heartpred1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, cross_val_score\n",
    "from sklearn import linear_model, tree, ensemble\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder,TargetEncoder, LabelEncoder, OrdinalEncoder \n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 as cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>ID</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>myocardial_infarctions_anamnesis</th>\n",
       "      <th>Exertional_angina_pectoris_anamnesis</th>\n",
       "      <th>FC_of_angina_pectoris</th>\n",
       "      <th>Coronary_heart_disease(CHD)</th>\n",
       "      <th>Heredity_on_CHD</th>\n",
       "      <th>essential_hypertension</th>\n",
       "      <th>Symptomatic_hypertension</th>\n",
       "      <th>...</th>\n",
       "      <th>supr_tach</th>\n",
       "      <th>ven_tach</th>\n",
       "      <th>ven_fib</th>\n",
       "      <th>th_d_av</th>\n",
       "      <th>pul_ede</th>\n",
       "      <th>myo_rup</th>\n",
       "      <th>dres_syn</th>\n",
       "      <th>ch_hf</th>\n",
       "      <th>re_myin</th>\n",
       "      <th>p_inf_ang</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6543</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>female</td>\n",
       "      <td>two</td>\n",
       "      <td>3y ago</td>\n",
       "      <td>II FC</td>\n",
       "      <td>unstable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stage 2</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6544</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>male</td>\n",
       "      <td>zero</td>\n",
       "      <td>never</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>none</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6545</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>female</td>\n",
       "      <td>zero</td>\n",
       "      <td>&gt;5y ago</td>\n",
       "      <td>II FC</td>\n",
       "      <td>exertional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stage 3</td>\n",
       "      <td>?</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6546</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>female</td>\n",
       "      <td>zero</td>\n",
       "      <td>?</td>\n",
       "      <td>IV FC</td>\n",
       "      <td>unstable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRUE</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6547</td>\n",
       "      <td>75.655137</td>\n",
       "      <td>female</td>\n",
       "      <td>zero</td>\n",
       "      <td>3y ago</td>\n",
       "      <td>none</td>\n",
       "      <td>exertional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stage 3</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID        age     sex myocardial_infarctions_anamnesis  \\\n",
       "0  6543  69.000000  female                              two   \n",
       "1  6544  58.000000    male                             zero   \n",
       "2  6545  72.000000  female                             zero   \n",
       "3  6546  73.000000  female                             zero   \n",
       "4  6547  75.655137  female                             zero   \n",
       "\n",
       "  Exertional_angina_pectoris_anamnesis FC_of_angina_pectoris  \\\n",
       "0                               3y ago                 II FC   \n",
       "1                                never                  none   \n",
       "2                              >5y ago                 II FC   \n",
       "3                                    ?                 IV FC   \n",
       "4                               3y ago                  none   \n",
       "\n",
       "  Coronary_heart_disease(CHD) Heredity_on_CHD essential_hypertension  \\\n",
       "0                    unstable             NaN                Stage 2   \n",
       "1                        none             NaN                   none   \n",
       "2                  exertional             NaN                Stage 3   \n",
       "3                    unstable             NaN                    NaN   \n",
       "4                  exertional             NaN                Stage 3   \n",
       "\n",
       "  Symptomatic_hypertension  ... supr_tach ven_tach ven_fib th_d_av pul_ede  \\\n",
       "0                    FALSE  ...        NO       NO      NO      NO      NO   \n",
       "1                    FALSE  ...        NO       NO      NO      NO      NO   \n",
       "2                        ?  ...        NO       NO      NO      NO      NO   \n",
       "3                     TRUE  ...        NO       NO      NO      NO      NO   \n",
       "4                    FALSE  ...        NO       NO      NO      NO      NO   \n",
       "\n",
       "  myo_rup dres_syn ch_hf re_myin p_inf_ang  \n",
       "0      NO       NO    NO      NO        NO  \n",
       "1      NO       NO    NO      NO        NO  \n",
       "2      NO       NO    NO     YES        NO  \n",
       "3      NO       NO   YES      NO        NO  \n",
       "4      NO       NO    NO      NO        NO  \n",
       "\n",
       "[5 rows x 123 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'two'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[111], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecomposition\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PCA\n\u001b[1;32m----> 4\u001b[0m pca \u001b[38;5;241m=\u001b[39m \u001b[43mPCA\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(np\u001b[38;5;241m.\u001b[39mcumsum(pca\u001b[38;5;241m.\u001b[39mexplained_variance_ratio_))\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of Components\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1145\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1147\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1148\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1150\u001b[0m     )\n\u001b[0;32m   1151\u001b[0m ):\n\u001b[1;32m-> 1152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\decomposition\\_pca.py:434\u001b[0m, in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    431\u001b[0m \u001b[38;5;129m@_fit_context\u001b[39m(prefer_skip_nested_validation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    432\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    433\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model with X and apply the dimensionality reduction on X.\u001b[39;00m\n\u001b[1;32m--> 434\u001b[0m \n\u001b[0;32m    435\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m    436\u001b[0m \u001b[38;5;124;03m    ----------\u001b[39;00m\n\u001b[0;32m    437\u001b[0m \u001b[38;5;124;03m    X : {array-like, sparse matrix} of shape (n_samples, n_features)\u001b[39;00m\n\u001b[0;32m    438\u001b[0m \u001b[38;5;124;03m        Training data, where `n_samples` is the number of samples\u001b[39;00m\n\u001b[0;32m    439\u001b[0m \u001b[38;5;124;03m        and `n_features` is the number of features.\u001b[39;00m\n\u001b[0;32m    440\u001b[0m \n\u001b[0;32m    441\u001b[0m \u001b[38;5;124;03m    y : Ignored\u001b[39;00m\n\u001b[0;32m    442\u001b[0m \u001b[38;5;124;03m        Ignored.\u001b[39;00m\n\u001b[0;32m    443\u001b[0m \n\u001b[0;32m    444\u001b[0m \u001b[38;5;124;03m    Returns\u001b[39;00m\n\u001b[0;32m    445\u001b[0m \u001b[38;5;124;03m    -------\u001b[39;00m\n\u001b[0;32m    446\u001b[0m \u001b[38;5;124;03m    X_new : ndarray of shape (n_samples, n_components)\u001b[39;00m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;124;03m        Transformed values.\u001b[39;00m\n\u001b[0;32m    448\u001b[0m \n\u001b[0;32m    449\u001b[0m \u001b[38;5;124;03m    Notes\u001b[39;00m\n\u001b[0;32m    450\u001b[0m \u001b[38;5;124;03m    -----\u001b[39;00m\n\u001b[0;32m    451\u001b[0m \u001b[38;5;124;03m    This method returns a Fortran-ordered array. To convert it to a\u001b[39;00m\n\u001b[0;32m    452\u001b[0m \u001b[38;5;124;03m    C-ordered array, use 'np.ascontiguousarray'.\u001b[39;00m\n\u001b[0;32m    453\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m    454\u001b[0m     U, S, Vt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit(X)\n\u001b[0;32m    455\u001b[0m     U \u001b[38;5;241m=\u001b[39m U[:, : \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components_]\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\decomposition\\_pca.py:483\u001b[0m, in \u001b[0;36m_fit\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    478\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msvd_solver \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marpack\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m is_array_api_compliant:\n\u001b[0;32m    479\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    480\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPCA with svd_solver=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124marpack\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not supported for Array API inputs.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    481\u001b[0m     )\n\u001b[1;32m--> 483\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(\n\u001b[0;32m    484\u001b[0m     X,\n\u001b[0;32m    485\u001b[0m     dtype\u001b[38;5;241m=\u001b[39m[xp\u001b[38;5;241m.\u001b[39mfloat64, xp\u001b[38;5;241m.\u001b[39mfloat32],\n\u001b[0;32m    486\u001b[0m     accept_sparse\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m    487\u001b[0m     ensure_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    488\u001b[0m     copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy,\n\u001b[0;32m    489\u001b[0m )\n\u001b[0;32m    491\u001b[0m \u001b[38;5;66;03m# Handle n_components==None\u001b[39;00m\n\u001b[0;32m    492\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_components \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:605\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    603\u001b[0m         out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    604\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 605\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    606\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n\u001b[0;32m    607\u001b[0m     out \u001b[38;5;241m=\u001b[39m _check_y(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\validation.py:836\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    831\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pandas_requires_conversion:\n\u001b[0;32m    832\u001b[0m     \u001b[38;5;66;03m# pandas dataframe requires conversion earlier to handle extension dtypes with\u001b[39;00m\n\u001b[0;32m    833\u001b[0m     \u001b[38;5;66;03m# nans\u001b[39;00m\n\u001b[0;32m    834\u001b[0m     \u001b[38;5;66;03m# Use the original dtype for conversion if dtype is None\u001b[39;00m\n\u001b[0;32m    835\u001b[0m     new_dtype \u001b[38;5;241m=\u001b[39m dtype_orig \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m dtype\n\u001b[1;32m--> 836\u001b[0m     array \u001b[38;5;241m=\u001b[39m \u001b[43marray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_dtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    837\u001b[0m     \u001b[38;5;66;03m# Since we converted here, we do not need to convert again later\u001b[39;00m\n\u001b[0;32m    838\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\generic.py:6534\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   6530\u001b[0m     results \u001b[38;5;241m=\u001b[39m [ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()]\n\u001b[0;32m   6532\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   6533\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[1;32m-> 6534\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6535\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m   6536\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:414\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    411\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[0;32m    412\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 414\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    415\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    416\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    417\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    418\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    419\u001b[0m \u001b[43m    \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    420\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:354\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m    352\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    353\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 354\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    355\u001b[0m     result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[0;32m    357\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:616\u001b[0m, in \u001b[0;36mBlock.astype\u001b[1;34m(self, dtype, copy, errors, using_cow)\u001b[0m\n\u001b[0;32m    596\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    597\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[0;32m    598\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    612\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[0;32m    613\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    614\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[1;32m--> 616\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    618\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[0;32m    620\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:238\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    235\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[0;32m    237\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 238\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    239\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m    240\u001b[0m     \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[0;32m    241\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[0;32m    242\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:183\u001b[0m, in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m    180\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m    182\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 183\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    185\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[0;32m    186\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:134\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[0;32m    132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mor\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m    133\u001b[0m     \u001b[38;5;66;03m# Explicit copy, or required since NumPy can't view from / to object.\u001b[39;00m\n\u001b[1;32m--> 134\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'two'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1190 entries, 0 to 1189\n",
      "Columns: 123 entries, ID to p_inf_ang\n",
      "dtypes: bool(1), float64(11), int64(1), object(110)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                        0\n",
       "age                                     250\n",
       "sex                                     246\n",
       "myocardial_infarctions_anamnesis          7\n",
       "Exertional_angina_pectoris_anamnesis    160\n",
       "                                       ... \n",
       "myo_rup                                   0\n",
       "dres_syn                                  0\n",
       "ch_hf                                     0\n",
       "re_myin                                   0\n",
       "p_inf_ang                                 0\n",
       "Length: 123, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                      1190\n",
       "age                                      246\n",
       "sex                                        2\n",
       "myocardial_infarctions_anamnesis           4\n",
       "Exertional_angina_pectoris_anamnesis       8\n",
       "                                        ... \n",
       "myo_rup                                    2\n",
       "dres_syn                                   2\n",
       "ch_hf                                      2\n",
       "re_myin                                    2\n",
       "p_inf_ang                                  2\n",
       "Length: 123, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>1190.0</td>\n",
       "      <td>7137.500000</td>\n",
       "      <td>343.667717</td>\n",
       "      <td>6543.000000</td>\n",
       "      <td>6840.25</td>\n",
       "      <td>7137.50</td>\n",
       "      <td>7434.750000</td>\n",
       "      <td>7732.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>940.0</td>\n",
       "      <td>63.242062</td>\n",
       "      <td>11.410026</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>56.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>94.455567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Systolic_BP_ECT</th>\n",
       "      <td>464.0</td>\n",
       "      <td>139.094828</td>\n",
       "      <td>49.406189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.00</td>\n",
       "      <td>140.00</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>260.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diastolic_BP_ECT</th>\n",
       "      <td>434.0</td>\n",
       "      <td>83.058493</td>\n",
       "      <td>19.999228</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>90.075954</td>\n",
       "      <td>197.953064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ser_K_cont</th>\n",
       "      <td>802.0</td>\n",
       "      <td>4.368080</td>\n",
       "      <td>1.056283</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>8.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ser_Na_cont</th>\n",
       "      <td>507.0</td>\n",
       "      <td>136.773176</td>\n",
       "      <td>8.775743</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>132.00</td>\n",
       "      <td>136.00</td>\n",
       "      <td>141.500000</td>\n",
       "      <td>169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ser_AIAT_cont</th>\n",
       "      <td>1041.0</td>\n",
       "      <td>0.574256</td>\n",
       "      <td>0.516971</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_AsAT_cont</th>\n",
       "      <td>829.0</td>\n",
       "      <td>0.357021</td>\n",
       "      <td>0.322560</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>2.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ser_cpk_cont</th>\n",
       "      <td>190.0</td>\n",
       "      <td>2.061053</td>\n",
       "      <td>0.994194</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.60</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wbc_count</th>\n",
       "      <td>902.0</td>\n",
       "      <td>9.468958</td>\n",
       "      <td>4.313427</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.40</td>\n",
       "      <td>8.40</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>27.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opioid_icu_3day</th>\n",
       "      <td>866.0</td>\n",
       "      <td>0.060644</td>\n",
       "      <td>0.274222</td>\n",
       "      <td>-0.065560</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nsaids_icu_3day</th>\n",
       "      <td>867.0</td>\n",
       "      <td>0.079963</td>\n",
       "      <td>0.344940</td>\n",
       "      <td>-0.116439</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.029597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min      25%  \\\n",
       "ID                1190.0  7137.500000  343.667717  6543.000000  6840.25   \n",
       "age                940.0    63.242062   11.410026    26.000000    56.00   \n",
       "Systolic_BP_ECT    464.0   139.094828   49.406189     0.000000   110.00   \n",
       "Diastolic_BP_ECT   434.0    83.058493   19.999228     0.000000    70.00   \n",
       "ser_K_cont         802.0     4.368080    1.056283     2.300000     3.70   \n",
       "ser_Na_cont        507.0   136.773176    8.775743   117.000000   132.00   \n",
       "ser_AIAT_cont     1041.0     0.574256    0.516971     0.030000     0.23   \n",
       "serum_AsAT_cont    829.0     0.357021    0.322560     0.040000     0.15   \n",
       "ser_cpk_cont       190.0     2.061053    0.994194     1.200000     1.25   \n",
       "wbc_count          902.0     9.468958    4.313427     2.000000     6.40   \n",
       "opioid_icu_3day    866.0     0.060644    0.274222    -0.065560     0.00   \n",
       "nsaids_icu_3day    867.0     0.079963    0.344940    -0.116439     0.00   \n",
       "\n",
       "                      50%          75%          max  \n",
       "ID                7137.50  7434.750000  7732.000000  \n",
       "age                 64.00    71.000000    94.455567  \n",
       "Systolic_BP_ECT    140.00   170.000000   260.000000  \n",
       "Diastolic_BP_ECT    80.00    90.075954   197.953064  \n",
       "ser_K_cont           4.20     4.900000     8.200000  \n",
       "ser_Na_cont        136.00   141.500000   169.000000  \n",
       "ser_AIAT_cont        0.38     0.750000     3.000000  \n",
       "serum_AsAT_cont      0.22     0.450000     2.150000  \n",
       "ser_cpk_cont         1.60     3.600000     3.600000  \n",
       "wbc_count            8.40    11.500000    27.900000  \n",
       "opioid_icu_3day      0.00     0.000000     2.000000  \n",
       "nsaids_icu_3day      0.00     0.000000     2.029597  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:70: SyntaxWarning: invalid escape sequence '\\d'\n",
      "<>:71: SyntaxWarning: invalid escape sequence '\\d'\n",
      "<>:70: SyntaxWarning: invalid escape sequence '\\d'\n",
      "<>:71: SyntaxWarning: invalid escape sequence '\\d'\n",
      "C:\\Users\\hrith\\AppData\\Local\\Temp\\ipykernel_30716\\1020746975.py:70: SyntaxWarning: invalid escape sequence '\\d'\n",
      "  X_train['time_beg_chd'] = X_train['time_beg_chd'].str.extract('(\\d+)', expand=False).astype(float).fillna(0).astype(int)\n",
      "C:\\Users\\hrith\\AppData\\Local\\Temp\\ipykernel_30716\\1020746975.py:71: SyntaxWarning: invalid escape sequence '\\d'\n",
      "  test_data['time_beg_chd'] = test_data['time_beg_chd'].str.extract('(\\d+)', expand=False).astype(float).fillna(0).astype(int)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>myocardial_infarctions_anamnesis</th>\n",
       "      <th>Exertional_angina_pectoris_anamnesis</th>\n",
       "      <th>FC_of_angina_pectoris</th>\n",
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       "      <th>Heredity_on_CHD</th>\n",
       "      <th>essential_hypertension</th>\n",
       "      <th>Symptomatic_hypertension</th>\n",
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       "      <th>...</th>\n",
       "      <th>nsaids_icu_1hour</th>\n",
       "      <th>nsaids_icu_2day</th>\n",
       "      <th>nsaids_icu_3day</th>\n",
       "      <th>lido_icu</th>\n",
       "      <th>beta_block_icu</th>\n",
       "      <th>calc_block_icu</th>\n",
       "      <th>heparin_icu</th>\n",
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       "      <th>ticlid_icu</th>\n",
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      "text/plain": [
       "         age  sex  myocardial_infarctions_anamnesis  \\\n",
       "0  69.000000  0.0                               2.0   \n",
       "1  58.000000  1.0                               0.0   \n",
       "2  72.000000  0.0                               0.0   \n",
       "3  73.000000  0.0                               0.0   \n",
       "4  75.655137  0.0                               0.0   \n",
       "\n",
       "   Exertional_angina_pectoris_anamnesis  FC_of_angina_pectoris  \\\n",
       "0                                   3.0                    2.0   \n",
       "1                                   0.0                    0.0   \n",
       "2                                   5.0                    2.0   \n",
       "3                                   0.0                    4.0   \n",
       "4                                   3.0                    0.0   \n",
       "\n",
       "   Coronary_heart_disease(CHD)  Heredity_on_CHD  essential_hypertension  \\\n",
       "0                          2.0              0.0                     2.0   \n",
       "1                          0.0              0.0                     0.0   \n",
       "2                          3.0              0.0                     3.0   \n",
       "3                          2.0              0.0                     0.0   \n",
       "4                          3.0              0.0                     3.0   \n",
       "\n",
       "   Symptomatic_hypertension  no_arterial_hypertension_duration  ...  \\\n",
       "0                       0.0                                0.0  ...   \n",
       "1                       0.0                                0.0  ...   \n",
       "2                       0.0                               10.0  ...   \n",
       "3                       1.0                                4.0  ...   \n",
       "4                       0.0                                0.0  ...   \n",
       "\n",
       "   nsaids_icu_1hour  nsaids_icu_2day  nsaids_icu_3day  lido_icu  \\\n",
       "0               0.0              1.0              0.0       0.0   \n",
       "1               0.0              0.0              0.0       0.0   \n",
       "2               3.0              0.0              0.0       0.0   \n",
       "3               0.0              0.0              0.0       1.0   \n",
       "4               3.0              0.0              0.0       1.0   \n",
       "\n",
       "   beta_block_icu  calc_block_icu  heparin_icu  acetylsal_icu  ticlid_icu  \\\n",
       "0             0.0             1.0          0.0            1.0         0.0   \n",
       "1             0.0             0.0          1.0            0.0         0.0   \n",
       "2             0.0             1.0          1.0            1.0         0.0   \n",
       "3             0.0             1.0          0.0            1.0         1.0   \n",
       "4             0.0             0.0          1.0            0.0         0.0   \n",
       "\n",
       "   trental_icu  \n",
       "0          0.0  \n",
       "1          0.0  \n",
       "2          0.0  \n",
       "3          0.0  \n",
       "4          0.0  \n",
       "\n",
       "[5 rows x 107 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')\n",
    "\n",
    "# Store the IDs for the final submission\n",
    "submission_ids = test_data['ID']  # Store original IDs for final submission\n",
    "\n",
    "# Drop irrelevant columns (ID)\n",
    "train_data.drop(columns=['ID'], inplace=True)\n",
    "test_data.drop(columns=['ID'], inplace=True)\n",
    "\n",
    "# Perform replacements and conversions\n",
    "replacement_mapping = {'?': 0, 'TRUE': 1, 'FALSE': 0, 'YES': 1, 'NO': 0, 'NaN':0, 'none':0, 'never':0, 'True': 1, 'False': 0, 'NA':0, 'inf':1, 'yes': 1, 'no': 0,}\n",
    "train_data.replace(replacement_mapping, inplace=True)\n",
    "test_data.replace(replacement_mapping, inplace=True)\n",
    "\n",
    "train_data=train_data.fillna(0)\n",
    "test_data=test_data.fillna(0)\n",
    "\n",
    "# Convert 'sex' column to binary (0, 1)\n",
    "train_data['sex'] = train_data['sex'].map({'female': 0, 'male': 1})\n",
    "test_data['sex'] = test_data['sex'].map({'female': 0, 'male': 1})\n",
    "\n",
    "# Define mapping for myocardial_infarctions_anamnesis\n",
    "infarction_mapping = {'zero': 0, 'one': 1, 'two': 2, 'three': 3}\n",
    "\n",
    "# Convert 'myocardial_infarctions_anamnesis' column to numerical values\n",
    "train_data['myocardial_infarctions_anamnesis'] = train_data['myocardial_infarctions_anamnesis'].map(infarction_mapping)\n",
    "test_data['myocardial_infarctions_anamnesis'] = test_data['myocardial_infarctions_anamnesis'].map(infarction_mapping)\n",
    "\n",
    "# Convert string numbers in 'essential_hypertension' to integers, remove 'Stage' from values, replace None and NaN with 0\n",
    "train_data['essential_hypertension'] = train_data['essential_hypertension'].replace({'none': 0, 'Stage ': ''}, regex=True).fillna(0)\n",
    "test_data['essential_hypertension'] = test_data['essential_hypertension'].replace({'none': 0, 'Stage ': ''}, regex=True).fillna(0)\n",
    "\n",
    "# Convert string numbers in 'chronic_Heart_failure(HF)' to integers, remove 'Stage' from values, replace None and NaN with 0\n",
    "train_data['chronic_Heart_failure(HF)'] = train_data['chronic_Heart_failure(HF)'].replace({'none': 0, ' stage': ''}, regex=True).fillna(0)\n",
    "test_data['chronic_Heart_failure(HF)'] = test_data['chronic_Heart_failure(HF)'].replace({'none': 0, ' stage': ''}, regex=True).fillna(0)\n",
    "\n",
    "train_data['FC_of_angina_pectoris'] = train_data['FC_of_angina_pectoris'].replace({'none': 0, ' FC': ''}, regex=True).fillna(0)\n",
    "test_data['FC_of_angina_pectoris'] = test_data['FC_of_angina_pectoris'].replace({'none': 0, ' FC': ''}, regex=True).fillna(0)\n",
    "\n",
    "infarction_mapping2 = {'I':1,'II': 2, 'III': 3, 'IV': 4,'IIB':3}\n",
    "train_data['FC_of_angina_pectoris'] = train_data['FC_of_angina_pectoris'].map(infarction_mapping2).fillna(0)\n",
    "test_data['FC_of_angina_pectoris'] = test_data['FC_of_angina_pectoris'].map(infarction_mapping2).fillna(0)\n",
    "\n",
    "train_data['chronic_Heart_failure(HF)'] = train_data['chronic_Heart_failure(HF)'].map(infarction_mapping2)\n",
    "test_data['chronic_Heart_failure(HF)'] = test_data['chronic_Heart_failure(HF)'].map(infarction_mapping2)\n",
    "\n",
    "# Define features and target variables\n",
    "X_train = train_data.drop(columns=['atr_fib', 'supr_tach', 'ven_tach', 'ven_fib', 'th_d_av', 'pul_ede', 'myo_rup', 'dres_syn', 'ch_hf', 're_myin', 'p_inf_ang','anterior_myocardial_infarction', 'lateral_myocardial_infarction', 'inferior_myocardial_infarction', 'posterior_myocardial_infarction'])\n",
    "y_train = train_data[['atr_fib', 'supr_tach', 'ven_tach', 'ven_fib', 'th_d_av', 'pul_ede', 'myo_rup', 'dres_syn', 'ch_hf', 're_myin', 'p_inf_ang']]\n",
    "test_data.drop(columns=['anterior_myocardial_infarction', 'lateral_myocardial_infarction', 'inferior_myocardial_infarction', 'posterior_myocardial_infarction'], inplace=True)\n",
    "\n",
    "# Extract integer values from 'Exertional_angina_pectoris_anamnesis'\n",
    "X_train['Exertional_angina_pectoris_anamnesis'] = X_train['Exertional_angina_pectoris_anamnesis'].str.extract(r'(\\d+)')\n",
    "X_train['Exertional_angina_pectoris_anamnesis'] = X_train['Exertional_angina_pectoris_anamnesis'].astype(float).fillna(0).astype(int)\n",
    "\n",
    "# Do the same for test data if needed\n",
    "test_data['Exertional_angina_pectoris_anamnesis'] = test_data['Exertional_angina_pectoris_anamnesis'].str.extract(r'(\\d+)')\n",
    "test_data['Exertional_angina_pectoris_anamnesis'] = test_data['Exertional_angina_pectoris_anamnesis'].astype(float).fillna(0).astype(int)\n",
    "\n",
    "# Convert 'no_arterial_hypertension_duration' column to numerical values\n",
    "X_train['no_arterial_hypertension_duration'] = X_train['no_arterial_hypertension_duration'].str.extract(r'(\\d+)')\n",
    "X_train['no_arterial_hypertension_duration'] = pd.to_numeric(X_train['no_arterial_hypertension_duration'], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "test_data['no_arterial_hypertension_duration'] = test_data['no_arterial_hypertension_duration'].str.extract(r'(\\d+)')\n",
    "test_data['no_arterial_hypertension_duration'] = pd.to_numeric(test_data['no_arterial_hypertension_duration'], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "# Extract integer value from time_beg_chd column\n",
    "X_train['time_beg_chd'] = X_train['time_beg_chd'].str.extract('(\\d+)', expand=False).astype(float).fillna(0).astype(int)\n",
    "test_data['time_beg_chd'] = test_data['time_beg_chd'].str.extract('(\\d+)', expand=False).astype(float).fillna(0).astype(int)\n",
    "\n",
    "# Mapping for relapse columns\n",
    "relapse_mapping = {'There is no relapse': 0, 'Only one': 1, '2 times': 2, '3 or more times': 3}\n",
    "\n",
    "# Apply mapping to the specified columns\n",
    "X_train['Relap_1hour'] = X_train['Relap_1hour'].map(relapse_mapping).fillna(0)\n",
    "X_train['relap_2day'] = X_train['relap_2day'].map(relapse_mapping).fillna(0)\n",
    "X_train['relap_3day'] = X_train['relap_3day'].map(relapse_mapping).fillna(0)\n",
    "\n",
    "test_data['Relap_1hour'] = test_data['Relap_1hour'].map(relapse_mapping).fillna(0)\n",
    "test_data['relap_2day'] = test_data['relap_2day'].map(relapse_mapping).fillna(0)\n",
    "test_data['relap_3day'] = test_data['relap_3day'].map(relapse_mapping).fillna(0)\n",
    "\n",
    "chd_mapping = {'none': 0, 'unstable': 2, 'exertional': 3}\n",
    "\n",
    "# Convert 'Coronary_heart_disease(CHD)' column to numerical values\n",
    "X_train['Coronary_heart_disease(CHD)'] = X_train['Coronary_heart_disease(CHD)'].map(chd_mapping).fillna(0).astype(int)\n",
    "test_data['Coronary_heart_disease(CHD)'] = test_data['Coronary_heart_disease(CHD)'].map(chd_mapping).fillna(0).astype(int)\n",
    "\n",
    "# Mapping for nsaids_icu_1hour column\n",
    "nsaids_mapping = {'once': 1, 'twice': 2, 'thrice': 3, 'four or more times': 4}\n",
    "\n",
    "# Apply mapping to the specified column\n",
    "X_train['nsaids_icu_1hour'] = X_train['nsaids_icu_1hour'].map(nsaids_mapping).fillna(0)\n",
    "test_data['nsaids_icu_1hour'] = test_data['nsaids_icu_1hour'].map(nsaids_mapping).fillna(0)\n",
    "\n",
    "# Define mapping dictionary for Heredity_on_CHD\n",
    "heredity_mapping = {'not burdened': 1, 'burdened': 2}\n",
    "\n",
    "# Convert 'Heredity_on_CHD' column to numerical values\n",
    "X_train['Heredity_on_CHD'] = X_train['Heredity_on_CHD'].map(heredity_mapping).fillna(0).astype(int)\n",
    "test_data['Heredity_on_CHD'] = test_data['Heredity_on_CHD'].map(heredity_mapping).fillna(0).astype(int)\n",
    "# Preprocessing pipeline for numerical features\n",
    "numeric_features = X_train.select_dtypes(include=['int64', 'float64']).columns\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', StandardScaler())])\n",
    "\n",
    "# Preprocessing pipeline for categorical features\n",
    "categorical_features = X_train.select_dtypes(include=['object']).columns\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
    "\n",
    "\n",
    "# Combine preprocessing pipelines\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)])\n",
    "X_train.replace(replacement_mapping, inplace=True)\n",
    "X_train.fillna(0, inplace=True)\n",
    "X_train = X_train.astype('float64')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA().fit(X_train)\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cumulative Explained Variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZKZfL5bcAAABIAQxHQ4YM0Y4dO7R9+3ZnGTBggDIzM52/w8PDlZ+f7zynpKREpaWl8ng8kiSPx6MdO3aovLzcmZOXlyeXy6XU1NQG7wkAADR+AbvmqE2bNurZs6ffutatW6tdu3bO+jFjxmjSpEmKi4uTy+XSuHHj5PF4NGjQIEnS0KFDlZqaqtGjR2vOnDnyer2aNm2asrOzFRkZ2eA9AQCAxi+gF2T/mKeeekqhoaEaOXKkqqqqlJ6ernnz5jnjLVq00IoVKzR27Fh5PB61bt1aWVlZmjVrVgCrBgAAjVlQhaMNGzb4PW7ZsqVyc3OVm5t71ud07NhRK1eurOfKAABAcxHw7zkCAAAIJoQjAAAAC+EIAADAQjgCAACwEI4AAAAshCMAAAAL4QgAAMBCOAIAALAQjgAAACyEIwAAAAvhCAAAwEI4AgAAsBCOAAAALIQjAAAAC+EIAADAQjgCAACwEI4AAAAshCMAAAAL4QgAAMBCOAIAALAQjgAAACyEIwAAAAvhCAAAwEI4AgAAsBCOAAAALIQjAAAAC+EIAADAQjgCAACwEI4AAAAs5xWOOnfurG+++ea09RUVFercufPPLgoAACBQziscffnll6qpqTltfVVVlfbv3/+ziwIAAAiUsHOZ/MYbbzh/r169WjExMc7jmpoa5efnq1OnTnVWHAAAQEM7p3A0fPhwSVJISIiysrL8xsLDw9WpUyf96U9/qrPiAAAAGto5haPa2lpJUkpKirZs2aL27dvXS1EAAACBck7h6JS9e/fWdR0AAABB4bzCkSTl5+crPz9f5eXlzhmlU1544YWfXRgAAEAgnFc4euSRRzRr1iwNGDBAiYmJCgkJqeu6AAAAAuK8wtGCBQu0ePFijR49uq7rAQAACKjz+p6j6upqXXHFFXVdCwAAQMCdVzi66667tHTp0rquBQAAIODO62O148eP67nnntPatWvVu3dvhYeH+40/+eSTdVIcAABAQzuvcPTxxx+rb9++kqTi4mK/MS7OBgAAjdl5haP169fXdR0AAABB4byuOQIAAGiqzuvM0dVXX/1PPz5bt27deRcEAAAQSOcVjk5db3TKiRMntH37dhUXF5/2g7QAAACNyXmFo6eeeuqM62fOnKkjR478rIIAAAACqU6vObr11lv5XTUAANCo1Wk4KigoUMuWLetykwAAAA3qvMLRiBEj/Jbf/va3GjRokO644w797ne/+8nbmT9/vnr37i2XyyWXyyWPx6O3337bGT9+/Liys7PVrl07RUdHa+TIkSorK/PbRmlpqTIyMtSqVSvFx8dr8uTJOnny5Pm0BQAAcH7XHMXExPg9Dg0NVdeuXTVr1iwNHTr0J2/noosu0hNPPKEuXbrIGKMXX3xRN954o7Zt26YePXpo4sSJeuutt7R8+XLFxMQoJydHI0aM0Pvvvy9JqqmpUUZGhhISErRp0yYdOHBAt912m8LDw/X444+fT2sAAKCZO69wtGjRojrZ+Q033OD3+LHHHtP8+fO1efNmXXTRRVq4cKGWLl2qwYMHO/vt3r27Nm/erEGDBmnNmjXatWuX1q5dK7fbrb59++rRRx/VlClTNHPmTEVERNRJnQAAoPn4WdccFRUVacmSJVqyZIm2bdv2swqpqanRsmXLdPToUXk8HhUVFenEiRNKS0tz5nTr1k3JyckqKCiQ9P01Tr169ZLb7XbmpKeny+fzaefOnWfdV1VVlXw+n98CAAAgneeZo/Lyco0aNUobNmxQbGysJKmiokJXX321li1bpgsuuOAnb2vHjh3yeDw6fvy4oqOj9dprryk1NVXbt29XRESEs/1T3G63vF6vJMnr9foFo1Pjp8bOZvbs2XrkkUd+co0AAKD5OK8zR+PGjdPhw4e1c+dOHTp0SIcOHVJxcbF8Pp/uu+++c9pW165dtX37dhUWFmrs2LHKysrSrl27zqesn2zq1KmqrKx0ln379tXr/gAAQONxXmeOVq1apbVr16p79+7OutTUVOXm5p7TBdmSFBERoYsvvliS1L9/f23ZskXPPPOMbr75ZlVXV6uiosLv7FFZWZkSEhIkSQkJCfrggw/8tnfqbrZTc84kMjJSkZGR51QnAABoHs7rzFFtba3Cw8NPWx8eHq7a2tqfVVBtba2qqqrUv39/hYeHKz8/3xkrKSlRaWmpPB6PJMnj8WjHjh0qLy935uTl5cnlcik1NfVn1QEAAJqn8zpzNHjwYI0fP14vv/yykpKSJEn79+/XxIkTNWTIkJ+8nalTp2rYsGFKTk7W4cOHtXTpUm3YsEGrV69WTEyMxowZo0mTJikuLk4ul0vjxo2Tx+PRoEGDJElDhw5VamqqRo8erTlz5sjr9WratGnKzs7mzBAAADgv5xWO/vu//1u/+c1v1KlTJ3Xo0EGStG/fPvXs2VNLliz5ydspLy/XbbfdpgMHDigmJka9e/fW6tWrdc0110j6/jfcQkNDNXLkSFVVVSk9PV3z5s1znt+iRQutWLFCY8eOlcfjUevWrZWVlaVZs2adT1sAAADnF446dOigDz/8UGvXrtUnn3wiSerevbvfbfc/xcKFC//peMuWLZWbm6vc3NyzzunYsaNWrlx5TvsFAAA4m3O65mjdunVKTU2Vz+dTSEiIrrnmGo0bN07jxo3TZZddph49eujdd9+tr1oBAADq3TmFo6efflp33323XC7XaWMxMTH63e9+pyeffLLOigMAAGho5xSOPvroI1177bVnHR86dKiKiop+dlEAAACBck7hqKys7Iy38J8SFhamr7/++mcXBQAAECjnFI4uvPBCFRcXn3X8448/VmJi4s8uCgAAIFDOKRxdd911evjhh3X8+PHTxo4dO6YZM2bo+uuvr7PiAAAAGto53co/bdo0vfrqq7rkkkuUk5Ojrl27SpI++eQT5ebmqqamRg899FC9FAoAANAQzikcud1ubdq0SWPHjtXUqVNljJEkhYSEKD09Xbm5uXK73fVSKAAAQEM45y+BPPWli99++60+++wzGWPUpUsXtW3btj7qAwAAaFDn9Q3ZktS2bVtddtlldVkLAABAwJ3TBdkAAABNHeEIAADAQjgCAACwEI4AAAAshCMAAAAL4QgAAMBCOAIAALAQjgAAACyEIwAAAAvhCAAAwEI4AgAAsBCOAAAALIQjAAAAC+EIAADAQjgCAACwEI4AAAAshCMAAAAL4QgAAMBCOAIAALAQjgAAACyEIwAAAAvhCAAAwEI4AgAAsBCOAAAALIQjAAAAC+EIAADAQjgCAACwEI4AAAAshCMAAAAL4QgAAMBCOAIAALAQjgAAACyEIwAAAAvhCAAAwEI4AgAAsBCOAAAALIQjAAAAC+EIAADAEtBwNHv2bF122WVq06aN4uPjNXz4cJWUlPjNOX78uLKzs9WuXTtFR0dr5MiRKisr85tTWlqqjIwMtWrVSvHx8Zo8ebJOnjzZkK0AAIAmIqDhaOPGjcrOztbmzZuVl5enEydOaOjQoTp69KgzZ+LEiXrzzTe1fPlybdy4UV999ZVGjBjhjNfU1CgjI0PV1dXatGmTXnzxRS1evFjTp08PREsAAKCRCwvkzletWuX3ePHixYqPj1dRUZF+9atfqbKyUgsXLtTSpUs1ePBgSdKiRYvUvXt3bd68WYMGDdKaNWu0a9curV27Vm63W3379tWjjz6qKVOmaObMmYqIiAhEawAAoJEKqmuOKisrJUlxcXGSpKKiIp04cUJpaWnOnG7duik5OVkFBQWSpIKCAvXq1Utut9uZk56eLp/Pp507dzZg9QAAoCkI6JkjW21trSZMmKArr7xSPXv2lCR5vV5FREQoNjbWb67b7ZbX63Xm2MHo1PipsTOpqqpSVVWV89jn89VVGwAAoJELmjNH2dnZKi4u1rJly+p9X7Nnz1ZMTIyzdOjQod73CQAAGoegCEc5OTlasWKF1q9fr4suushZn5CQoOrqalVUVPjNLysrU0JCgjPnh3evnXp8as4PTZ06VZWVlc6yb9++OuwGAAA0ZgENR8YY5eTk6LXXXtO6deuUkpLiN96/f3+Fh4crPz/fWVdSUqLS0lJ5PB5Jksfj0Y4dO1ReXu7MycvLk8vlUmpq6hn3GxkZKZfL5bcAAABIAb7mKDs7W0uXLtXf/vY3tWnTxrlGKCYmRlFRUYqJidGYMWM0adIkxcXFyeVyady4cfJ4PBo0aJAkaejQoUpNTdXo0aM1Z84ceb1eTZs2TdnZ2YqMjAxkewAAoBEKaDiaP3++JOnXv/613/pFixbp9ttvlyQ99dRTCg0N1ciRI1VVVaX09HTNmzfPmduiRQutWLFCY8eOlcfjUevWrZWVlaVZs2Y1VBsAAKAJCWg4Msb86JyWLVsqNzdXubm5Z53TsWNHrVy5si5LAwAAzVRQXJANAAAQLAhHAAAAFsIRAACAhXAEAABgIRwBAABYCEcAAAAWwhEAAICFcAQAAGAhHAEAAFgIRwAAABbCEQAAgIVwBAAAYCEcAQAAWAhHAAAAFsIRAACAhXAEAABgIRwBAABYCEcAAAAWwhEAAICFcAQAAGAhHAEAAFgIRwAAABbCEQAAgIVwBAAAYCEcAQAAWAhHAAAAFsIRAACAhXAEAABgIRwBAABYCEcAAAAWwhEAAICFcAQAAGAhHAEAAFgIRwAAABbCEQAAgIVwBAAAYCEcAQAAWAhHAAAAFsIRAACAhXAEAABgIRwBAABYCEcAAAAWwhEAAICFcAQAAGAhHAEAAFgIRwAAABbCEQAAgIVwBAAAYCEcAQAAWAIajt555x3dcMMNSkpKUkhIiF5//XW/cWOMpk+frsTEREVFRSktLU179uzxm3Po0CFlZmbK5XIpNjZWY8aM0ZEjRxqwCwAA0JQENBwdPXpUffr0UW5u7hnH58yZo7lz52rBggUqLCxU69atlZ6eruPHjztzMjMztXPnTuXl5WnFihV65513dM899zRUCwAAoIkJC+TOhw0bpmHDhp1xzBijp59+WtOmTdONN94oSfqf//kfud1uvf766xo1apR2796tVatWacuWLRowYIAk6dlnn9V1112n//qv/1JSUlKD9QIAAJqGoL3maO/evfJ6vUpLS3PWxcTEaODAgSooKJAkFRQUKDY21glGkpSWlqbQ0FAVFhaeddtVVVXy+Xx+CwAAgBTE4cjr9UqS3G6333q32+2Meb1excfH+42HhYUpLi7OmXMms2fPVkxMjLN06NChjqsHAACNVdCGo/o0depUVVZWOsu+ffsCXRIAAAgSQRuOEhISJEllZWV+68vKypyxhIQElZeX+42fPHlShw4dcuacSWRkpFwul98CAAAgBXE4SklJUUJCgvLz8511Pp9PhYWF8ng8kiSPx6OKigoVFRU5c9atW6fa2loNHDiwwWsGAACNX0DvVjty5Ig+++wz5/HevXu1fft2xcXFKTk5WRMmTNAf/vAHdenSRSkpKXr44YeVlJSk4cOHS5K6d++ua6+9VnfffbcWLFigEydOKCcnR6NGjeJONQAAcF4CGo62bt2qq6++2nk8adIkSVJWVpYWL16sBx54QEePHtU999yjiooKXXXVVVq1apVatmzpPOell15STk6OhgwZotDQUI0cOVJz585t8F4AAEDTENBw9Otf/1rGmLOOh4SEaNasWZo1a9ZZ58TFxWnp0qX1UR4AAGiGgvaaIwAAgEAgHAEAAFgIRwAAABbCEQAAgIVwBAAAYCEcAQAAWAhHAAAAFsIRAACAhXAEAABgIRwBAABYCEcAAAAWwhEAAICFcAQAAGAhHAEAAFgIRwAAABbCEQAAgIVwBAAAYCEcAQAAWAhHAAAAFsIRAACAhXAEAABgIRwBAABYCEcAAAAWwhEAAICFcAQAAGAhHAEAAFgIRwAAABbCEQAAgIVwBAAAYCEcAQAAWAhHAAAAFsIRAACAhXAEAABgIRwBAABYCEcAAAAWwhEAAICFcAQAAGAhHAEAAFgIRwAAABbCEQAAgIVwBAAAYCEcAQAAWAhHAAAAFsIRAACAhXAEAABgIRwBAABYwgJdAAAA/0xpaakOHjwY6DL87N69O9AloB4RjgAAQau0tFTdunXXsWPfBbqUMzpRVR3oElAPmkw4ys3N1R//+Ed5vV716dNHzz77rC6//PJAlwUA+BkOHjyoY8e+08A7Z8iV2CnQ5TgO7ChQ8RvP6eTJk4EuBfWgSYSj//u//9OkSZO0YMECDRw4UE8//bTS09NVUlKi+Pj4QJcHAPiZXImdFJfcNdBlOHwHvgx0CahHTeKC7CeffFJ333237rjjDqWmpmrBggVq1aqVXnjhhUCXBgAAGplGH46qq6tVVFSktLQ0Z11oaKjS0tJUUFAQwMoAAEBj1Og/Vjt48KBqamrkdrv91rvdbn3yySdnfE5VVZWqqqqcx5WVlZIkn89Xp7UdOXJEknTo7yU6WXWsTrf9c/i8pZKkoqIip8ZgEBoaqtra2kCXcRrq+umCsSYpOOsKxpqk4KurpKREUhC+jh74uySpcv8ehYeFBLia/y8Y6zr1nnPkyJE6f589tT1jTJ1uV6aR279/v5FkNm3a5Ld+8uTJ5vLLLz/jc2bMmGEksbCwsLCwsDSBZd++fXWaLRr9maP27durRYsWKisr81tfVlamhISEMz5n6tSpmjRpkvO4trZWhw4dUrt27RQSEhxJ+8f4fD516NBB+/btk8vlCnQ5Dao59y7Rf3Puvzn3LtF/c+7/bL0bY3T48GElJSXV6f4afTiKiIhQ//79lZ+fr+HDh0v6Puzk5+crJyfnjM+JjIxUZGSk37rY2Nh6rrR+uFyuZvcfySnNuXeJ/ptz/825d4n+m3P/Z+o9JiamzvfT6MORJE2aNElZWVkaMGCALr/8cj399NM6evSo7rjjjkCXBgAAGpkmEY5uvvlmff3115o+fbq8Xq/69u2rVatWnXaRNgAAwI9pEuFIknJycs76MVpTFBkZqRkzZpz28WBz0Jx7l+i/OfffnHuX6L8599/QvYcYU9f3vwEAADRejf5LIAEAAOoS4QgAAMBCOAIAALAQjgAAACyEowB65513dMMNNygpKUkhISF6/fXX/caNMZo+fboSExMVFRWltLQ07dmzx2/OoUOHlJmZKZfLpdjYWI0ZM+a030v7+OOP9ctf/lItW7ZUhw4dNGfOnPpu7UfNnj1bl112mdq0aaP4+HgNHz7c+Q2lU44fP67s7Gy1a9dO0dHRGjly5GnfhF5aWqqMjAy1atVK8fHxmjx5sk6ePOk3Z8OGDbr00ksVGRmpiy++WIsXL67v9n7U/Pnz1bt3b+cLzTwej95++21nvCn3/kNPPPGEQkJCNGHCBGddU+5/5syZCgkJ8Vu6devmjDfl3iVp//79uvXWW9WuXTtFRUWpV69e2rp1qzPelF/3OnXqdNqxDwkJUXZ2tqSmf+xramr08MMPKyUlRVFRUfrFL36hRx991O930YLm+Nfpj5HgnKxcudI89NBD5tVXXzWSzGuvveY3/sQTT5iYmBjz+uuvm48++sj85je/MSkpKebYsWPOnGuvvdb06dPHbN682bz77rvm4osvNrfccoszXllZadxut8nMzDTFxcXm5ZdfNlFRUebPf/5zQ7V5Runp6WbRokWmuLjYbN++3Vx33XUmOTnZHDlyxJlz7733mg4dOpj8/HyzdetWM2jQIHPFFVc44ydPnjQ9e/Y0aWlpZtu2bWblypWmffv2ZurUqc6cL774wrRq1cpMmjTJ7Nq1yzz77LOmRYsWZtWqVQ3a7w+98cYb5q233jKffvqpKSkpMf/5n/9pwsPDTXFxsTGmafdu++CDD0ynTp1M7969zfjx4531Tbn/GTNmmB49epgDBw44y9dff+2MN+XeDx06ZDp27Ghuv/12U1hYaL744guzevVq89lnnzlzmvLrXnl5ud9xz8vLM5LM+vXrjTFN+9gbY8xjjz1m2rVrZ1asWGH27t1rli9fbqKjo80zzzzjzAmW4084ChI/DEe1tbUmISHB/PGPf3TWVVRUmMjISPPyyy8bY4zZtWuXkWS2bNnizHn77bdNSEiI2b9/vzHGmHnz5pm2bduaqqoqZ86UKVNM165d67mjc1NeXm4kmY0bNxpjvu81PDzcLF++3Jmze/duI8kUFBQYY74Pl6Ghocbr9Tpz5s+fb1wul9PvAw88YHr06OG3r5tvvtmkp6fXd0vnrG3btuYvf/lLs+n98OHDpkuXLiYvL8/8y7/8ixOOmnr/M2bMMH369DnjWFPvfcqUKeaqq64663hze90bP368+cUvfmFqa2ub/LE3xpiMjAxz5513+q0bMWKEyczMNMYE1/HnY7UgtXfvXnm9XqWlpTnrYmJiNHDgQBUUFEiSCgoKFBsbqwEDBjhz0tLSFBoaqsLCQmfOr371K0VERDhz0tPTVVJSom+//baBuvlxlZWVkqS4uDhJUlFRkU6cOOHXf7du3ZScnOzXf69evfy+CT09PV0+n087d+505tjbODXn1DaCQU1NjZYtW6ajR4/K4/E0m96zs7OVkZFxWo3Nof89e/YoKSlJnTt3VmZmpkpLSyU1/d7feOMNDRgwQDfddJPi4+PVr18/Pf/88854c3rdq66u1pIlS3TnnXcqJCSkyR97SbriiiuUn5+vTz/9VJL00Ucf6b333tOwYcMkBdfxJxwFKa/XK0mn/QSK2+12xrxer+Lj4/3Gw8LCFBcX5zfnTNuw9xFotbW1mjBhgq688kr17NlT0ve1RUREnPaDwD/s/8d6O9scn8+nY8eO1Uc7P9mOHTsUHR2tyMhI3XvvvXrttdeUmpraLHpftmyZPvzwQ82ePfu0sabe/8CBA7V48WKtWrVK8+fP1969e/XLX/5Shw8fbvK9f/HFF5o/f766dOmi1atXa+zYsbrvvvv04osvSmper3uvv/66KioqdPvtt0tq+v/eS9KDDz6oUaNGqVu3bgoPD1e/fv00YcIEZWZmSgqu499kfj4EjVd2draKi4v13nvvBbqUBtW1a1dt375dlZWV+utf/6qsrCxt3Lgx0GXVu3379mn8+PHKy8tTy5YtA11Ogzv1f8mS1Lt3bw0cOFAdO3bUK6+8oqioqABWVv9qa2s1YMAAPf7445Kkfv36qbi4WAsWLFBWVlaAq2tYCxcu1LBhw5SUlBToUhrMK6+8opdeeklLly5Vjx49tH37dk2YMEFJSUlBd/w5cxSkEhISJOm0OxXKysqcsYSEBJWXl/uNnzx5UocOHfKbc6Zt2PsIpJycHK1YsULr16/XRRdd5KxPSEhQdXW1Kioq/Ob/sP8f6+1sc1wuV8DfiCIiInTxxRerf//+mj17tvr06aNnnnmmyfdeVFSk8vJyXXrppQoLC1NYWJg2btyouXPnKiwsTG63u0n3/0OxsbG65JJL9NlnnzX5Y5+YmKjU1FS/dd27d3c+Vmwur3t///vftXbtWt11113OuqZ+7CVp8uTJztmjXr16afTo0Zo4caJzBjmYjj/hKEilpKQoISFB+fn5zjqfz6fCwkJ5PB5JksfjUUVFhYqKipw569atU21trQYOHOjMeeedd3TixAlnTl5enrp27aq2bds2UDenM8YoJydHr732mtatW6eUlBS/8f79+ys8PNyv/5KSEpWWlvr1v2PHDr//UPLy8uRyuZwXYI/H47eNU3NObSOY1NbWqqqqqsn3PmTIEO3YsUPbt293lgEDBigzM9P5uyn3/0NHjhzR559/rsTExCZ/7K+88srTvrLj008/VceOHSU1/de9UxYtWqT4+HhlZGQ465r6sZek7777TqGh/rGjRYsWqq2tlRRkx/+cLzdHnTl8+LDZtm2b2bZtm5FknnzySbNt2zbz97//3Rjz/S2NsbGx5m9/+5v5+OOPzY033njGWxr79etnCgsLzXvvvWe6dOnid0tjRUWFcbvdZvTo0aa4uNgsW7bMtGrVKuC3tI4dO9bExMSYDRs2+N3a+t133zlz7r33XpOcnGzWrVtntm7dajwej/F4PM74qdtahw4darZv325WrVplLrjggjPe1jp58mSze/duk5ubGxS3tT744INm48aNZu/evebjjz82Dz74oAkJCTFr1qwxxjTt3s/EvlvNmKbd//333282bNhg9u7da95//32TlpZm2rdvb8rLy40xTbv3Dz74wISFhZnHHnvM7Nmzx7z00kumVatWZsmSJc6cpvy6Z4wxNTU1Jjk52UyZMuW0saZ87I0xJisry1x44YXOrfyvvvqqad++vXnggQecOcFy/AlHAbR+/Xoj6bQlKyvLGPP9bY0PP/ywcbvdJjIy0gwZMsSUlJT4beObb74xt9xyi4mOjjYul8vccccd5vDhw35zPvroI3PVVVeZyMhIc+GFF5onnniioVo8qzP1LcksWrTImXPs2DHzH//xH6Zt27amVatW5re//a05cOCA33a+/PJLM2zYMBMVFWXat29v7r//fnPixAm/OevXrzd9+/Y1ERERpnPnzn77CJQ777zTdOzY0URERJgLLrjADBkyxAlGxjTt3s/kh+GoKfd/8803m8TERBMREWEuvPBCc/PNN/t9z09T7t0YY958803Ts2dPExkZabp162aee+45v/Gm/LpnjDGrV682kk7ryZimf+x9Pp8ZP368SU5ONi1btjSdO3c2Dz30kN8t98Fy/EOMsb6aEgAAoJnjmiMAAAAL4QgAAMBCOAIAALAQjgAAACyEIwAAAAvhCAAAwEI4AgAAsBCOAAAALIQjAE3G7bffruHDhzt/h4SEKCQkROHh4XK73brmmmv0wgsvOL/lBABnQjgC0GRde+21OnDggL788ku9/fbbuvrqqzV+/Hhdf/31OnnyZKDLAxCkwgJdAADUl8jISCUkJEiSLrzwQl166aUaNGiQhgwZosWLF+uuu+4KcIUAghFnjgA0K4MHD1afPn306quvBroUAEGKcASg2enWrZu+/PLLQJcBIEgRjgA0O8YYhYSEBLoMAEGKcASg2dm9e7dSUlICXQaAIEU4AtCsrFu3Tjt27NDIkSMDXQqAIMXdagCarKqqKnm9XtXU1KisrEyrVq3S7Nmzdf311+u2224LdHkAghThCECTtWrVKiUmJiosLExt27ZVnz59NHfuXGVlZSk0lBPnAM4sxBhjAl0EAABAsOB/nQAAACyEIwAAAAvhCAAAwEI4AgAAsBCOAAAALIQjAAAAC+EIAADAQjgCAACwEI4AAAAshCMAAAAL4QgAAMBCOAIAALD8P9k6FxmkmdYNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cHE2cOFGRkZEqLi7WT3/6U916660aPXq0JCk5OVljxozRrFmztHz5ctXW1mru3LmaNGkSd2IBAABJHr6ys2vXLvXv31/9+/eXJGVmZqp///5auHChOnbsqP379+u73/2uevXqpZkzZ2rAgAHKz8+X3W4351i9erWSkpI0YsQIjR07Vvfcc4/eeOMNT/1JAADAy3j0yk56eroMw2iy/6OPPrruHKGhoVqzZo07ywIAABbiU2t2AAAAmouwAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALM2jYWf79u26//77FR0dLZvNpvXr15t9tbW1WrBggfr27aubbrpJ0dHRevjhh3Xy5EmnOeLj42Wz2Zy2F198sY3/EgAA4K08GnYuXLigfv36admyZQ36qqqqtGfPHj3//PPas2eP1q1bp6KiIn33u99tcOzixYtVWlpqbvPmzWuL8gEAgA/o5MlfnpGRoYyMjEb7goODtXnzZqe23/zmNxo0aJBKSkoUGxtrtgcGBioyMrJVawUAAL7Jp9bsnDt3TjabTSEhIU7tL774orp3767+/ftryZIlqquru+Y8NTU1cjgcThsAALAmj17ZaY6LFy9qwYIFeuihhxQUFGS2P/XUU7rzzjsVGhqqzz77TFlZWSotLdUrr7zS5Fy5ubnKyclpi7IBAICH+UTYqa2t1Q9/+EMZhqHXX3/dqS8zM9P8OSUlRf7+/nrssceUm5sru93e6HxZWVlO4xwOh2JiYlqneAAA4FFeH3auBp0vv/xSH3/8sdNVncakpaWprq5OR48eVe/evRs9xm63NxmEAACAtXh12LkadA4dOqRPPvlE3bt3v+6YvXv3qkOHDgoPD2+DCgEAgLfzaNiprKzU4cOHzf0jR45o7969Cg0NVVRUlL7//e9rz5492rhxoy5fvqyysjJJUmhoqPz9/VVQUKCdO3dq+PDhCgwMVEFBgebPn6+pU6eqW7dunvqzAACAF/Fo2Nm1a5eGDx9u7l9dRzNt2jQtWrRIGzZskCSlpqY6jfvkk0+Unp4uu92ud955R4sWLVJNTY0SEhI0f/58p/U4aLmSkhKVl5e7NLawsNDN1QAA0DweDTvp6ekyDKPJ/mv1SdKdd96pHTt2uLssfENJSYmSkpJVXV3Vonlqay65qSIAAJrHq9fswPPKy8tVXV2ltBnZCoqKb/b40i8KdGDDG9d99hEAAK2FsIMbEhQVr9DYxu9uuxZH6VH3FwMAQDP41BOUAQAAmouwAwAALI2vsdAutOSusLCwMKcXzwIAfAthB5ZWfe6MJJumTp3q8hwBAV108GAhgQcAfBRhB5ZWW3VekqHUyQvUIyGp2eMdpUe1c0WOysvLCTsA4KMIO2gXuobHunQ3GQDA97FAGQAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBoPFQRuAO/WAgDfRdgBroF3awGA7yPsANfgrndr5efnKzk52aUauDIEAC1D2AFugKvv1uLKEAB4HmEHaEW8dR0API+wA7QB3roOAJ7DrecAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSXAo7PXv21JkzZxq0V1RUqGfPni0uCgAAwF1cCjtHjx7V5cuXG7TX1NToxIkTNzzP9u3bdf/99ys6Olo2m03r16936jcMQwsXLlRUVJQCAgI0cuRIHTp0yOmYs2fPasqUKQoKClJISIhmzpypyspKV/4sAABgQZ2ac/CGDRvMnz/66CMFBweb+5cvX9bWrVsVHx9/w/NduHBB/fr104wZMzRhwoQG/S+//LJ+/etf63e/+50SEhL0/PPPa/To0fqf//kfde7cWZI0ZcoUlZaWavPmzaqtrdUjjzyi2bNna82aNc350wAAgEU1K+yMHz9ekmSz2TRt2jSnPj8/P8XHx+uXv/zlDc+XkZGhjIyMRvsMw9DSpUv13HPP6Xvf+54k6a233lJERITWr1+vSZMmqbCwUJs2bdLnn3+uu+66S5L02muvaezYsfrFL36h6Ojo5vx5AADAgpr1NVZ9fb3q6+sVGxur06dPm/v19fWqqalRUVGR7rvvPrcUduTIEZWVlWnkyJFmW3BwsNLS0lRQUCBJKigoUEhIiBl0JGnkyJHq0KGDdu7c2eTcNTU1cjgcThsAALAml9bsHDlyRGFhYe6uxUlZWZkkKSIiwqk9IiLC7CsrK1N4eLhTf6dOnRQaGmoe05jc3FwFBwebW0xMjJurBwAA3qJZX2N909atW7V161bzCs83rVixosWFtaasrCxlZmaa+w6Hg8ADAIBFuRR2cnJytHjxYt11112KioqSzWZzd12KjIyUJJ06dUpRUVFm+6lTp5Sammoec/r0aadxdXV1Onv2rDm+MXa7XXa73e01AwAA7+NS2Fm+fLlWrVqlH/3oR+6ux5SQkKDIyEht3brVDDcOh0M7d+7UE088IUkaPHiwKioqtHv3bg0YMECS9PHHH6u+vl5paWmtVhsAAPAdLoWdS5cu6dvf/naLf3llZaUOHz5s7h85ckR79+5VaGioYmNj9fTTT+uFF17QbbfdZt56Hh0dbd4VlpycrDFjxmjWrFlavny5amtrNXfuXE2aNIk7sQAAgCQXFyg/+uijbnmOza5du9S/f3/1799fkpSZman+/ftr4cKFkqSf/vSnmjdvnmbPnq2BAweqsrJSmzZtMp+xI0mrV69WUlKSRowYobFjx+qee+7RG2+80eLaAACANbh0ZefixYt64403tGXLFqWkpMjPz8+p/5VXXrmhedLT02UYRpP9NptNixcv1uLFi5s8JjQ0lAcIAgCAJrkUdvbv32+uozlw4IBTX2ssVgYAAHCVS2Hnk08+cXcdAAAArcKlNTsAAAC+wqUrO8OHD7/m11Uff/yxywUBAAC4k0th5+p6natqa2u1d+9eHThwoMELQgEAADzJpbDz6quvNtq+aNEiVVZWtqggAAAAd3Lrmp2pU6d6/XuxAABA++LWsFNQUOD0wD8AAABPc+lrrAkTJjjtG4ah0tJS7dq1S88//7xbCgMAAHAHl8JOcHCw036HDh3Uu3dvLV68WKNGjXJLYQAAAO7gUthZuXKlu+sAAABoFS6Fnat2796twsJCSdIdd9xhvtATAADAW7gUdk6fPq1JkyYpLy9PISEhkqSKigoNHz5c77zzjnr06OHOGgEAAFzm0t1Y8+bN0/nz5/WPf/xDZ8+e1dmzZ3XgwAE5HA499dRT7q4RAADAZS5d2dm0aZO2bNmi5ORks+3222/XsmXLWKAMAAC8iktXdurr6+Xn59eg3c/PT/X19S0uCgAAwF1cCjv33nuvfvzjH+vkyZNm24kTJzR//nyNGDHCbcUBAAC0lEth5ze/+Y0cDofi4+OVmJioxMREJSQkyOFw6LXXXnN3jQAAAC5zac1OTEyM9uzZoy1btujgwYOSpOTkZI0cOdKtxQEAALRUs67sfPzxx7r99tvlcDhks9n0ne98R/PmzdO8efM0cOBA3XHHHcrPz2+tWgEAAJqtWWFn6dKlmjVrloKCghr0BQcH67HHHtMrr7zituIAAABaqllhZ9++fRozZkyT/aNGjdLu3btbXBQAAIC7NCvsnDp1qtFbzq/q1KmTvvrqqxYXBQAA4C7NCjs333yzDhw40GT//v37FRUV1eKiAAAA3KVZYWfs2LF6/vnndfHixQZ91dXVys7O1n333ee24gAAAFqqWbeeP/fcc1q3bp169eqluXPnqnfv3pKkgwcPatmyZbp8+bL+/d//vVUKBdqzwsJCl8eGhYUpNjbWjdUAgG9pVtiJiIjQZ599pieeeEJZWVkyDEOSZLPZNHr0aC1btkwRERGtUijQHlWfOyPJpqlTp7o8R0BAFx08WEjgAdBuNfuhgnFxcfrggw/09ddf6/DhwzIMQ7fddpu6devWGvUB7Vpt1XlJhlInL1CPhKRmj3eUHtXOFTkqLy8n7ABot1x6grIkdevWTQMHDnRnLQCa0DU8VqGxvT1dBgD4JJfejQUAAOArCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSvD7sxMfHy2azNdjmzJkjSUpPT2/Q9/jjj3u4agAA4C1cfl1EW/n88891+fJlc//AgQP6zne+ox/84Adm26xZs7R48WJzv0uXLm1aIwAA8F5eH3Z69OjhtP/iiy8qMTFRw4YNM9u6dOmiyMjIti4NAAD4AK//GuubLl26pD/84Q+aMWOGbDab2b569WqFhYWpT58+ysrKUlVV1TXnqampkcPhcNoAAIA1ef2VnW9av369KioqNH36dLNt8uTJiouLU3R0tPbv368FCxaoqKhI69ata3Ke3Nxc5eTktEHFAADA03wq7Lz55pvKyMhQdHS02TZ79mzz5759+yoqKkojRoxQcXGxEhMTG50nKytLmZmZ5r7D4VBMTEzrFQ4AADzGZ8LOl19+qS1btlzzio0kpaWlSZIOHz7cZNix2+2y2+1urxEAAHgfn1mzs3LlSoWHh2vcuHHXPG7v3r2SpKioqDaoCgAAeDufuLJTX1+vlStXatq0aerU6f9KLi4u1po1azR27Fh1795d+/fv1/z58zV06FClpKR4sGIAAOAtfCLsbNmyRSUlJZoxY4ZTu7+/v7Zs2aKlS5fqwoULiomJ0cSJE/Xcc895qFIAAOBtfCLsjBo1SoZhNGiPiYnRtm3bPFARAADwFT6zZgcAAMAVhB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBpnTxdAIDWV1hY6PLYsLAwxcbGurEaAGhbhB3AwqrPnZFk09SpU12eIyCgiw4eLCTwAPBZhB3AwmqrzksylDp5gXokJDV7vKP0qHauyFF5eTlhB4DPIuwA7UDX8FiFxvb2dBkA4BEsUAYAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJbGrecArosnMAPwZYQdAE3iCcwArICwA6BJPIEZgBUQdgBcF09gBuDLWKAMAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjVvPLa6kpETl5eUuj2/Jk3MBAPAGhB0LKykpUVJSsqqrq1o8V23NJTdUBABA2yPsWFh5ebmqq6uUNiNbQVHxLs1R+kWBDmx4Q3V1de4tDgCANuLVYWfRokXKyclxauvdu7cOHjwoSbp48aL+7d/+Te+8845qamo0evRo/cd//IciIiI8Ua7XCoqKd/npt47So+4tBgCANub1C5TvuOMOlZaWmtunn35q9s2fP1/vv/++1q5dq23btunkyZOaMGGCB6sFAADexquv7EhSp06dFBkZ2aD93LlzevPNN7VmzRrde++9kqSVK1cqOTlZO3bs0Le+9a22LhUAAHghr7+yc+jQIUVHR6tnz56aMmWKSkpKJEm7d+9WbW2tRo4caR6blJSk2NhYFRQUXHPOmpoaORwOpw0AAFiTV4edtLQ0rVq1Sps2bdLrr7+uI0eOaMiQITp//rzKysrk7++vkJAQpzEREREqKyu75ry5ubkKDg42t5iYmFb8KwAAgCd59ddYGRkZ5s8pKSlKS0tTXFyc/vSnPykgIMDlebOyspSZmWnuOxwOAg8AABbl1Vd2/lVISIh69eqlw4cPKzIyUpcuXVJFRYXTMadOnWp0jc832e12BQUFOW0AAMCafCrsVFZWqri4WFFRURowYID8/Py0detWs7+oqEglJSUaPHiwB6sEAADexKu/xvrJT36i+++/X3FxcTp58qSys7PVsWNHPfTQQwoODtbMmTOVmZmp0NBQBQUFad68eRo8eDB3YgEAAJNXh53jx4/roYce0pkzZ9SjRw/dc8892rFjh3r06CFJevXVV9WhQwdNnDjR6aGCALxLS9+xFhYWptjYWDdVA6C98eqw884771yzv3Pnzlq2bJmWLVvWRhUBaI7qc2ck2TR16tQWzRMQ0EUHDxYSeAC4xKvDDgDfVlt1XpKh1MkL1CMhyaU5HKVHtXNFjsrLywk7AFxC2AHQ6rqGx7r8fjYAaCmfuhsLAACguQg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0jp5ugAAuBGFhYUujw0LC1NsbKwbqwHgSwg7ALxa9bkzkmyaOnWqy3MEBHTRwYOFBB6gnSLsAPBqtVXnJRlKnbxAPRKSmj3eUXpUO1fkqLy8nLADtFOEHQA+oWt4rEJje3u6DAA+iAXKAADA0gg7AADA0gg7AADA0gg7AADA0ligDADXUVJSovLycpfH85wfwLMIOwBwDSUlJUpKSlZ1dZXLc/CcH8CzCDsAcA3l5eWqrq5S2oxsBUXFN3s8z/kBPI+wAwA3ICgqnuf8AD6KBcoAAMDSCDsAAMDSCDsAAMDSvDrs5ObmauDAgQoMDFR4eLjGjx+voqIip2PS09Nls9mctscff9xDFQMAAG/j1WFn27ZtmjNnjnbs2KHNmzertrZWo0aN0oULF5yOmzVrlkpLS83t5Zdf9lDFAADA23j13VibNm1y2l+1apXCw8O1e/duDR061Gzv0qWLIiMj27o8AADgA7z6ys6/OnfunCQpNDTUqX316tUKCwtTnz59lJWVpaqqaz/8q6amRg6Hw2kDAADW5NVXdr6pvr5eTz/9tO6++2716dPHbJ88ebLi4uIUHR2t/fv3a8GCBSoqKtK6deuanCs3N1c5OTltUTYAAPAwnwk7c+bM0YEDB/Tpp586tc+ePdv8uW/fvoqKitKIESNUXFysxMTERufKyspSZmamue9wOBQTE9M6hQMAAI/yibAzd+5cbdy4Udu3b9ctt9xyzWPT0tIkSYcPH24y7NjtdtntdrfXCQAAvI9Xhx3DMDRv3jy9++67ysvLU0JCwnXH7N27V5IUFRXVytUBQNvgretAy3h12JkzZ47WrFmj9957T4GBgSorK5MkBQcHKyAgQMXFxVqzZo3Gjh2r7t27a//+/Zo/f76GDh2qlJQUD1cPAC3HW9eBlvPqsPP6669LuvLgwG9auXKlpk+fLn9/f23ZskVLly7VhQsXFBMTo4kTJ+q5557zQLUA4H68dR1oOa8OO4ZhXLM/JiZG27Zta6NqAMBzeOs64DqvDjsAYBWFhYVtOg7A/yHsAEArqj53RpJNU6dObdE8tTWX3FMQ0A4RdgCgFdVWnZdkKHXyAvVISGr2+NIvCnRgwxuqq6tzf3FAO0HYAYA20DU81qU1N47So+4vBmhnfOrdWAAAAM1F2AEAAJbG11gA2gXuhgLaL8IOAEvjbigAhB0AlsbdUAAIOwDaBe6GAtovFigDAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABL49ZzL1dSUqLy8nKXxvLkVwAACDteraSkRElJyaqurmrRPDz5FQDQnhF2vFh5ebmqq6uUNiNbQVHxzR7Pk18BACDs+ISgqHie/AoAgIsIOwAAS2vJ2serwsLCFBsb66aK0NYIOwAAy3LX2seAgC46eLCQwOOjCDsAAMtq6dpH6cqSgJ0rclReXk7Y8VGEHQCA5bm69hHWwEMFAQCApRF2AACApRF2AACApRF2AACApbFAGQDagZa8K8/Tz5jhHYFoKcIOAFhY9bkzkmyaOnWqy3N48hkzvCMQ7kDYAQALq606L8lQ6uQF6pGQ1OzxV58xk5+fr+TkZJdqaMmVId4RCHcg7ABAO9A1PNal58x4y5Uh3hGIliDsAACa5K4rQzx9GJ5E2AEAXJerV4YAb0DYaWXcRQAA1uDLd7S1VEvfHO/pv5+w04q4iwAAfJ+3rFvyFHf8W+bpv5+w04q4iwAArnD1qog3XOH29TvaWqql/5Z5w7otwk4b4C4CAO2VO66KSN5xhdvX72hrKV9+c7xlws6yZcu0ZMkSlZWVqV+/fnrttdc0aNAgT5cFAO1aS6+KWOEKN3e0eZ4lws4f//hHZWZmavny5UpLS9PSpUs1evRoFRUVKTw83NPlAUC75+pVEStd4eaONs+xxItAX3nlFc2aNUuPPPKIbr/9di1fvlxdunTRihUrPF0aAADwMJ8PO5cuXdLu3bs1cuRIs61Dhw4aOXKkCgoKPFgZAADwBj7/NVZ5ebkuX76siIgIp/aIiAgdPHiw0TE1NTWqqakx98+dOydJcjgcbq2tsrJSknT2yyLV1VQ3e7yj9EtJ0rkTh+TXydbm472hBsa37/HeUAPj2/d4b6jBUVYiSdq9e7f570pzdejQQfX19S6NLSoqktSCf8v+f/2VlZVu/3f26nyGYVz7QMPHnThxwpBkfPbZZ07tzzzzjDFo0KBGx2RnZxuS2NjY2NjY2CywHTt27JpZweev7ISFhaljx446deqUU/upU6cUGRnZ6JisrCxlZmaa+/X19Tp79qy6d+8um8215N8eOBwOxcTE6NixYwoKCvJ0OZbCuW1dnN/Ww7ltPZzb6zMMQ+fPn1d0dPQ1j/P5sOPv768BAwZo69atGj9+vKQr4WXr1q2aO3duo2PsdrvsdrtTW0hISCtXah1BQUH8D6+VcG5bF+e39XBuWw/n9tqCg4Ove4zPhx1JyszM1LRp03TXXXdp0KBBWrp0qS5cuKBHHnnE06UBAAAPs0TYefDBB/XVV19p4cKFKisrU2pqqjZt2tRg0TIAAGh/LBF2JGnu3LlNfm0F97Db7crOzm7wFSBajnPbuji/rYdz23o4t+5jM4zr3a8FAADgu3z+oYIAAADXQtgBAACWRtgBAACWRtgBAACWRtjBdS1atEg2m81pS0pK8nRZPmn79u26//77FR0dLZvNpvXr1zv1G4ahhQsXKioqSgEBARo5cqQOHTrkmWJ9zPXO7fTp0xt8jseMGeOZYn1Mbm6uBg4cqMDAQIWHh2v8+PHm+5KuunjxoubMmaPu3bura9eumjhxYoMn26OhGzm36enpDT67jz/+uIcq9k2EHdyQO+64Q6Wlpeb26aeferokn3ThwgX169dPy5Yta7T/5Zdf1q9//WstX75cO3fu1E033aTRo0fr4sWLbVyp77neuZWkMWPGOH2O33777Tas0Hdt27ZNc+bM0Y4dO7R582bV1tZq1KhRunDhgnnM/Pnz9f7772vt2rXatm2bTp48qQkTJniwat9wI+dWkmbNmuX02X355Zc9VLGPcsvbOGFp2dnZRr9+/TxdhuVIMt59911zv76+3oiMjDSWLFlitlVUVBh2u914++23PVCh7/rXc2sYhjFt2jTje9/7nkfqsZrTp08bkoxt27YZhnHlc+rn52esXbvWPKawsNCQZBQUFHiqTJ/0r+fWMAxj2LBhxo9//GPPFWUBXNnBDTl06JCio6PVs2dPTZkyRSUlJZ4uyXKOHDmisrIyjRw50mwLDg5WWlqaCgoKPFiZdeTl5Sk8PFy9e/fWE088oTNnzni6JJ907tw5SVJoaKgkaffu3aqtrXX67CYlJSk2NpbPbjP967m9avXq1QoLC1OfPn2UlZWlqqoqT5TnsyzzBGW0nrS0NK1atUq9e/dWaWmpcnJyNGTIEB04cECBgYGeLs8yysrKJKnBa04iIiLMPrhuzJgxmjBhghISElRcXKyf/exnysjIUEFBgTp27Ojp8nxGfX29nn76ad19993q06ePpCufXX9//wYvVOaz2zyNnVtJmjx5suLi4hQdHa39+/drwYIFKioq0rp16zxYrW8h7OC6MjIyzJ9TUlKUlpamuLg4/elPf9LMmTM9WBlw4yZNmmT+3LdvX6WkpCgxMVF5eXkaMWKEByvzLXPmzNGBAwdYt9cKmjq3s2fPNn/u27evoqKiNGLECBUXFysxMbGty/RJfI2FZgsJCVGvXr10+PBhT5diKZGRkZLU4A6WU6dOmX1wn549eyosLIzPcTPMnTtXGzdu1CeffKJbbrnFbI+MjNSlS5dUUVHhdDyf3RvX1LltTFpamiTx2W0Gwg6arbKyUsXFxYqKivJ0KZaSkJCgyMhIbd261WxzOBzauXOnBg8e7MHKrOn48eM6c+YMn+MbYBiG5s6dq3fffVcff/yxEhISnPoHDBggPz8/p89uUVGRSkpK+Oxex/XObWP27t0rSXx2m4GvsXBdP/nJT3T//fcrLi5OJ0+eVHZ2tjp27KiHHnrI06X5nMrKSqf/N3bkyBHt3btXoaGhio2N1dNPP60XXnhBt912mxISEvT8888rOjpa48eP91zRPuJa5zY0NFQ5OTmaOHGiIiMjVVxcrJ/+9Ke69dZbNXr0aA9W7RvmzJmjNWvW6L333lNgYKC5Dic4OFgBAQEKDg7WzJkzlZmZqdDQUAUFBWnevHkaPHiwvvWtb3m4eu92vXNbXFysNWvWaOzYserevbv279+v+fPna+jQoUpJSfFw9T7E07eDwfs9+OCDRlRUlOHv72/cfPPNxoMPPmgcPnzY02X5pE8++cSQ1GCbNm2aYRhXbj9//vnnjYiICMNutxsjRowwioqKPFu0j7jWua2qqjJGjRpl9OjRw/Dz8zPi4uKMWbNmGWVlZZ4u2yc0dl4lGStXrjSPqa6uNp588kmjW7duRpcuXYwHHnjAKC0t9VzRPuJ657akpMQYOnSoERoaatjtduPWW281nnnmGePcuXOeLdzH2AzDMNoyXAEAALQl1uwAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wA8JhVq1Y1eFM2ALgbYQcAWlFeXp5sNluDl2QCaDuEHQAAYGmEHQButXHjRoWEhOjy5cuSrryh2Waz6dlnnzWPefTRRzV16lRzf/369brtttvUuXNnjR49WseOHXOa8/3339fAgQPVuXNnhYWF6YEHHrihWmpqarRgwQLFxMTIbrfr1ltv1Ztvvmn2b9u2TYMGDZLdbldUVJSeffZZ1dXVmf3x8fFaunSp05ypqalatGiRuW+z2fSf//mfeuCBB9SlSxfddttt2rBhgyTp6NGjGj58uCSpW7dustlsmj59+g3VDsB9CDsA3GrIkCE6f/68/v73v0u6EijCwsKUl5dnHrNt2zalp6dLkqqqqvTzn/9cb731lv7617+qoqJCkyZNMo/9y1/+ogceeEBjx47V3//+d23dulWDBg26oVoefvhhvf322/r1r3+twsJC/fa3v1XXrl0lSSdOnNDYsWM1cOBA7du3T6+//rrefPNNvfDCC83+m3NycvTDH/5Q+/fv19ixYzVlyhSdPXtWMTEx+vOf/yxJKioqUmlpqX71q181e34ALeTpN5ECsJ4777zTWLJkiWEYhjF+/Hjj5z//ueHv72+cP3/eOH78uCHJ+Oc//2msXLnSkGTs2LHDHFtYWGhIMnbu3GkYhmEMHjzYmDJlSrNrKCoqMiQZmzdvbrT/Zz/7mdG7d2+jvr7ebFu2bJnRtWtX4/Lly4ZhGEZcXJzx6quvOo3r16+fkZ2dbe5LMp577jlzv7Ky0pBkfPjhh4Zh/N/b2L/++utm/w0A3IMrOwDcbtiwYcrLy5NhGMrPz9eECROUnJysTz/9VNu2bVN0dLRuu+02SVKnTp00cOBAc2xSUpJCQkJUWFgo6crXYCNGjGh2DXv37lXHjh01bNiwRvsLCws1ePBg2Ww2s+3uu+9WZWWljh8/3qzflZKSYv580003KSgoSKdPn252zQBaRydPFwDAetLT07VixQrt27dPfn5+SkpKUnp6uvLy8vT11183GUAaExAQ4FINro77pg4dOsgwDKe22traBsf5+fk57dtsNtXX17f49wNwD67sAHC7q+t2Xn31VTPYXA07eXl55nodSaqrq9OuXbvM/aKiIlVUVCg5OVnSlasmW7dubXYNffv2VX19vbZt29Zof3JysgoKCpzCzF//+lcFBgbqlltukST16NFDpaWlZr/D4dCRI0eaVYe/v78kmQu2AbQ9wg4At+vWrZtSUlK0evVqM9gMHTpUe/bs0T//+U+nKzt+fn6aN2+edu7cqd27d2v69On61re+ZS5Czs7O1ttvv63s7GwVFhbqiy++0EsvvXTdGuLj4zVt2jTNmDFD69ev15EjR5SXl6c//elPkqQnn3xSx44d07x583Tw4EG99957ys7OVmZmpjp0uPKfxnvvvVe///3vlZ+fry+++ELTpk1Tx44dm3Uu4uLiZLPZtHHjRn311VeqrKxs1ngALUfYAdAqhg0bpsuXL5thJzQ0VLfffrsiIyPVu3dv87guXbpowYIFmjx5su6++2517dpVf/zjH83+9PR0rV27Vhs2bFBqaqruvfde/e1vf7uhGl5//XV9//vf15NPPqmkpCTNmjVLFy5ckCTdfPPN+uCDD/S3v/1N/fr10+OPP66ZM2fqueeeM8dnZWVp2LBhuu+++zRu3DiNHz9eiYmJzToPN998s3JycvTss88qIiJCc+fObdZ4AC1nM/71C2kAAAAL4coOAACwNMIOAJ+Un5+vrl27NrkBwFV8jQXAJ1VXV+vEiRNN9t96661tWA0Ab0bYAQAAlsbXWAAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNL+Hz1YVmxcRbbXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "Could not interpret value `Label` for `y`. An entry with this name does not appear in `data`.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[110], line 61\u001b[0m\n\u001b[0;32m     58\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msex\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m'\u001b[39m)  \u001b[38;5;66;03m# Example of plotting categorical data\u001b[39;00m\n\u001b[0;32m     60\u001b[0m \u001b[38;5;66;03m# Bivariate analysis\u001b[39;00m\n\u001b[1;32m---> 61\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatterplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mage\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mLabel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Example of scatter plot\u001b[39;00m\n\u001b[0;32m     62\u001b[0m sns\u001b[38;5;241m.\u001b[39mboxplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msex\u001b[39m\u001b[38;5;124m'\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage\u001b[39m\u001b[38;5;124m'\u001b[39m, data\u001b[38;5;241m=\u001b[39mdata)  \u001b[38;5;66;03m# Example of box plot\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;66;03m# Visualizations\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\relational.py:603\u001b[0m, in \u001b[0;36mscatterplot\u001b[1;34m(data, x, y, hue, size, style, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, style_order, legend, ax, **kwargs)\u001b[0m\n\u001b[0;32m    594\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatterplot\u001b[39m(\n\u001b[0;32m    595\u001b[0m     data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m    596\u001b[0m     x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, style\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    600\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    601\u001b[0m ):\n\u001b[1;32m--> 603\u001b[0m     p \u001b[38;5;241m=\u001b[39m \u001b[43m_ScatterPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    604\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    605\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    606\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlegend\u001b[49m\n\u001b[0;32m    607\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    609\u001b[0m     p\u001b[38;5;241m.\u001b[39mmap_hue(palette\u001b[38;5;241m=\u001b[39mpalette, order\u001b[38;5;241m=\u001b[39mhue_order, norm\u001b[38;5;241m=\u001b[39mhue_norm)\n\u001b[0;32m    610\u001b[0m     p\u001b[38;5;241m.\u001b[39mmap_size(sizes\u001b[38;5;241m=\u001b[39msizes, order\u001b[38;5;241m=\u001b[39msize_order, norm\u001b[38;5;241m=\u001b[39msize_norm)\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\relational.py:390\u001b[0m, in \u001b[0;36m_ScatterPlotter.__init__\u001b[1;34m(self, data, variables, legend)\u001b[0m\n\u001b[0;32m    381\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, variables\u001b[38;5;241m=\u001b[39m{}, legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    382\u001b[0m \n\u001b[0;32m    383\u001b[0m     \u001b[38;5;66;03m# TODO this is messy, we want the mapping to be agnostic about\u001b[39;00m\n\u001b[0;32m    384\u001b[0m     \u001b[38;5;66;03m# the kind of plot to draw, but for the time being we need to set\u001b[39;00m\n\u001b[0;32m    385\u001b[0m     \u001b[38;5;66;03m# this information so the SizeMapping can use it\u001b[39;00m\n\u001b[0;32m    386\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_default_size_range \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    387\u001b[0m         np\u001b[38;5;241m.\u001b[39mr_[\u001b[38;5;241m.5\u001b[39m, \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39msquare(mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m    388\u001b[0m     )\n\u001b[1;32m--> 390\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    392\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend \u001b[38;5;241m=\u001b[39m legend\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:634\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m    629\u001b[0m \u001b[38;5;66;03m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[0;32m    630\u001b[0m \u001b[38;5;66;03m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[0;32m    631\u001b[0m \u001b[38;5;66;03m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[0;32m    632\u001b[0m \u001b[38;5;66;03m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[0;32m    633\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_var_ordered \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m}  \u001b[38;5;66;03m# alt., used DefaultDict\u001b[39;00m\n\u001b[1;32m--> 634\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    636\u001b[0m \u001b[38;5;66;03m# TODO Lots of tests assume that these are called to initialize the\u001b[39;00m\n\u001b[0;32m    637\u001b[0m \u001b[38;5;66;03m# mappings to default values on class initialization. I'd prefer to\u001b[39;00m\n\u001b[0;32m    638\u001b[0m \u001b[38;5;66;03m# move away from that and only have a mapping when explicitly called.\u001b[39;00m\n\u001b[0;32m    639\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhue\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyle\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:679\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m    674\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    675\u001b[0m     \u001b[38;5;66;03m# When dealing with long-form input, use the newer PlotData\u001b[39;00m\n\u001b[0;32m    676\u001b[0m     \u001b[38;5;66;03m# object (internal but introduced for the objects interface)\u001b[39;00m\n\u001b[0;32m    677\u001b[0m     \u001b[38;5;66;03m# to centralize / standardize data consumption logic.\u001b[39;00m\n\u001b[0;32m    678\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 679\u001b[0m     plot_data \u001b[38;5;241m=\u001b[39m \u001b[43mPlotData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    680\u001b[0m     frame \u001b[38;5;241m=\u001b[39m plot_data\u001b[38;5;241m.\u001b[39mframe\n\u001b[0;32m    681\u001b[0m     names \u001b[38;5;241m=\u001b[39m plot_data\u001b[38;5;241m.\u001b[39mnames\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_core\\data.py:58\u001b[0m, in \u001b[0;36mPlotData.__init__\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[0;32m     52\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m     53\u001b[0m     data: DataSource,\n\u001b[0;32m     54\u001b[0m     variables: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, VariableSpec],\n\u001b[0;32m     55\u001b[0m ):\n\u001b[0;32m     57\u001b[0m     data \u001b[38;5;241m=\u001b[39m handle_data_source(data)\n\u001b[1;32m---> 58\u001b[0m     frame, names, ids \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_assign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     60\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframe \u001b[38;5;241m=\u001b[39m frame\n\u001b[0;32m     61\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;241m=\u001b[39m names\n",
      "File \u001b[1;32mc:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_core\\data.py:232\u001b[0m, in \u001b[0;36mPlotData._assign_variables\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m    230\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    231\u001b[0m         err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn entry with this name does not appear in `data`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 232\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[0;32m    234\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    235\u001b[0m \n\u001b[0;32m    236\u001b[0m     \u001b[38;5;66;03m# Otherwise, assume the value somehow represents data\u001b[39;00m\n\u001b[0;32m    237\u001b[0m \n\u001b[0;32m    238\u001b[0m     \u001b[38;5;66;03m# Ignore empty data structures\u001b[39;00m\n\u001b[0;32m    239\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, Sized) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(val) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "\u001b[1;31mValueError\u001b[0m: Could not interpret value `Label` for `y`. An entry with this name does not appear in `data`."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the training dataset\n",
    "train_df = pd.read_csv(\"train.csv\")\n",
    "\n",
    "# Load the test dataset\n",
    "test_df = pd.read_csv(\"test.csv\")\n",
    "\n",
    "# Combine the datasets for preprocessing and feature engineering\n",
    "combined_df = pd.concat([train_df, test_df], ignore_index=True)\n",
    "\n",
    "# Data Preprocessing\n",
    "# Convert age to numeric type\n",
    "combined_df['age'] = pd.to_numeric(combined_df['age'], errors='coerce')\n",
    "\n",
    "# Convert categorical variables to binary\n",
    "combined_df['sex'] = combined_df['sex'].map({'female': 0, 'male': 1})\n",
    "\n",
    "# Fill missing values with appropriate methods, e.g., mean, median, mode\n",
    "\n",
    "# Feature Engineering\n",
    "# You can create new features or transform existing ones based on domain knowledge\n",
    "# For example, you can create a new feature based on the duration of hypertension\n",
    "combined_df['hypertension_duration'] = np.where(combined_df['Symptomatic_hypertension'] == 'TRUE',\n",
    "                                                combined_df['no_arterial_hypertension_duration'],\n",
    "                                                np.nan)\n",
    "\n",
    "# Exploratory Data Analysis (EDA)\n",
    "# Visualize the distribution of numerical features\n",
    "numeric_cols = combined_df.select_dtypes(include=np.number).columns\n",
    "for col in numeric_cols:\n",
    "    sns.histplot(combined_df[col])\n",
    "    plt.title(col)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the dataset (assuming it's in CSV format)\n",
    "data = pd.read_csv(\"train.csv\")\n",
    "\n",
    "# Perform data preprocessing\n",
    "# Handle missing values\n",
    "# Encode categorical variables\n",
    "\n",
    "# Perform feature engineering\n",
    "# Create new features\n",
    "# Transform existing features\n",
    "\n",
    "# Exploratory Data Analysis (EDA)\n",
    "# Univariate analysis\n",
    "data.describe()  # Summary statistics for numerical features\n",
    "data['sex'].value_counts().plot(kind='bar')  # Example of plotting categorical data\n",
    "\n",
    "# Bivariate analysis\n",
    "sns.scatterplot(x='age', y='Label', data=data)  # Example of scatter plot\n",
    "sns.boxplot(x='sex', y='age', data=data)  # Example of box plot\n",
    "\n",
    "# Visualizations\n",
    "sns.histplot(data['age'], bins=20, kde=True)  # Histogram\n",
    "sns.pairplot(data)  # Pairplot for multiple variables\n",
    "\n",
    "# Summary statistics\n",
    "print(data.describe())  # Summary statistics for numerical features\n",
    "print(data['sex'].value_counts())  # Frequency table for a categorical feature\n",
    "\n",
    "# Show plots\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Visualize the correlation matrix\n",
    "correlation_matrix = combined_df.corr()\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Visualize the distribution of categorical features\n",
    "categorical_cols = combined_df.select_dtypes(include='object').columns\n",
    "for col in categorical_cols:\n",
    "    sns.countplot(y=combined_df[col])\n",
    "    plt.title(col)\n",
    "    plt.show()\n",
    "\n",
    "# Explore relationships between features and target variable (if available)\n",
    "# For example, you can use scatter plots, box plots, or violin plots\n",
    "sns.scatterplot(data=combined_df, x='age', y='Coronary_heart_disease(CHD)')\n",
    "plt.title('Relationship between Age and Coronary Heart Disease')\n",
    "plt.show()\n",
    "\n",
    "# After EDA, you can proceed with feature selection, modeling, etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.89243697 0.89243697 0.89411765 0.89411765 0.89327731 0.89243697\n",
      " 0.88907563 0.88823529 0.88739496        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.89159664 0.89411765 0.89411765 0.89243697 0.89159664 0.89243697\n",
      " 0.88655462 0.89243697 0.88655462        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.89243697 0.89243697 0.89243697 0.89411765 0.8907563  0.89243697\n",
      " 0.88655462 0.88823529 0.89243697        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.89159664 0.89243697 0.89327731 0.89243697 0.89243697 0.89243697\n",
      " 0.88991597 0.8907563  0.88571429        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.9907563 0.9907563 0.9907563 0.9907563 0.9907563 0.9907563 0.9907563\n",
      " 0.9907563 0.9907563       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan 0.9907563 0.9907563 0.9907563 0.9907563 0.9907563 0.9907563\n",
      " 0.9907563 0.9907563 0.9907563       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan 0.9907563 0.9907563 0.9907563 0.9907563 0.9907563\n",
      " 0.9907563 0.9907563 0.9907563 0.9907563       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan 0.9907563 0.9907563 0.9907563 0.9907563\n",
      " 0.9907563 0.9907563 0.9907563 0.9907563 0.9907563       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan       nan       nan       nan\n",
      "       nan       nan       nan       nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.97394958 0.97394958 0.97394958 0.97310924 0.97394958 0.97394958\n",
      " 0.97310924 0.97310924 0.97310924        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97394958 0.97394958 0.97394958 0.97394958 0.97394958 0.97394958\n",
      " 0.97394958 0.97394958 0.97310924        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97394958 0.97394958 0.97394958 0.97394958 0.97394958 0.97394958\n",
      " 0.97310924 0.97310924 0.97226891        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.97394958 0.97394958 0.97394958 0.97394958 0.97394958 0.97394958\n",
      " 0.97226891 0.97310924 0.97310924        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.9605042  0.9605042  0.9605042  0.95966387 0.9605042  0.9605042\n",
      " 0.95462185 0.95378151 0.95882353        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9605042  0.9605042  0.9605042  0.95966387 0.95966387 0.9605042\n",
      " 0.95462185 0.95462185 0.95546218        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9605042  0.9605042  0.9605042  0.95966387 0.9605042  0.9605042\n",
      " 0.95714286 0.95294118 0.95294118        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9605042  0.9605042  0.9605042  0.9605042  0.9605042  0.9605042\n",
      " 0.95630252 0.95714286 0.95714286        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.96470588 0.96386555 0.96470588 0.96218487 0.96386555 0.96302521\n",
      " 0.95966387 0.95882353 0.95798319        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.96470588 0.96470588 0.96386555 0.96302521 0.96218487 0.96386555\n",
      " 0.95882353 0.95798319 0.95546218        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.96470588 0.96470588 0.96470588 0.96218487 0.96302521 0.96302521\n",
      " 0.95798319 0.95630252 0.95798319        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.96470588 0.96470588 0.96470588 0.96302521 0.96302521 0.96302521\n",
      " 0.95882353 0.95714286 0.95714286        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.90504202 0.90504202 0.90420168 0.89159664 0.89411765 0.89159664\n",
      " 0.87394958 0.8789916  0.87983193        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.90504202 0.90420168 0.90420168 0.89327731 0.89747899 0.89411765\n",
      " 0.87647059 0.87226891 0.87394958        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.90504202 0.90420168 0.90504202 0.88991597 0.89579832 0.89495798\n",
      " 0.87226891 0.8789916  0.87478992        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.90504202 0.90420168 0.90420168 0.89159664 0.89159664 0.89243697\n",
      " 0.87058824 0.87478992 0.87563025        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.9697479  0.9697479  0.9697479  0.9697479  0.9697479  0.9697479\n",
      " 0.96722689 0.96806723 0.96806723        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9697479  0.9697479  0.9697479  0.9697479  0.96890756 0.96890756\n",
      " 0.96470588 0.96638655 0.96722689        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9697479  0.9697479  0.9697479  0.9697479  0.9697479  0.96890756\n",
      " 0.96638655 0.96722689 0.96722689        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9697479  0.9697479  0.9697479  0.9697479  0.9697479  0.9697479\n",
      " 0.96722689 0.96806723 0.96722689        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.9605042  0.95882353 0.95798319 0.95042017 0.95546218 0.95042017\n",
      " 0.93613445 0.93529412 0.9394958         nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9605042  0.95882353 0.95798319 0.9512605  0.95210084 0.95210084\n",
      " 0.93697479 0.93781513 0.9394958         nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9605042  0.95966387 0.95714286 0.95294118 0.95210084 0.95042017\n",
      " 0.93697479 0.9394958  0.93529412        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9605042  0.95882353 0.95798319 0.95378151 0.9512605  0.95042017\n",
      " 0.93781513 0.93613445 0.93781513        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.79747899 0.79747899 0.79243697 0.77731092 0.78571429 0.77731092\n",
      " 0.75546218 0.74453782 0.75210084        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.79579832 0.79831933 0.79411765 0.78235294 0.78739496 0.7789916\n",
      " 0.75462185 0.7487395  0.7605042         nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.79831933 0.79747899 0.79327731 0.77478992 0.77731092 0.78907563\n",
      " 0.7487395  0.7605042  0.75630252        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.79495798 0.8        0.79327731 0.78067227 0.77647059 0.78487395\n",
      " 0.7512605  0.75798319 0.75210084        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.9092437  0.91008403 0.91092437 0.90840336 0.90168067 0.90504202\n",
      " 0.89327731 0.89579832 0.89159664        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9092437  0.91008403 0.91008403 0.90756303 0.90672269 0.90588235\n",
      " 0.8907563  0.88823529 0.89915966        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9092437  0.9092437  0.91092437 0.90588235 0.90336134 0.90756303\n",
      " 0.89159664 0.89243697 0.89327731        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9092437  0.91092437 0.91008403 0.90588235 0.90672269 0.90588235\n",
      " 0.89243697 0.89243697 0.89747899        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "540 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 427, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 402, in fit\n",
      "    raise ValueError(\n",
      "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.91848739 0.92016807 0.91764706 0.90252101 0.90588235 0.90084034\n",
      " 0.88907563 0.88235294 0.88907563        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.91848739 0.91932773 0.91848739 0.90756303 0.90672269 0.9092437\n",
      " 0.89243697 0.89411765 0.8907563         nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.91764706 0.91848739 0.91764706 0.90672269 0.90504202 0.90588235\n",
      " 0.89579832 0.88991597 0.89159664        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.91848739 0.91848739 0.91764706 0.89915966 0.90588235 0.90504202\n",
      " 0.88823529 0.8907563  0.88991597        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "c:\\Users\\hrith\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:780: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight (\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for atr_fib: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 6}\n",
      "Best parameters for supr_tach: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 2}\n",
      "Best parameters for ven_tach: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 2}\n",
      "Best parameters for ven_fib: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 2}\n",
      "Best parameters for th_d_av: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 2}\n",
      "Best parameters for pul_ede: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 2}\n",
      "Best parameters for myo_rup: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 2}\n",
      "Best parameters for dres_syn: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 2}\n",
      "Best parameters for ch_hf: {'classifier__max_depth': None, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 4}\n",
      "Best parameters for re_myin: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 6}\n",
      "Best parameters for p_inf_ang: {'classifier__max_depth': 50, 'classifier__max_samples': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 4}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.metrics import fbeta_score, make_scorer\n",
    "\n",
    "# Load the data\n",
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')\n",
    "\n",
    "# Store the IDs for the final submission\n",
    "submission_ids = test_data['ID']  # Store original IDs for final submission\n",
    "\n",
    "# Drop irrelevant columns (ID)\n",
    "train_data.drop(columns=['ID'], inplace=True)\n",
    "test_data.drop(columns=['ID'], inplace=True)\n",
    "\n",
    "# Define features and target variables\n",
    "X_train = train_data.drop(columns=['atr_fib', 'supr_tach', 'ven_tach', 'ven_fib', 'th_d_av', 'pul_ede', 'myo_rup', 'dres_syn', 'ch_hf', 're_myin', 'p_inf_ang'])\n",
    "y_train = train_data[['atr_fib', 'supr_tach', 'ven_tach', 'ven_fib', 'th_d_av', 'pul_ede', 'myo_rup', 'dres_syn', 'ch_hf', 're_myin', 'p_inf_ang']]\n",
    "\n",
    "# Preprocessing pipeline for numerical features\n",
    "numeric_features = X_train.select_dtypes(include=['int64', 'float64']).columns\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler())])\n",
    "\n",
    "# Preprocessing pipeline for categorical features\n",
    "categorical_features = X_train.select_dtypes(include=['object']).columns\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
    "\n",
    "# Combine preprocessing pipelines\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)])\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'classifier__max_depth': [50, 60, 70, None],\n",
    "    'classifier__min_samples_split': [2, 4, 6],\n",
    "    'classifier__min_samples_leaf': [1, 3, 5]\n",
    "}\n",
    "\n",
    "# Define models for each target variable with GridSearchCV\n",
    "models = {}\n",
    "for column in y_train.columns:\n",
    "    model = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                            ('classifier', RandomForestClassifier(n_estimators=150,\n",
    "                                                                  criterion='entropy',\n",
    "                                                                  max_features='sqrt',\n",
    "                                                                  warm_start=True,\n",
    "                                                                  class_weight='balanced_subsample',\n",
    "                                                                  bootstrap=False,\n",
    "                                                                  n_jobs=-1,max_samples=None))])\n",
    "    grid_search = GridSearchCV(model, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
    "    grid_search.fit(X_train, y_train[column])\n",
    "    models[column] = grid_search\n",
    "\n",
    "# Display the best parameters\n",
    "for column, grid_model in models.items():\n",
    "    print(f\"Best parameters for {column}: {grid_model.best_params_}\")\n",
    "\n",
    "# Make predictions on the test dataset using the models with the best parameters\n",
    "predictions = {}\n",
    "for column, grid_model in models.items():\n",
    "    predictions[column] = grid_model.predict_proba(test_data)[:, 1]\n",
    "\n",
    "# Prepare submission file\n",
    "submission_df = pd.DataFrame({'ID': submission_ids})  # Use original IDs for submission\n",
    "for column in y_train.columns:\n",
    "    submission_df[column] = predictions[column]\n",
    "\n",
    "# Save submission file\n",
    "submission_df.to_csv('submissionwith.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3.2\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Load the data\n",
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')\n",
    "\n",
    "# Store the IDs for the final submission\n",
    "submission_ids = test_data['ID']  # Store original IDs for final submission\n",
    "\n",
    "# Drop irrelevant columns (ID)\n",
    "train_data.drop(columns=['ID'], inplace=True)\n",
    "test_data.drop(columns=['ID'], inplace=True)\n",
    "\n",
    "# Perform replacements and conversions\n",
    "replacement_mapping = {'?': 0, 'TRUE': 1, 'FALSE': 0, 'YES': 1, 'NO': 0, 'NaN':0, 'none':0, 'never':0, 'True': 1, 'False': 0, 'NA':0, 'inf':1, 'yes': 1, 'no': 0,}\n",
    "train_data.replace(replacement_mapping, inplace=True)\n",
    "test_data.replace(replacement_mapping, inplace=True)\n",
    "\n",
    "train_data=train_data.fillna(0)\n",
    "test_data=test_data.fillna(0)\n",
    "\n",
    "# Convert 'sex' column to binary (0, 1)\n",
    "train_data['sex'] = train_data['sex'].map({'female': 0, 'male': 1})\n",
    "test_data['sex'] = test_data['sex'].map({'female': 0, 'male': 1})\n",
    "\n",
    "# Define mapping for myocardial_infarctions_anamnesis\n",
    "infarction_mapping = {'zero': 0, 'one': 1, 'two': 2, 'three': 3}\n",
    "\n",
    "# Convert 'myocardial_infarctions_anamnesis' column to numerical values\n",
    "train_data['myocardial_infarctions_anamnesis'] = train_data['myocardial_infarctions_anamnesis'].map(infarction_mapping)\n",
    "test_data['myocardial_infarctions_anamnesis'] = test_data['myocardial_infarctions_anamnesis'].map(infarction_mapping)\n",
    "\n",
    "# Convert string numbers in 'essential_hypertension' to integers, remove 'Stage' from values, replace None and NaN with 0\n",
    "train_data['essential_hypertension'] = train_data['essential_hypertension'].replace({'none': 0, 'Stage ': ''}, regex=True).fillna(0)\n",
    "test_data['essential_hypertension'] = test_data['essential_hypertension'].replace({'none': 0, 'Stage ': ''}, regex=True).fillna(0)\n",
    "\n",
    "# Convert string numbers in 'chronic_Heart_failure(HF)' to integers, remove 'Stage' from values, replace None and NaN with 0\n",
    "train_data['chronic_Heart_failure(HF)'] = train_data['chronic_Heart_failure(HF)'].replace({'none': 0, ' stage': ''}, regex=True).fillna(0)\n",
    "test_data['chronic_Heart_failure(HF)'] = test_data['chronic_Heart_failure(HF)'].replace({'none': 0, ' stage': ''}, regex=True).fillna(0)\n",
    "\n",
    "train_data['FC_of_angina_pectoris'] = train_data['FC_of_angina_pectoris'].replace({'none': 0, ' FC': ''}, regex=True).fillna(0)\n",
    "test_data['FC_of_angina_pectoris'] = test_data['FC_of_angina_pectoris'].replace({'none': 0, ' FC': ''}, regex=True).fillna(0)\n",
    "\n",
    "infarction_mapping2 = {'I':1,'II': 2, 'III': 3, 'IV': 4,'IIB':3}\n",
    "train_data['FC_of_angina_pectoris'] = train_data['FC_of_angina_pectoris'].map(infarction_mapping2).fillna(0)\n",
    "test_data['FC_of_angina_pectoris'] = test_data['FC_of_angina_pectoris'].map(infarction_mapping2).fillna(0)\n",
    "\n",
    "train_data['chronic_Heart_failure(HF)'] = train_data['chronic_Heart_failure(HF)'].map(infarction_mapping2)\n",
    "test_data['chronic_Heart_failure(HF)'] = test_data['chronic_Heart_failure(HF)'].map(infarction_mapping2)\n",
    "\n",
    "# Define features and target variables\n",
    "X_train = train_data.drop(columns=['atr_fib', 'supr_tach', 'ven_tach', 'ven_fib', 'th_d_av', 'pul_ede', 'myo_rup', 'dres_syn', 'ch_hf', 're_myin', 'p_inf_ang','anterior_myocardial_infarction', 'lateral_myocardial_infarction', 'inferior_myocardial_infarction', 'posterior_myocardial_infarction'])\n",
    "y_train = train_data[['atr_fib', 'supr_tach', 'ven_tach', 'ven_fib', 'th_d_av', 'pul_ede', 'myo_rup', 'dres_syn', 'ch_hf', 're_myin', 'p_inf_ang']]\n",
    "test_data.drop(columns=['anterior_myocardial_infarction', 'lateral_myocardial_infarction', 'inferior_myocardial_infarction', 'posterior_myocardial_infarction'], inplace=True)\n",
    "\n",
    "# Extract integer values from 'Exertional_angina_pectoris_anamnesis'\n",
    "X_train['Exertional_angina_pectoris_anamnesis'] = X_train['Exertional_angina_pectoris_anamnesis'].str.extract(r'(\\d+)')\n",
    "X_train['Exertional_angina_pectoris_anamnesis'] = X_train['Exertional_angina_pectoris_anamnesis'].astype(float).fillna(0).astype(int)\n",
    "\n",
    "# Do the same for test data if needed\n",
    "test_data['Exertional_angina_pectoris_anamnesis'] = test_data['Exertional_angina_pectoris_anamnesis'].str.extract(r'(\\d+)')\n",
    "test_data['Exertional_angina_pectoris_anamnesis'] = test_data['Exertional_angina_pectoris_anamnesis'].astype(float).fillna(0).astype(int)\n",
    "\n",
    "# Convert 'no_arterial_hypertension_duration' column to numerical values\n",
    "X_train['no_arterial_hypertension_duration'] = X_train['no_arterial_hypertension_duration'].str.extract(r'(\\d+)')\n",
    "X_train['no_arterial_hypertension_duration'] = pd.to_numeric(X_train['no_arterial_hypertension_duration'], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "test_data['no_arterial_hypertension_duration'] = test_data['no_arterial_hypertension_duration'].str.extract(r'(\\d+)')\n",
    "test_data['no_arterial_hypertension_duration'] = pd.to_numeric(test_data['no_arterial_hypertension_duration'], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "# Extract integer value from time_beg_chd column\n",
    "X_train['time_beg_chd'] = X_train['time_beg_chd'].str.extract('(\\d+)', expand=False).astype(float).fillna(0).astype(int)\n",
    "test_data['time_beg_chd'] = test_data['time_beg_chd'].str.extract('(\\d+)', expand=False).astype(float).fillna(0).astype(int)\n",
    "\n",
    "# Mapping for relapse columns\n",
    "relapse_mapping = {'There is no relapse': 0, 'Only one': 1, '2 times': 2, '3 or more times': 3}\n",
    "\n",
    "# Apply mapping to the specified columns\n",
    "X_train['Relap_1hour'] = X_train['Relap_1hour'].map(relapse_mapping).fillna(0)\n",
    "X_train['relap_2day'] = X_train['relap_2day'].map(relapse_mapping).fillna(0)\n",
    "X_train['relap_3day'] = X_train['relap_3day'].map(relapse_mapping).fillna(0)\n",
    "\n",
    "test_data['Relap_1hour'] = test_data['Relap_1hour'].map(relapse_mapping).fillna(0)\n",
    "test_data['relap_2day'] = test_data['relap_2day'].map(relapse_mapping).fillna(0)\n",
    "test_data['relap_3day'] = test_data['relap_3day'].map(relapse_mapping).fillna(0)\n",
    "\n",
    "chd_mapping = {'none': 0, 'unstable': 2, 'exertional': 3}\n",
    "\n",
    "# Convert 'Coronary_heart_disease(CHD)' column to numerical values\n",
    "X_train['Coronary_heart_disease(CHD)'] = X_train['Coronary_heart_disease(CHD)'].map(chd_mapping).fillna(0).astype(int)\n",
    "test_data['Coronary_heart_disease(CHD)'] = test_data['Coronary_heart_disease(CHD)'].map(chd_mapping).fillna(0).astype(int)\n",
    "\n",
    "# Mapping for nsaids_icu_1hour column\n",
    "nsaids_mapping = {'once': 1, 'twice': 2, 'thrice': 3, 'four or more times': 4}\n",
    "\n",
    "# Apply mapping to the specified column\n",
    "X_train['nsaids_icu_1hour'] = X_train['nsaids_icu_1hour'].map(nsaids_mapping).fillna(0)\n",
    "test_data['nsaids_icu_1hour'] = test_data['nsaids_icu_1hour'].map(nsaids_mapping).fillna(0)\n",
    "\n",
    "# Define mapping dictionary for Heredity_on_CHD\n",
    "heredity_mapping = {'not burdened': 1, 'burdened': 2}\n",
    "\n",
    "# Convert 'Heredity_on_CHD' column to numerical values\n",
    "X_train['Heredity_on_CHD'] = X_train['Heredity_on_CHD'].map(heredity_mapping).fillna(0).astype(int)\n",
    "test_data['Heredity_on_CHD'] = test_data['Heredity_on_CHD'].map(heredity_mapping).fillna(0).astype(int)\n",
    "# Preprocessing pipeline for numerical features\n",
    "numeric_features = X_train.select_dtypes(include=['int64', 'float64']).columns\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler())])\n",
    "\n",
    "# Preprocessing pipeline for categorical features\n",
    "categorical_features = X_train.select_dtypes(include=['object']).columns\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', KNNImputer(n_neighbors=5,weights='distance')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
    "\n",
    "# Combine preprocessing pipelines\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)])\n",
    "X_train.replace(replacement_mapping, inplace=True)\n",
    "X_train.fillna(0, inplace=True)\n",
    "X_train = X_train.astype('float64')\n",
    "# Define models for each target variable\n",
    "models = {}\n",
    "for column in y_train.columns:\n",
    "    model = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                            ('classifier', SVC(probability=True))])  \n",
    "    model.fit(X_train, y_train[column])\n",
    "    models[column] = model\n",
    "\n",
    "# Make predictions on the test dataset\n",
    "X_test = test_data\n",
    "predictions = {}\n",
    "for column, model in models.items():\n",
    "    predictions[column] = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Prepare submission file\n",
    "submission_df = pd.DataFrame({'ID': submission_ids})  # Use original IDs for submission\n",
    "for column in y_train.columns:\n",
    "    submission_df[column] = predictions[column]\n",
    "\n",
    "# Save submission file\n",
    "submission_df.to_csv('submissionwith.csv', index=False)"
   ]
  }
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