cancer_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SMOKING</th>\n",
       "      <th>YELLOW_FINGERS</th>\n",
       "      <th>ANXIETY</th>\n",
       "      <th>PEER_PRESSURE</th>\n",
       "      <th>CHRONIC DISEASE</th>\n",
       "      <th>FATIGUE</th>\n",
       "      <th>ALLERGY</th>\n",
       "      <th>WHEEZING</th>\n",
       "      <th>ALCOHOL CONSUMING</th>\n",
       "      <th>COUGHING</th>\n",
       "      <th>SHORTNESS OF BREATH</th>\n",
       "      <th>SWALLOWING DIFFICULTY</th>\n",
       "      <th>CHEST PAIN</th>\n",
       "      <th>LUNG_CANCER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>59</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0      M   69        1               2        2              1   \n",
       "1      M   74        2               1        1              1   \n",
       "2      F   59        1               1        1              2   \n",
       "3      M   63        2               2        2              1   \n",
       "4      F   63        1               2        1              1   \n",
       "\n",
       "   CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "0                1         2         1         2                  2         2   \n",
       "1                2         2         2         1                  1         1   \n",
       "2                1         2         1         2                  1         2   \n",
       "3                1         1         1         1                  2         1   \n",
       "4                1         1         1         2                  1         2   \n",
       "\n",
       "   SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN LUNG_CANCER  \n",
       "0                    2                      2           2         YES  \n",
       "1                    2                      2           2         YES  \n",
       "2                    2                      1           2          NO  \n",
       "3                    1                      2           2          NO  \n",
       "4                    2                      1           1          NO  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"survey lung cancer.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309, 16)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>AGE</th>\n",
       "      <th>SMOKING</th>\n",
       "      <th>YELLOW_FINGERS</th>\n",
       "      <th>ANXIETY</th>\n",
       "      <th>PEER_PRESSURE</th>\n",
       "      <th>CHRONIC DISEASE</th>\n",
       "      <th>FATIGUE</th>\n",
       "      <th>ALLERGY</th>\n",
       "      <th>WHEEZING</th>\n",
       "      <th>ALCOHOL CONSUMING</th>\n",
       "      <th>COUGHING</th>\n",
       "      <th>SHORTNESS OF BREATH</th>\n",
       "      <th>SWALLOWING DIFFICULTY</th>\n",
       "      <th>CHEST PAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>62.673139</td>\n",
       "      <td>1.563107</td>\n",
       "      <td>1.569579</td>\n",
       "      <td>1.498382</td>\n",
       "      <td>1.501618</td>\n",
       "      <td>1.504854</td>\n",
       "      <td>1.673139</td>\n",
       "      <td>1.556634</td>\n",
       "      <td>1.556634</td>\n",
       "      <td>1.556634</td>\n",
       "      <td>1.579288</td>\n",
       "      <td>1.640777</td>\n",
       "      <td>1.469256</td>\n",
       "      <td>1.556634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.210301</td>\n",
       "      <td>0.496806</td>\n",
       "      <td>0.495938</td>\n",
       "      <td>0.500808</td>\n",
       "      <td>0.500808</td>\n",
       "      <td>0.500787</td>\n",
       "      <td>0.469827</td>\n",
       "      <td>0.497588</td>\n",
       "      <td>0.497588</td>\n",
       "      <td>0.497588</td>\n",
       "      <td>0.494474</td>\n",
       "      <td>0.480551</td>\n",
       "      <td>0.499863</td>\n",
       "      <td>0.497588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AGE     SMOKING  YELLOW_FINGERS     ANXIETY  PEER_PRESSURE  \\\n",
       "count  309.000000  309.000000      309.000000  309.000000     309.000000   \n",
       "mean    62.673139    1.563107        1.569579    1.498382       1.501618   \n",
       "std      8.210301    0.496806        0.495938    0.500808       0.500808   \n",
       "min     21.000000    1.000000        1.000000    1.000000       1.000000   \n",
       "25%     57.000000    1.000000        1.000000    1.000000       1.000000   \n",
       "50%     62.000000    2.000000        2.000000    1.000000       2.000000   \n",
       "75%     69.000000    2.000000        2.000000    2.000000       2.000000   \n",
       "max     87.000000    2.000000        2.000000    2.000000       2.000000   \n",
       "\n",
       "       CHRONIC DISEASE    FATIGUE     ALLERGY     WHEEZING  ALCOHOL CONSUMING  \\\n",
       "count       309.000000  309.000000  309.000000  309.000000         309.000000   \n",
       "mean          1.504854    1.673139    1.556634    1.556634           1.556634   \n",
       "std           0.500787    0.469827    0.497588    0.497588           0.497588   \n",
       "min           1.000000    1.000000    1.000000    1.000000           1.000000   \n",
       "25%           1.000000    1.000000    1.000000    1.000000           1.000000   \n",
       "50%           2.000000    2.000000    2.000000    2.000000           2.000000   \n",
       "75%           2.000000    2.000000    2.000000    2.000000           2.000000   \n",
       "max           2.000000    2.000000    2.000000    2.000000           2.000000   \n",
       "\n",
       "         COUGHING  SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  \n",
       "count  309.000000           309.000000             309.000000  309.000000  \n",
       "mean     1.579288             1.640777               1.469256    1.556634  \n",
       "std      0.494474             0.480551               0.499863    0.497588  \n",
       "min      1.000000             1.000000               1.000000    1.000000  \n",
       "25%      1.000000             1.000000               1.000000    1.000000  \n",
       "50%      2.000000             2.000000               1.000000    2.000000  \n",
       "75%      2.000000             2.000000               2.000000    2.000000  \n",
       "max      2.000000             2.000000               2.000000    2.000000  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 309 entries, 0 to 308\n",
      "Data columns (total 16 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   GENDER                 309 non-null    object\n",
      " 1   AGE                    309 non-null    int64 \n",
      " 2   SMOKING                309 non-null    int64 \n",
      " 3   YELLOW_FINGERS         309 non-null    int64 \n",
      " 4   ANXIETY                309 non-null    int64 \n",
      " 5   PEER_PRESSURE          309 non-null    int64 \n",
      " 6   CHRONIC DISEASE        309 non-null    int64 \n",
      " 7   FATIGUE                309 non-null    int64 \n",
      " 8   ALLERGY                309 non-null    int64 \n",
      " 9   WHEEZING               309 non-null    int64 \n",
      " 10  ALCOHOL CONSUMING      309 non-null    int64 \n",
      " 11  COUGHING               309 non-null    int64 \n",
      " 12  SHORTNESS OF BREATH    309 non-null    int64 \n",
      " 13  SWALLOWING DIFFICULTY  309 non-null    int64 \n",
      " 14  CHEST PAIN             309 non-null    int64 \n",
      " 15  LUNG_CANCER            309 non-null    object\n",
      "dtypes: int64(14), object(2)\n",
      "memory usage: 38.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY', 'PEER_PRESSURE', 'CHRONIC DISEASE', 'FATIGUE ', 'ALLERGY ', 'WHEEZING', 'ALCOHOL CONSUMING', 'COUGHING', 'SHORTNESS OF BREATH', 'SWALLOWING DIFFICULTY', 'CHEST PAIN', 'LUNG_CANCER']\n"
     ]
    }
   ],
   "source": [
    "column_names = data.columns.tolist()\n",
    "print(column_names) # Get the column names as a list print(column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GENDER                   0\n",
       "AGE                      0\n",
       "SMOKING                  0\n",
       "YELLOW_FINGERS           0\n",
       "ANXIETY                  0\n",
       "PEER_PRESSURE            0\n",
       "CHRONIC DISEASE          0\n",
       "FATIGUE                  0\n",
       "ALLERGY                  0\n",
       "WHEEZING                 0\n",
       "ALCOHOL CONSUMING        0\n",
       "COUGHING                 0\n",
       "SHORTNESS OF BREATH      0\n",
       "SWALLOWING DIFFICULTY    0\n",
       "CHEST PAIN               0\n",
       "LUNG_CANCER              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.isnull().sum()\n",
    "# print(cancer_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
      "0      M   64        2               2        1              2   \n",
      "\n",
      "   CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
      "0                2         2         2         2                  2         2   \n",
      "\n",
      "   SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN LUNG_CANCER  \n",
      "0                    2                      1           2         YES  \n"
     ]
    }
   ],
   "source": [
    "modes = data.mode()\n",
    "print(modes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1oKZRVdQ03ry+x1glNZXNN5hZgbrnAy4AAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEovwLcuWLYsZM2bEsmXL6nsUADZCG+LfM2IRvuX999+Pk046Kd5///36HgWAjdCG+PeMWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCArPL6HmBVVFdXx9SpU2PevHmx6aabRpcuXaKsrKy+x2ID4hwCgNpZ72PxxRdfjBtvvDE++eSTwraWLVvGoEGDYr/99qvHydhQOIcAoPbW65ehX3zxxRg+fHh06NAhbrjhhnjyySfjhhtuiA4dOsTw4cPjxRdfrO8RWc85hwBgzay3sVhdXR033nhjdOvWLUaOHBmdO3eOTTbZJDp37hwjR46Mbt26xU033RTV1dX1PSrrKecQAKy5VX4Z+quvvoqvvvqq8PXChQvXykArTJ06NT755JO48MILo7S0uGlLS0ujb9++MXjw4Jg6dWp07dp1rc7ChmlNzqH33ntvXY7K94hza/3n3xFr04Z4fq1yLF5++eVx8cUXr81ZisybNy8iItq3b/+d+1dsX3Ec/Lk1OYcuu+yytTcYsF7z5x+KrXIsnnfeeXHmmWcWvl64cGG0adNmrQwVEbHppptGRMTs2bOjc+fOK+2fPXt20XHw59bkHBo2bFi0a9du7Q7I99J7770nRtZz/vyzNm2I/w1Y5VisqKiIioqKtTlLkS5dukTLli1jzJgxMXLkyKKXEWtqamLMmDHRqlWr6NKlyzqbiQ3LmpxD7dq1ix122GFdjgusJ/z5h2Lr7QdcysrKYtCgQTFp0qS44IIL4s0334wvv/wy3nzzzbjgggti0qRJccopp7hWHlnOIQBYc+v1dRb322+/uPjii+PGG2+MwYMHF7a3atUqLr74YtfI469yDgHAmlmvYzHif/+y79Gjh9++Qa05hwCg9tb7WIz435cTXR6HNeEcAoDaWW/fswgAQP0TiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWIRvadu2bdx8883Rtm3b+h4FgI3Qhvj3THl9DwDrk0aNGsUOO+xQ32MAsJHaEP+e8cwiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZ5fU9AMD3UemyBfU9wl9VunR+0f9u7DaEfydQH8QiwDpUVVUVDRpWRLwzob5HWWWVs1+s7xHWmQYNK6Kqqqq+x4D1ilgEWIdatGgRd47+TSxY4Fms9VFVVVW0aNGivseA9YpYBFjHWrRoIUiADYYPuAAAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIKq/tHVNKERGxcOHCOhsGAIC6s6LTVnRbbdQ6FhctWhQREW3atKn1NwcAYO1btGhRVFVV1eq+JamWqVlTUxMfffRRNG3aNEpKSmr1zb9t4cKF0aZNm/jggw+iWbNma/x4WNO1wZquHda17lnTtcO61j1rWve+vaZNmzaNRYsWRevWraO0tHbvPqz1M4ulpaWx9dZb1/buWc2aNXOy1DFrWves6dphXeueNV07rGvds6Z1b8Wa1vYZxRV8wAUAgCyxCABA1noTixUVFTF8+PCoqKio71E2Gta07lnTtcO61j1runZY17pnTeteXa9prT/gAgDAxm+9eWYRAID1j1gEACBLLAIAkCUWAQDIWqexePnll8cee+wRTZs2jS233DKOPPLImD59etExy5Yti8GDB8dmm20WTZo0iaOOOirmzJmzLsfcoNx0003RpUuXwoU3u3XrFmPHji3st55r7oorroiSkpI4/fTTC9us6+obMWJElJSUFN06depU2G9Na+fDDz+ME044ITbbbLOorKyMH/3oR/Hqq68W9qeU4qKLLopWrVpFZWVl9OrVK2bOnFmPE6//ttlmm5XO1ZKSkhg8eHBEOFdro7q6Oi688MJo3759VFZWxrbbbhuXXnpp0e8rdq6uvkWLFsXpp58e7dq1i8rKyujevXu88sorhf11tqZpHerdu3e6/fbb07Rp09LkyZPToYcemtq2bZsWL15cOOZnP/tZatOmTXr22WfTq6++mvbee+/UvXv3dTnmBuXRRx9NTzzxRJoxY0aaPn16Ov/881ODBg3StGnTUkrWc029/PLLaZtttkldunRJp512WmG7dV19w4cPT507d04ff/xx4fbpp58W9lvT1Tdv3rzUrl271L9///TSSy+ld955Jz399NNp1qxZhWOuuOKKVFVVlR5++OE0ZcqUdPjhh6f27dunpUuX1uPk67e5c+cWnafjxo1LEZGef/75lJJztTYuu+yytNlmm6XHH388zZ49O91///2pSZMm6dprry0c41xdfT/+8Y/TTjvtlCZMmJBmzpyZhg8fnpo1a5b+9Kc/pZTqbk3XaSz+ublz56aISBMmTEgppTR//vzUoEGDdP/99xeO+cMf/pAiIk2aNKm+xtzg/OAHP0j/+Z//aT3X0KJFi9L222+fxo0bl/bff/9CLFrX2hk+fHjaeeedv3OfNa2dc845J+2zzz7Z/TU1Nally5bpqquuKmybP39+qqioSHffffe6GHGjcNppp6Vtt9021dTUOFdrqU+fPmnAgAFF2/7hH/4h9e3bN6XkXK2NL7/8MpWVlaXHH3+8aPuuu+6ahg0bVqdrWq/vWVywYEFERGy66aYREfHaa6/FN998E7169Soc06lTp2jbtm1MmjSpXmbckFRXV8c999wTS5YsiW7dulnPNTR48ODo06dP0fpFOE/XxMyZM6N169bRoUOH6Nu3b7z//vsRYU1r69FHH43dd989jjnmmNhyyy2ja9euccsttxT2z549Oz755JOida2qqoq99trLuq6ir7/+Ou68884YMGBAlJSUOFdrqXv37vHss8/GjBkzIiJiypQpMXHixDjkkEMiwrlaG8uXL4/q6upo1KhR0fbKysqYOHFina5peZ1MXAs1NTVx+umnR48ePeKHP/xhRER88skn0bBhw2jevHnRsS1atIhPPvmkHqbcMLzxxhvRrVu3WLZsWTRp0iQeeuih2GmnnWLy5MnWs5buueeeeP3114ve+7GC87R29tprrxg1alR07NgxPv7447j44otj3333jWnTplnTWnrnnXfipptuijPPPDPOP//8eOWVV2Lo0KHRsGHD6NevX2HtWrRoUXQ/67rqHn744Zg/f370798/Ivz5r61zzz03Fi5cGJ06dYqysrKorq6Oyy67LPr27RsR4VythaZNm0a3bt3i0ksvjR133DFatGgRd999d0yaNCm22267Ol3TeovFwYMHx7Rp02LixIn1NcJGo2PHjjF58uRYsGBBPPDAA9GvX7+YMGFCfY+1wfrggw/itNNOi3Hjxq30/9iovRXPIEREdOnSJfbaa69o165d3HfffVFZWVmPk224ampqYvfdd49//dd/jYiIrl27xrRp0+JXv/pV9OvXr56n2zjceuutccghh0Tr1q3re5QN2n333RdjxoyJu+66Kzp37hyTJ0+O008/PVq3bu1cXQOjR4+OAQMGxFZbbRVlZWWx6667xvHHHx+vvfZanX6fenkZesiQIfH444/H888/H1tvvXVhe8uWLePrr7+O+fPnFx0/Z86caNmy5TqecsPRsGHD2G677WK33XaLyy+/PHbeeee49tprrWctvfbaazF37tzYddddo7y8PMrLy2PChAnxy1/+MsrLy6NFixbWtQ40b948dthhh5g1a5ZztZZatWoVO+20U9G2HXfcsfDy/oq1+/NP6lrXVfPee+/F+PHjY+DAgYVtztXaOfvss+Pcc8+N4447Ln70ox/FT37ykzjjjDPi8ssvjwjnam1tu+22MWHChFi8eHF88MEH8fLLL8c333wTHTp0qNM1XaexmFKKIUOGxEMPPRTPPfdctG/fvmj/brvtFg0aNIhnn322sG369Onx/vvvR7du3dblqBu0mpqa+Oqrr6xnLR144IHxxhtvxOTJkwu33XffPfr27Vv4Z+u65hYvXhxvv/12tGrVyrlaSz169Fjp8mMzZsyIdu3aRURE+/bto2XLlkXrunDhwnjppZes6yq4/fbbY8stt4w+ffoUtjlXa+fLL7+M0tLi5CgrK4uampqIcK6uqcaNG0erVq3iiy++iKeffjqOOOKIul3TuvhEzqo65ZRTUlVVVXrhhReKLkvw5ZdfFo752c9+ltq2bZuee+659Oqrr6Zu3bqlbt26rcsxNyjnnntumjBhQpo9e3aaOnVqOvfcc1NJSUl65plnUkrWs658+9PQKVnX2jjrrLPSCy+8kGbPnp1+97vfpV69eqXNN988zZ07N6VkTWvj5ZdfTuXl5emyyy5LM2fOTGPGjEmbbLJJuvPOOwvHXHHFFal58+bpkUceSVOnTk1HHHGEy5Gsgurq6tS2bdt0zjnnrLTPubr6+vXrl7baaqvCpXMefPDBtPnmm6ef//znhWOcq6vvqaeeSmPHjk3vvPNOeuaZZ9LOO++c9tprr/T111+nlOpuTddpLEbEd95uv/32wjFLly5NgwYNSj/4wQ/SJptskv7+7/8+ffzxx+tyzA3KgAEDUrt27VLDhg3TFltskQ488MBCKKZkPevKn8eidV19xx57bGrVqlVq2LBh2mqrrdKxxx5bdD1Aa1o7jz32WPrhD3+YKioqUqdOndLNN99ctL+mpiZdeOGFqUWLFqmioiIdeOCBafr06fU07Ybj6aefThHxnWvlXF19CxcuTKeddlpq27ZtatSoUerQoUMaNmxY+uqrrwrHOFdX37333ps6dOiQGjZsmFq2bJkGDx6c5s+fX9hfV2taktK3Lp8OAADf4ndDAwCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRaB74VJkyZFWVlZ9OnTp75HAdig+HV/wPfCwIEDo0mTJnHrrbfG9OnTo3Xr1vU9EsAGwTOLwEZv8eLFce+998Ypp5wSffr0iVGjRhXtf/TRR2P77bePRo0aRc+ePeOOO+6IkpKSmD9/fuGYiRMnxr777huVlZXRpk2bGDp0aCxZsmTd/iAA9UAsAhu9++67Lzp16hQdO3aME044IW677bZY8aLK7Nmz4+ijj44jjzwypkyZEieffHIMGzas6P5vv/12HHzwwXHUUUfF1KlT4957742JEyfGkCFD6uPHAVinvAwNbPR69OgRP/7xj+O0006L5cuXR6tWreL++++PAw44IM4999x44okn4o033igcf8EFF8Rll10WX3zxRTRv3jwGDhwYZWVl8etf/7pwzMSJE2P//fePJUuWRKNGjerjxwJYJzyzCGzUpk+fHi+//HIcf/zxERFRXl4exx57bNx6662F/XvssUfRffbcc8+ir6dMmRKjRo2KJk2aFG69e/eOmpqamD179rr5QQDqSXl9DwCwNt16662xfPnyog+0pJSioqIirr/++lV6jMWLF8fJJ58cQ4cOXWlf27Zt62xWgPWRWAQ2WsuXL4/f/OY38e///u9x0EEHFe078sgj4+67746OHTvGk08+WbTvlVdeKfp61113jbfeeiu22267tT4zwPrGexaBjdbDDz8cxx57bMydOzeqqqqK9p1zzjnx3HPPxX333RcdO3aMM844I0488cSYPHlynHXWWfGnP/0p5s+fH1VVVTF16tTYe++9Y8CAATFw4MBo3LhxvPXWWzFu3LhVfnYSYEPlPYvARuvWW2+NXr16rRSKERFHHXVUvPrqq7Fo0aJ44IEH4sEHH4wuXbrETTfdVPg0dEVFRUREdOnSJSZMmBAzZsyIfffdN7p27RoXXXSRazUC3wueWQT4M5dddln86le/ig8++KC+RwGod96zCHzv3XjjjbHHHnvEZpttFr/73e/iqquucg1FgP8jFoHvvZkzZ8bIkSNj3rx50bZt2zjrrLPivPPOq++xANYLXoYGACDLB1wAAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQ9f8AThRWPZBtdFMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))  # Set the size of the plot\n",
    "sns.boxplot(x=data['AGE'])  # Create the boxplot for the 'AGE' column\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Boxplot of Age')\n",
    "plt.xlabel('Age')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_treatment(col):\n",
    "    \n",
    "    sorted(col)\n",
    "    Q1,Q3 = np.percentile(col , [25,75])\n",
    "    IQR = Q3 - Q1\n",
    "    lower_range = Q1 - (1.5 * IQR)\n",
    "    upper_range = Q3 + (1.5 * IQR)\n",
    "    return lower_range,upper_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lower Range: 41.5\n",
      "Upper Range: 85.5\n"
     ]
    }
   ],
   "source": [
    "lower_range,upper_range = outlier_treatment(data['AGE'])\n",
    "print(\"Lower Range:\",lower_range)\n",
    "print(\"Upper Range:\",upper_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261    39\n",
       "Name: AGE, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_data = data[data['AGE'].values < lower_range]\n",
    "lower_data['AGE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "277    87\n",
       "Name: AGE, dtype: int64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_data = data[data['AGE'].values > upper_range]\n",
    "upper_data['AGE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Outliers: 2\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lower_outliers = lower_data.value_counts().sum(axis=0)\n",
    "upper_outliers = upper_data.value_counts().sum(axis=0)\n",
    "total_outliers = lower_outliers + upper_outliers\n",
    "print(\"Total Number of Outliers:\",total_outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[261, 277]\n"
     ]
    }
   ],
   "source": [
    "lower_index = list(data[ data['AGE'] < lower_range ].index)\n",
    "upper_index = list(data[ data['AGE'] > upper_range ].index)\n",
    "total_index = list(lower_index + upper_index)\n",
    "print(total_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape Before Dropping Outlier Rows: (307, 16)\n",
      "Shape After Dropping Outlier Rows: (305, 16)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape Before Dropping Outlier Rows:\", data.shape)\n",
    "data.drop(total_index, inplace = True)\n",
    "print(\"Shape After Dropping Outlier Rows:\", data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data size: 305\n",
      "Cleaned data size: 305\n"
     ]
    }
   ],
   "source": [
    "Q1 = data['AGE'].quantile(0.25)\n",
    "Q3 = data['AGE'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "cleaned_data = data[(data['AGE'] >= lower_bound) & (data['AGE'] <= upper_bound)]\n",
    "\n",
    "print(f\"Original data size: {data.shape[0]}\")\n",
    "print(f\"Cleaned data size: {cleaned_data.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))  # Set the size of the plot\n",
    "sns.boxplot(x=data['AGE'])  # Create the boxplot for the 'AGE' column\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Boxplot of Age')\n",
    "plt.xlabel('Age')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))  # Set the size of the plot\n",
    "sns.boxplot(x=data['AGE'])  # Create the boxplot for the 'AGE' column\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Boxplot of Age without Outliers')\n",
    "plt.xlabel('Age')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(3, 2))\n",
    "sns.histplot(data)\n",
    "plt.title('Pairplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>AGE</th>\n",
       "      <th>SMOKING</th>\n",
       "      <th>YELLOW_FINGERS</th>\n",
       "      <th>ANXIETY</th>\n",
       "      <th>PEER_PRESSURE</th>\n",
       "      <th>CHRONIC DISEASE</th>\n",
       "      <th>FATIGUE</th>\n",
       "      <th>ALLERGY</th>\n",
       "      <th>WHEEZING</th>\n",
       "      <th>ALCOHOL CONSUMING</th>\n",
       "      <th>COUGHING</th>\n",
       "      <th>SHORTNESS OF BREATH</th>\n",
       "      <th>SWALLOWING DIFFICULTY</th>\n",
       "      <th>CHEST PAIN</th>\n",
       "      <th>LUNG_CANCER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>F</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>M</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>M</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>M</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>M</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "304      F   56        1               1        1              2   \n",
       "305      M   70        2               1        1              1   \n",
       "306      M   58        2               1        1              1   \n",
       "307      M   67        2               1        2              1   \n",
       "308      M   62        1               1        1              2   \n",
       "\n",
       "     CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  \\\n",
       "304                2         2         1         1                  2   \n",
       "305                1         2         2         2                  2   \n",
       "306                1         1         2         2                  2   \n",
       "307                1         2         2         1                  2   \n",
       "308                1         2         2         2                  2   \n",
       "\n",
       "     COUGHING  SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  \\\n",
       "304         2                    2                      2           1   \n",
       "305         2                    2                      1           2   \n",
       "306         2                    1                      1           2   \n",
       "307         2                    2                      1           2   \n",
       "308         1                    1                      2           1   \n",
       "\n",
       "    LUNG_CANCER  \n",
       "304         YES  \n",
       "305         YES  \n",
       "306         YES  \n",
       "307         YES  \n",
       "308         YES  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert 'GENDER' column: 'M' -> 1, 'F' -> 0\n",
    "data['GENDER'] = data['GENDER'].map({'M': 1, 'F': 0})\n",
    "\n",
    "# Convert 'LUNG_CANCER' column: 'YES' -> 1, 'NO' -> 0\n",
    "data['LUNG_CANCER'] = data['LUNG_CANCER'].map({'YES': 1, 'NO': 0})\n",
    "\n",
    "# Now calculate the correlation matrix for numeric columns\n",
    "corr_matrix = data.corr()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
    "\n",
    "# Show the plot\n",
    "plt.title(\"Correlation Heatmap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
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       "   GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0       1   69        1               2        2              1   \n",
       "1       1   74        2               1        1              1   \n",
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       "3       1   63        2               2        2              1   \n",
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       "\n",
       "   CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "0                1         2         1         2                  2         2   \n",
       "1                2         2         2         1                  1         1   \n",
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       "\n",
       "   SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  LUNG_CANCER  \n",
       "0                    2                      2           2            1  \n",
       "1                    2                      2           2            1  \n",
       "2                    2                      1           2            0  \n",
       "3                    1                      2           2            0  \n",
       "4                    2                      1           1            0  "
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     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   AGE  AGE_BINARY\n",
       "0   69           1\n",
       "1   74           1\n",
       "2   59           0\n",
       "3   63           1\n",
       "4   63           1"
      ]
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     "execution_count": 213,
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   ],
   "source": [
    "threshold_age = 60\n",
    "\n",
    "# Convert 'AGE' to binary: 1 if AGE >= threshold_age, otherwise 0\n",
    "data['AGE_BINARY'] = (data['AGE'] >= threshold_age).astype(int)\n",
    "\n",
    "# Display the first few rows to verify\n",
    "data[['AGE', 'AGE_BINARY']].head()"
   ]
  },
  {
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       "   GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0       1   69        1               2        2              1   \n",
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       "   CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
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       "3                    1                      2           2            0  \n",
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     "execution_count": 216,
     "metadata": {},
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    }
   ],
   "source": [
    "data = data.drop(columns=['AGE_BINARY'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
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       "   GENDER  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  CHRONIC DISEASE  \\\n",
       "0       1        1               2        2              1                1   \n",
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       "   FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
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       "\n",
       "   SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  LUNG_CANCER  \\\n",
       "0                    2                      2           2            1   \n",
       "1                    2                      2           2            1   \n",
       "2                    2                      1           2            0   \n",
       "3                    1                      2           2            0   \n",
       "4                    2                      1           1            0   \n",
       "\n",
       "   AGE_BINARY  \n",
       "0           1  \n",
       "1           1  \n",
       "2           0  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_B = data.drop(columns=['AGE'])\n",
    "data_B.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 305 entries, 0 to 308\n",
      "Data columns (total 17 columns):\n",
      " #   Column                 Non-Null Count  Dtype\n",
      "---  ------                 --------------  -----\n",
      " 0   GENDER                 305 non-null    int64\n",
      " 1   AGE                    305 non-null    int64\n",
      " 2   SMOKING                305 non-null    int64\n",
      " 3   YELLOW_FINGERS         305 non-null    int64\n",
      " 4   ANXIETY                305 non-null    int64\n",
      " 5   PEER_PRESSURE          305 non-null    int64\n",
      " 6   CHRONIC DISEASE        305 non-null    int64\n",
      " 7   FATIGUE                305 non-null    int64\n",
      " 8   ALLERGY                305 non-null    int64\n",
      " 9   WHEEZING               305 non-null    int64\n",
      " 10  ALCOHOL CONSUMING      305 non-null    int64\n",
      " 11  COUGHING               305 non-null    int64\n",
      " 12  SHORTNESS OF BREATH    305 non-null    int64\n",
      " 13  SWALLOWING DIFFICULTY  305 non-null    int64\n",
      " 14  CHEST PAIN             305 non-null    int64\n",
      " 15  LUNG_CANCER            305 non-null    int64\n",
      " 16  AGE_BINARY             305 non-null    int64\n",
      "dtypes: int64(17)\n",
      "memory usage: 51.0 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL SELECTION AND TRAINING\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into features and target\n",
    "X = data.drop(columns=['LUNG_CANCER'])\n",
    "y = data['LUNG_CANCER']  # Encode target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, 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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model = LogisticRegression(max_iter=1000, random_state=42)\n",
    "lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_pred = lr_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 3  0]\n",
      " [ 2 56]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      1.00      0.75         3\n",
      "           1       1.00      0.97      0.98        58\n",
      "\n",
      "    accuracy                           0.97        61\n",
      "   macro avg       0.80      0.98      0.87        61\n",
      "weighted avg       0.98      0.97      0.97        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_test, lr_pred))\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, lr_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 96.72%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, lr_pred)\n",
    "print(f\"Model Accuracy: {accuracy * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ACCURACY of the Logistic Regression is 96.72%\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Train Random Forest Classifier\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_pred_test = rf_model.predict(X_test)\n",
    "rf_pred_train = rf_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 3  0]\n",
      " [ 3 55]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      1.00      0.67         3\n",
      "           1       1.00      0.95      0.97        58\n",
      "\n",
      "    accuracy                           0.95        61\n",
      "   macro avg       0.75      0.97      0.82        61\n",
      "weighted avg       0.98      0.95      0.96        61\n",
      "\n",
      "Model Accuracy on Test Data: 95.08%\n",
      "Model Accuracy on Train Data: 100.00%\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_test, rf_pred))\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, rf_pred))\n",
    "\n",
    "#accuracy\n",
    "test_accuracy = accuracy_score(y_test, rf_pred_test)\n",
    "train_accuracy = accuracy_score(y_train, rf_pred_train)\n",
    "print(f\"Model Accuracy on Test Data: {test_accuracy * 100:.2f}%\")\n",
    "print(f\"Model Accuracy on Train Data: {train_accuracy * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy of Random Forest is 95.08%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(max_iter=1000, 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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(max_iter=1000, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "nn_model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000, random_state=42)\n",
    "nn_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_pred_test =nn_model.predict(X_test)\n",
    "nn_pred_train =nn_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 1  2]\n",
      " [ 3 55]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.33      0.29         3\n",
      "           1       0.96      0.95      0.96        58\n",
      "\n",
      "    accuracy                           0.92        61\n",
      "   macro avg       0.61      0.64      0.62        61\n",
      "weighted avg       0.93      0.92      0.92        61\n",
      "\n",
      "Model Accuracy on Test Data: 91.80%\n",
      "Model Accuracy on Train Data: 89.34%\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_test, nn_pred))\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, nn_pred))\n",
    "\n",
    "test_accuracy = accuracy_score(y_test, nn_pred_test)\n",
    "train_accuracy = accuracy_score(y_train, nn_pred_train)\n",
    "print(f\"Model Accuracy on Test Data: {test_accuracy * 100:.2f}%\")\n",
    "print(f\"Model Accuracy on Train Data: {train_accuracy * 100:.2f}%\")"
   ]
  }
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