Heart Disease Cleveland.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f130564a",
   "metadata": {},
   "source": [
    "# 1. Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b3d8493",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import StratifiedKFold,train_test_split,GridSearchCV\n",
    "from sklearn.metrics import precision_score, recall_score, roc_auc_score, precision_recall_curve,plot_confusion_matrix,auc\n",
    "from sklearn.metrics import f1_score, fbeta_score, accuracy_score, average_precision_score,confusion_matrix,roc_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from lofo import LOFOImportance, Dataset, plot_importance\n",
    "from yellowbrick.model_selection import LearningCurve\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d7f23db",
   "metadata": {},
   "source": [
    "# 2. Reading The Data and Check Missing Values/Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ede3fd8",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>282</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   69    1   0       160   234    1        2      131      0     0.10      1   \n",
       "1   69    0   0       140   239    0        0      151      0     1.80      0   \n",
       "2   66    0   0       150   226    0        0      114      0     2.60      2   \n",
       "3   65    1   0       138   282    1        2      174      0     1.40      1   \n",
       "4   64    1   0       110   211    0        2      144      1     1.80      1   \n",
       "\n",
       "   ca  thal  condition  \n",
       "0   1     0          0  \n",
       "1   2     0          0  \n",
       "2   0     0          0  \n",
       "3   1     0          1  \n",
       "4   0     0          0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"heart_cleveland_upload.csv\")\n",
    "df = data.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc3aa0f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "sex          0\n",
       "cp           0\n",
       "trestbps     0\n",
       "chol         0\n",
       "fbs          0\n",
       "restecg      0\n",
       "thalach      0\n",
       "exang        0\n",
       "oldpeak      0\n",
       "slope        0\n",
       "ca           0\n",
       "thal         0\n",
       "condition    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Missing Values:\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ba0fec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 297 entries, 0 to 296\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   age        297 non-null    int64  \n",
      " 1   sex        297 non-null    int64  \n",
      " 2   cp         297 non-null    int64  \n",
      " 3   trestbps   297 non-null    int64  \n",
      " 4   chol       297 non-null    int64  \n",
      " 5   fbs        297 non-null    int64  \n",
      " 6   restecg    297 non-null    int64  \n",
      " 7   thalach    297 non-null    int64  \n",
      " 8   exang      297 non-null    int64  \n",
      " 9   oldpeak    297 non-null    float64\n",
      " 10  slope      297 non-null    int64  \n",
      " 11  ca         297 non-null    int64  \n",
      " 12  thal       297 non-null    int64  \n",
      " 13  condition  297 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 32.6 KB\n"
     ]
    }
   ],
   "source": [
    "# Checking Data Types:\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "785866c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    201\n",
      "0     96\n",
      "Name: sex, dtype: int64\n",
      "3    142\n",
      "2     83\n",
      "1     49\n",
      "0     23\n",
      "Name: cp, dtype: int64\n",
      "0    254\n",
      "1     43\n",
      "Name: fbs, dtype: int64\n",
      "0    147\n",
      "2    146\n",
      "1      4\n",
      "Name: restecg, dtype: int64\n",
      "0    200\n",
      "1     97\n",
      "Name: exang, dtype: int64\n",
      "0    139\n",
      "1    137\n",
      "2     21\n",
      "Name: slope, dtype: int64\n",
      "0    174\n",
      "1     65\n",
      "2     38\n",
      "3     20\n",
      "Name: ca, dtype: int64\n",
      "0    164\n",
      "2    115\n",
      "1     18\n",
      "Name: thal, dtype: int64\n",
      "0    160\n",
      "1    137\n",
      "Name: condition, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking if features are really numeric:\n",
    "\n",
    "for i in df.columns:\n",
    "    if df[i].nunique() < 10:\n",
    "        print(df[i].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276864e8",
   "metadata": {},
   "source": [
    "**So far we saw that,**\n",
    "* No missing values.\n",
    "* Data types are correct.\n",
    "* Having only 297 observations.\n",
    "* Actual numerical features: age, trestbps, chol, thalach, oldpeak\n",
    "* Categorical features: sex, cp, fbs, restecg, exang, slope, ca, thal\n",
    "* Target: condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d4862ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's note the numerical features:\n",
    "really_nums = [\"age\",\"trestbps\",\"chol\",\"thalach\",\"oldpeak\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "93052357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the balance for our target:\n",
    "sns.countplot(df.condition,label=\"Count\"); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a1c7f2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Condition 1 ratio: % 53.87\n"
     ]
    }
   ],
   "source": [
    "print(\"Condition 1 ratio: %\", round(df.condition.value_counts()[0] / df.condition.count(),4)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6a9dae",
   "metadata": {},
   "source": [
    "**Good, target is not imbalanced. No need to worry. Let's start building base model:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deef1175",
   "metadata": {},
   "source": [
    "# Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "58af53a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 266 candidates, totalling 2660 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=40, shuffle=True),\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(remainder='passthrough',\n",
       "                                                          transformers=[('num',\n",
       "                                                                         Pipeline(steps=[('scaler',\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['age',\n",
       "                                                                          'trestbps',\n",
       "                                                                          'chol',\n",
       "                                                                          'thalach',\n",
       "                                                                          'oldpeak'])])),\n",
       "                                       ('classifier',\n",
       "                                        LogisticRegression(random_state=40))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid=[...\n",
       "                          'classifier__penalty': ['l1'],\n",
       "                          'classifier__solver': ['liblinear', 'saga']},\n",
       "                         {'classifier__C': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  , 1.05, 1.1 , 1.15,\n",
       "       1.2 , 1.25, 1.3 , 1.35, 1.4 , 1.45, 1.5 , 1.55, 1.6 , 1.65, 1.7 ,\n",
       "       1.75, 1.8 , 1.85, 1.9 , 1.95]),\n",
       "                          'classifier__penalty': ['l2'],\n",
       "                          'classifier__solver': ['liblinear', 'newton-cg',\n",
       "                                                 'lbfgs', 'sag', 'saga']}],\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 40 \n",
    "train, test = train_test_split(df, test_size=0.30, random_state=seed, stratify=df.condition)\n",
    "\n",
    "y_train = train['condition']\n",
    "X_train = train.drop('condition', axis=1)\n",
    "y_test = test['condition']\n",
    "X_test = test.drop('condition', axis=1)\n",
    "\n",
    "num_transformer = Pipeline(steps=[('scaler', StandardScaler())\n",
    "                                 ])\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[('num', num_transformer, really_nums)],\n",
    "                                 remainder = 'passthrough') \n",
    "\n",
    "\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('classifier',   LogisticRegression(random_state=seed))\n",
    "                          ]\n",
    "                   )\n",
    "\n",
    "params = [{'classifier__C'      : np.arange(0.1, 2.0, 0.05), \n",
    "           'classifier__penalty': ['l1'], \n",
    "           'classifier__solver' : ['liblinear', 'saga']},\n",
    "          \n",
    "          {'classifier__C'      : np.arange(0.1, 2.0, 0.05), \n",
    "           'classifier__penalty': ['l2'], \n",
    "           'classifier__solver' : ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga']}\n",
    "         ]\n",
    "\n",
    "kfold = StratifiedKFold(n_splits = 10, random_state = seed, shuffle=True) \n",
    "grid = GridSearchCV(pipeline, param_grid = params, cv = kfold, verbose = 1, n_jobs = -1)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb22acfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training accuracy: 0.855072463768116\n",
      "Test accuracy    : 0.8333333333333334\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAAFXCAYAAAD9BrYbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVHUlEQVR4nO3dfXQVhZnH8d/cmxfIC1LzgrxIFChvhhRjKb4s6IEqqxZQq+K2xLejtKbgloNZyjlJiIAUQagLWKroWlBsYasSMHDQaHexqEWxQbJixGooEQISScibJGRm/+B4LUJ6tX1uLpN+P39lZsidh3sOX2bmvozjeZ4nAMA/LBDtAQCgsyCoAGCEoAKAEYIKAEYIKgAYiYn2AJLkuq4aGxsVGxsrx3GiPQ4AnJbneWptbVViYqICgVOPR8+IoDY2Nur999+P9hgA8JUMHDhQycnJp6w/I4IaGxsrSRqQOltxwU+jPA06i9tGDo32COhkuqUn657HJ4ea9WVnRFA/P82PC36q+JhPojwNOosjB+qiPQI6qfYuTfKiFAAYIagAYISgAoARggoARggqABghqABghKACgBGCCgBGCCoAGCGoAGCEoAKAEYIKAEYIKgAYIagAYISgAoARggoARggqABghqABghKACgBGCCgBGCCoAGCGoAGCEoAKAEYIKAEYIKgAYIagAYISgAoARggoARggqABghqABghKACgBGCCgBGCCoAGCGoAGCEoAKAEYIKAEYIKgAYIagAYISgAoARggoARggqABghqABghKACgBGCCgBGCCoAGCGoAGCEoAKAEYIKAEYIKgAYIagAYISgAoARggoARggqABghqABghKACgBGCCgBGCCoAGCGoAGCEoAKAEYIKAEYIKgAYIagAYISgAoARggoARggqABghqABghKACgJGYaA+A8F7bfJYW3ttX6/fsUuPRgJbM6Kt9H8TLcx1996ZPNWnqoWiPCJ+aUrhfo8bXqr42KEmq+nO85v/4vOgO5WMRC6rruioqKlJFRYXi4uI0b948ZWRkRGp3ndbHH8bpsbm95Hknllct7KnUnq0qWFmpz5oCmnLFYA27uEFDv90U3UHhS0O+3aif35Ohd99KjPYonULETvlLS0vV0tKitWvXasaMGVqwYEGkdtVpfdbk6MFpGfrR7I9D6+6Z+7GmFJ5YrjkYo9YWR4nd2qI1InwsNs7VgMxm3ZR7SL96uUIFKyuV1rsl2mP5WsSCumPHDo0aNUqSNHz4cJWXl0dqV53W0pnn6trJNTp/6GehdY4jBWOkB6f21Y/GDFbWJQ3q0/9YFKeEX53do1Vl25L06wfP0Y/HDtTutxNU9ORHkrxoj+ZbEQtqQ0ODkpKSQsvBYFDHjx+P1O46nY2/TlEg6Gncv3162u0zl/9F/11ervraoNYsOaeDp0NncHBfvApy+mlvRVdJjn63Ik09M1rU41yOUv9eEbuGmpSUpMbGxtCy67qKieE1sK/qxXVn61hzQPd8d5COtzpq+ezEzzdMOaTs0fVKOee4uia6uuK6Wv2h5KxojwsfOn9Is/oNbdbLz54dWuc4UttxJ4pT+VvEjlCzs7O1detWSVJZWZkGDhwYqV11Sss27dFjv6/QitIKzX36Q8V1cbWitEK73kjS00vOkedJLcccbd3YXcP/pSHa48KHXFe6Z+5+9Tj3xCWj791Wo492d9HhA3FRnsy/InbIeOWVV2rbtm265ZZb5Hme5s+fH6ld/VOZMnu/ls7sox+NGSRJuuzqOl131ydRngp+tLeiq36Z31tzVlUqEPR0+ECsfp7LO3H+EY7neVG/An3s2DGVl5draI9pio8hDrAxrtfwaI+ATuYbPc/SzA33KDMzU/Hx8ads55NSAGCEoAKAEYIKAEYIKgAYIagAYISgAoARggoARggqABghqABghKACgBGCCgBGCCoAGCGoAGCEoAKAEYIKAEYIKgAYIagAYISgAoARggoARggqABghqABghKACgBGCCgBGCCoAGCGoAGCEoAKAEYIKAEYIKgAYIagAYISgAoARggoARggqABghqABghKACgBGCCgBGCCoAGCGoAGCEoAKAEYIKAEYIKgAYIagAYISgAoARggoARggqABghqABghKACgBGCCgBGYtrbsH///r/5i7169TIfBgD8rN2gTp48WY7jyPO8U7Y5jqOXX345ooMBgN+0G9RXXnmlI+cAAN8Lew21rq5O+fn5uvXWW1VbW6tZs2bp6NGjHTEbAPhK2KAWFBRo2LBhqq2tVUJCgtLT03Xfffd1xGwA4Cthg1pVVaVJkyYpEAgoLi5O06dPV3V1dUfMBgC+EjaowWBQ9fX1chxHklRZWalAgHdbAcCXtfui1Ofuvfde5eTk6MCBA8rNzVVZWZnmz5/fEbMBgK+EDeqoUaN0wQUX6J133pHrupozZ45SU1M7YjYA8JWwQW1tbVVJSYm2b9+umJgY1dTU6MYbbwxdAgAAnBA2qHPmzFFDQ4Ouv/56ua6r4uJiVVRUKD8/vyPmAwDfCBvUsrIybdy4MbQ8ZswYTZw4MaJDAYAfhX25vkePHtq3b19o+dChQ0pLS4voUADgR+0eoebk5MhxHB05ckQTJkzQiBEjFAgE9Pbbb+ub3/xmR84IAL7QblCnTZt22vV33nlnxIYBAD9rN6jf+c53Qj+/++67ampqkud5amtrU1VV1UnbAQBf4UWp/Px8bd++XXV1derXr5/ee+89ZWdn68Ybb+yI+QDAN8K+KPXaa6+ppKRE48aN09y5c7V69Wp99tlnHTEbAPhK2KCmp6crNjZW/fv3V0VFhYYNG6b6+vqOmA0AfCXsKX+PHj306KOP6pJLLtGiRYskSS0tLREfDAD8JuwR6gMPPKA+ffooKytLV111lV544QUVFRV1wGgA4C9f6SZ9F154ofbv36+xY8dq7NixHTIYAPjN17pJ3+fL3KQPAE51Rt2k7/ac8aqtaerw/aJzCv6ee5/BVsBJ/NvbO2gOAOj0CCoAGCGoAGAkbFDr6uqUn5+vW2+9VbW1tZo1a5bq6uo6YjYA8JWwQS0oKNCwYcNUW1urhIQEpaenKy8vryNmAwBfCRvUqqoqTZo0SYFAQHFxcZo+fbqqq6s7YjYA8JWwQQ0Gg6qvrw/dlK+yslKBAJdeAeDLwn6Wf9q0acrJydGBAweUm5ursrIyzZ8/vyNmAwBfCRvU0aNHKzMzU++8847a2to0Z84cpaamdsRsAOArYYO6fPnyk5Z3794tSZo6dWpkJgIAn/paF0NbW1v1yiuvqKamJlLzAIBvhT1C/fKR6E9+8hNu1AcAp/G1X65vbGw86av9AAAnhD1CHTNmTOgtU57nqa6uTnfddVfEBwMAvwkb1IcfflgpKSmSTnwfardu3ZSUlBTxwQDAb8IGdebMmdq8eXNHzAIAvhY2qIMHD9b69euVlZWlLl26hNb36tUrooMBgN+EDerOnTu1c+fOk9ZxCxQAOFW7QX3++ed1/fXXR+VWKADgR+2+bWr16tUdOQcA+B5fGwUARto95d+zZ4/Gjh17ynpuIw0Ap9duUDMyMvTYY4915CwA4GvtBjU2Nla9e/fuyFkAwNfavYaanZ3dkXMAgO+1G9TCwsKOnAMAfI9X+QHACEEFACMEFQCMEFQAMEJQAcAIQQUAIwQVAIwQVAAwQlABwAhBBQAjBBUAjBBUADBCUAHACEEFACMEFQCMEFQAMEJQAcAIQQUAIwQVAIwQVAAwQlABwAhBBQAjBBUAjBBUADBCUAHACEEFACMEFQCMEFQAMEJQAcAIQQUAIwQVAIwQVAAwQlABwAhBBQAjBBUAjBBUADBCUAHACEEFACMEFQCMEFQAMEJQAcAIQQUAIwQVAIzERHsAhONpRt52VX50lp793WAFAq7unlKmi0ZUKxjw9OzvBmlTyYBoDwkfcZ9vlFfcKDmSesUocN9ZUkJA3sO18t5rlTzJGRIr56fd5cQ70R7XVwjqGezcc48qd9oODR5co8qPzpIkXX3tn9W7T4N+fPe/KiHhuJb8Z6k++OAber8iJcrTwg+8ihZ5axsUeDxNTlJA7oo6ef9VL3UPSG1S4Ik0yZO8+bXy1tTLubNbtEf2lYie8u/cuVM5OTmR3EWn9r0Je7Rlcz+9uvXc0LpLL/tYL245X64bUENDnP73f/pqzNi9UZwSfuIMilPg6XQ5SQF5LZ502JW6BeRkxcnJSZYTcOQEHWlAjHSwLdrj+k7EjlBXrlypDRs2qGvXrpHaRae34pGLJEnZF1WH1qWlNenwJ188p4c/6arzz6/t6NHgY06MI+8PzXIX1UlxUuCOVDl9vkiBV31c3rONCszoHr0hfSpiR6h9+/bVsmXLIvXw/7Qcx5Mn56+WJdflOhe+HudfuipYfI6c25Ll/keNPNeTdOKSgPvvNXKuS5RzSZcoT+k/EQvquHHjFBPDJVprnxxKVEpKc2j57JRmHT6cEMWJ4Cfex8fl7ToWWnauTjhxal/vyX2lWW5ejZy7kxWYnBzFKf2Lt035zBuv99JV4z5SIOAqMbFFl1/xF72+rXe0x4Jf1LTJnXNEXt2J66NeabN0Xoz0fy3yltUpsChFge/yH/Tfi0NIn3lh4wD17NmgX/5qi2JiXW0u6a9du9KjPRZ8wsmKlzM5We5Pa6SgpNSgAvPOljuzRvIkd1HtF382M06Bn3aP1qi+RFB9YMlDI0M/u25Aj/4qO4rTwO8CExOliYknrQuu7hGlaTqXiJ7y9+nTR+vWrYvkLgDgjME1VAAwQlABwAhBBQAjBBUAjBBUADBCUAHACEEFACMEFQCMEFQAMEJQAcAIQQUAIwQVAIwQVAAwQlABwAhBBQAjBBUAjBBUADBCUAHACEEFACMEFQCMEFQAMEJQAcAIQQUAIwQVAIwQVAAwQlABwAhBBQAjBBUAjBBUADBCUAHACEEFACMEFQCMEFQAMEJQAcAIQQUAIwQVAIwQVAAwQlABwAhBBQAjBBUAjBBUADBCUAHACEEFACMEFQCMEFQAMEJQAcAIQQUAIwQVAIwQVAAwQlABwAhBBQAjBBUAjBBUADBCUAHACEEFACMEFQCMEFQAMEJQAcAIQQUAIwQVAIwQVAAwQlABwAhBBQAjBBUAjBBUADBCUAHACEEFACMEFQCMEFQAMBIT7QEkyfM8SVK3b3SJ8iToTBynLdojoJPp5iRI+qJZX+Z47W3pQPX19Xr//fejPQYAfCUDBw5UcnLyKevPiKC6rqvGxkbFxsbKcZxojwMAp+V5nlpbW5WYmKhA4NQrpmdEUAGgM+BFKQAwQlABwAhBBQAjBBUAjBBUn3BdV4WFhZo0aZJycnK0d+/eaI+ETmLnzp3KycmJ9hidwhnxxn6EV1paqpaWFq1du1ZlZWVasGCBVqxYEe2x4HMrV67Uhg0b1LVr12iP0ilwhOoTO3bs0KhRoyRJw4cPV3l5eZQnQmfQt29fLVu2LNpjdBoE1ScaGhqUlJQUWg4Ggzp+/HgUJ0JnMG7cOMXEcKJqhaD6RFJSkhobG0PLruvyDwE4wxBUn8jOztbWrVslSWVlZRo4cGCUJwLwZRzi+MSVV16pbdu26ZZbbpHneZo/f360RwLwJXyWHwCMcMoPAEYIKgAYIagAYISgAoARggoARggqzFRVVSkzM1MTJ07Uddddp2uvvVZ33HGHqqur/+7HfO655/Szn/1MknT33Xfr4MGD7f7ZpUuX6q233vpajz9o0KBT1i1btizsxzHHjBmjqqqqr7yfr/KY8D+CClPp6ekqLi7W+vXrVVJSokGDBmnhwoUmj71y5Ur16NGj3e1vvvmm2tq40ymihzf2I6JGjhypJUuWSDpxVJeVlaXdu3frmWee0auvvqpVq1bJdV1dcMEFmj17tuLj47V+/XqtWLFCSUlJ6t27txISEkK/v3r1aqWlpen+++/Xjh07FBsbq9zcXLW0tKi8vFz5+flavny5unTpoqKiItXW1qpLly4qKCjQ0KFDVVVVpby8PDU1Nelb3/pW2PmffvppFRcXq7m5WbGxsVq8eLH69esnSVq+fLnee+89xcfH6/7779fgwYN1+PBhFRYWqrq6Wo7jaMaMGbr00ksj9wTjjMIRKiKmtbVVW7Zs0fDhw0PrRo8erS1btujTTz/VunXr9Nvf/lbFxcVKSUnRE088oYMHD+qhhx7SmjVrtHbt2pO+v+BzTz31lJqamrR582Y9+eSTeuSRR3TNNdcoMzNT8+bN06BBgzRz5kzl5eXp+eef19y5czV9+nRJ0ty5c3XDDTeouLhY2dnZf3P+hoYGlZaW6qmnntILL7ygK664QmvWrAltz8jI0Pr165Wbmxu6LPHAAw/o+9//vp577jmtWLFChYWFamhoMHg24QccocLUoUOHNHHiRElSS0uLsrKyNGPGjND2z48K//jHP2rv3r26+eabJZ2I79ChQ/WnP/1JF154oVJTUyVJ48eP1xtvvHHSPt58803dfPPNCgQCSktLU0lJyUnbGxsbVV5erlmzZoXWNTU16ciRI9q+fbsWL14sSZowYYLy8/Pb/bskJSVp8eLFKikpUWVlpV599VUNGTIktP2mm26SJF1++eXKy8vT0aNH9dprr+nDDz/U0qVLJUnHjx/Xvn37vsYzCD8jqDD1+TXU9sTHx0uS2tradPXVV4eC1tjYqLa2Nr3++uv6609Dn+4btWJiYuQ4Tmh579696tmzZ2jZdV3FxcWdNEd1dbW6d+8uSaHHdxzntPdW/9yBAweUk5OjyZMna/To0UpNTdXu3btD24PBYOhnz/MUExMj13W1atWq0L4OHTqklJQUlZaWtrsfdB6c8iMqRo4cqZdeekk1NTXyPE9FRUVatWqVLrroIpWVlengwYNyXVebNm065XdHjBihTZs2yfM81dTUaPLkyWppaVEwGFRbW5uSk5N13nnnhYK6bds2/fCHP5QkXXrppdqwYYMk6cUXX9SxY8fanXHXrl3KyMjQ7bffrmHDhqm0tPSkF702btwoSXrppZfUv39/JSQk6OKLL9YzzzwjSfrggw80fvx4NTc32zxpOONxhIqoGDx4sKZOnarbbrtNrutqyJAhmjJliuLj45Wfn6/bb79dXbt21YABA0753R/84AeaN2+eJkyYIEkqKChQUlKSRo0apdmzZ+vBBx/UokWLVFRUpMcff1yxsbH6xS9+IcdxVFhYqLy8PK1du1aZmZlKTExsd8bLLrtMv/nNb3TNNdfI8zyNGDFCe/bsCW2vrKzUxIkTlZiYqAULFkiS8vPzVVhYqPHjx0uSFi5ceNIXg6Nz49umAMAIp/wAYISgAoARggoARggqABghqABghKACgBGCCgBGCCoAGPl/GAFbqdXNf7QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('\\nTraining accuracy:', grid.score(X_train, y_train))\n",
    "print('Test accuracy    :', grid.score(X_test, y_test))\n",
    "\n",
    "plot_confusion_matrix(grid, X_test, y_test, colorbar=False)\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59b1db8e",
   "metadata": {},
   "source": [
    "### Creating a Dataframe to follow models' results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "43b0e859",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(headline, true_value, pred, probs):\n",
    "    scores=[]\n",
    "    CM = confusion_matrix(true_value, pred)\n",
    "    scores.append(headline)\n",
    "    scores.append(accuracy_score(true_value, pred))\n",
    "    scores.append(int(CM[1,1]))\n",
    "    scores.append(int(CM[0,1]))\n",
    "    scores.append(int(CM[0][0]))\n",
    "    scores.append(int(CM[1][0]))\n",
    "    scores.append(precision_score(true_value, pred))\n",
    "    scores.append(recall_score(true_value, pred))\n",
    "    scores.append(roc_auc_score(true_value, probs))\n",
    "    p, r, _ = precision_recall_curve(true_value, probs)\n",
    "    scores.append(auc(r,p))\n",
    "    scores.append(f1_score(true_value, pred))\n",
    "    scores.append(fbeta_score(true_value, pred, beta=2)) \n",
    "    return scores\n",
    "\n",
    "score_names = ['method','accuracy','TP','FP','TN','FN','precision','recall','roc_auc','pr_auc','f1', 'f2']\n",
    "dfAcc = pd.DataFrame(data=np.zeros(shape=(0,12)), columns = score_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7348342",
   "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>method</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline model</td>\n",
       "      <td>0.83</td>\n",
       "      <td>32.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           method  accuracy    TP   FP    TN    FN  precision  recall  \\\n",
       "0  Baseline model      0.83 32.00 5.00 43.00 10.00       0.86    0.76   \n",
       "\n",
       "   roc_auc  pr_auc   f1   f2  \n",
       "0     0.92    0.92 0.81 0.78  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding first model:\n",
    "pred_probs = grid.predict_proba(X_test)[:,1]\n",
    "scores = print_results('Baseline model', y_test, grid.predict(X_test), pred_probs)\n",
    "dftmp = pd.DataFrame([scores], columns=score_names)\n",
    "dfAcc = pd.concat([dfAcc, dftmp], ignore_index=True)\n",
    "dfAcc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39271ee9",
   "metadata": {},
   "source": [
    "### Feature Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20887ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking correlations:\n",
    "\n",
    "sns.set(font_scale = 1.25)\n",
    "correlation_matrix = df.corr()\n",
    "plt.figure(figsize=(13,13))\n",
    "ax = sns.heatmap(correlation_matrix, vmax=1, cbar=True, square=True, annot=True, fmt='.2f', \n",
    "                 annot_kws={'size': 12}, cmap='coolwarm')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "plt.yticks(rotation=0)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6479fa5c",
   "metadata": {},
   "source": [
    "#### As can be seen, \"fbs\" and \"chol\" have no correlations with the target. These two features will likely be dropped.\n",
    "#### Let's check features with Lofo Importance (Leave One Feature Out):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "edc18aef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classifier__C': 0.1,\n",
       " 'classifier__penalty': 'l2',\n",
       " 'classifier__solver': 'newton-cg'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a5c0f251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d07bf6e580da4d84ac25d1b7c07eb820",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/13 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model1 = LogisticRegression(random_state=0,C=0.1,penalty=\"l2\",solver=\"newton-cg\")\n",
    "model1.fit(X_train,y_train)\n",
    "\n",
    "dataset = Dataset(df=train, target='condition', features=[col for col in df.columns if col != 'condition'])\n",
    "lofo_imp = LOFOImportance(dataset, model=model1,scoring='neg_mean_squared_error')\n",
    "importance_df = lofo_imp.get_importance()\n",
    "plot_importance(importance_df, figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f491d0",
   "metadata": {},
   "source": [
    "**As we guessed, \"fbs\" and \"chol\" turned out to be unimportant. Since \"age\" has some correlation with the target, we'll keep it for now.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c77d16",
   "metadata": {},
   "source": [
    "# Model 1 (Dropping 2 features acc. to the LOFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d6d2d8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 266 candidates, totalling 2660 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=40, shuffle=True),\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(remainder='passthrough',\n",
       "                                                          transformers=[('num',\n",
       "                                                                         Pipeline(steps=[('scaler',\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['age',\n",
       "                                                                          'trestbps',\n",
       "                                                                          'thalach',\n",
       "                                                                          'oldpeak'])])),\n",
       "                                       ('classifier',\n",
       "                                        LogisticRegression(random_state=40))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid=[{'class...\n",
       "                          'classifier__penalty': ['l1'],\n",
       "                          'classifier__solver': ['liblinear', 'saga']},\n",
       "                         {'classifier__C': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  , 1.05, 1.1 , 1.15,\n",
       "       1.2 , 1.25, 1.3 , 1.35, 1.4 , 1.45, 1.5 , 1.55, 1.6 , 1.65, 1.7 ,\n",
       "       1.75, 1.8 , 1.85, 1.9 , 1.95]),\n",
       "                          'classifier__penalty': ['l2'],\n",
       "                          'classifier__solver': ['liblinear', 'newton-cg',\n",
       "                                                 'lbfgs', 'sag', 'saga']}],\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_model2 = df.drop([\"chol\",\"fbs\"],axis=1)\n",
    "really_nums = [\"age\",\"trestbps\",\"thalach\",\"oldpeak\"]\n",
    "\n",
    "seed = 40 \n",
    "train, test = train_test_split(df_for_model2, test_size=0.30, random_state=seed, stratify=df.condition)\n",
    "\n",
    "y_train = train['condition']\n",
    "X_train = train.drop('condition', axis=1)\n",
    "y_test = test['condition']\n",
    "X_test = test.drop('condition', axis=1)\n",
    "\n",
    "num_transformer = Pipeline(steps=[('scaler', StandardScaler())\n",
    "                                 ])\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[('num', num_transformer, really_nums)],\n",
    "                                 remainder = 'passthrough') \n",
    "\n",
    "\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('classifier',   LogisticRegression(random_state=seed))\n",
    "                          ]\n",
    "                   )\n",
    "\n",
    "params = [{'classifier__C'      : np.arange(0.1, 2.0, 0.05), \n",
    "           'classifier__penalty': ['l1'], \n",
    "           'classifier__solver' : ['liblinear', 'saga']},\n",
    "          \n",
    "          {'classifier__C'      : np.arange(0.1, 2.0, 0.05), \n",
    "           'classifier__penalty': ['l2'], \n",
    "           'classifier__solver' : ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga']}\n",
    "         ]\n",
    "\n",
    "kfold = StratifiedKFold(n_splits = 10, random_state = seed, shuffle=True) \n",
    "grid2 = GridSearchCV(pipeline, param_grid = params, cv = kfold, verbose = 1, n_jobs = -1)\n",
    "grid2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df2cd20d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training accuracy: 0.8502415458937198\n",
      "Test accuracy    : 0.8444444444444444\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('\\nTraining accuracy:', grid2.score(X_train, y_train))\n",
    "print('Test accuracy    :', grid2.score(X_test, y_test))\n",
    "\n",
    "plot_confusion_matrix(grid2, X_test, y_test, colorbar=False)\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8c83b6c",
   "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>method</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline model</td>\n",
       "      <td>0.83</td>\n",
       "      <td>32.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dropping 2 Cols(Lofo)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>33.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  method  accuracy    TP   FP    TN    FN  precision  recall  \\\n",
       "0         Baseline model      0.83 32.00 5.00 43.00 10.00       0.86    0.76   \n",
       "1  Dropping 2 Cols(Lofo)      0.84 33.00 5.00 43.00  9.00       0.87    0.79   \n",
       "\n",
       "   roc_auc  pr_auc   f1   f2  \n",
       "0     0.92    0.92 0.81 0.78  \n",
       "1     0.93    0.93 0.82 0.80  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_probs = grid2.predict_proba(X_test)[:,1]\n",
    "scores = print_results('Dropping 2 Cols(Lofo)', y_test, grid2.predict(X_test), pred_probs)\n",
    "dftmp = pd.DataFrame([scores], columns=score_names)\n",
    "dfAcc = pd.concat([dfAcc, dftmp], ignore_index=True)\n",
    "dfAcc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c31cdca",
   "metadata": {},
   "source": [
    "### There is a very small change but in a good way. Let's check the features with RFE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "883d91a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False  True False  True False  True  True  True  True  True]\n",
      "['age', 'sex', 'cp', 'trestbps', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']\n"
     ]
    }
   ],
   "source": [
    "a = 1 \n",
    "score_list =[]\n",
    "\n",
    "for n in range(1,len(df_for_model2.columns)):\n",
    "    X_trn, X_tst, y_trn, y_tst = train_test_split(X_train, y_train, test_size = 0.30, random_state = 0)\n",
    "    model = LogisticRegression()\n",
    "    rfe = RFE(model,n_features_to_select=n)\n",
    "    X_trn_rfe = rfe.fit_transform(X_trn,y_trn)\n",
    "    X_tst_rfe = rfe.transform(X_tst)\n",
    "    model.fit(X_trn_rfe,y_trn)\n",
    "    score = model.score(X_tst_rfe,y_tst)\n",
    "    score_list.append(score)\n",
    "\n",
    "model = LogisticRegression()\n",
    "rfe = RFE(model, n_features_to_select=score_list.index(max(score_list))+1)                 \n",
    "X_rfe = rfe.fit_transform(X_train, y_train)  \n",
    "model.fit(X_rfe,y_train)        \n",
    "b = X_train.columns.to_list()\n",
    "print(rfe.support_)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae632a89",
   "metadata": {},
   "outputs": [],
   "source": [
    "to_drop = ['age', 'sex','trestbps','thalach']\n",
    "to_stay = ['cp','restecg','exang', 'oldpeak', 'slope', 'ca', 'thal']\n",
    "\n",
    "df_for_model3 = df_for_model2.drop(to_drop,axis=1)\n",
    "really_nums = [\"oldpeak\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d7830cd",
   "metadata": {},
   "source": [
    "# Model 2 (Dropping 4 more features acc. to the RFE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52d99d69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 266 candidates, totalling 2660 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=40, shuffle=True),\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(remainder='passthrough',\n",
       "                                                          transformers=[('num',\n",
       "                                                                         Pipeline(steps=[('scaler',\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['oldpeak'])])),\n",
       "                                       ('classifier',\n",
       "                                        LogisticRegression(random_state=40))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid=[{'classifier__C': array([0.1 , 0.15, 0...\n",
       "                          'classifier__penalty': ['l1'],\n",
       "                          'classifier__solver': ['liblinear', 'saga']},\n",
       "                         {'classifier__C': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  , 1.05, 1.1 , 1.15,\n",
       "       1.2 , 1.25, 1.3 , 1.35, 1.4 , 1.45, 1.5 , 1.55, 1.6 , 1.65, 1.7 ,\n",
       "       1.75, 1.8 , 1.85, 1.9 , 1.95]),\n",
       "                          'classifier__penalty': ['l2'],\n",
       "                          'classifier__solver': ['liblinear', 'newton-cg',\n",
       "                                                 'lbfgs', 'sag', 'saga']}],\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 40 \n",
    "train, test = train_test_split(df_for_model3, test_size=0.30, random_state=seed, stratify=df.condition)\n",
    "\n",
    "y_train = train['condition']\n",
    "X_train = train.drop('condition', axis=1)\n",
    "y_test = test['condition']\n",
    "X_test = test.drop('condition', axis=1)\n",
    "\n",
    "num_transformer = Pipeline(steps=[('scaler', StandardScaler())\n",
    "                                 ])\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[('num', num_transformer, really_nums)],\n",
    "                                 remainder = 'passthrough') \n",
    "\n",
    "\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('classifier',   LogisticRegression(random_state=seed))\n",
    "                          ]\n",
    "                   )\n",
    "\n",
    "params = [{'classifier__C'      : np.arange(0.1, 2.0, 0.05), \n",
    "           'classifier__penalty': ['l1'], \n",
    "           'classifier__solver' : ['liblinear', 'saga']},\n",
    "          \n",
    "          {'classifier__C'      : np.arange(0.1, 2.0, 0.05), \n",
    "           'classifier__penalty': ['l2'], \n",
    "           'classifier__solver' : ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga']}\n",
    "         ]\n",
    "\n",
    "kfold = StratifiedKFold(n_splits = 10, random_state = seed, shuffle=True) \n",
    "grid3 = GridSearchCV(pipeline, param_grid = params, cv = kfold, verbose = 1, n_jobs = -1)\n",
    "grid3.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9d096d91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training accuracy: 0.8405797101449275\n",
      "Test accuracy    : 0.8444444444444444\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('\\nTraining accuracy:', grid3.score(X_train, y_train))\n",
    "print('Test accuracy    :', grid3.score(X_test, y_test))\n",
    "\n",
    "plot_confusion_matrix(grid3, X_test, y_test, colorbar=False)\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d02678d",
   "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>method</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline model</td>\n",
       "      <td>0.83</td>\n",
       "      <td>32.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dropping 2 Cols(Lofo)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>33.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dropping 2+4 Cols</td>\n",
       "      <td>0.84</td>\n",
       "      <td>34.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  method  accuracy    TP   FP    TN    FN  precision  recall  \\\n",
       "0         Baseline model      0.83 32.00 5.00 43.00 10.00       0.86    0.76   \n",
       "1  Dropping 2 Cols(Lofo)      0.84 33.00 5.00 43.00  9.00       0.87    0.79   \n",
       "2      Dropping 2+4 Cols      0.84 34.00 6.00 42.00  8.00       0.85    0.81   \n",
       "\n",
       "   roc_auc  pr_auc   f1   f2  \n",
       "0     0.92    0.92 0.81 0.78  \n",
       "1     0.93    0.93 0.82 0.80  \n",
       "2     0.93    0.92 0.83 0.82  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_probs = grid3.predict_proba(X_test)[:,1]\n",
    "scores = print_results('Dropping 2+4 Cols', y_test, grid3.predict(X_test), pred_probs)\n",
    "dftmp = pd.DataFrame([scores], columns=score_names)\n",
    "dfAcc = pd.concat([dfAcc, dftmp], ignore_index=True)\n",
    "dfAcc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29db094d",
   "metadata": {},
   "source": [
    "#### No difference. But, the simpler the better! We will continue with Model 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a784586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RRUWF3G631q1bJ0latWqVrrrqqnSVBQAAAFgubT3ZgwcP1g9+8ANNnTpV0WhUY8eO1fjx4yVJCxYs0Ny5cxUMBjVkyBBNnz49XWUBAAAAlkt5yH7rrbeSj2+44QbdcMMNRzxn8ODBWrFiRapLAQAAANKCKz4CAAAAFiNkAwAAABYjZAMAAAAWI2QDAAAAFiNkAwAAABYjZAMAAAAWI2QDAAAAFiNkAwAAABYjZAMAAAAWI2QDAAAAFiNkAwAAABYjZAMAAAAWI2QDAAAAFiNkAwAAABYjZAMAAAAWI2QDAAAAFiNkAwAAABYjZAMAAAAWS2nIDgaDGj9+vKqrqyVJy5Yt0/jx4zVhwgTdd999CofDkqTHH39co0eP1sSJEzVx4kQtXbo0lWUBAAAAKeVI1QuvX79ec+fOVVVVlSRp+/btWrRokZ5//nl5PB7de++9evbZZzVjxgxt3LhRv/jFLzRs2LBUlQMAAACkTcp6spcvX6558+appKREkuRyuTRv3jx5vV4ZhqHzzjtPNTU1kqSNGzfqySef1IQJEzR//nyFQqFUlQUAAACkXMpC9gMPPKDLL788uVxRUaErr7xSklRfX6+lS5fqG9/4hlpaWnTBBRfo7rvv1sqVK9XU1KQnnngiVWUBAAAAKZey4SLHUltbq1tvvVXf/va3NXz4cEnSU089ldx+8803a86cOZo9e/Zp7ae42Hta//5UeTzujOw3k/x+X6ZLOK5sr6+nol2yD22SnWiX7EObZKdsa5e0huytW7fq1ltv1Y033qibb75ZklRTU6M1a9bo+uuvlySZpimH4/TL2r8/qHjcPO3XORl+v08tLT1vqEsg0JzpEo7J7/dldX09Fe2SfWiT7ES7ZB/aJDtlol1sNuO4nbppm8IvGAzqlltu0Z133pkM2JKUk5OjRx55RLt27ZJpmlq6dKnGjBmTrrIAAAAAy6WtJ3vFihWqq6vT4sWLtXjxYknS1VdfrTvvvFPz58/XbbfdpkgkoksvvVQ33XRTusoCAAAALJfykP3WW29JkmbMmKEZM2Yc9TmVlZWqrKxMdSkAAABAWnDFRwAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYl0K2c8884yCwWCqawEAAADOCF0K2Zs3b1ZlZaXuv/9+ffLJJ6muCQAAAOjWujRP9s9+9jMFg0G99NJL+slPfiLTNDV16lRNmDBBbrc71TUCAAAA3UqXx2R7vV5dc801Gj9+vBoaGvTss8/qmmuuSV5sBgAAAEBCl3qy165dq2XLlmnt2rWqrKzUwoULNXjwYO3cuVPTpk3T1Vdfneo6AQAAgG6jSyH7Jz/5iaZNm6af/vSn8vl8yfX9+/fX5MmTU1YcAAAA0B11abjIiy++qMLCQvl8PgUCAT399NOKx+OSpDvuuCOlBQIAAADdTZdC9k9/+lO9/fbbiX9gs2ndunV68MEHU1kXAAAA0G11abjIRx99pJdfflmSVFxcrF/96leaOHFiSgsDAAAAuqsu9WRHIhGFw+HkcjQaTVlBAAAAQHfXpZ7sf/iHf9Att9yiiRMnyjAMvfzyyxo1alSqawMAAAC6pS6F7HvuuUdLly7Vm2++KYfDoTFjxmjKlCmprg0AAADolroUsu12u6ZPn67p06enuh4AAACg2+vSmOw33nhDV199tS677DJdeumlyduJBINBjR8/XtXV1ZKkNWvWaMKECRo7dqweffTR5PM2bdqkSZMmqbKyUvfffz9jvgEAANCtdSlkP/LII7r33nu1atUqvfTSS8nb8axfv15Tp05VVVWVJKm9vV1z5szRE088oT/84Q/auHGj3nnnHUnS3XffrR//+Md69dVXZZqmli9ffno/FQAAAJBBXQrZ+fn5Gjt2rPr27auKiork7XiWL1+uefPmqaSkRJK0YcMGnXXWWerXr58cDocmTJig1atXa/fu3Wpvb9fQoUMlSZMmTdLq1atP76cCAAAAMqhLIfuSSy5J9jp31QMPPKDLL788ubxv3z75/f7kcklJiWpra49Y7/f7VVtbe1L7AgAAALJJl058fOedd7RkyRI5nU45nU6ZpinDMPThhx92eUfxeFyGYSSXD77GsdafruJi72m/xqnweNwZ2W8m+f2+TJdwXNleX09Fu2Qf2iQ70S7ZhzbJTtnWLl0K2U8//fRp76isrEyBQCC5HAgEVFJScsT6urq65BCT07F/f1DxuHnar3My/H6fWlpCad1nNggEmjNdwjH5/b6srq+nol2yD22SnWiX7EObZKdMtIvNZhy3U7dLw0UqKir0ySefaPny5erVq5c++uijE47J/rJLLrlE27dv144dOxSLxfTyyy/rqquuUkVFhdxut9atWydJWrVqla666qqTem0AAAAgm3SpJ/s3v/mN/vKXv2jv3r2aMWOGHn/8ce3YsUOzZs3q8o7cbrcefvhh/fCHP1QoFNKoUaN0zTXXSJIWLFiguXPnKhgMasiQIczHDQAAgG6tSyH7f/7nf/S73/1OkydPVlFRkZYvX67vfve7XQrZb731VvLxyJEj9eKLLx7xnMGDB2vFihUnUTYAAACQvbo0XMThcMjlciWX8/Pz5XB0KZ8DAAAAPU6XknJ5ebnefvttGYahcDisRYsWnfSYbAAAAKCn6FLI/tGPfqR77rlHmzdv1tChQ3XJJZdowYIFqa4NAAAA6Ja6FLJLS0v13//932pra1MsFpPXm5k5qAEAAIDuoEshe/HixUddf9NNN1laDAAAAHAm6FLI/vzzz5OPw+Gw/va3v2nkyJEpKwoAAADozroUsh966KFOy7W1tbr//vtTUhAAAADQ3XVpCr8vKy0t1e7du62uBQAAADgjnPSYbNM0tXHjRhUXF6esKAAAAKA7O+kx2VJi3ux77rknJQUBAAAA3d0pjckGAAAAcGxdCtk33nijDMM45vbf/va3lhUEAAAAdHddCtkXXXSRtm7dqsmTJ8vpdGrVqlWKRqMaN25cqusDAAAAup0uhewPP/xQzz77rOx2uyTp61//uiZPnqzKysqUFgcAAAB0R12awq++vl6hUCi53NLSovb29pQVBQAAAHRnXerJHj9+vL773e9qzJgxMk1Tr7zyiqZPn57q2gAAAIBuqUsh+84779SFF16o9957T263W/Pnz9cVV1yR6toAAACAbqnLV3wsLS3VoEGDdNddd8npdKayJgAAAKBb61JP9u9//3v913/9l0KhkMaMGaN/+Zd/0ezZszV58uST3uHvfvc7LVmyJLlcXV2tiRMnqq2tTevWrVNubq4k6fbbb9eYMWNO+vUBAACATOtSyF6yZImWLVumf/7nf1ZxcbGef/553XrrracUsr/zne/oO9/5jiRpy5YtmjVrlm6//XZ973vf05IlS1RSUnLSrwkAAABkky4NF7HZbPJ6vcnl8vLy5HR+p+M//uM/NHv2bOXm5qqmpkZz5szRhAkT9Nhjjykej5/26wMAAACZ0KWQXVhYqE2bNiWv+vjiiy+qoKDgtHa8Zs0atbe365vf/Kbq6uo0YsQIPfjgg1q+fLk++OADrVix4rReHwAAAMgUwzRN80RP2rp1q+68807t3LlT+fn5crvdeuKJJ3T++eef8o7vuOMOjR07VuPHjz9i2+uvv64XXnhBCxcuPOXXz5RnX/0s0yWk1bTKwZkuAQAAIOt0aUx2e3u7Vq1apaqqKsViMQ0YMOC0ZhgJh8P629/+pocffliStHnzZlVVVSWvIGmaphyOLpV2TPv3BxWPn/Dzg6X8fp9aWkInfuIZJhBoznQJx+T3+7K6vp6Kdsk+tEl2ol2yD22SnTLRLjaboeJi77G3d+VF/v3f/112u10DBw7Ueeedd9pT+G3evFlnn3228vLyJCVC9YMPPqjGxkZFIhEtW7aMmUUAAADQbXWpu/j888/XSy+9pMsuuywZjKXEWO1TsWvXLpWVlSWXBw8erB/84AeaOnWqotHoMYeRAAAAAN1Bl0L2m2++qdWrV3daZxiGNm3adEo7vfbaa3Xttdd2WnfDDTfohhtuOKXXAwAAALJJl0L2J598kuo6ur3n/7RVf1i7QzIM2QzJYbfJ5bTL7bQpx+WQJ8chT65TvlynvHlO2TpmagEAAMCZ57gh+0c/+pF++tOfSpLq6+vVq1evtBTVHQ0b5FdenlvrNu2VGTcViZkKR2IKRWJqamlTKBJLPtduM5TvcanI61JxQa56F7jldJz+vOMAAADIDscN2Rs3bkw+vuWWW7Ry5cqUF9RdDSjP1xUXV6i9LXzU7dFYXC3tUTW3hNXQElZDMKQdtUFt25M4E7bQ61JZrzyVFefJl+tMzkkOAACA7ue4IfvwKbS7MJ02jsNht6nA41KBx6W+HeticVMNzSEFGtsUONCmz3Y26LOdDfLkONTX71Vfv0ee3NObyQUAAADp1+XJqOlZtZ7dZqi4IEfFBTka3L9I7aGo9ta3qmZ/qzbvatDmXQ3q5XPrrDKf+vTOk93WpRkXAQAAkGHHDdnxeFyNjY0yTVOxWCz5+KBTncIPR5fjdujs8nydXZ6vtlBU1YEW7axt1kdb6vTpdpv6lXo1oDxfee7Tu1APcLIKCvPkclp73oDf77P09awWjsTU2NCa6TIAAN3UcdPa559/rhEjRiSD9fDhw5PbTmcKP5xYrtuhQX0LdG5Fvuoa21W1t1nbdjdpW02TKnp7NLCiQAUeV6bLRA/hctr11PPrLXs9j8ed9VdH/f6kSzJdAgCgGztuyP7ss8/SVQeOwTAM+Qtz5S/MVWt7VNtqmrSjtlnVgRaV9crT4P6FyidsAwAAZBXGHXQjeTkOXXROL53Xr0Db9zRra02j3v64VRW9PTq/f6G8nCQJAACQFQjZ3ZDLadf5/Qs1oNynL3Y3avueZtXUtahfiVfn9S9kzDYAAECGkca6MZfTrgvP7qVz+uRrS3WjduxtVnUgqAF98nVe30I5HcxGAgAAkAmE7DNAjsuhr5xTrHMrCvTZzgZt3d2k6n0tuvDsIvX1e5h+EQAAIM3o6jyD5LodGjaot75+cbny3HZ9tKVOf/5krw40Z/csDgAAAGcaQvYZqMjn1v+6uFxDB/VWa3tE727Yo4+31CkUjmW6NAAAgB6B4SJnKMMw1L/Eq/Jeefp8V4O27WnSnvpWDTm7SP1KvAwhAQAASCF6ss9wTodNQwb00j8M7SNfrlMff7Ffaz+tVbAtkunSAAAAzliE7B7Cl+fSlV8p08UDi9UQDOntj2u0pbpB8biZ6dIAAADOOAwX6UEMw9DZZT6VFeXqk+312rSjQdWBFg09t7eKfO5MlwcAAHDGyEjIvvHGG1VfXy+HI7H7+fPnq6WlRQ899JBCoZC++c1vavbs2ZkorUfIcTv01cEl2rO/RZ9sq9e7G/bo3IoCnd+/UHYbY7UBAABOV9pDtmmaqqqq0h//+MdkyG5vb9c111yjZ555RuXl5Zo5c6beeecdjRo1Kt3l9SjlxR71LsjVp9vr9cXuRtUeaNWwc3urkF5tAACA05L2Mdnbtm2TJN188836p3/6Jy1ZskQbNmzQWWedpX79+snhcGjChAlavXp1ukvrkZwOm4YO6q3hF5YoEo3r3Q17tGnHAcUYqw0AAHDK0h6ym5qaNHLkSC1cuFBPP/20nnvuOdXU1Mjv9yefU1JSotra2nSX1qOVFuVp9LA+6lvi1ZbqRv1pfY0aglzEBgAA4FSkfbjIsGHDNGzYsOTy9ddfr8cee0yXXXZZcp1pmqc9j3Nxsfe0/v2p8ni691CL/zU0T7sDQf3t73v17oY9unBAsYacU3zcsdp+vy+NFZ68bK+vu7D6vd0djpWe9t7paT9vd0G7ZB/aJDtlW7ukPWR/8MEHikQiGjlypKREoK6oqFAgEEg+JxAIqKSk5LT2s39/MO3T0/n9PrW0dP/e38I8p0YN7aON2+r16bb92rW3ScMG9VaB9+ihKBBoTnOFXef3+7K6vu7C6ve2x+PuFsdKT3rvcKxkJ9ol+9Am2SkT7WKzGcft1E37cJHm5mb9/Oc/VygUUjAY1MqVK/Wv//qv2r59u3bs2KFYLKaXX35ZV111VbpLw2FcDrsuPc+vKy4oUSgS15827NFnOw8wrzYAAEAXpL0ne/To0Vq/fr2uu+46xeNxTZs2TcOGDdPDDz+sH/7whwqFQho1apSuueaadJeGoyjrladew9zauL1en+9q1N76tkSvtseV6dIAAACyVkbmyb7rrrt01113dVo3cuRIvfjii5koByfgciZ6tcuLPdqwtU5/Wl+j8/oValBFQaZLAwAAyEpc8RFdVl6cp+L8Cn2yrV6bdzZo7/5WVV55jjwOLmADAABwuLSPyUb35nLaddn5fl0+2K+2cFSzH31bL62pUiwez3RpAAAAWYOebJySPsUeFefnqCUU08o/bdNHnwd0y7gLVOHPzNSJAAAA2YSebJwyt9Ou/2/6V3XbdReprrFdP3n6b/qftfRqAwAA0JON0/bVwSU6v1+hnnlts37/zjZ9+Hmdbhl3gfr09mS6NAAAgIygJxuWyPe49C/XXaT/PXGIAg1t+o/Ff9WLf9muaIxebQAA0PPQkw3LGIahKy4o1fn9i/Ts65/rhXe366+b9mnGNYN1bl+m+wMAAD0HPdmwXIHHpduuu0h3XH+x2sNRPbRknZ55dbNa26OZLg0AACAt6MlGygw9t7cG9y/Uyj9t1xvrdumjLQHdMOZ8XXpebxkGc2sDyKyCwjy5nPZMl3FCfr/PstcKR2JqbGi17PUAHBshGymV43Jo6j8O0oghpXr6lc+0cOUn+so5xZr6j4NU1isv0+UB6MFcTrueen59pss4Lo/HrZaWkGWv9/1Jl1j2WgCOj+EiSIsB5fn60fcu13evPldbqhv0o//3vn739hdqDzOEBAAAnHnoyUbaOOw2VV7RXyMuLNWKt7fqlfd2au3GvZo8+lwNv7CUISQAAOCMQU820q7A69Yt4y/UnBsvU4HXrd+89Hf9n6UfavuepkyXBgAAYAlCNjLm3IoC/Wj65freNeerZn+rfvrfH+iJlZ9obz0n5QAAgO6N4SLIKJvN0KihFbriglK9+tedevWvu/Th53X6+iXl+qcrB6jI5850iQAAACeNkI2skOt26Lqvn6PRl/bVy2uq9PZHu7V241794+X9dM3w/vLmOjNdIgAAQJcRspFVCjwu3TDmPI35aj+98O42vfLeDr25rlqjhvZR5RX96dkGAADdAiEbWamkMFc/mDBE1444S6+8t0NvfFCtN9dV62sXlembI85ijm0AAJDVMhKyH3/8cb3yyiuSpFGjRumee+7Rfffdp3Xr1ik3N1eSdPvtt2vMmDGZKA9ZpK/fq+9PGKLrvn6OXv3rTr27YY/+vGGPLjvfr7Ff7a+BFflM/QcAALJO2kP2mjVr9Oc//1krV66UYRi69dZb9frrr2vjxo1asmSJSkpK0l0SugF/Ya7+eez5mnDlAL3xwS699WG1PtgcUF+/R6OGVmjkkFLl5TBuO1PicVPRWFzRWMd9PC4zLsVNU6Ypmeq4N00ZMpTXFlU4FJHNZiRuhiG73ZDLYZPDbuODEwCg20t7yPb7/br33nvlcrkkSQMHDlRNTY1qamo0Z84c1dbWasyYMbr99ttlszHDIDor8Lj07VEDNW7kWXr/77V6++MaLX39c/3uj1/oqxeU6B+GVuicPvRuny7TNNUaiqq+KaT6pnZFvtivzbsaFI7EFIrEFI7EFep4HI3GFTet3b/TYUvc7Da5nXbluDpubkfyca7bIZeDQA4AyE5pD9mDBg1KPq6qqtIrr7yipUuX6q9//avmzZsnn8+nmTNnasWKFZo8eXK6y0M3keNyaNTQCo0aWqGqvU165+Mavff3Wv3lk70q65Wnywf7dfn5Jerd25vpUrNWNBZXoKFNtfVt2negVbUH2rSvoU31Te2qbwopFIkd8W8cdkNup11up12eHKd6+dxydPQ+O+xGx33isc0wZBiGDEOJx5IMQzIludxOtbaGFI8nesHjZqIHPBLtuMXiCnc8DkdiamoNqz189Ho8OU55chzy5B66z89zyumwp/6XCADAMRimaVrcB9U1W7Zs0cyZM/XDH/5Q3/rWtzpte/311/XCCy9o4cKFmSjttDz76meZLiGtplUOznQJSa3tEf3po9169+Pd2ri1TnFTKi/26GsXl+trF/fRoH6FPa7XMxKNq7a+RTV1LdpT16KaQFA1dYnlugOtnXqgPblOlff2yF+YK39hrnp33PyFuSouyNXq97bLnsFvl+JxU+3hqNpCUbW2R9XSHlGwNXFrbg2rpT2iw/+a5bkdKvC6VeB1qcDrVqHPrQKvW3Zb194D2fTeRurwNxtAqmTkxMd169bpjjvu0Jw5czRu3Dht3rxZVVVVqqyslJT4qtrhOL3S9u8PKm71d9gn4Pf71NISSus+s0Eg0JzpEpIuO7dYl51brKbWsD7eUqcN2+r1wjtb9fs/fqECr0uD+xfp/P6FuqB/kUqKcs+I0B2NxVXX2J7oja5vU+3BXukDraprbO8UPHPdDpUW5WpAmU/DB5eotFeuSovyVNorT54cxzF/H/6iXLW3RSyr2eNxn/KxkuOwKcfrUi+vq9P6eDwxxCXYFlFzR/Buao2otr4l+WHCZkj5HpcKvW4VehP3vjznMX/ubHpvp5rf7+tRP6/UPf5mn86xciw9rZ2t1hOPle4gE+1isxkqLj72N+ZpD9l79uzRrFmz9Oijj2rkyJGSEqH6wQcf1IgRI5SXl6dly5Yd0bsNnIz8PJeuuqSPvv2P52v7znqt/6JOG7fX67MdB/T+32slSUU+t87vX6hBfQvVr8Srit4e5bqzc1bL1vao6hrbFGhoV6ChTXWNiaEd++rbVNfYrvhhSTrXbVdJUZ4GlOdr+IVlKi3KVWmvPJUU5cqXe+xA2d3ZbIa8uU55c50q63Vofdw01dIWUVNLRA0tITU0h1UdCKpqb+J3ZrcZKvC6VOhxq9DnUi+fO2vfBwCA7iPt/5MsWrRIoVBIDz/8cHLdlClT9IMf/EBTp05VNBrV2LFjNX78+HSXhjOUN9epK79Sriu/Ui7TNLW3vlWf7WzQ5p0H9Pft9Xrv09rkc3sX5KhfiVd9/V716e1Rr3y3inxuFXrdcthTM1QiEo2rsSWkxmBYDcGwGltC2t/YrkBjR6BuaFNLe7TTv8l12+UvzNVZZT5dcWGJSosSIbq0KO+4PbM9kc0w5MtzyZfnUoXfIynxwT7YFlFDMKyGYEgNwbB21DZr255E8M5x2XWgJaL+fk/yQ5iti8NMAACQMhCy586dq7lz5x512w033JDmatDTGIah8mKPyos9Gj2sQqZpan9ju6oDLdoVCKp6X1DVgaA+/qKu0zALQ1K+N9HLWeBxK8dtV47TLlfHzBdul10uh12maSpuJoYuJB6bisVNtYdjagt1jCcOHRpX3NQSPiJAS4kT+ooLcuUvyNGA8nz5C3I6xknnqHdB7nGHduDEjMOCd7+SxFd9cdNUc0tY9c0h1TeFtHnnAf15fY2kROge2Cdfg/oWalDfAp3Tp0BuFydWAgCOje9E0aMZhpE8wW/ooN7J9eFITPsa2nSgOaQDzYlp7A4+rmtsVygSVSgSVygcO+osHF/msNuU505MO5eX41Cu26FCr1uD+xclhip43SroGCtc4HUp3+OSjRCdVjbD6DhR0q0B5dL3J12iz74IaMvuBm2pbtQX1Y1a9eftMjue27/Umwzdg/oVqsDjOuE+ACAdCgrz5HL2rI6AWCye6RKOQMgGjsLltKuvPzFs5ETipqlIx7zRhqHkxVVshiGbLRHkUzXUBKlVXJCj4oIyjbiwTFJibPzWmsaO0N2gdz7erdc/2CVJKi3KTYbu8/oVnjEn1gLoflxOu556fn2my0ir70+6JNMlHIGQDZwmm2HI3TFkBGe2vByHvnJOsb5yTrGkxMwuO2qbtWVXo7ZUN+jjL+r050/2SJLy85yderr7l3ozOgUiACC9CNkAcIocdpsG9inQwD4FumZ4f5mmqT37W7WlOjHEZEt1g9Z9HpAkuZ12ndMnX+f1OziuO185Lv4EA8CZir/wAGARwzDUp7dHfXp7NGpohSTpQHMoEbo7ertfPGxc91llh8Z1n9uXcd1nGrPjxOdoLK5ozFS042qmcdOUGU8MNYt3nCxtxs3ksnlwaKkhud0ORTqudnrw6ql2uyG7zZDdZjvs8aFlh93gWxMgCxCyASCFinxuXXFBqa64oFTS4eO6E8H7jx/t1mt/S4zr7pXv1oCyfJ1d7tOA8nydXeZTXo4zk+XjMKZpKhKNqz0cS9wiMYXC0eRyKBJLBumDoTojl1RWYv53l8Mmp9Mml8OeeOyw6bd/+LtspilvrlOFHdOTFnndynXbOYcAsBghGwDS6MvjuiPRxLjuL6obVbW3SVV7mpNDTKTECZUDyvN1dkfo7uv3Ki+HP91WMk1T4Uhc7ZGY2sNRhQ6G6MPC88H1R7uQsMNuKMflkNtpV16OUw67IafDJof94M2Q026Tw5F43OnkaMOQYUv0UBsdJ0snZxYypdw8l1paQzJNddxMxWKmYvG4YvFET/mXlyPRuMLRuCLRmMKRxOOm1rDCkbh+/9aWo/4MLqet40qoiauhHrw+QO+CxLShvQtz5OEDH3BS+EsNABnkdNh0bkWBzq0oSK4LtkW0Y2+ztu9p0vY9Tdq8q0Hv/f3QRZOK892J2W9KvKrwe9TX71VpUZ6cDoYIHC4eN9XcGk5edKixJayG5pAaOu5bQlHtqm1WKBLrNC/+QU6HLTEPvtOu4vwc5bjsiTDtSsyPn+NMnPCcytmDXE67Ig7rTqq+5bqLtWv3ATW1RtQYDOlAMHEV1MRFmUJqaA6pak+zPtpSp0i085RouW6H/AU5Kj44b/9hAbx3QQ7nGABfwhEBAFnGm+vUkAG9NGTAoevDNwRD2rG3WdWBoKoDLaoOBLVxe71iHd2ShpG4YmlprzyVFeWprDhPpb3y5C/IUZEv54wK4NFYXM2tkURwDh4WEINhNQY7QnQwpKaW8FHDszfXqUKvS/6iPPkLc5NBOsd16OZ22c/Icc02m6G8HKfycpwq65V3zOeZpqmW9mji6rMNbaprbFddY+K+9kCbPt1er/CXQrg31yl/Ya5KinIT94c9LvS6GI6CHoeQDQDdQKHXrcJz3brk3EMXTYrG4tqzv1W7A0HtrW/V3vpW1da3aUv1HoXCnS+SVOB1qTg/J3kr9Lrk87iUn5e4+JHD7VQsHk97sDTNQ1dEbW1PXBG1pS2iptawmlrCamqNJO5bwsl1R7tKqiHJl+fsuKCTW/1KvMmhDwUetwp9LhV6Ehd7Otjz7Pf7etxcwl1lGIa8uU55c506q8x3xHbTNNXcGlGgsa1TEA80tGnr7kb9dVNtpw84LodN/sLcTiH84OPeBTlcSwBnJEI2AHRTDrtN/Uq8yUvDH2SaphqCYdXWt2p/U7v2N7arrqld9U3t2lmbGAoQPcrV0QxJOW6HcjuuTprrcijHbVdux3hjh92QvWOM8cHxxjYjMVY43pGoEh3rh8YGR6JxhSOJkwEjkZjC0bhaQ1G1tkfU2h5VWyiW/LdHk+t2KN/jUkGeUxW9PbrgrKLkB4ODV0st9Lrly3MS1NLIMAzlexLtMLBPwRHbo7G49je1K3CgTfsa2rTvQJsCDYnHf99Rr3AkfthrSb18OYd6wIsSveAHgzjnIKC74p0LAGcYwzBU5HOryOc+6nbTNNUWiqqxJazmjp7iuM2mmtomtbRH1R6Kqq2jd7mlLaJAQ7vCkZhiB2fNiMc7TrY7MhwbHfu32SSnwy6nw5ac2cLpsMnltKvA41J5cZ7y3A7l5TiU53Z23Hcs5zjky3Up3+OU08LxyEgfh92m0qI8lRYdOSTFNE01toQPBe+O+0BDmz7aElBza6TT8xmGgu6KkA0APYxhHBqXW56Y5ER+v0+BQPNJvU7cNBWPm7LZjGS4Bk7EMIzkNxDn9Ss8YntbKNopfB/sCT/WMJTehYd6vhPhO3EeQpHPLU+Og/dlFojHD304T84bH+tYjieWY7F4Ys5400zOpGMqcX/E+sO+PZMSM/LUBILKtvlvCNkAgFNiMwzZ7AQYWCvX7VD/Up/6lx45Fjwai2t/Y3vnISgd93+vOvJkTKfDpiKvW4Ud3+wU+RLzghf5EkOMfHku+fKc8uQ6D02d2MMdLRAf/i1Wp+VYXNF45+VYx7pkiO4Iz6fCMBIfygwdnOayY9no2CYj+bwDzSGV+LLrgl6EbAAA0C047DaV9krMnPNlB4eh1DW060AwpANNHffNiakJt9U06kBz+OjnIxiSL/dQ6PZ23Oe5E8OYctwO5boS5yqUN4fV3hpKrnc7E+cnpLPHPB43Feo4xyESiSkUjSsciSVu0bhy9jRr175gxzzqnYd5depJTl6R9NQCsd1mdFxl1CaHreOcDYdNOXab7DYjOU+8w95xNVLbobnjk+d32A4uGx0B2pCt41d5Mr/TIecUn/S3calGyAYAAN3e4cNQjsU0TQXbImoIJmaraW5NnJfQ3BpRsDUxm01za1jV+4Jqbg2f8MTcwx0MkwcvROQ87LHN1tH7KkkHe2A7lg+PkbG42dEzfNgFhg57HI2ZikRjisZOvmv4qIHYbijH5TyFQJy4ZyjO8RGyAQBAj2AYRkdvddeGFRy8GmhbOKq2UGI2HHeuU3v3Nau1YzkSjSVm0onFFY0meoMj0XjyPhKLKx5PjC9Wcpzxodc/nNNhk72jF9jeEYS//NjltMntsMvltMvltMnl6Lh32uXuOLm4tMSnF/64pVN4JhCnHyEbAADgKAzDkLvj4kQHe8j9fp8CRbkZruz4/H6fvLnZdhpgz5NVk4q+9NJLuvbaazV27FgtXbo00+UAAAAApyRrerJra2v16KOP6vnnn5fL5dKUKVM0fPhwnXvuuZkuDQAAADgpWdOTvWbNGo0YMUKFhYXKy8tTZWWlVq9enemyAAAAgJOWNT3Z+/btk9/vTy6XlJRow4YNp/x6NltmBvh783reGKhM/a67Ktvr6y6sfG/n5ThlmEdOo5Vtetp7p6f9vFL2/81OxbHSE9vZat3hd5jt7+1USHe7nGh/WROy4/F4pzNfTdM8rTNhi4o8VpR10qZec2FG9ptJxcXeTJdwXNleX3fBe/vM19N+Xon3NU5Nd/gd8t7OvKwZLlJWVqZAIJBcDgQCKikpyWBFAAAAwKnJmpD9ta99TWvXrlV9fb3a2tr02muv6aqrrsp0WQAAAMBJy5rhIqWlpZo9e7amT5+uSCSi66+/XhdffHGmywIAAABOmmF++XJDAAAAAE5L1gwXAQAAAM4UhGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoTsU/DSSy/p2muv1dixY7V06dIjtm/atEmTJk1SZWWl7r//fkWj0QxU2bOcqE0ef/xxjR49WhMnTtTEiROP+hxYLxgMavz48aqurj5iG8dJ5hyvXThW0u/xxx/XuHHjNG7cOP385z8/YjvHSmacqF04VjLjV7/6la699lqNGzdOixcvPmJ7Vh0vJk7K3r17zdGjR5sHDhwwW1pazAkTJphbtmzp9Jxx48aZH330kWmapnnfffeZS5cuzUClPUdX2mTmzJnmhx9+mKEKe6aPP/7YHD9+vDlkyBBz165dR2znOMmME7ULx0p6/eUvfzG/+93vmqFQyAyHw+b06dPN1157rdNzOFbSryvtwrGSfu+//745ZcoUMxKJmG1tbebo0aPNrVu3dnpONh0v9GSfpDVr1mjEiBEqLCxUXl6eKisrtXr16uT23bt3q729XUOHDpUkTZo0qdN2WO9EbSJJGzdu1JNPPqkJEyZo/vz5CoVCGaq251i+fLnmzZunkpKSI7ZxnGTO8dpF4lhJN7/fr3vvvVcul0tOp1MDBw5UTU1NcjvHSmacqF0kjpVMuOKKK/Tb3/5WDodD+/fvVywWU15eXnJ7th0vhOyTtG/fPvn9/uRySUmJamtrj7nd7/d32g7rnahNWlpadMEFF+juu+/WypUr1dTUpCeeeCITpfYoDzzwgC6//PKjbuM4yZzjtQvHSvoNGjQoGQiqqqr0yiuvaNSoUcntHCuZcaJ24VjJHKfTqccee0zjxo3TyJEjVVpamtyWbccLIfskxeNxGYaRXDZNs9PyibbDeif6nXs8Hj311FMaOHCgHA6Hbr75Zr3zzjuZKBUdOE6yE8dK5mzZskU333yz7rnnHp199tnJ9RwrmXWsduFYyaw77rhDa9eu1Z49e7R8+fLk+mw7XgjZJ6msrEyBQCC5HAgEOn3t+uXtdXV1x/xaFtY4UZvU1NRoxYoVyWXTNOVwONJaIzrjOMlOHCuZsW7dOs2YMUP/9m//pm9961udtnGsZM7x2oVjJTO2bt2qTZs2SZJyc3M1duxYbd68Obk9244XQvZJ+trXvqa1a9eqvr5ebW1teu2113TVVVclt1dUVMjtdmvdunWSpFWrVnXaDuudqE1ycnL0yCOPaNeuXTJNU0uXLtWYMWMyWDE4TrITx0r67dmzR7NmzdKCBQs0bty4I7ZzrGTGidqFYyUzqqurNXfuXIXDYYXDYb355pu67LLLktuz7XjhY9dJKi0t1ezZszV9+nRFIhFdf/31uvjii/X9739fd9xxh77yla9owYIFmjt3roLBoIYMGaLp06dnuuwzWlfaZP78+brtttsUiUR06aWX6qabbsp02T0Sx0l24ljJnEWLFikUCunhhx9OrpsyZYreeustjpUM6kq7cKyk36hRo7RhwwZdd911stvtGjt2rMaNG5e1/7cYpmmaGds7AAAAcAZiuAgAAABgMUI2AAAAYDFCNgAAAGAxQjYAAABgMUI2AAAAYDFCNgAAAGAxQjYAAABgMS5GAwA90IoVK7R48WLZbDYVFRXpoYce0uLFi7V+/Xq1tLTINE397Gc/63Q1NQBA13ExGgDoYT777DPNmDFDK1euVHl5uZ5++mm9++678ng8+uUvfymbzabf/OY3+vDDD/XrX/860+UCQLdEyAaAHmbx4sX69NNPtWDBgk7rt23bpvfee0+7du3S+++/L4/Ho2eeeSZDVQJA98aYbADoYex2uwzDSC63t7dr6dKlmjlzpiTpG9/4hqZOnZqp8gDgjEDIBoAeZvjw4Vq7dq327dsnSXruuef07rvvavTo0Zo2bZouuugivfHGG4rFYhmuFAC6L4aLAEAPtGrVKi1atEiS5Pf7NWvWLM2fP1+xWEzRaFRXXnmlXnvtNb399tuy2eiPAYCTRcgGAAAALEb3BAAAAGAxQjYAAABgMUI2AAAAYDFCNgAAAGAxQjYAAABgMUI2AAAAYDFCNgAAAGAxQjYAAABgsf8fJOrBF+YfeV8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking boxplots and histplots of remaining features:\n",
    "\n",
    "for i in df_for_model3.columns:\n",
    "    plt.figure()\n",
    "    plt.tight_layout()\n",
    "    sns.set(rc={\"figure.figsize\":(12, 8)})\n",
    "    f, (ax_box, ax_hist) = plt.subplots(2, sharex=True)\n",
    "    plt.gca().set(xlabel= i,ylabel='Frequency')\n",
    "    sns.boxplot(df_for_model3[i], ax=ax_box , linewidth= 1.0)\n",
    "    sns.histplot(df_for_model3[i], ax=ax_hist , bins = 10,kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04b6200a",
   "metadata": {},
   "source": [
    "**Outliers are known to cause problems in classification models. But our data is very small, so I want to try the suppression method instead of the deletion method about outliers.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0d3951",
   "metadata": {},
   "source": [
    "#### Suppressing Outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7e6c36b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAHmCAYAAAC26dynAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUj0lEQVR4nO3df6wXdL3H8TdwKNGrEoonZ94sytbIVjMrglS6S5EDQ2mT5haZy6m5uVwpBFmbd5gmxTTWwnQ0jUolwnJEbFZeQ2qNasNNTd1EEDCUX8rPA+d7//B66tsBD+cWfF94Ho//vudzzvl+eJ/3zp778h0MaDQajQIAgFADW30BAAB4PYIVAIBoghUAgGiCFQCAaIIVAIBoghUAgGiCFQCAaG29fcLmzdurq+vw/1OtJ5zwH/XSS68c9uc9UplX35hX35hX35hX35hX35hX35hX37RqXgMHDqi3vOWYA573GqxdXY2WBOtrz83BM6++Ma++Ma++Ma++Ma++Ma++Ma++SZyXtwQAABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQra3VF9ifH/3o7tqwYW11du5r9VWOGIMHDzqi5rV165aqqjr++KEtef4jbV6t9q/M69RT316XXDL133wjAPqTyGBds2Z1PfnU0zXoqKGtvgqHyL5dW6qqauO2va29CIfUaz9nAPhXRAZrVdWgo4bW0W//r1Zfg0Nkx+qHqqr8jN/gXvs5A8C/wntYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiCZYAQCIJlgBAIgmWAEAiNbW6gvsz9atW6pr765WXwMAoN9Yvvx/6rjjhtQZZ5zV6qv0EBqsW6uxt7PV1wAA6Dd+97uHa/DgQZHB6i0BAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEE6wAAEQTrAAARBOsAABEa2v1BYA3rq69u+q551bXLbf8d6uvclgNHjyoOjv3tfoaRwzz6hvz6hvzOnjPPbe6TjhhWKuvsV9eYQUAIJpXWIFDZmDbUfWfp55Y06bd0OqrHFbDhx9bGze+3OprHDHMq2/Mq2/M6+Ddcst/1+DBg1p9jf3yCisAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANHaWn2B/Tn++ONr16ZXWn0NAIB+Y8yYc+q444a0+hr7FRqsQ2vjtr2tvgYAQL8xevTZNXz4sbVx48utvkoP3hIAAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBANMEKAEA0wQoAQDTBCgBAtLZWX+BA9u3aUjtWP9Tqa3CI7Nu1parKz/gN7tWf84mtvgYAR7jIYD311LfX4MGDqrNzX6uvcsQ40ua1deurq3f88UNb8vxH2rxa7f8/rxPr1FPf/m+/DwD9S2SwXnLJ1Bo+/NjauPHlVl/liGFefWNefWNeALSS97ACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEE2wAgAQTbACABBNsAIAEK2tt08YOHDA4bhH3HMficyrb8yrb8yrb8yrb8yrb8yrb8yrb1oxr96ec0Cj0WgcprsAAECfeUsAAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0QQrAADRBCsAANEEKwAA0VoarL/4xS9q/Pjxdd5559WCBQt6nD/++OM1efLkOv/882vmzJm1d+/eFtwyS28zmzt3bo0dO7YmTZpUkyZN2u/n9CevvPJKTZgwodauXdvjzH719Hrzsls9zZ07tzo6Oqqjo6O++c1v9ji3Y816m5cda3bbbbfV+PHjq6Ojo+bPn9/j3H41621e9mv/brnllpo+fXqPj8ftV6NFNmzY0Bg7dmxj8+bNje3btzcmTpzYeOqpp5o+p6Ojo/HnP/+50Wg0Gl/5ylcaCxYsaMFNcxzMzK644orGn/70pxbdMMtf/vKXxoQJExojR45srFmzpse5/WrW27zsVrPly5c3pkyZ0ti9e3djz549jalTpzaWLVvW9Dl27O8OZl527O/+8Ic/ND796U83Ojs7Gzt37myMHTu28cwzzzR9jv36u4OZl/3q6dFHH2185CMfaUybNq3HWdp+tewV1kcffbQ++tGP1tChQ+voo4+u888/v5YuXdp9/vzzz9euXbvqAx/4QFVVTZ48uem8P+ptZlVVjz32WM2bN68mTpxYN954Y+3evbtFt229++67r77+9a/XSSed1OPMfvX0evOqslv/bPjw4TV9+vR605veVIMHD64RI0bUunXrus/tWLPe5lVlx/7Rhz/84br77rurra2tXnrppdq3b18dffTR3ef2q1lv86qyX/9sy5YtNWfOnLryyit7nCXuV8uC9W9/+1sNHz68+/FJJ51UL7zwwgHPhw8f3nTeH/U2s+3bt9d73/veuu666+pnP/tZbdu2rb773e+24qoRZs2aVR/60If2e2a/enq9edmtnt797nd3/zJ/9tln65e//GWdc8453ed2rFlv87JjPQ0ePLhuv/326ujoqFGjRlV7e3v3mf3q6fXmZb96+trXvlbXXnttHXfccT3OEverZcHa1dVVAwYM6H7caDSaHvd23h/1NpNjjjmmvv/979eIESOqra2tLrvssnr44YdbcdV49qtv7NaBPfXUU3XZZZfV9ddfX6eddlr3x+3Y/h1oXnZs/6655ppasWJFrV+/vu67777uj9uv/TvQvOxXs/vvv79OPvnkGjVq1H7PE/erZcH61re+tTZu3Nj9eOPGjU1/FfnP5y+++OIB/6qyv+htZuvWrauFCxd2P240GtXW1nZY73iksF99Y7f2b+XKlXXppZfWl770pbrooouazuxYT683LzvW7JlnnqnHH3+8qqqGDBlS5513Xj355JPd5/arWW/zsl/NlixZUsuXL69JkybV7bffXr/+9a/rpptu6j5P3K+WBevHPvaxWrFiRW3atKl27txZy5Ytq7PPPrv7/JRTTqk3v/nNtXLlyqqqeuCBB5rO+6PeZnbUUUfVrbfeWmvWrKlGo1ELFiyoT37yky28cS771Td2q6f169fX1VdfXbNnz66Ojo4e53asWW/zsmPN1q5dW1/96ldrz549tWfPnnrooYfqzDPP7D63X816m5f9ajZ//vx68MEH64EHHqhrrrmmPvGJT9SMGTO6zxP3q2XB2t7eXtdee21NnTq1LrzwwpowYUK9//3vr8svv7xWrVpVVVWzZ8+ub3zjGzVu3LjasWNHTZ06tVXXjdDbzIYNG1Y33nhjXXXVVTVu3LhqNBr1uc99rtXXjmK/+sZuHdhdd91Vu3fvrptvvrn7n8n58Y9/bMcOoLd52bFm55xzTp177rl14YUX1qc+9an64Ac/WB0dHfbrAHqbl/06OMn7NaDRaDRaegMAAHgd/qcrAACiCVYAAKIJVgAAoglWAACiCVYAAKIJVoD9mD59et111109Pr5p06Z6z3vec0iec9GiRXXFFVccku8NcCQTrAAAROu//y8ZwP+5995765577qmBAwfWiSeeWDfccEPT+bJly2rOnDk1ZMiQet/73tf98UWLFtXSpUurq6ur1q1bV+3t7XXzzTdXe3t7vfzyyzVr1qz661//Wp2dnTVq1Ki6/vrrq62trRYuXFj33ntvdXZ21tatW+vyyy+vSy65pOk5ly5dWrNnz6477rij3vnOdx6WOQCk8gor0K+tWLGi7rzzzrr77rvr5z//eU2YMKGuvvrqeu3/VHnxxRdrxowZ9Z3vfKcWLVpUp5xyStPX//GPf6yZM2fWkiVLauTIkTVr1qyqqrrppptq5MiRtWjRolq8eHFt3ry55s+fX9u3b6/777+/7rjjjlq8eHHNmTOnbr311qbv+eCDD9bcuXPrnnvuEasA5RVWoJ975JFHavz48TVs2LCqqpo8eXLNmjWrzjjjjKqqWrlyZZ1++un1rne9q6qqpkyZUt/+9re7v3706NH1jne8o6qqLr744po0aVJVVf32t7+tVatW1cKFC6uqateuXVVVdcwxx9T3vve9evjhh+vZZ5+tJ554onbs2NH9/VatWlWPPPJIzZgxo04++eRD/KcHODIIVqBf6+rq6vGxRqNRe/fubXr8mra25l+bgwYNavperz3u6uqq2267rUaMGFFVVdu2basBAwbUhg0basqUKXXxxRfXmWeeWePGjavf/OY33d/j2GOPrW9961v1xS9+sc4999x629ve9u/5gwIcwbwlAOjXPv7xj9eSJUtq06ZNVVX105/+tIYOHdodnmeddVY9/fTT9cQTT1TVq+9b/Ue///3v64UXXqiqqp/85Cc1duzYqqoaM2ZM/eAHP6hGo1F79uypq666qn74wx/WY489VsOGDasvfOELNWbMmO5Y3bdvX1VVnXbaaTVq1Kj6zGc+U9OmTdtvUAP0N4IV6NdGjx5dl156aX32s5+tjo6OWrx4cc2bN68GDnz11+OwYcNq9uzZ9eUvf7kuuuiiWrt2bdPXt7e313XXXVcXXHBBPf/88zVjxoyqqpo5c2bt2LGjJk6cWBMnTqzTTz+9Pv/5z9fo0aOrvb29xo0bVxdccEGtX7++hg0bVqtXr276vldeeWXt3Lmz7rzzzsMzCIBgAxr/+HddABy0RYsW1a9+9auaN29eq68C8IbmFVYAAKJ5hRUAgGheYQUAIJpgBQAgmmAFACCaYAUAIJpgBQAgmmAFACDa/wLiR8s2WXK7EwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier_list = ['cp', 'oldpeak', 'ca']\n",
    "\n",
    "for i in outlier_list:\n",
    "    Q1 = df_for_model3[i].quantile(0.25)\n",
    "    Q3 = df_for_model3[i].quantile(0.75)\n",
    "\n",
    "    IQR = Q3 - Q1\n",
    "    ups = Q3 + 1.5*IQR\n",
    "    lows = Q1 - 1.5*IQR\n",
    "\n",
    "    outlier_up = df_for_model3[i] > ups\n",
    "    outlier_low = df_for_model3[i] < lows\n",
    "\n",
    "    df_for_model3.loc[outlier_up,i] = ups\n",
    "    df_for_model3.loc[outlier_low,i] = lows\n",
    "\n",
    "    sns.boxplot(df_for_model3[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c180e9c",
   "metadata": {},
   "source": [
    "# Model 3 - Suppressed Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1b204d97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 266 candidates, totalling 2660 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=40, shuffle=True),\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(remainder='passthrough',\n",
       "                                                          transformers=[('num',\n",
       "                                                                         Pipeline(steps=[('scaler',\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['oldpeak'])])),\n",
       "                                       ('classifier',\n",
       "                                        LogisticRegression(random_state=40))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid=[{'classifier__C': array([0.1 , 0.15, 0...\n",
       "                          'classifier__penalty': ['l1'],\n",
       "                          'classifier__solver': ['liblinear', 'saga']},\n",
       "                         {'classifier__C': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  , 1.05, 1.1 , 1.15,\n",
       "       1.2 , 1.25, 1.3 , 1.35, 1.4 , 1.45, 1.5 , 1.55, 1.6 , 1.65, 1.7 ,\n",
       "       1.75, 1.8 , 1.85, 1.9 , 1.95]),\n",
       "                          'classifier__penalty': ['l2'],\n",
       "                          'classifier__solver': ['liblinear', 'newton-cg',\n",
       "                                                 'lbfgs', 'sag', 'saga']}],\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 40 \n",
    "train, test = train_test_split(df_for_model3, test_size=0.30, random_state=seed, stratify=df.condition)\n",
    "\n",
    "y_train = train['condition']\n",
    "X_train = train.drop('condition', axis=1)\n",
    "y_test = test['condition']\n",
    "X_test = test.drop('condition', axis=1)\n",
    "\n",
    "num_transformer = Pipeline(steps=[('scaler', StandardScaler())\n",
    "                                 ])\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[('num', num_transformer, really_nums)],\n",
    "                                 remainder = 'passthrough') \n",
    "\n",
    "\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('classifier',   LogisticRegression(random_state=seed))\n",
    "                          ]\n",
    "                   )\n",
    "\n",
    "params = [{'classifier__C'      : np.arange(0.1, 2.0, 0.05), \n",
    "           'classifier__penalty': ['l1'], \n",
    "           'classifier__solver' : ['liblinear', 'saga']},\n",
    "          \n",
    "          {'classifier__C'      : np.arange(0.1, 2.0, 0.05), \n",
    "           'classifier__penalty': ['l2'], \n",
    "           'classifier__solver' : ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga']}\n",
    "         ]\n",
    "\n",
    "kfold = StratifiedKFold(n_splits = 10, random_state = seed, shuffle=True) \n",
    "grid4 = GridSearchCV(pipeline, param_grid = params, cv = kfold, verbose = 1, n_jobs = -1)\n",
    "grid4.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7b3136ca",
   "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>method</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline model</td>\n",
       "      <td>0.83</td>\n",
       "      <td>32.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dropping 2 Cols(Lofo)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>33.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dropping 2+4 Cols</td>\n",
       "      <td>0.84</td>\n",
       "      <td>34.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Outliers</td>\n",
       "      <td>0.83</td>\n",
       "      <td>33.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  method  accuracy    TP   FP    TN    FN  precision  recall  \\\n",
       "0         Baseline model      0.83 32.00 5.00 43.00 10.00       0.86    0.76   \n",
       "1  Dropping 2 Cols(Lofo)      0.84 33.00 5.00 43.00  9.00       0.87    0.79   \n",
       "2      Dropping 2+4 Cols      0.84 34.00 6.00 42.00  8.00       0.85    0.81   \n",
       "3               Outliers      0.83 33.00 6.00 42.00  9.00       0.85    0.79   \n",
       "\n",
       "   roc_auc  pr_auc   f1   f2  \n",
       "0     0.92    0.92 0.81 0.78  \n",
       "1     0.93    0.93 0.82 0.80  \n",
       "2     0.93    0.92 0.83 0.82  \n",
       "3     0.93    0.92 0.81 0.80  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_probs = grid4.predict_proba(X_test)[:,1]\n",
    "scores = print_results('Outliers', y_test, grid4.predict(X_test), pred_probs)\n",
    "dftmp = pd.DataFrame([scores], columns=score_names)\n",
    "dfAcc = pd.concat([dfAcc, dftmp], ignore_index=True)\n",
    "dfAcc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db27e51",
   "metadata": {},
   "source": [
    "### Results are not good. That's why I will continue with the manual feature analysis on the 2nd model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "24da0d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_for_model4 = df_for_model2.drop(['age', 'sex','trestbps','thalach'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ef6358",
   "metadata": {},
   "source": [
    "**I want to see the effect of features on condition in bar graphs:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1e7e8dd6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in df_for_model4.columns:\n",
    "    if i != \"condition\":\n",
    "        (df_for_model4.groupby(i)['condition'].value_counts(normalize=True).unstack('condition').plot.bar(stacked=True,figsize=(15,5)));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0deb576f",
   "metadata": {},
   "source": [
    "**As a result of my investigations I saw that I can group feature as follows;**\n",
    "- \"oldpeak\" : Three groups, 100%, 50%-99%, and others\n",
    "- \"cp\" : Two groups, 3 or not\n",
    "- \"restecg\" : Two groups, 1 or not\n",
    "- \"slope\" : 0 or not\n",
    "- \"ca\" : 0 or not\n",
    "- \"thal\" : 0 or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0cabe178",
   "metadata": {},
   "outputs": [],
   "source": [
    "newpeak_list = []\n",
    "for i in df_for_model4.oldpeak.values:\n",
    "    a = len(df_for_model4[(df_for_model4.oldpeak == i) & (df_for_model4.condition == 1)]) / len(df_for_model4[(df_for_model4.oldpeak == i)])\n",
    "    if a == 1:\n",
    "        newpeak_list.append(2)\n",
    "    elif a > 0.50:\n",
    "        newpeak_list.append(1)\n",
    "    else:\n",
    "        newpeak_list.append(0)\n",
    "df_for_model4[\"new_peak\"] = newpeak_list\n",
    "\n",
    "\n",
    "new_cp_list = []\n",
    "for i in df_for_model4.cp.values:\n",
    "    if i == 3:\n",
    "        new_cp_list.append(1)\n",
    "    else:\n",
    "        new_cp_list.append(0)   \n",
    "df_for_model4[\"cp_is_3\"] = new_cp_list\n",
    "\n",
    "\n",
    "new_res_list = []\n",
    "for i in df_for_model4.restecg.values:\n",
    "    if i == 1:\n",
    "        new_res_list.append(1)\n",
    "    else:\n",
    "        new_res_list.append(0)      \n",
    "df_for_model4[\"res_is_1\"] = new_res_list\n",
    "\n",
    "\n",
    "new_slope_list = []\n",
    "for i in df_for_model4.slope.values:\n",
    "    if i == 0:\n",
    "        new_slope_list.append(1)\n",
    "    else:\n",
    "        new_slope_list.append(0)       \n",
    "df_for_model4[\"slope_is_0\"] = new_slope_list\n",
    "\n",
    "\n",
    "new_ca_list = []\n",
    "for i in df_for_model4.ca.values:\n",
    "    if i == 0:\n",
    "        new_ca_list.append(1)\n",
    "    else:\n",
    "        new_ca_list.append(0)\n",
    "df_for_model4[\"ca_is_0\"] = new_ca_list\n",
    "\n",
    "new_thal_list = []\n",
    "for i in df_for_model4.thal.values:\n",
    "    if i == 0:\n",
    "        new_thal_list.append(1)\n",
    "    else:\n",
    "        new_thal_list.append(0)        \n",
    "df_for_model4[\"thal_is_0\"] = new_thal_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cc3e2d",
   "metadata": {},
   "source": [
    "# Model 4 - New grouping features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "368be783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 190 candidates, totalling 1900 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=40, shuffle=True),\n",
       "             estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={'C': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  , 1.05, 1.1 , 1.15,\n",
       "       1.2 , 1.25, 1.3 , 1.35, 1.4 , 1.45, 1.5 , 1.55, 1.6 , 1.65, 1.7 ,\n",
       "       1.75, 1.8 , 1.85, 1.9 , 1.95]),\n",
       "                         'penalty': ['l2'],\n",
       "                         'solver': ['liblinear', 'newton-cg', 'lbfgs', 'sag',\n",
       "                                    'saga']},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_model4 = df_for_model4.drop([\"oldpeak\",\"cp\",\"restecg\",\"slope\",\"ca\",\"thal\"],axis=1)\n",
    "\n",
    "seed = 40 \n",
    "train, test = train_test_split(df_for_model4, test_size=0.30, random_state=seed, stratify=df.condition)\n",
    "\n",
    "y_train = train['condition']\n",
    "X_train = train.drop('condition', axis=1)\n",
    "y_test = test['condition']\n",
    "X_test = test.drop('condition', axis=1)\n",
    "\n",
    "params =  {'C'      : np.arange(0.1, 2.0, 0.05), \n",
    "           'penalty': ['l2'], \n",
    "           'solver' : ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga']}\n",
    "model = LogisticRegression()\n",
    "kfold = StratifiedKFold(n_splits = 10, random_state = seed, shuffle=True) \n",
    "grid4 = GridSearchCV(estimator=model, param_grid = params, cv = kfold, verbose = 1, n_jobs = -1)\n",
    "grid4.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "54d069af",
   "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>method</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline model</td>\n",
       "      <td>0.83</td>\n",
       "      <td>32.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dropping 2 Cols(Lofo)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>33.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dropping 2+4 Cols</td>\n",
       "      <td>0.84</td>\n",
       "      <td>34.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Outliers</td>\n",
       "      <td>0.83</td>\n",
       "      <td>33.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grouping Method</td>\n",
       "      <td>0.89</td>\n",
       "      <td>36.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  method  accuracy    TP   FP    TN    FN  precision  recall  \\\n",
       "0         Baseline model      0.83 32.00 5.00 43.00 10.00       0.86    0.76   \n",
       "1  Dropping 2 Cols(Lofo)      0.84 33.00 5.00 43.00  9.00       0.87    0.79   \n",
       "2      Dropping 2+4 Cols      0.84 34.00 6.00 42.00  8.00       0.85    0.81   \n",
       "3               Outliers      0.83 33.00 6.00 42.00  9.00       0.85    0.79   \n",
       "4        Grouping Method      0.89 36.00 4.00 44.00  6.00       0.90    0.86   \n",
       "\n",
       "   roc_auc  pr_auc   f1   f2  \n",
       "0     0.92    0.92 0.81 0.78  \n",
       "1     0.93    0.93 0.82 0.80  \n",
       "2     0.93    0.92 0.83 0.82  \n",
       "3     0.93    0.92 0.81 0.80  \n",
       "4     0.96    0.96 0.88 0.87  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_probs = grid4.predict_proba(X_test)[:,1]\n",
    "scores = print_results('Grouping Method', y_test, grid4.predict(X_test), pred_probs)\n",
    "dftmp = pd.DataFrame([scores], columns=score_names)\n",
    "dfAcc = pd.concat([dfAcc, dftmp], ignore_index=True)\n",
    "dfAcc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "029ebdc4",
   "metadata": {},
   "source": [
    "### Looks good, improvement has been made. What about learning curve and optimizing train size?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9f28512f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.7000000000000002, 'penalty': 'l2', 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid4.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1925f651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.7000000000000002, random_state=40, solver='liblinear')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 40 \n",
    "train, test = train_test_split(df_for_model4, test_size=0.30, random_state=seed, stratify=df.condition)\n",
    "\n",
    "y_train = train['condition']\n",
    "X_train = train.drop('condition', axis=1)\n",
    "y_test = test['condition']\n",
    "X_test = test.drop('condition', axis=1)\n",
    "\n",
    "model = LogisticRegression(random_state=40,C=0.7000000000000002,penalty=\"l2\",solver=\"liblinear\")\n",
    "model.fit(X_train,y_train)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "72cb2161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seed = 40 \n",
    "y = df_for_model4.condition\n",
    "X = df_for_model4.drop(\"condition\",axis=1)\n",
    "\n",
    "visualizer = LearningCurve(model, cv=10, scoring=\"accuracy\", train_sizes=np.linspace(0.1, 1.0, 10), n_jobs=-1)\n",
    "visualizer.fit(X, y)\n",
    "visualizer.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a96f7a8",
   "metadata": {},
   "source": [
    "**As seen in the learning curve, the optimum point corresponds to approximately 60% train and 40% test size. Let's try!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "71584dfc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline model</td>\n",
       "      <td>0.83</td>\n",
       "      <td>32.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dropping 2 Cols(Lofo)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>33.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dropping 2+4 Cols</td>\n",
       "      <td>0.84</td>\n",
       "      <td>34.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Outliers</td>\n",
       "      <td>0.83</td>\n",
       "      <td>33.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grouping Method</td>\n",
       "      <td>0.89</td>\n",
       "      <td>36.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Best Model</td>\n",
       "      <td>0.90</td>\n",
       "      <td>47.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  method  accuracy    TP   FP    TN    FN  precision  recall  \\\n",
       "0         Baseline model      0.83 32.00 5.00 43.00 10.00       0.86    0.76   \n",
       "1  Dropping 2 Cols(Lofo)      0.84 33.00 5.00 43.00  9.00       0.87    0.79   \n",
       "2      Dropping 2+4 Cols      0.84 34.00 6.00 42.00  8.00       0.85    0.81   \n",
       "3               Outliers      0.83 33.00 6.00 42.00  9.00       0.85    0.79   \n",
       "4        Grouping Method      0.89 36.00 4.00 44.00  6.00       0.90    0.86   \n",
       "5             Best Model      0.90 47.00 4.00 60.00  8.00       0.92    0.85   \n",
       "\n",
       "   roc_auc  pr_auc   f1   f2  \n",
       "0     0.92    0.92 0.81 0.78  \n",
       "1     0.93    0.93 0.82 0.80  \n",
       "2     0.93    0.92 0.83 0.82  \n",
       "3     0.93    0.92 0.81 0.80  \n",
       "4     0.96    0.96 0.88 0.87  \n",
       "5     0.97    0.97 0.89 0.87  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 40 \n",
    "train, test = train_test_split(df_for_model4, test_size=0.4, random_state=seed, stratify=df.condition)\n",
    "\n",
    "y_train = train['condition']\n",
    "X_train = train.drop('condition', axis=1)\n",
    "y_test = test['condition']\n",
    "X_test = test.drop('condition', axis=1)\n",
    "\n",
    "bestmodel = LogisticRegression(random_state=40,C=0.7000000000000002,penalty=\"l2\",solver=\"liblinear\")\n",
    "bestmodel.fit(X_train,y_train)    \n",
    "\n",
    "pred_probs = bestmodel.predict_proba(X_test)[:,1]\n",
    "scores = print_results('Best Model', y_test, bestmodel.predict(X_test), pred_probs)\n",
    "dftmp = pd.DataFrame([scores], columns=score_names)\n",
    "dfAcc = pd.concat([dfAcc, dftmp], ignore_index=True)\n",
    "dfAcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "603d37f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "auc = roc_auc_score(y_test,pred_probs)\n",
    "fpr, tpr,thresold = roc_curve(y_test,bestmodel.predict_proba(X_test)[:,1])\n",
    "plt.plot(fpr,tpr,label=\"Model AUC (Alan=%0.2f)\" % auc)\n",
    "plt.plot([0,1],[0,1],\"r--\")\n",
    "plt.xlabel(\"False Positive Ratio\")\n",
    "plt.ylabel(\"True Positive Ratio\")\n",
    "plt.title(\"ROC\")\n",
    "plt.legend(loc = \"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fdea996",
   "metadata": {},
   "source": [
    "# We started with 83% accuracy and 92% roc score in our base model. At this point, it is seen again that it is our best model with 90% accuracy and 96% roc score."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}