "source": [
    "lr_model = LogisticRegression(random_state=42, C= 0.001, penalty= 'l2' ,class_weight='balanced')\n",
    "lr_model.fit(X_train, y_train)\n",
    "lr_preds = lr_model.predict(X_test)\n",
    "\n",
    "\n",
    "lr_cm = confusion_matrix(y_test, lr_preds)\n",
    "plt.figure()\n",
    "sns.heatmap(lr_cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Logistic Regression Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, lr_preds))\n",
    "print(f'Logistic Regression Accuracy: {accuracy_score(y_test, lr_preds)}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leave-One-Out Cross Validation (LOOCV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOOCV (SVM Linear) Accuracy: 0.74\n",
      "LOOCV (SVM Poly) Accuracy: 0.64\n",
      "LOOCV (SVM RBF) Accuracy: 0.68\n",
      "LOOCV (RF) Accuracy: 0.71\n",
      "LOOCV (RF Boosted) Accuracy: 0.69\n",
      "LOOCV (LR) Accuracy: 0.69\n",
      "LOOCV (GDA) Accuracy: 0.69\n"
     ]
    }
   ],
   "source": [
    "loo = LeaveOneOut()\n",
    "\n",
    "scores_svm_linear = cross_val_score(svm_model_linear, X, y, cv=loo)\n",
    "scores_svm_poly = cross_val_score(svm_model_poly, X, y, cv=loo)\n",
    "scores_svm_rbf = cross_val_score(svm_model_rbf, X, y, cv=loo)\n",
    "scores_rf = cross_val_score(rf_model, X, y, cv=loo)\n",
    "scores_rf_boosted = cross_val_score(rf_boosted, X, y, cv=loo)\n",
    "scores_lr = cross_val_score(lr_model, X, y, cv=loo)\n",
    "scores_gda = cross_val_score(gda, X, y, cv=loo)\n",
    "\n",
    "\n",
    "# Print the results\n",
    "print(\"LOOCV (SVM Linear) Accuracy:\", scores_svm_linear.mean())\n",
    "print(\"LOOCV (SVM Poly) Accuracy:\", scores_svm_poly.mean())\n",
    "print(\"LOOCV (SVM RBF) Accuracy:\", scores_svm_rbf.mean())\n",
    "print(\"LOOCV (RF) Accuracy:\", scores_rf.mean())\n",
    "print(\"LOOCV (RF Boosted) Accuracy:\", scores_rf_boosted.mean())\n",
    "print(\"LOOCV (LR) Accuracy:\", scores_lr.mean())\n",
    "print(\"LOOCV (GDA) Accuracy:\", scores_gda.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the effects\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "models = ['SVM Linear', 'SVM Poly', 'SVM RBF', 'Random Forest', 'RF Boosted', 'Logistic Regression', 'GDA']\n",
    "scores = [scores_svm_linear.mean(), scores_svm_poly.mean(), scores_svm_rbf.mean(), scores_rf.mean(), scores_rf_boosted.mean(), scores_lr.mean(), scores_gda.mean()]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(models, scores, color='blue')\n",
    "plt.xlabel('Models')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('LOOCV Accuracy Comparison')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['encoder.pkl']"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#saving all the models\n",
    "import joblib\n",
    "joblib.dump(svm_model_linear, 'svm_model_linear.pkl')\n",
    "joblib.dump(svm_model_poly, 'svm_model_poly.pkl')\n",
    "joblib.dump(svm_model_rbf, 'svm_model_rbf.pkl')\n",
    "joblib.dump(rf_model, 'rf_model.pkl')\n",
    "joblib.dump(rf_boosted, 'rf_boosted.pkl')\n",
    "joblib.dump(lr_model, 'lr_model.pkl')\n",
    "joblib.dump(gda, 'gda.pkl')\n",
    "joblib.dump(scaler, 'scaler.pkl')\n",
    "joblib.dump(encoder, 'encoder.pkl')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving test data to another file for using in dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>Gender</th>\n",
       "      <th>Family history</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Obese/non obese</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Triglycerides level</th>\n",
       "      <th>LDL level</th>\n",
       "      <th>VLDL level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>82.0</td>\n",
       "      <td>27.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>39.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.64</td>\n",
       "      <td>82.0</td>\n",
       "      <td>30.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>18.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>76.0</td>\n",
       "      <td>26.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>94.0</td>\n",
       "      <td>32.91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.53</td>\n",
       "      <td>77.0</td>\n",
       "      <td>32.89</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>21.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Family history  Height  Weight    BMI  Obese/non obese  \\\n",
       "0     0.0             1.0    1.73    82.0  27.40              0.0   \n",
       "1     0.0             1.0    1.64    82.0  30.49              1.0   \n",
       "2     1.0             0.0    1.70    76.0  26.30              0.0   \n",
       "3     1.0             1.0    1.69    94.0  32.91              1.0   \n",
       "4     0.0             1.0    1.53    77.0  32.89              1.0   \n",
       "\n",
       "   Cholesterol  Triglycerides level  LDL level  VLDL level  \n",
       "0        226.0                246.0      190.0       39.36  \n",
       "1        187.0                118.0      142.0       18.88  \n",
       "2        132.0                125.0      160.0       20.00  \n",
       "3        199.0                106.0      113.0       16.00  \n",
       "4        201.0                136.0      190.0       21.76  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = pd.DataFrame(X_test, columns=X.columns)\n",
    "# X_test.head()\n",
    "X_test.reset_index(drop=True, inplace=True)\n",
    "X_test[numerical_columns] = scaler.inverse_transform(X_test[numerical_columns])\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaler mean shape: (10,)\n",
      "Scaler scale shape: (10,)\n",
      "Test data columns: Index(['Gender', 'Family history', 'Height', 'Weight', 'BMI',\n",
      "       'Obese/non obese', 'Cholesterol', 'Triglycerides level', 'LDL level',\n",
      "       'VLDL level'],\n",
      "      dtype='object')\n",
      "Numerical columns: ['Gender', 'Family history', 'Height', 'Weight', 'BMI', 'Obese/non obese', 'Cholesterol', 'Triglycerides level', 'LDL level', 'VLDL level']\n"
     ]
    }
   ],
   "source": [
    "# Check the shape of scaler parameters\n",
    "print(\"Scaler mean shape:\", scaler.mean_.shape)\n",
    "print(\"Scaler scale shape:\", scaler.scale_.shape)\n",
    "\n",
    "# Check the columns of the test dataset\n",
    "print(\"Test data columns:\", X_test.columns)\n",
    "\n",
    "# Check numerical columns list\n",
    "print(\"Numerical columns:\", numerical_columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Health_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Health_status\n",
       "0            1.0\n",
       "1            1.0\n",
       "2            1.0\n",
       "3            0.0\n",
       "4            1.0"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = pd.DataFrame(y_test, columns=['Health_status'])\n",
    "y_test.reset_index(drop=True, inplace=True)\n",
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Health_status\n",
       "1.0              15\n",
       "0.0              10\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 1 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Health_status  25 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 332.0 bytes\n"
     ]
    }
   ],
   "source": [
    "y_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Family history</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Obese/non obese</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Triglycerides level</th>\n",
       "      <th>LDL level</th>\n",
       "      <th>VLDL level</th>\n",
       "      <th>Health_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>82.0</td>\n",
       "      <td>27.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>39.36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.64</td>\n",
       "      <td>82.0</td>\n",
       "      <td>30.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>18.88</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>76.0</td>\n",
       "      <td>26.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>94.0</td>\n",
       "      <td>32.91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.53</td>\n",
       "      <td>77.0</td>\n",
       "      <td>32.89</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>21.76</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Family history  Height  Weight    BMI  Obese/non obese  \\\n",
       "0     0.0             1.0    1.73    82.0  27.40              0.0   \n",
       "1     0.0             1.0    1.64    82.0  30.49              1.0   \n",
       "2     1.0             0.0    1.70    76.0  26.30              0.0   \n",
       "3     1.0             1.0    1.69    94.0  32.91              1.0   \n",
       "4     0.0             1.0    1.53    77.0  32.89              1.0   \n",
       "\n",
       "   Cholesterol  Triglycerides level  LDL level  VLDL level  Health_status  \n",
       "0        226.0                246.0      190.0       39.36            1.0  \n",
       "1        187.0                118.0      142.0       18.88            1.0  \n",
       "2        132.0                125.0      160.0       20.00            1.0  \n",
       "3        199.0                106.0      113.0       16.00            0.0  \n",
       "4        201.0                136.0      190.0       21.76            1.0  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.concat([X_test, y_test], axis=1)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 11 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Gender               25 non-null     float64\n",
      " 1   Family history       25 non-null     float64\n",
      " 2   Height               25 non-null     float64\n",
      " 3   Weight               25 non-null     float64\n",
      " 4   BMI                  25 non-null     float64\n",
      " 5   Obese/non obese      25 non-null     float64\n",
      " 6   Cholesterol          25 non-null     float64\n",
      " 7   Triglycerides level  25 non-null     float64\n",
      " 8   LDL level            25 non-null     float64\n",
      " 9   VLDL level           25 non-null     float64\n",
      " 10  Health_status        25 non-null     float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 2.3 KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.0\n",
       "1     1.0\n",
       "2     1.0\n",
       "3     0.0\n",
       "4     1.0\n",
       "5     1.0\n",
       "6     0.0\n",
       "7     1.0\n",
       "8     1.0\n",
       "9     1.0\n",
       "10    1.0\n",
       "11    1.0\n",
       "12    1.0\n",
       "13    0.0\n",
       "14    1.0\n",
       "15    0.0\n",
       "16    0.0\n",
       "17    1.0\n",
       "18    0.0\n",
       "19    0.0\n",
       "20    0.0\n",
       "21    0.0\n",
       "22    0.0\n",
       "23    1.0\n",
       "24    1.0\n",
       "Name: Health_status, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['Health_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Family history</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Obese/non obese</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Triglycerides level</th>\n",
       "      <th>LDL level</th>\n",
       "      <th>VLDL level</th>\n",
       "      <th>Health_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.73</td>\n",
       "      <td>82.0</td>\n",
       "      <td>27.40</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>226.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>39.36</td>\n",
       "      <td>patient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.64</td>\n",
       "      <td>82.0</td>\n",
       "      <td>30.49</td>\n",
       "      <td>Obese</td>\n",
       "      <td>187.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>18.88</td>\n",
       "      <td>patient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>1.70</td>\n",
       "      <td>76.0</td>\n",
       "      <td>26.30</td>\n",
       "      <td>Non-obese</td>\n",
       "      <td>132.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>patient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.69</td>\n",
       "      <td>94.0</td>\n",
       "      <td>32.91</td>\n",
       "      <td>Obese</td>\n",
       "      <td>199.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.53</td>\n",
       "      <td>77.0</td>\n",
       "      <td>32.89</td>\n",
       "      <td>Obese</td>\n",
       "      <td>201.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>21.76</td>\n",
       "      <td>patient</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Family history  Height  Weight    BMI Obese/non obese  Cholesterol  \\\n",
       "0  Female            Yes    1.73    82.0  27.40       Non-obese        226.0   \n",
       "1  Female            Yes    1.64    82.0  30.49           Obese        187.0   \n",
       "2    Male             No    1.70    76.0  26.30       Non-obese        132.0   \n",
       "3    Male            Yes    1.69    94.0  32.91           Obese        199.0   \n",
       "4  Female            Yes    1.53    77.0  32.89           Obese        201.0   \n",
       "\n",
       "   Triglycerides level  LDL level  VLDL level Health_status  \n",
       "0                246.0      190.0       39.36       patient  \n",
       "1                118.0      142.0       18.88       patient  \n",
       "2                125.0      160.0       20.00       patient  \n",
       "3                106.0      113.0       16.00       healthy  \n",
       "4                136.0      190.0       21.76       patient  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[categorical_columns]=encoder.inverse_transform(test_data[categorical_columns].astype(int))\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Female'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9312\\3884169254.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtest_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnumerical_columns\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnumerical_columns\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Usman\\Assignment\\ml_env\\Lib\\site-packages\\sklearn\\preprocessing\\_data.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, X, copy)\u001b[0m\n\u001b[0;32m   1102\u001b[0m         \"\"\"\n\u001b[0;32m   1103\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1105\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1106\u001b[1;33m         X = check_array(\n\u001b[0m\u001b[0;32m   1107\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1108\u001b[0m             \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1109\u001b[0m             \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Usman\\Assignment\\ml_env\\Lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_all_finite, ensure_non_negative, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m   1052\u001b[0m                         \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1054\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1055\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1056\u001b[1;33m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1057\u001b[0m                 raise ValueError(\n\u001b[0;32m   1058\u001b[0m                     \u001b[1;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1059\u001b[0m                 \u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Usman\\Assignment\\ml_env\\Lib\\site-packages\\sklearn\\utils\\_array_api.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(array, dtype, order, copy, xp, device)\u001b[0m\n\u001b[0;32m    828\u001b[0m         \u001b[1;31m# Use NumPy API to support order\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    829\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    830\u001b[0m             \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    831\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 832\u001b[1;33m             \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    833\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    834\u001b[0m         \u001b[1;31m# At this point array is a NumPy ndarray. We convert it to an array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    835\u001b[0m         \u001b[1;31m# container that is consistent with the input's namespace.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Usman\\Assignment\\ml_env\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, dtype, copy)\u001b[0m\n\u001b[0;32m   2149\u001b[0m     def __array__(\n\u001b[0;32m   2150\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool_t\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2151\u001b[0m     \u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2152\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2153\u001b[1;33m         \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2154\u001b[0m         if (\n\u001b[0;32m   2155\u001b[0m             \u001b[0mastype_is_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2156\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Female'"
     ]
    }
   ],
   "source": [
    "test_data[numerical_columns] = scaler.inverse_transform(test_data[numerical_columns])\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.to_excel('test_data.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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