implementation of algos in scania aps data.ipynb

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  {
   "cell_type": "markdown",
   "id": "46bb730c",
   "metadata": {},
   "source": [
    "# Scania trucks air pressure system failure dataset implementation of Algos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8339bd92",
   "metadata": {},
   "source": [
    "***1. Introduction***\n",
    "* Air Pressure System (APS) in trucks generates pressurized air that is used for various functions of trucks such as braking and gear changes. Lot of money is spent on un-necessary checks done by service person on APS system in trucks, which can be prevented by developing a machine learning model that can predict the failure of component in APS. In this way, service can be restricted to the trucks whose APS component is predicted to be failed by the model.\n",
    "\n",
    "* The given dataset positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS. Attributes of dataset are anonymized for proprietary reasons. This is a binary classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfd506c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import csv\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import numpy as np\n",
    "import re\n",
    "import os\n",
    "import datetime as dt\n",
    "import pickle\n",
    "from datetime import datetime\n",
    "from scipy import stats\n",
    "import random\n",
    "from sklearn.model_selection import KFold\n",
    "from IPython.display import display, Markdown\n",
    "import pickle\n",
    "from pandas import Series\n",
    "from collections import defaultdict\n",
    "import math\n",
    "from scipy.stats import randint, uniform\n",
    "import missingno as msno\n",
    "import optuna\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.impute import MissingIndicator\n",
    "from sklearn.impute import KNNImputer\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import f1_score, auc, accuracy_score, recall_score, precision_score,\\\n",
    "confusion_matrix, classification_report, roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2507a2e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(\"C:\\\\Users\\\\shekh\\\\Downloads\\\\aps_failure_training_set (1).csv\", skiprows=20)\n",
    "test=pd.read_csv(\"C:\\\\Users\\\\shekh\\\\Downloads\\\\aps_failure_test_set.csv\", skiprows=20)"
   ]
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [class, aa_000, ab_000, ac_000, ad_000, ae_000, af_000, ag_000, ag_001, ag_002, ag_003, ag_004, ag_005, ag_006, ag_007, ag_008, ag_009, ah_000, ai_000, aj_000, ak_000, al_000, am_0, an_000, ao_000, ap_000, aq_000, ar_000, as_000, at_000, au_000, av_000, ax_000, ay_000, ay_001, ay_002, ay_003, ay_004, ay_005, ay_006, ay_007, ay_008, ay_009, az_000, az_001, az_002, az_003, az_004, az_005, az_006, az_007, az_008, az_009, ba_000, ba_001, ba_002, ba_003, ba_004, ba_005, ba_006, ba_007, ba_008, ba_009, bb_000, bc_000, bd_000, be_000, bf_000, bg_000, bh_000, bi_000, bj_000, bk_000, bl_000, bm_000, bn_000, bo_000, bp_000, bq_000, br_000, bs_000, bt_000, bu_000, bv_000, bx_000, by_000, bz_000, ca_000, cb_000, cc_000, cd_000, ce_000, cf_000, cg_000, ch_000, ci_000, cj_000, ck_000, cl_000, cm_000, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 171 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for the duplicates\n",
    "train[train.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed5e1081",
   "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>class</th>\n",
       "      <th>aa_000</th>\n",
       "      <th>ab_000</th>\n",
       "      <th>ac_000</th>\n",
       "      <th>ad_000</th>\n",
       "      <th>ae_000</th>\n",
       "      <th>af_000</th>\n",
       "      <th>ag_000</th>\n",
       "      <th>ag_001</th>\n",
       "      <th>ag_002</th>\n",
       "      <th>...</th>\n",
       "      <th>ee_002</th>\n",
       "      <th>ee_003</th>\n",
       "      <th>ee_004</th>\n",
       "      <th>ee_005</th>\n",
       "      <th>ee_006</th>\n",
       "      <th>ee_007</th>\n",
       "      <th>ee_008</th>\n",
       "      <th>ee_009</th>\n",
       "      <th>ef_000</th>\n",
       "      <th>eg_000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 171 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [class, aa_000, ab_000, ac_000, ad_000, ae_000, af_000, ag_000, ag_001, ag_002, ag_003, ag_004, ag_005, ag_006, ag_007, ag_008, ag_009, ah_000, ai_000, aj_000, ak_000, al_000, am_0, an_000, ao_000, ap_000, aq_000, ar_000, as_000, at_000, au_000, av_000, ax_000, ay_000, ay_001, ay_002, ay_003, ay_004, ay_005, ay_006, ay_007, ay_008, ay_009, az_000, az_001, az_002, az_003, az_004, az_005, az_006, az_007, az_008, az_009, ba_000, ba_001, ba_002, ba_003, ba_004, ba_005, ba_006, ba_007, ba_008, ba_009, bb_000, bc_000, bd_000, be_000, bf_000, bg_000, bh_000, bi_000, bj_000, bk_000, bl_000, bm_000, bn_000, bo_000, bp_000, bq_000, br_000, bs_000, bt_000, bu_000, bv_000, bx_000, by_000, bz_000, ca_000, cb_000, cc_000, cd_000, ce_000, cf_000, cg_000, ch_000, ci_000, cj_000, ck_000, cl_000, cm_000, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 171 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c54f2d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing na with NaN because na is in category type\n",
    "train.replace(to_replace=['na', ' nan'], value=np.NaN, inplace=True)\n",
    "test.replace(to_replace=['na', ' nan'], value=np.NaN, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08c187ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fe0c11ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoding the target column in binary form o and 1 as it has only 2 outputs\n",
    "train['class']=le.fit_transform(train['class'])\n",
    "test['class']=le.fit_transform(test['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff40bf73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "646fd266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "794f5448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='class', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5acc616a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing the null values in train data if the there are more na than 42000, it will remove the columns if it has more than 42000 na values\n",
    "# we did this after observing the Eda and seeing how much percent of data is missing\n",
    "train.dropna(axis = 1, thresh=42000, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d7882e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 161)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0efb4f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.dropna(axis = 1, thresh=11000, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3d7a7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 161)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b399f9c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for train dataset\n",
    "y_train = train['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "473f84ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['class'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "93c7cb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for test dataset\n",
    "y_test = test['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd12694c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(['class'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd6cfc4",
   "metadata": {},
   "source": [
    "## Handling the missing value by mean imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "873ebd72",
   "metadata": {},
   "outputs": [],
   "source": [
    "impute_mean = SimpleImputer(missing_values= np.nan, strategy='mean',copy = True, verbose= 2)\n",
    "train_mean_imputed = pd.DataFrame(impute_mean.fit_transform(train),columns=train.columns)\n",
    "test_mean_imputed = pd.DataFrame(impute_mean.transform(test),columns=test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "838e841e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>ag_002</th>\n",
       "      <th>ag_003</th>\n",
       "      <th>ag_004</th>\n",
       "      <th>...</th>\n",
       "      <th>ee_002</th>\n",
       "      <th>ee_003</th>\n",
       "      <th>ee_004</th>\n",
       "      <th>ee_005</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>76698.0</td>\n",
       "      <td>2.130706e+09</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>37250.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1240520.0</td>\n",
       "      <td>493384.0</td>\n",
       "      <td>721044.0</td>\n",
       "      <td>469792.0</td>\n",
       "      <td>339156.0</td>\n",
       "      <td>157956.0</td>\n",
       "      <td>73224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33058.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>190620.639314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18254.0</td>\n",
       "      <td>...</td>\n",
       "      <td>421400.0</td>\n",
       "      <td>178064.0</td>\n",
       "      <td>293306.0</td>\n",
       "      <td>245416.0</td>\n",
       "      <td>133654.0</td>\n",
       "      <td>81140.0</td>\n",
       "      <td>97576.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41040.0</td>\n",
       "      <td>2.280000e+02</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>...</td>\n",
       "      <td>277378.0</td>\n",
       "      <td>159812.0</td>\n",
       "      <td>423992.0</td>\n",
       "      <td>409564.0</td>\n",
       "      <td>320746.0</td>\n",
       "      <td>158022.0</td>\n",
       "      <td>95128.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>2212.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60874.0</td>\n",
       "      <td>1.368000e+03</td>\n",
       "      <td>458.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43752.0</td>\n",
       "      <td>...</td>\n",
       "      <td>622012.0</td>\n",
       "      <td>229790.0</td>\n",
       "      <td>405298.0</td>\n",
       "      <td>347188.0</td>\n",
       "      <td>286954.0</td>\n",
       "      <td>311560.0</td>\n",
       "      <td>433954.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    aa_000        ac_000         ad_000  ae_000  af_000  ag_000  ag_001  \\\n",
       "0  76698.0  2.130706e+09     280.000000     0.0     0.0     0.0     0.0   \n",
       "1  33058.0  0.000000e+00  190620.639314     0.0     0.0     0.0     0.0   \n",
       "2  41040.0  2.280000e+02     100.000000     0.0     0.0     0.0     0.0   \n",
       "3     12.0  7.000000e+01      66.000000     0.0    10.0     0.0     0.0   \n",
       "4  60874.0  1.368000e+03     458.000000     0.0     0.0     0.0     0.0   \n",
       "\n",
       "   ag_002  ag_003   ag_004  ...     ee_002    ee_003    ee_004    ee_005  \\\n",
       "0     0.0     0.0  37250.0  ...  1240520.0  493384.0  721044.0  469792.0   \n",
       "1     0.0     0.0  18254.0  ...   421400.0  178064.0  293306.0  245416.0   \n",
       "2     0.0     0.0   1648.0  ...   277378.0  159812.0  423992.0  409564.0   \n",
       "3     0.0   318.0   2212.0  ...      240.0      46.0      58.0      44.0   \n",
       "4     0.0     0.0  43752.0  ...   622012.0  229790.0  405298.0  347188.0   \n",
       "\n",
       "     ee_006    ee_007    ee_008  ee_009  ef_000  eg_000  \n",
       "0  339156.0  157956.0   73224.0     0.0     0.0     0.0  \n",
       "1  133654.0   81140.0   97576.0  1500.0     0.0     0.0  \n",
       "2  320746.0  158022.0   95128.0   514.0     0.0     0.0  \n",
       "3      10.0       0.0       0.0     0.0     4.0    32.0  \n",
       "4  286954.0  311560.0  433954.0  1218.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 160 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_mean_imputed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee8cf804",
   "metadata": {},
   "source": [
    "## Machine learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "74e94eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def result(cm): \n",
    "    sns.set(rc={'figure.figsize':(5,5)})\n",
    "    class_label = [\"negative\", \"positive\"]\n",
    "    df_cm = pd.DataFrame(cm, index = class_label, columns = class_label)\n",
    "    sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.show()\n",
    "\n",
    "    print(\"Number of False Positive = \", cm[0][1])\n",
    "    print(\"Number of False Negative = \", cm[1][0])\n",
    "    Total_cost = cm[0][1] * 10 + cm[1][0] * 500\n",
    "    print(\"Total cost = \",Total_cost )\n",
    "    return Total_cost\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f7a66c",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "59a9b90d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n",
      "Best HyperParameter:  {'C': 10}\n"
     ]
    }
   ],
   "source": [
    "clf=LogisticRegression(penalty='l2', class_weight = 'balanced')\n",
    "\n",
    "C_values = [10**x for x in range(-4,5)]\n",
    "tuned_parameters = [{'C': C_values}]\n",
    "\n",
    "gsvl = GridSearchCV(clf,tuned_parameters,cv=3,verbose=1,scoring='roc_auc', return_train_score=True, n_jobs=-1)\n",
    "gsvl.fit(train_mean_imputed,y_train)\n",
    "print(\"Best HyperParameter: \",gsvl.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bb003897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of False Positive =  523\n",
      "Number of False Negative =  26\n",
      "Total cost =  18230\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "18230"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(penalty='l2', C=0.1 ,n_jobs= -1, class_weight = 'balanced')\n",
    "clf.fit(train_mean_imputed,y_train)\n",
    "y_pred = clf.predict(test_mean_imputed)\n",
    "cm =confusion_matrix (y_test, y_pred)\n",
    "result(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "543ccd60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98     15625\n",
      "           1       0.40      0.93      0.56       375\n",
      "\n",
      "    accuracy                           0.97     16000\n",
      "   macro avg       0.70      0.95      0.77     16000\n",
      "weighted avg       0.98      0.97      0.97     16000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f170013d",
   "metadata": {},
   "source": [
    "### SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "04995421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(class_weight='balanced')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc=SVC(class_weight='balanced')\n",
    "\n",
    "svc.fit(train_mean_imputed, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8f7902e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_pred=svc.predict(test_mean_imputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "51c1d05f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.96      0.98     15625\n",
      "           1       0.35      0.94      0.51       375\n",
      "\n",
      "    accuracy                           0.96     16000\n",
      "   macro avg       0.67      0.95      0.74     16000\n",
      "weighted avg       0.98      0.96      0.97     16000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bbf6f175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC score: 0.9467519999999999\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, svc_pred)\n",
    "roc_auc = roc_auc_score(y_test, svc_pred)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "\n",
    "# Print the AUC-ROC score\n",
    "print('AUC-ROC score:', roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "330a24b3",
   "metadata": {},
   "source": [
    "### Decision Tree( hyperparameter tunning by oputna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ed5cd50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(trial):\n",
    "    # Define the hyperparameters to optimize\n",
    "    max_depth = trial.suggest_int('max_depth', 1, 10)\n",
    "    min_samples_split = trial.suggest_int('min_samples_split', 2, 20)\n",
    "    min_samples_leaf = trial.suggest_int('min_samples_leaf', 1, 10)\n",
    "\n",
    "    # Create the Decision Tree classifier with the optimized hyperparameters\n",
    "    clf = DecisionTreeClassifier(max_depth=max_depth,\n",
    "                                 min_samples_split=min_samples_split,\n",
    "                                 min_samples_leaf=min_samples_leaf)\n",
    "\n",
    "    # Evaluate the classifier with cross-validation\n",
    "    score = cross_val_score(clf, train_mean_imputed, y_train, cv=5).mean()\n",
    "\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "56a7ff7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-04-06 17:57:41,214]\u001b[0m A new study created in memory with name: no-name-67769ab7-c200-4315-a3f5-af0448679dbc\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 17:58:07,112]\u001b[0m Trial 0 finished with value: 0.9906166666666666 and parameters: {'max_depth': 10, 'min_samples_split': 20, 'min_samples_leaf': 10}. Best is trial 0 with value: 0.9906166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 17:58:27,149]\u001b[0m Trial 1 finished with value: 0.9907 and parameters: {'max_depth': 9, 'min_samples_split': 6, 'min_samples_leaf': 8}. Best is trial 1 with value: 0.9907.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 17:58:45,021]\u001b[0m Trial 2 finished with value: 0.9905666666666667 and parameters: {'max_depth': 8, 'min_samples_split': 2, 'min_samples_leaf': 10}. Best is trial 1 with value: 0.9907.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 17:58:54,270]\u001b[0m Trial 3 finished with value: 0.9882333333333333 and parameters: {'max_depth': 4, 'min_samples_split': 16, 'min_samples_leaf': 7}. Best is trial 1 with value: 0.9907.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 17:59:10,191]\u001b[0m Trial 4 finished with value: 0.9907499999999999 and parameters: {'max_depth': 7, 'min_samples_split': 17, 'min_samples_leaf': 2}. Best is trial 4 with value: 0.9907499999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 17:59:23,795]\u001b[0m Trial 5 finished with value: 0.9904833333333333 and parameters: {'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 1}. Best is trial 4 with value: 0.9907499999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 17:59:43,841]\u001b[0m Trial 6 finished with value: 0.991 and parameters: {'max_depth': 9, 'min_samples_split': 11, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 17:59:57,507]\u001b[0m Trial 7 finished with value: 0.9901500000000001 and parameters: {'max_depth': 6, 'min_samples_split': 6, 'min_samples_leaf': 10}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:00:00,256]\u001b[0m Trial 8 finished with value: 0.9833333333333332 and parameters: {'max_depth': 1, 'min_samples_split': 11, 'min_samples_leaf': 6}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:00:20,384]\u001b[0m Trial 9 finished with value: 0.9907499999999999 and parameters: {'max_depth': 9, 'min_samples_split': 11, 'min_samples_leaf': 7}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:00:29,727]\u001b[0m Trial 10 finished with value: 0.9882333333333333 and parameters: {'max_depth': 4, 'min_samples_split': 14, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:00:45,586]\u001b[0m Trial 11 finished with value: 0.9907999999999999 and parameters: {'max_depth': 7, 'min_samples_split': 16, 'min_samples_leaf': 3}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:01:03,548]\u001b[0m Trial 12 finished with value: 0.9908833333333333 and parameters: {'max_depth': 8, 'min_samples_split': 13, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:01:25,739]\u001b[0m Trial 13 finished with value: 0.9909166666666666 and parameters: {'max_depth': 10, 'min_samples_split': 8, 'min_samples_leaf': 4}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:01:47,958]\u001b[0m Trial 14 finished with value: 0.9908833333333333 and parameters: {'max_depth': 10, 'min_samples_split': 8, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:02:10,211]\u001b[0m Trial 15 finished with value: 0.9908333333333335 and parameters: {'max_depth': 10, 'min_samples_split': 9, 'min_samples_leaf': 3}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:02:19,608]\u001b[0m Trial 16 finished with value: 0.9882666666666667 and parameters: {'max_depth': 4, 'min_samples_split': 9, 'min_samples_leaf': 5}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:02:22,306]\u001b[0m Trial 17 finished with value: 0.9833333333333332 and parameters: {'max_depth': 1, 'min_samples_split': 13, 'min_samples_leaf': 1}. Best is trial 6 with value: 0.991.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:02:42,425]\u001b[0m Trial 18 finished with value: 0.9911333333333333 and parameters: {'max_depth': 9, 'min_samples_split': 7, 'min_samples_leaf': 3}. Best is trial 18 with value: 0.9911333333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:03:00,393]\u001b[0m Trial 19 finished with value: 0.9910333333333334 and parameters: {'max_depth': 8, 'min_samples_split': 5, 'min_samples_leaf': 2}. Best is trial 18 with value: 0.9911333333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:03:18,463]\u001b[0m Trial 20 finished with value: 0.9910666666666668 and parameters: {'max_depth': 8, 'min_samples_split': 2, 'min_samples_leaf': 2}. Best is trial 18 with value: 0.9911333333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:03:36,472]\u001b[0m Trial 21 finished with value: 0.99125 and parameters: {'max_depth': 8, 'min_samples_split': 3, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:03:52,286]\u001b[0m Trial 22 finished with value: 0.9910833333333333 and parameters: {'max_depth': 7, 'min_samples_split': 2, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:04:08,126]\u001b[0m Trial 23 finished with value: 0.99095 and parameters: {'max_depth': 7, 'min_samples_split': 4, 'min_samples_leaf': 3}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:04:19,598]\u001b[0m Trial 24 finished with value: 0.9904166666666667 and parameters: {'max_depth': 5, 'min_samples_split': 3, 'min_samples_leaf': 1}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:04:35,374]\u001b[0m Trial 25 finished with value: 0.9908833333333333 and parameters: {'max_depth': 7, 'min_samples_split': 6, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:04:46,794]\u001b[0m Trial 26 finished with value: 0.9902833333333334 and parameters: {'max_depth': 5, 'min_samples_split': 4, 'min_samples_leaf': 3}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:05:06,904]\u001b[0m Trial 27 finished with value: 0.9911666666666668 and parameters: {'max_depth': 9, 'min_samples_split': 2, 'min_samples_leaf': 1}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:05:27,083]\u001b[0m Trial 28 finished with value: 0.9910333333333332 and parameters: {'max_depth': 9, 'min_samples_split': 7, 'min_samples_leaf': 1}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:05:47,143]\u001b[0m Trial 29 finished with value: 0.9910500000000001 and parameters: {'max_depth': 9, 'min_samples_split': 3, 'min_samples_leaf': 1}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:06:09,429]\u001b[0m Trial 30 finished with value: 0.9910166666666665 and parameters: {'max_depth': 10, 'min_samples_split': 5, 'min_samples_leaf': 3}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:06:27,497]\u001b[0m Trial 31 finished with value: 0.9910499999999999 and parameters: {'max_depth': 8, 'min_samples_split': 2, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:06:47,643]\u001b[0m Trial 32 finished with value: 0.99115 and parameters: {'max_depth': 9, 'min_samples_split': 3, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:07:07,729]\u001b[0m Trial 33 finished with value: 0.9906666666666666 and parameters: {'max_depth': 9, 'min_samples_split': 20, 'min_samples_leaf': 1}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:07:27,901]\u001b[0m Trial 34 finished with value: 0.99115 and parameters: {'max_depth': 9, 'min_samples_split': 3, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:07:50,109]\u001b[0m Trial 35 finished with value: 0.9907166666666667 and parameters: {'max_depth': 10, 'min_samples_split': 3, 'min_samples_leaf': 9}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:08:08,136]\u001b[0m Trial 36 finished with value: 0.9911833333333334 and parameters: {'max_depth': 8, 'min_samples_split': 5, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:08:21,778]\u001b[0m Trial 37 finished with value: 0.9904166666666667 and parameters: {'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 1}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:08:39,668]\u001b[0m Trial 38 finished with value: 0.99125 and parameters: {'max_depth': 8, 'min_samples_split': 5, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-04-06 18:08:57,619]\u001b[0m Trial 39 finished with value: 0.9907833333333335 and parameters: {'max_depth': 8, 'min_samples_split': 5, 'min_samples_leaf': 5}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:09:02,481]\u001b[0m Trial 40 finished with value: 0.9866666666666667 and parameters: {'max_depth': 2, 'min_samples_split': 6, 'min_samples_leaf': 6}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:09:20,629]\u001b[0m Trial 41 finished with value: 0.99115 and parameters: {'max_depth': 8, 'min_samples_split': 2, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:09:36,414]\u001b[0m Trial 42 finished with value: 0.991 and parameters: {'max_depth': 7, 'min_samples_split': 5, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:09:54,340]\u001b[0m Trial 43 finished with value: 0.9908999999999999 and parameters: {'max_depth': 8, 'min_samples_split': 4, 'min_samples_leaf': 1}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:10:14,437]\u001b[0m Trial 44 finished with value: 0.9910666666666665 and parameters: {'max_depth': 9, 'min_samples_split': 3, 'min_samples_leaf': 3}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:10:30,276]\u001b[0m Trial 45 finished with value: 0.9909333333333332 and parameters: {'max_depth': 7, 'min_samples_split': 7, 'min_samples_leaf': 4}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:10:43,910]\u001b[0m Trial 46 finished with value: 0.9902 and parameters: {'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 8}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:11:01,775]\u001b[0m Trial 47 finished with value: 0.9910166666666667 and parameters: {'max_depth': 8, 'min_samples_split': 2, 'min_samples_leaf': 2}. Best is trial 21 with value: 0.99125.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:11:24,186]\u001b[0m Trial 48 finished with value: 0.9913666666666666 and parameters: {'max_depth': 10, 'min_samples_split': 5, 'min_samples_leaf': 1}. Best is trial 48 with value: 0.9913666666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:11:46,486]\u001b[0m Trial 49 finished with value: 0.9913333333333334 and parameters: {'max_depth': 10, 'min_samples_split': 9, 'min_samples_leaf': 1}. Best is trial 48 with value: 0.9913666666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:12:08,863]\u001b[0m Trial 50 finished with value: 0.9913833333333333 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 1}. Best is trial 50 with value: 0.9913833333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:12:31,151]\u001b[0m Trial 51 finished with value: 0.9909166666666668 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 1}. Best is trial 50 with value: 0.9913833333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:12:53,946]\u001b[0m Trial 52 finished with value: 0.9913500000000001 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 1}. Best is trial 50 with value: 0.9913833333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:13:16,425]\u001b[0m Trial 53 finished with value: 0.9912000000000001 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 1}. Best is trial 50 with value: 0.9913833333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:13:38,776]\u001b[0m Trial 54 finished with value: 0.9909166666666666 and parameters: {'max_depth': 10, 'min_samples_split': 15, 'min_samples_leaf': 1}. Best is trial 50 with value: 0.9913833333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:14:01,139]\u001b[0m Trial 55 finished with value: 0.9911666666666668 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 1}. Best is trial 50 with value: 0.9913833333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:14:23,585]\u001b[0m Trial 56 finished with value: 0.99115 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 1}. Best is trial 50 with value: 0.9913833333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:14:45,892]\u001b[0m Trial 57 finished with value: 0.9910166666666667 and parameters: {'max_depth': 10, 'min_samples_split': 9, 'min_samples_leaf': 3}. Best is trial 50 with value: 0.9913833333333333.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:15:08,336]\u001b[0m Trial 58 finished with value: 0.9914166666666666 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 2}. Best is trial 58 with value: 0.9914166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:15:30,696]\u001b[0m Trial 59 finished with value: 0.9913166666666667 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 2}. Best is trial 58 with value: 0.9914166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:15:53,115]\u001b[0m Trial 60 finished with value: 0.9912000000000001 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 1}. Best is trial 58 with value: 0.9914166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:16:15,469]\u001b[0m Trial 61 finished with value: 0.9912833333333333 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 2}. Best is trial 58 with value: 0.9914166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:16:37,924]\u001b[0m Trial 62 finished with value: 0.99145 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 1}. Best is trial 62 with value: 0.99145.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:17:00,122]\u001b[0m Trial 63 finished with value: 0.9908833333333332 and parameters: {'max_depth': 10, 'min_samples_split': 13, 'min_samples_leaf': 1}. Best is trial 62 with value: 0.99145.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:17:20,310]\u001b[0m Trial 64 finished with value: 0.99095 and parameters: {'max_depth': 9, 'min_samples_split': 11, 'min_samples_leaf': 1}. Best is trial 62 with value: 0.99145.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:17:42,675]\u001b[0m Trial 65 finished with value: 0.9912333333333333 and parameters: {'max_depth': 10, 'min_samples_split': 9, 'min_samples_leaf': 1}. Best is trial 62 with value: 0.99145.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:18:02,796]\u001b[0m Trial 66 finished with value: 0.9911666666666668 and parameters: {'max_depth': 9, 'min_samples_split': 11, 'min_samples_leaf': 2}. Best is trial 62 with value: 0.99145.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:18:09,883]\u001b[0m Trial 67 finished with value: 0.9892000000000001 and parameters: {'max_depth': 3, 'min_samples_split': 8, 'min_samples_leaf': 7}. Best is trial 62 with value: 0.99145.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:18:29,973]\u001b[0m Trial 68 finished with value: 0.9909666666666667 and parameters: {'max_depth': 9, 'min_samples_split': 14, 'min_samples_leaf': 3}. Best is trial 62 with value: 0.99145.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:18:52,273]\u001b[0m Trial 69 finished with value: 0.99125 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 1}. Best is trial 62 with value: 0.99145.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:19:12,473]\u001b[0m Trial 70 finished with value: 0.9906666666666666 and parameters: {'max_depth': 9, 'min_samples_split': 13, 'min_samples_leaf': 2}. Best is trial 62 with value: 0.99145.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:19:34,744]\u001b[0m Trial 71 finished with value: 0.9914999999999999 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:19:57,013]\u001b[0m Trial 72 finished with value: 0.99125 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 1}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:20:19,258]\u001b[0m Trial 73 finished with value: 0.9912833333333333 and parameters: {'max_depth': 10, 'min_samples_split': 11, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:20:41,525]\u001b[0m Trial 74 finished with value: 0.9912166666666667 and parameters: {'max_depth': 10, 'min_samples_split': 14, 'min_samples_leaf': 1}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:21:01,631]\u001b[0m Trial 75 finished with value: 0.9907999999999999 and parameters: {'max_depth': 9, 'min_samples_split': 17, 'min_samples_leaf': 4}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:21:23,960]\u001b[0m Trial 76 finished with value: 0.9913333333333334 and parameters: {'max_depth': 10, 'min_samples_split': 8, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:21:44,041]\u001b[0m Trial 77 finished with value: 0.9911 and parameters: {'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 3}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-04-06 18:22:06,386]\u001b[0m Trial 78 finished with value: 0.9911166666666666 and parameters: {'max_depth': 10, 'min_samples_split': 8, 'min_samples_leaf': 1}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:22:26,665]\u001b[0m Trial 79 finished with value: 0.9908999999999999 and parameters: {'max_depth': 9, 'min_samples_split': 8, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:22:48,983]\u001b[0m Trial 80 finished with value: 0.9905999999999999 and parameters: {'max_depth': 10, 'min_samples_split': 11, 'min_samples_leaf': 10}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:23:11,527]\u001b[0m Trial 81 finished with value: 0.9910166666666667 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:23:33,851]\u001b[0m Trial 82 finished with value: 0.9912666666666666 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 3}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:23:56,224]\u001b[0m Trial 83 finished with value: 0.9911666666666668 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:24:16,535]\u001b[0m Trial 84 finished with value: 0.9910833333333333 and parameters: {'max_depth': 9, 'min_samples_split': 7, 'min_samples_leaf': 1}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:24:38,882]\u001b[0m Trial 85 finished with value: 0.9912333333333333 and parameters: {'max_depth': 10, 'min_samples_split': 9, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:25:01,187]\u001b[0m Trial 86 finished with value: 0.9907999999999999 and parameters: {'max_depth': 10, 'min_samples_split': 13, 'min_samples_leaf': 1}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:25:21,374]\u001b[0m Trial 87 finished with value: 0.9909166666666666 and parameters: {'max_depth': 9, 'min_samples_split': 11, 'min_samples_leaf': 3}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:25:32,879]\u001b[0m Trial 88 finished with value: 0.9903666666666666 and parameters: {'max_depth': 5, 'min_samples_split': 9, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:25:55,156]\u001b[0m Trial 89 finished with value: 0.9910833333333334 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 1}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:26:15,305]\u001b[0m Trial 90 finished with value: 0.9911666666666668 and parameters: {'max_depth': 9, 'min_samples_split': 8, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:26:37,669]\u001b[0m Trial 91 finished with value: 0.9912166666666666 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 2}. Best is trial 71 with value: 0.9914999999999999.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:27:00,030]\u001b[0m Trial 92 finished with value: 0.9915166666666666 and parameters: {'max_depth': 10, 'min_samples_split': 11, 'min_samples_leaf': 2}. Best is trial 92 with value: 0.9915166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:27:22,379]\u001b[0m Trial 93 finished with value: 0.9911166666666666 and parameters: {'max_depth': 10, 'min_samples_split': 11, 'min_samples_leaf': 1}. Best is trial 92 with value: 0.9915166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:27:44,615]\u001b[0m Trial 94 finished with value: 0.99125 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 2}. Best is trial 92 with value: 0.9915166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:28:06,917]\u001b[0m Trial 95 finished with value: 0.9909666666666667 and parameters: {'max_depth': 10, 'min_samples_split': 11, 'min_samples_leaf': 1}. Best is trial 92 with value: 0.9915166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:28:27,063]\u001b[0m Trial 96 finished with value: 0.9908833333333333 and parameters: {'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 1}. Best is trial 92 with value: 0.9915166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:28:49,541]\u001b[0m Trial 97 finished with value: 0.9911 and parameters: {'max_depth': 10, 'min_samples_split': 10, 'min_samples_leaf': 3}. Best is trial 92 with value: 0.9915166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:29:09,841]\u001b[0m Trial 98 finished with value: 0.9908166666666667 and parameters: {'max_depth': 9, 'min_samples_split': 13, 'min_samples_leaf': 1}. Best is trial 92 with value: 0.9915166666666666.\u001b[0m\n",
      "\u001b[32m[I 2023-04-06 18:29:32,535]\u001b[0m Trial 99 finished with value: 0.9912833333333333 and parameters: {'max_depth': 10, 'min_samples_split': 12, 'min_samples_leaf': 2}. Best is trial 92 with value: 0.9915166666666666.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Define the study for Optuna to optimize the hyperparameters\n",
    "study = optuna.create_study(direction='maximize')\n",
    "study.optimize(objective, n_trials=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "49ca0538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters:  {'max_depth': 10, 'min_samples_split': 11, 'min_samples_leaf': 2}\n"
     ]
    }
   ],
   "source": [
    "print('Best hyperparameters: ', study.best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9df3198c",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_clf = DecisionTreeClassifier(max_depth=study.best_params['max_depth'],\n",
    "                                   min_samples_split=study.best_params['min_samples_split'],\n",
    "                                   min_samples_leaf=study.best_params['min_samples_leaf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "80a61fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=10, min_samples_leaf=2, min_samples_split=11)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf.fit(train_mean_imputed, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bda28689",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_optuna_predict=best_clf.predict(test_mean_imputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a94dfcb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98925"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, dt_optuna_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c9cce2ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99     15625\n",
      "           1       0.87      0.63      0.73       375\n",
      "\n",
      "    accuracy                           0.99     16000\n",
      "   macro avg       0.93      0.81      0.86     16000\n",
      "weighted avg       0.99      0.99      0.99     16000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, dt_optuna_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b38a510e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8149120000000001"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, dt_optuna_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "804d998a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC score: 0.8149120000000001\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, dt_optuna_predict)\n",
    "roc_auc = roc_auc_score(y_test, dt_optuna_predict)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "\n",
    "# Print the AUC-ROC score\n",
    "print('AUC-ROC score:', roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ea794b",
   "metadata": {},
   "source": [
    "### Random forest classifier(by gridsearchcv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2751fb98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 18 candidates, totalling 54 fits\n",
      "Best HyperParameter:  {'max_depth': 6, 'n_estimators': 15}\n"
     ]
    }
   ],
   "source": [
    "clf=RandomForestClassifier(class_weight = 'balanced')\n",
    "\n",
    "depth=list(range(3, 10 ,3))\n",
    "nEstimator = list(range(3, 20 ,3))\n",
    "tuned_parameters = [{'max_depth':depth,'n_estimators':nEstimator}]\n",
    "gsvr = GridSearchCV(clf,tuned_parameters,cv=3,verbose=1,scoring='roc_auc', return_train_score=True, n_jobs=-1)\n",
    "gsvr.fit(train_mean_imputed,y_train)\n",
    "print(\"Best HyperParameter: \",gsvr.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "51893d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ANm3acPz4cRYsWMDQoUOBuHOvLxojoZRgRUTktdGtWzcePXrEwIEDiYyMxNPTk4CAABwcHABwcXFh7ty5jBgxAl9fX7Jly0bfvn3x9fVN8BgJZWcymUyGfjobFXP9fHKHIG8Il7xVkzsEeUPcfWDc91rkwfi7Ir0sp7KNDRsrJdMMVkREtNm/FWgVsYiIiBVoBisiIja/ijglUoIVERGViK1AJWIREREr0AxWREQ0g7UCJVgREXnl28xJfCoRi4iIWIFmsCIiohKxFSjBioiILtOxApWIRURErEAzWBERUYnYCpRgRUREJWIrUIlYRETECjSDFRERlYitQAlWRERUIrYClYhFRESsQDNYERFRidgKlGBFREQJ1gpUIhYREbECzWBFRESLnKxACVZERFQitgKViEVERKxAM1gREVGJ2AqUYEVERCViK1CJWERExAo0gxUREZWIrUAJVkREVCK2ApWIRURErEAzWBER0QzWCpRgRUQETKbkjuC1oxKxiIiIFWgGKyIiKhFbgRKsiIgowVqBTZWId+3aRfPmzalYsSJXrlzB39+fn376KbnDEhERSTSbSbB79+6lS5cu5MqVi7t37xIbG8ujR4/o378/69atS+7wREReb6ZY4x4C2FCJ2N/fn169etGyZUu2bNkCQI8ePUifPj0BAQE0aNAgeQMUEXmdqURsOJuZwZ46dQofH594x2vWrMk///yTDBGJiIi8PJtJsBkyZCA8PDze8bNnz5IpU6ZkiEhE5A1iMhn3EMCGEmzdunUZOXIkwcHB2NnZ8eDBA3bv3s3w4cOpXbt2cocnIvJ6i4017mGQWbNm0bx5c4tjO3bsoFGjRnh4eODj48OYMWOIjIw0t0dFRTF06FDKly+Ph4cHvXr14ubNmxZj7Nu3j4YNG+Lu7k7NmjXZuHGjRXtCxkgIm0mwX3/9Nfnz56dBgwY8fPgQX19f2rVrR6FChejRo0dyhyciIklo6dKlTJo0yeJYYGAgXbp0oVq1avz4448MHjyYTZs2MXToUHOfIUOG8Pvvv+Pv78/ChQs5f/483bp1M7efO3eO9u3bU6lSJdauXUvjxo3p27cv+/btS/AYCWVnMtnWfP7ixYsEBQURGxtLoUKFePfddw0ZN+b6eUPGEXkRl7xVkzsEeUPcfWDc91pEQG/Dxkrb2u+lXxsWFsbgwYM5cOAArq6uZM2alcWLFwPQu3dvbty4wfz58839161bx8CBAzly5Ai3bt3C29ubmTNn8tFHHwEQEhJCzZo1WbFiBR4eHgwaNIigoCBWr15tHqNXr17cvn2bgIAAwsLCXjhGQtnMDLZp06asWbOGrFmzUrNmTWrXrm1YchURkRewkct0/v77bxwcHFi/fj3u7u4WbV999RXffPONxbFUqVIRExPD/fv3OXz4MADlypUzt+fPn58cOXJw6NAhIG4WXL58eYsxypUrx+HDhzGZTAkaI6Fs5jKdPHnyMGrUKL7//nuqVatGw4YNLT6giIikDFWqVHlu+/bt25/Z5uPj89QrSgCKFStm8TwmJoYFCxZQokQJsmTJQlhYGJkzZyZNmjQW/bJnz05oaCgAoaGhuLq6xmuPiIjg1q1bCRojoWxmBjtq1Cj++OMPRo4cyf3792nTpg0+Pj5MmTKFS5cuJXd4IiKvNVOsybBHUnj06BF9+/blzJkzDB48GICIiAgcHR3j9U2TJg1RUVEAREZGxuvz5Hl0dHSCxkgom5nBQtyHrFWrFrVq1eLOnTv8+OOP+Pv7M2PGDIKCgpI7PBGR15eBq3+fN0M1wv379/n66685ePAgU6dOxc3NDQAnJyeio6Pj9Y+KiiJt2rRAXKL8b58nz9OmTZugMRLKphIsxH2I7du3s379evbu3UvOnDlp06ZNcoclIiI2IDw8nLZt23LlyhUCAgLw9PQ0t7m6unL79m2io6MtZqHh4eHkyJEDgJw5c8bbcyE8PBxnZ2cyZMiQoDESymYS7J49e/j555/Ztm0bJpOJmjVrMn/+fMqUKZPcoYmIvP5SwB7Cd+7c4csvv+T+/fssXbqUwoULW7S///77xMbGcvjwYfNCppCQEMLCwsyJuEyZMhw8eNDidfv376d06dKkSpUqQWMklM0k2Hbt2uHp6cl3331HjRo1Ej0VFxGRV5BE505fxahRo7h06RJz584lS5YsXLt2zdyWJUsWcuTIwccff8zAgQMZOXIkadOmZfDgwZQtW5ZSpUoB0Lx5c3x9ffHz88PX15ddu3bxyy+/MHfuXIAEjZFQNnMd7OXLl8mdO7fVxtd1sJJUdB2sJBUjr4N9OK2LYWM5d55qyDj9+vXjypUrLF68mMePH+Ph4fHMhUbbt28nd+7cPHz4kJEjR5pvGvPhhx8ycOBAMmfObO67e/duxo0bx4ULF8idOzddu3a12DEwIWMkRLIm2KlTp9K6dWvSpk3L1KnP/w/Spcur/cdXgpWkogQrScXQBOvfybCxnLtON2yslCxZS8Rr166ladOmpE2blrVr1z6zn52d3SsnWBEReQ7drs5wyZpgd+zY8dSf/ytW/+FFRCSFsZmNJqpUqcLt27fjHQ8LC4u3rZU8X2j4NcrX+ISDR45bHG/esRclKtSK9zgRdPqp4/QY8D0Dvh8f7/j1GzcZPHoy1Rp+iWdVXz79qiubt+2y6BMdHc3EGfOp4tuc9yvXp3GrLmzattOwzygpy5JlM/jr5G6LYzVr+vDbrh8JvxFE0Om9jBozkHTpnOO9tmv3Nhw9toPwG0EcPrqNtu2ax+sjBtDt6gyXrDPYTZs2sWfPHgCuXLnCsGHD4m1PdeXKFezs7JIjvBTpatg12vccwL37DyyOm0wmTp8N4csmDaleuaJFW4F8eSyex8bGMnbKbH7duZf6tSzPJ0ZHR9O+53fcu3+fLm2akS2rC7/u/J0+g0cT8+gR9WrGbZHWe/Bodu89yJefN6JcmVKcPHWGwaMmcevWHZo2rm+FTy626rMm9alXvwYXL142H6tTtzpLlk1nz579fNm8C46OjvTt14UNG5dQrUpjHj9+DMDwEf3o2KklI76fxOHAY1Sv4c34iUPjtsibvyK5PtLrSZVCwyVrgvXw8GDFihU8WWf177//4uDgYG63s7PD2dmZMWPGJFeIKUZsbCzrN2/Hb9pcnrZu7dKVqzx4GEGl8mVwL1H0meOcOhvCqInTORF0Bqf//GMHYNcfhzh19jzL506iZNG4a9A+KFuaq2HXmLdkNfVqViHo9Fl27N5H13YtaP/l5wCU9/QgrZMTE2fOp27NKmTMkN6gTy62zNU1O2PHDeby5X8tjvcf0J1TwWdpWL8VMTExAPzxxyGO/fUbzZp/wsIFK8mTJxdduramd88hBMxdCsDuXfvIlSsnVap+qAQrNi9ZE2zOnDlZtGgREHdt0tSpU8mUKVNyhpRinT4bwjA/f5r41qFcmVJ06jPYoj34zDkAirxX8LnjfDvcD2dnJ5bOnkjXb4bEa0+fzpnG9WtTokghi+P58+Tm0P8vSZ+/ELd3tHcFy5s1lC3tTkREJIeOHqfKhx8k6vNJyjR1+mh2bN9DZFQUFSv97++hcOGCzJ612JxcAa6FX+f0qXPUqFmZhQtWUrdeDSIjo1i8aLXFmK2+TPx9OSUBUsB1sCmNzWw08eR+f0/ztLsfiKWcrtnZtDIA1+zZ4p17BQg+cx7ntGnxmzqXnXsP8DAiAq/S7vTt1p78ef93/fGoQb0pVDD/M9+nvKcH5T0t74cY8+gRu/cdomD+vAC8lSkjAFdDwyj87v/GunTlKgCX/03cHSkkZWrx5aeU8ihB2TI1GDGyv0XbjRu3eOedXBbHUqdOTe533sYxTdz2dCXdinLu3AUqVCzL0OF9KV68MP/+G8b4cdM1e7WGFLCTU0pjMwn20qVLjBkzhtOnT5vPv5hMJqKjo7l58yYnT55M5ghtW6aMGciUMcMz24PPnOdhRAQZM6Rn8sjv+DcsnBnzlvJlp978sGAa2bO5ADw3uT7LhGkBXLx0hYkjBgJQtrQbud92ZeTEGTg5paFE0UKcOhPChBnzsLOzIyIi8uU+pKQY77zzNiNHD6BTh2+4eeNWvPYli1bT55sufN2zPYsXrSatkxPfDe5JxowZePD/1w9kzerC2zlzMHfeBEaNmMLp0+do9EkdpkwdCaAkKzbPZlYRDxs2jFOnTlGjRg3CwsL4+OOPKV68ONevX2fIkCHJHV6K163dlyyYNpa+3drxfqkS1K3hw6wJ33PvwQOWrF73UmOaTCbGTwtg8ap1tPriE6p5VwDAwcGB2RNHkDNHdtp0/5Zy1T+h96BRdG0bt/rTySn+uV15vUybOZZft+xk/U+/PLV95IjJTBg/k4Hf9SDkYiB//rWD+/cesPHnX3n4//8B5uDoQNZsLnzd7TvmzlnC7l376N51ANt+3UW/b1UmNlysybiHADY0gz1y5AjTp0/Hy8uLPXv2ULVqVdzc3Jg4cSK7du3i008/Te4QU7Qi7xWId+ydXDkpkDcPp86GJHq86OhoBoyYwOZtu2j1xSf06tzaoj1P7rdZOH0cN27d5vadu+TNnYurYeGYTKbnzrQl5WvXvjklihemnFct7O3tAcxXAtjb2xMbG8vjx48ZMmgso0ZMIl/+PIReDePOnXts3rKCWzdvA3D/3n1iY2PZuuU3i/G3/bqbqtU+Ilv2rFwLv56kn00kMWwmwUZHR5MnT9zlIvnz5+fUqVO4ubnRoEEDmjfXdW+v4tGjx2zcuoO8eXJT6j8riKOiosj8VuIWlt27/4BOfQZx7EQw33RvT/NPG1i0R0ZF8evOvXiULEbut11xyfwWAEGnzwJQtNC7L/1ZxPbVb1CLrNlcOHv+YLy2W3fPMGrEZPbs2U+aNI5s37aHU8Fxfxf29vYUK16YZUvXAHDu3AVSpUqFo6MjUVH/uz9naoe4r61InWowlEmX6RjOZkrEuXLl4vTpuA0P8ufPb77BemxsLA8ePHjeS+UFUqe2Z8b8ZYyfNtfi+MlTZ/nnylXKlnZL8FiPHj2mS98h/HXyNH7D+sVLrgAOqVMzcsJ0Vv+02eJ1y35Yzzu5clKoYL6X/SiSAnTvNoCPKta3eGzetJ2rV8P4qGJ95s9bToMGtZgydRSpU//v3/jNWzQmc+ZM/LxhKwBbt+wEoNEndSzGr127Kn/9FcS9e/eT7DO9EVQiNpzNzGB9fX3p27cvY8eOxdvbmxYtWvD222+zd+/eePf8k8Tr+FVTBnw/nv7D/ahbw4d/Q8OZNncxRd4rEG8ziedZvnYDh4+doHH92uTIlpVjJ4Is2t1LFMXe3p7PfOuwZNU6XLNnJV+e3Cxfu4Gjf51k8qhBpEplM/+uEys4eyb+KYebN28RHR3D0aN/ARAQsIwvW33GzNnjWLxoNSVKFmXosD6sWb2Bvb/HzXx/33OATRu3mXd4OnnyNJ9/4Uu58u/T5NN2SfqZRF6GzSTYdu3akSZNGkwmE25ubnTq1IkZM2aQM2dOxo4dm9zhpXj1a1UljaMj85b+QPf+w0jr5ESVjz7g6w6tzOfJEmLbzr0ArP5pE6t/2hSv/cTeuFlr5zbNSJXKjoClq7lz9x5F3ivA9HHDqOD1vjEfSFK0oJOn+fSTtgwe2oeVq+cQFnYNv7HT8RtneReWL5t3od+33enc9SuyZnUhOPgMTT/vyC+bn713ubwkXaZjOJu5H6y16XZ1klR0uzpJKkberu7BsKaGjZVu0FLDxkrJbGYG+6z7wdrZ2eHg4ICrqysffvghb731VtIGJiIi8hJsJsEeOnSIQ4cO4eDgQP78cZsdXLx4kcjISHLmzMnt27dJkyYNixYt4r333kvmaEVEXjNaRWw4m1lt4ubmxvvvv8+OHTtYt24d69atY8eOHXzwwQf4+vpy4MABvL298fPzS+5QRUReP1pFbDibSbA//PAD3377LS4uLuZjmTNnpk+fPixbtgwHBwdat27NkSNHkjFKERGRhLGZEvGjR48s7qzxRFRUFJGRcReUOzo6EqsyhoiI8bSK2HA2M4OtWLEiQ4cO5eLFi+ZjISEhfP/991SsWJHHjx+zfPlyXRMrImINKhEbzmZmsN999x3t27enZs2aZMyYEZPJxL1793B3d2fQoEHs2bOHFStWMGvWrOQOVURE5IVsJsFmyZKFVatWceDAAYKCgrC3t6dIkSKULVsWAHd3d3bv3k2GDNooXkTEaNqL2Hg2k2Ah7prXPHnyEBMTg6enp8UexJkzZ07GyEREXnMq7RrOZhJsdHQ033zzDZs3byZVqlRs2bKFMWPG8ODBA/z9/UmfPn1yhygiIpJgNrPIacaMGQQHB7Nw4ULSpIm7IXfz5s25ePGirn0VEbE2LXIynM0k2I0bN/Ldd9/h5eVlPubl5cWIESPYvn17MkYmIvIGMMUa9xDAhhJsWFiY+Ybr/1fOnDm5c+dOMkQkIiLy8mwmwRYsWJB9+/bFO75x40befffdZIhIROQNohKx4WxmkVPXrl3p0aMHZ8+e5fHjx/z444+EhISwZcsWJk6cmNzhiYi81kxKjIazmRls5cqVmTJlCidOnMDe3p6AgAAuXbrExIkTqVGjRnKHJyIikig2M4MF+PDDD/nwww+TOwwRkTePZrCGs6kEGxgYyJEjR4iJicFksvyP3aVLl2SKSkTkDaCdnAxnMwl22rRp+Pv7kzFjxnibStjZ2SnBiohIimIzCXb58uX06NGD9u3bJ3coIiJvHpWIDWczCfbevXvUqVMnucMQEXkzKcEazmZWEZcuXZqjR48mdxgiIiKGsJkZbJ06dRg+fDgnTpygQIECODo6WrQ3aNAgeQITEXkD/Hdhqbw6O5ON/FaLFCnyzDY7OzuCgoJeafyY6+df6fUiCeWSt2pyhyBviLsPjPteu9u2umFjZZyz1bCxUjKbKREHBwc/8/GqyVVERFKeWbNm0bx5c4tjQUFBNGvWjFKlSuHj48OiRYss2mNjY5kyZQqVKlWiVKlStG3blkuXLhk+RkLYTIIVEZFkZGN7ES9dupRJkyZZHLt16xatWrUiT548rFmzhs6dO+Pn58eaNWvMfaZPn86yZcsYPnw4K1asIDY2ljZt2hAdHW3YGAllM+dgRUQk+djKXsRhYWEMHjyYAwcOkC9fPou2VatW4eDgwLBhw0idOjUFCxbk4sWLzJ49m0aNGhEdHc28efPo3bs33t7eAEycOJFKlSqxdetW6tSpY8gYCaUZrIiI2Iy///4bBwcH1q9fj7u7u0VbYGAgZcuWJXXq/80Ny5Urx4ULF7h+/TrBwcE8ePCA8uXLm9szZsxIsWLFOHTokGFjJJRmsCIiYuh1sFWqVHlu+/bt25/Z5uPjg4+Pz1PbQkNDKVSokMWx7NmzA3D16lVCQ0OBuPuI/7fPkzYjxkgozWBFRARiDXxYSWRkZLxLONOkSQNAVFQUERERAE/tExUVZdgYCaUZrIiIGOp5M9RX4eTkFG+h0ZOk5+zsjJOTEwDR0dHmn5/0SZs2rWFjJJRmsCIiginWZNjDWlxdXQkPD7c49uR5jhw5zGXdp/XJkSOHYWMklBKsiIjY3GU6T+Pp6cnhw4d5/Pix+dj+/fvJnz8/Li4uFClShPTp03PgwAFz+927dzl58iSenp6GjZFQSrAiIpIiNGrUiPv37zNgwADOnj3L2rVrWbBggfkubI6OjjRr1gw/Pz+2b99OcHAwPXr0wNXVlerVqxs2RkLpHKyIiFh1cZJRXFxcmDt3LiNGjMDX15ds2bLRt29ffH19zX26devGo0ePGDhwIJGRkXh6ehIQEICDg4NhYySUzexFbG3ai1iSivYilqRi5F7Etxp7GzZW5tU7DRsrJVOJWERExApUIhYRkRRRIk5plGBFRMRm9iJ+nahELCIiYgWawYqIiErEVqAEKyIimJRgDacSsYiIiBVoBisiIioRW4ESrIiIqERsBSoRi4iIWIFmsCIiohKxFSjBioiISsRWoBKxiIiIFWgGKyIimsFagRKsiIgowVqBSsQiIiJWoBmsiIiAyS65I3jtJCjBtmjRIsED2tnZsXDhwpcOSEREkp5KxMZLUII1mRJ+n8DE9BUREXldJSjBLl682NpxiIhIMjLFqkRstJc+B3vu3Dn27t3LtWvXaNasGZcuXaJIkSKkT5/eyPhERCQJqERsvEQn2NjYWAYNGsSaNWswmUzY2dlRs2ZNpk+fzsWLF1m6dCmurq7WiFVERCTFSPRlOtOnT2fDhg18//337N2713zOtU+fPphMJiZOnGh4kCIiYl0mk51hD4mT6AS7Zs0aunXrRqNGjXjrrbfMx4sWLUq3bt3Yu3evkfGJiEgSMMUa95A4iU6w169fp2jRok9ty5EjB3fv3n3loERERFK6RCfYvHnzsmvXrqe2HTx4kLx5875yUCIikrRMsXaGPSROohc5ffnllwwaNIiYmBgqV66MnZ0dFy9e5MCBA8ybN49+/fpZI04REbEibWFgvEQn2MaNG3Pz5k1mzJjB8uXLMZlM9OzZEwcHB9q0acPnn39ujThFRERSFDvTS269dP/+fY4ePcrt27fJmDEj7u7uFouebE3M9fPJHYK8IVzyVk3uEOQNcfeBcd9rF0sb93eb98g2w8ZKyV56o4nY2FjzdbCOjo44ODgYGZeIiCQhnTs13kttNDF27FiWLVtGTEyM+TrYtGnT0rFjR9q1a2d4kCIiIilNohPstGnTWLx4Mc2aNaNatWq4uLhw/fp1fv75ZyZNmkS6dOlo2rSpNWIVEREr0SIn4yU6wa5Zs4aOHTvSpUsX87H8+fPj6elJ+vTpmT9/vhKsiEgKoxKx8RJ9HeytW7fw8PB4alulSpW4du3aKwclIiKS0iU6wZYvX57Nmzc/te2PP/6gdOnSrxyUiIgkLe1FbLwElYjXrVtn/rlUqVJMnTqVGzduUKtWLbJly8bt27fZtWsXW7ZsYcCAAdaKVURErER7CBsvQdfBFilSJOED2tkRFBT0SkFZg66DlaSi62AlqRh5HezZYjUMG+vdk1sMGyslS9AMdvv27daOQ0REklGsSruGS1CCzZUrV4IHfMmNoUREJBnp3KnxXmonp02bNnHw4EGio6PNCdVkMvHw4UP+/PNPdu/ebWiQIiLyZnj06BHTpk1j3bp13L59m2LFitGnTx9KlSoFQFBQECNGjODEiRNkyZKFli1b0qJFC/PrY2NjmTp1KqtXr+bevXt4enoyaNAg3nnnHXOfF41hlEQn2KlTpzJ16lQyZMjAo0ePcHBwIHXq1Ny8eZNUqVLRuHFjw4MUERHrspXrYGfMmMHq1asZPXo077zzDnPmzKFNmzZs2rQJBwcHWrVqhY+PD0OHDuXPP/9k6NChpEuXjkaNGgEwffp0li1bxujRo3F1dWXcuHG0adOGDRs24OjoyK1bt144hlESfZnOjz/+SIMGDTh48CAtW7akcuXK/PHHH/zwww+89dZbvPfee4YGKCIi1mcyGfd4Fdu2baNOnTpUrFiRvHnz0q9fP+7du8eff/7JqlWrcHBwYNiwYRQsWJBGjRrRsmVLZs+eDUB0dDTz5s2jW7dueHt7U6RIESZOnEhoaChbt24FeOEYRkp0gg0LC6Nu3brY2dlRtGhRjh49CkCJEiXo0KEDq1evNjxIERF5M7i4uPDbb79x+fJlHj9+zMqVK3F0dKRIkSIEBgZStmxZUqf+X/G1XLlyXLhwgevXrxMcHMyDBw8oX768uT1jxowUK1aMQ4cOAbxwDCMlukTs7OyMnV1cKSFv3rxcvnyZyMhInJycKFq0KJcvXzY0QBERsT4jS8RVqlR5bvvzrkwZMGAA3bt3p0qVKtjb25MqVSr8/f3JkycPoaGhFCpUyKJ/9uzZAbh69SqhoaEA5MyZM16fJ20vGiNr1qwJ+IQJk+gZbMmSJc0bT+TPnx97e3v27dsHwLlz53B0dDQsOBERSRqxJjvDHq/i7NmzZMiQgWnTprFy5UoaNmxI7969CQoKIjIyMl6OSZMmDQBRUVFEREQAPLVPVFQUwAvHMFKiZ7AdOnSgVatW3L17l5kzZ1KvXj2++eYbvLy8+P3336laVRfZi4i8yV5274SrV6/Sq1cvFixYQJkyZYC4Sd3Zs2fx9/fHycmJ6Ohoi9c8SYrOzs44OTkBcedin/z8pE/atGkBXjiGkRKdYD09Pfnhhx84deoUAIMGDSJVqlQcOXKEmjVr0q9fP0MDFBER67OF62CPHTtGTEwMJUuWtDju7u7O7t27efvttwkPD7doe/I8R44cPHr0yHwsT548Fn0KFy4MgKur63PHMNJLXQdbpEgR8/aJadKkYfjw4YYGJSIiScsW9ghydXUF4NSpU7i5uZmPnz59mnz58uHu7s6KFSt4/Pgx9vb2AOzfv5/8+fPj4uJChgwZSJ8+PQcOHDAn2Lt373Ly5EmaNWsGxE0SnzeGkRJ9DvZ5fvzxR2rUMG4/SxEReXO4ubnx/vvv880337B//34uXLjApEmT2LdvH+3ataNRo0bcv3+fAQMGcPbsWdauXcuCBQto3749EHfutVmzZvj5+bF9+3aCg4Pp0aMHrq6uVK9eHeCFYxjppWawz3L37l3++ecfI4cUEZEkYAt7EadKlYoZM2YwadIk+vfvz507dyhUqBALFizA3d0dgLlz5zJixAh8fX3Jli0bffv2xdfX1zxGt27dePToEQMHDiQyMhJPT08CAgJwcHAA4i4DetEYRknQ3XQSauHChYwePVp305E3mu6mI0nFyLvpHM1T37CxPP75ybCxUjJDS8QiIiISx9ASsYiIpEy2sMjpdaMEKyIiNnEO9nWToARbpEgR8/aIz2MymRLUT0RE5HWXoATbuXPnFJ84075dKblDkDeEg70KQ5Ly2MJGE6+bBH0TdO3a1dpxiIhIMlKJ2HhaRSwiImIFqmWJiAhaRGw8JVgREVGJ2ApUIhYREbECzWBFRESriK3gpRLszZs3CQgI4I8//uDatWvMnTuXbdu2UaRIEd1wXUQkBYpN7gBeQ4kuEV+6dIl69eqxatUqcuTIwY0bN3j8+DEhISF069aNnTt3WiFMERGRlCXRM9gxY8bg4uLC4sWLcXZ2pkSJEgCMHz+eqKgoZs6cibe3t9FxioiIFZlQidhoiZ7B7tu3j06dOpExY8Z4uzt99tlnnDlzxrDgREQkacSajHtInJdaRZw69dMnvtHR0Sl+S0UREREjJDrBlilThlmzZvHw4UPzMTs7O2JjY1m+fDmlS5c2NEAREbG+WOwMe0icRJ+D7dWrF59//jnVq1fHy8sLOzs7AgICOHfuHBcvXmTZsmXWiFNERKxI52CNl+gZbKFChVizZg1eXl4cOHAAe3t7/vjjD/LkycOKFSsoWrSoNeIUERFJUV7qOth8+fIxfvx4o2MREZFkoutgjZfoBPvvv/++sM/bb7/9UsGIiEjyUInYeIlOsD4+Pi9cKRwUFPTSAYmIiLwOEp1gR44cGS/BPnz4kMDAQA4cOMDIkSMNC05ERJKGSsTGszOZTIZdFjxq1CiuX79uk+dnUzvmSu4Q5A3hYK97aEjSiIi4aNhYm3I0MWys2mErDBsrJTP0dnU+Pj7ai1hERASDb1d37NixZ+7yJCIitkuLnIyX6GzYv3//eMdiY2MJDQ3l0KFDfPLJJ4YEJiIiSSdW+dVwiU6wBw4ciHfMzs6O9OnT07ZtWzp06GBIYCIiIilZohPsnDlzKFiwoDViERGRZKI9hI2X6EVOX3zxBevWrbNCKCIiklxMBj4kTqITrIODA5kzZ7ZGLCIiIq+NRJeIu3fvztixY7l37x5FihTB2dk5Xh9tlSgikrJoownjJXqjieLFi/P48ePnbpdoi1slaqMJSSraaEKSipEbTfyQs6lhY31ydalhY6Vkif4m+P77760Rh4iIyGslQQm2RYsWDB48mIIFC+Lr62vtmEREJIlpcZLxEpRgDx48yIMHD6wdi4iIJBOdgzWeoXsRi4iISBytxhAREW2VaAUJTrCdO3fG0dHxhf3s7OzYtm3bKwUlIiJJy5Z2clq3bh2zZ8/m0qVL5MmThy5dulCrVi0ALl++zPDhwzl06BDOzs588skndO3aFXt7e/Prly5dyrx587h27RolSpRg4MCBFCtWzNyekDGMkOAEW6xYMbJkyWLom4uIiPxfP/30EwMGDODbb7+lUqVKbNy4kZ49e+Lq6kqJEiVo3bo1+fLlY8WKFfzzzz8MGDCAVKlS0a1bNwB+/PFHxo4dy/DhwylWrBizZ8+mVatWbN68mSxZshATE/PCMYySqBmsm5uboW8uIiK2wRZWEZtMJiZPnkyLFi1o2jTuutyOHTsSGBjIwYMHuXLlCv/++y+rVq0iU6ZMFCpUiBs3bjB27Fg6dOiAo6MjM2fOpFmzZtSrVw+AkSNHUrVqVVavXk379u3ZsmXLC8cwihY5iYgIsXbGPV5WSEgIV65coW7duhbHAwICaN++PYGBgRQvXpxMmTKZ28qVK8f9+/cJCgrixo0bXLhwgfLly5vbU6dOTZkyZTh06BDAC8cwkhKsiIjYhJCQEAAePnxI69atKV++PI0bN2bHjh0AhIaG4urqavGa7NmzA3D16lVCQ0MByJkzZ7w+T9peNIaRElQi9vX11Qb/IiKvMSOvg61Spcpz27dv3/7U4/fv3wfgm2++oUuXLvTu3ZstW7bQqVMn5s+fT2RkJBkzZrR4TZo0aQCIiooiIiICIF6ZN02aNERFRQG8cAwjJSjBjho1ytA3FRER22IL52AdHBwAaN26tXnXwKJFi3Ly5Enmz5+Pk5MT0dHRFq95khSdnZ1xcnICeGqftGnTArxwDCPpOlgRETHUs2aoL5IjRw4AChUqZHH83XffZefOnZQtW5bTp09btIWHh5tf+6Q0HB4eTsGCBS36PBnb1dX1uWMYSedgRUTEJhY5FS9enHTp0nHs2DGL46dPnyZPnjx4enpy8uRJcykZYP/+/aRLl44iRYrg4uJC/vz5OXDggLn90aNHBAYG4unpCfDCMYykBCsiIsQa+HhZTk5OtGnThmnTpvHzzz/zzz//MGPGDPbu3UurVq2oWrUq2bJl4+uvvyY4OJht27YxYcIEvvrqK/N516+++or58+fz448/cvbsWb799lsiIyP55JNPABI0hlESfT/YlEr3g5WkovvBSlIx8n6wc3I3M2ystpeXvNLr58+fz5IlSwgLC6NgwYJ07dqVqlWrAnDx4kWGDh1KYGAgmTJlMu/ClCrV/+aLAQEBLFq0iNu3b5t3cipatKi5PSFjGEEJVsRgSrCSVIxMsLMMTLDtXzHBvi70TSAiIphsZyvi14bOwYqIiFiBZrAiIqIbrluBEqyIiCjBWoFKxCIiIlagGayIiNjEVomvGyVYERF5pR2Y5OlUIhYREbECzWBFRESLnKxACVZERJRgrUAlYhERESvQDFZERLSK2AqUYEVERKuIrUAlYhERESvQDFZERLTIyQqUYEVEROdgrUAlYhERESvQDFZERIjVHNZwSrAiIqJzsFagErGIiIgVaAYrIiIqEFuBEqyIiKhEbAUqEYuIiFiBZrAiIqKtEq1ACVZERHSZjhWoRCwiImIFNplgo6OjkzsEEZE3isnAh8SxqQS7fPlyfHx8KFWqFJcuXWLw4MFMnz49ucMSEXntxRr4kDg2k2A3bNjA+PHj8fX1xcHBAYCCBQsyc+ZM5s2bl8zRiYiIJI7NJNh58+YxYMAAunbtSqpUcWG1aNGCQYMGsXLlymSOTkTk9RaLybCHxLGZBBsSEkKZMmXiHffy8uLq1avJEJGIyJtD52CNZzMJNmvWrISEhMQ7fvToUbJnz54MEYmIiLw8m0mwn332GcOGDWP79u0AnD9/nuXLlzNixAgaNmyYzNGJiLzetMjJeDaz0UTbtm25d+8ePXv2JCoqivbt25M6dWqaNGlChw4dkjs8EZHXms6dGs/OZDLZ1G81IiKCs2fPYjKZKFCgAOnTpzdk3NSOuQwZR+RFHOxt5t+t8pqLiLho2Fg98zUxbKwJF1YYNlZKZjPfBD4+PjRo0ABfX19KliyZ3OGIiLxRbGqm9ZqwmXOwDRs2ZNOmTVSvXp2mTZuyZs0aHjx4kNxhiYi8EXQO1ng2VyI+fvw469ev55dffuHBgwdUr14dX19fypUr90rjqkQsSUUlYkkqRpaIuxtYIp6sEjFggwn2idjYWJYvX86ECRN4+PAhQUFBrzSeEqwkFSVYSSpGJthu+T4zbKwpF7Q5ENhQifiJq1evMnv2bOrXr8+IESMoWbIkY8aMSe6wRERea7ZYIg4JCcHDw4O1a9eajwUFBdGsWTNKlSqFj48PixYtsvwcsbFMmTKFSpUqUapUKdq2bculS5cs+rxoDKPYzD+1V6xYwc8//8yRI0fIlSuXecHT22+/ndyhiYhIEouJiaF37948fPjQfOzWrVu0atUKHx8fhg4dyp9//snQoUNJly4djRo1AmD69OksW7aM0aNH4+rqyrhx42jTpg0bNmzA0dExQWMYxWYS7JgxY6hZsybdu3fH09MzucMREXmj2Np1sP7+/vEu01y1ahUODg4MGzaM1KlTU7BgQS5evMjs2bNp1KgR0dHRzJs3j969e+Pt7Q3AxIkTqVSpElu3bqVOnTovHMNINlMi3rt3L6NGjVJyFRFJBra0F/GhQ4dYuXIlo0ePtjgeGBhI2bJlSZ36f3PDcuXKceHCBa5fv05wcDAPHjygfPny5vaMGTNSrFgxDh06lKAxjJSsM9j+/fszYMAA0qdPz/Dhw5/bd9SoUUkU1ZvFzs6Otm2a0aFDCwrkz0t4+HU2bNjKkGF+3Lt336Kvvb09u3euY8vW3xg2fEIyRSwpgZ2dHd27t6V16y/IlSsnZ86cZ+LEWaxYsc7c5+zZ/eTKlTPea3PnLsWNG7fiHe/cuRV+fkMoXLgC//xz2ZrhSzK6e/cuffv2ZeDAgeTMafn3ERoaSqFChSyOPdmr/urVq4SGhgLEe1327NnNbS8aI2vWrIZ9lmRNsJcvXyY2Ntb8syS9Pr07MWxoX8ZPmMGOHXt5770CDB3Sh+LFC1Oz9ufmfmnSpGHB/Ml4eZVmy9bfkjFiSQkGDepFz57tGT58AoGBx6lZszLz508mNjaWVavW4+KSmVy5ctK//wj++OOQxWtv374bb7x3383PsGHfJFX4byQjS8RVqlR5bvuTPeefZsiQIXh4eFC3bt14bZGRkTg6OlocS5MmDQBRUVFEREQAPLXPnTt3EjSGkZI1wS5evPipP//XtWvXkiKcN46dnR19endi9pwlDBgYV4rZvmMPN27eYvnSGbxf2o3DR45TsUJZJk8eQe6nzDZE/ittWie6dPmKadPm4+c3A4CdO/fi4VGCTp1asWrVetzcigHw00+/EBLyz3PHS5UqFXPmjOfmzVs4O6e1evxvKlvYIGLdunUEBgayYcOGp7Y7OTkRHR1tcexJUnR2dsbJyQmA6Oho889P+qRNmzZBYxjJZhY5FS1alL1795IlSxaL45cvX6Zu3bocPXo0mSJ7fWXMmIElS9ewevV6i+OnTp0FoEDBfBw+cpwf185n7x+H8G3YkvNnDyZHqJKCREVFU7lyQ65du2FxPDo6howZMwLg7l6cu3fvvTC5AvTo0Y7s2bMybtx0Jk/+3ioxi7GeN0N9njVr1nDjxg3zAqUnBg8ezKZNm3B1dSU8PNyi7cnzHDly8OjRI/OxPHnyWPQpXLgwwAvHMFKyJtgffviB9evjvtxNJhOdO3fGwcHBok94eLj5/5RirDt37tKj56B4x+vXqwHAyZOnAKhcpREnTgQnaWyScsXGxlr8vWTPnpUWLRrj41ORLl36A+DmVoxbt26zfPlMKleugL29Pb/8soM+fYYRGvq/L7+iRd9jwIAe1KvXgnz53knyz/ImMdnAKmI/Pz8iIyMtjlWvXp1u3bpRr149fvrpJ1asWMHjx4+xt7cHYP/+/eTPnx8XFxcyZMhA+vTpOXDggDnB3r17l5MnT9KsWTMAPD09nzuGkZJ1FXHVqlXJlSsXuXLF7bLk6upqfv7kUbFiRaZNm5acYb5Rynp60LdPZzb8vJW//45LsEqu8rI+/bQeFy8eZvjwfvzyy28sX/4jEJdg337blSNH/qJRo6/45pvhVKzoxdatK81lYHt7e+bOnciCBSv4/fcDyfkx3gi2sNFEjhw5yJs3r8UDwMXFhRw5ctCoUSPu37/PgAEDOHv2LGvXrmXBggW0b98eiDv32qxZM/z8/Ni+fTvBwcH06NEDV1dXqlevDvDCMYyUrDPYt956y2J18JMVxZI8Pihfhp/WLSTkwiVat+mZ3OHIa+DQoT+pWrUxJUsWZdCgXqxfv4jq1T+jc+d+PHr0iMOHjwOwd+8hgoLOsGPHGpo2bcScOUv45psuvPVWRgYOHP2Cd5E3hYuLC3PnzmXEiBH4+vqSLVs2+vbti6+vr7lPt27dePToEQMHDiQyMhJPT08CAgLM1dGEjGGUZE2w//77Lzlz5sTOzo6uXbty9+5d7t6Nv4IQ0I5OVta4cT3mzZ3A6TMhfFynKTdvxr9MQiSxQkL+ISTkH/buPcjdu/cICJhIhQpl2bs3/rn8ffsCuX37DiVLFsXdvTh9+3amQYNWREVFY29vT6pUcQU3e/tUpEqVynwFghjDFkrET3Pq1CmL525ubqxc+ey9ju3t7enTpw99+vR5Zp8XjWGUZE2wVapU4ffff8fFxQUfHx/s7Ozi9TGZTNjZ2b3yZv/ybD17tGf0qIHs2rWPRo1bc/fuveQOSVKwrFmzUL26N7/+ustiodOff54AIF++dyhYMB+BgX9y8uRpc7udnR2Ojo5cv36TOnWqkSZNGjZvXhZv/JMn97B79z5q1DDu7i8i1pCsCXbhwoVkypQJwGqbLcvztW3TjLFjBrFy1U+0bNWdmJiY5A5JUri0aZ0ICJjId9+Nwc9vuvl41aofAnD48HH27t3A+vVbaNWqu7m9Tp1qODunZdeufZw+fZbNmy1XotaqVYWBA3vQqNFXnDkTkjQf5g2ieoDxkjXBli1b9qk/P3Hz5s14l+2IcXLkyMZ4vyGEhPzD9OnzKe1R0qL93PkLXL9+M5mik5Tq0qV/WbBgJd9+251Hjx7x559/U6GCJ717d2T+/BUEB5/Bz286gwb1Ijz8Or/8soMSJYowYMDXbNiwhV27/gDg6lXLSymKFYu7zOLEiVPayckKYm3zzqUpms1cB3v37l3GjRtHs2bNePfdd2nTpg379+8nX758zJ49m3fe0RJ9o9Wq6YOzc1ry58/Drp3r4rV/1boHixavSvrAJMXr1m0AISH/8NVXn5MnTy4uX77K8OETmDhxNgCjR/tz/fpN2rdvQdu2zbh58xZz5y7l++8nJnPkIsaxmRuu9+/fn8DAQGbOnMmZM2fo06cPI0eOZNOmTaROnRp/f/9XGl83XJekohuuS1Ix8obrzfI2NGysJRfXvrjTG8Bmvgl27drFtGnTKFiwIHPmzKFChQrUrVuXwoUL07Rp0+QOT0TktWZrt6t7HdjM7eoePnxovgPC3r17+eCDD4C4fSMfP36cnKGJiIgkms3MYAsWLMjOnTvJmTMn165d48MP41Ycrlq1ioIFCyZzdCIirzdbvQ42JbOZBNutWze6du1KTEwMderUIV++fIwaNYqlS5dqq0QRESvTZTrGs5lFTgC3bt0iLCyMIkWKAHD8+HHSpUtnyAxWi5wkqWiRkyQVIxc5fZa3gWFjrby4zrCxUjKb+ibInDkzN2/eZPPmzTg4OFCgQAEKFCiQ3GGJiLz2tMjJeDaTYKOioujVqxfbt2/nyaTazs6OypUrM2nSpHh3oBcREePoHKzxbGYV8cSJEzl+/DhTp07l0KFDHDhwAH9/f06ePPnK18CKiIgkNZuZwf78888MHz6cypUrm49VrVoVe3t7hg4dSq9evZIxOhGR15sWORnPZhLsgwcPnnq+NX/+/Ny8qf1wRUSsyYbWu742bKZEXKhQIX755Zd4xzdv3kz+/PmTISIREZGXZzMz2I4dO9KpUyeCgoIoXbo0AIcPH+bXX39l/PjxyRydiMjrTauIjWczCdbb25vJkyczZ84cdu7ciclkonDhwkyaNInq1asnd3giIq81nYM1ns0kWIBq1apRrVq15A5DRETkldlUgv3zzz9ZvHgxp0+fxt7enuLFi9OyZUvee++95A5NROS1putgjWczi5x27NjBF198weXLl6lQoQKenp6cOnWKhg0bEhgYmNzhiYi81mIxGfaQODYzg504cSKtW7eOd73rmDFjGDduHCtXrkymyERERBLPZmawFy9epFGjRvGOf/bZZwQHBydDRCIibw6TyWTYQ+LYTIItWrQo+/bti3f8xIkTOgcrImJlsQY+JI7NlIjr1auHn58f58+fx8vLi9SpU/PXX3+xcOFCmjRpwrp168x9GzRokGxxioiIJITN3A/2yT1gX8TOzo6goKBEj6/7wUpS0f1gJakYeT/Y6u/UNGysrZfi78r3JrKZbwKdZxURST5a/Ws8mzkHKyIi8jqxmRmsiIgkHxs5W/haUYIVERGViK1AJWIREREr0AxWRES0F7EVKMGKiAixOgdrOJWIRURErEAzWBERUYHYCpRgRUREq4itQCViERERK9AMVkRENIO1AiVYERHRTk5WoBKxiIjYjNu3bzNo0CA+/PBDSpcuzeeff05gYKC5fd++fTRs2BB3d3dq1qzJxo0bLV4fFRXF0KFDKV++PB4eHvTq1YubN29a9HnRGEZRghUREWIxGfZ4FT179uTo0aNMmDCBNWvWULRoUVq3bs358+c5d+4c7du3p1KlSqxdu5bGjRvTt29f9u3bZ379kCFD+P333/H392fhwoWcP3+ebt26mdsTMoZRbOZ+sNam+8FKUtH9YCWpGHk/WM+3PzRsrEP/7n6p1128eJHq1auzbNky3n//fSCudF29enXq1KnDjRs3CAoKYvXq1ebX9OrVi9u3bxMQEEBYWBje3t7MnDmTjz76CICQkBBq1qzJihUr8PDwYNCgQc8dw0iawYqIiE3InDkzs2fPpmTJkuZjdnZ22NnZcffuXQIDAylfvrzFa8qVK8fhw4cxmUwcPnzYfOyJ/PnzkyNHDg4dOgTwwjGMpAQrIiKYTCbDHi8rY8aMfPTRRzg6OpqPbdmyhYsXL1KpUiVCQ0NxdXW1eE327NmJiIjg1q1bhIWFkTlzZtKkSROvT2hoKMALxzCSalkiImLoZTpVqlR5bvv27dsTNM6RI0fo378/1atXx9vbm8jISIvkC5ifR0dHExEREa8dIE2aNERFRQG8cAwjaQYrIiI2Z9u2bXz11VeUKlUKPz8/IC5R/jcJPnmeNm1anJycnpoko6KiSJs2bYLGMJJmsCIiYuj5x4TOUJ9lyZIljBgxgpo1azJmzBjzDDNnzpyEh4db9A0PD8fZ2ZkMGTLg6urK7du3iY6OtpilhoeHkyNHjgSNYSTNYEVExGYu01m2bBnDhw+nadOmTJgwwSJRlilThoMHD1r0379/P6VLlyZVqlS8//77xMbGmhc7Qdwq4rCwMDw9PRM0hpGUYEVExCaEhIQwcuRIqlWrRvv27bl+/TrXrl3j2rVr3Lt3j+bNm3P8+HH8/Pw4d+4c8+bN45dffqFNmzYA5MiRg48//piBAwdy4MABjh8/Ts+ePSlbtiylSpUCeOEYRtJ1sCIG03WwklSMvA7WzbX8izsl0PHQl9u0YebMmUycOPGpbb6+vowePZrdu3czbtw4Lly4QO7cuenatSu1a9c293v48CEjR45ky5YtAHz44YcMHDiQzJkzm/u8aAyjKMGKGEwJVpKKkQm2RI5yL+6UQCfC9hs2VkqmErGIiIgV6J/aIiKCSberM5wSrIiIEPtmnC1MUioRi4iIWIFmsCIiohKxFSjBioiISsRWoBKxiIiIFWgGKyIiKhFbgRKsiIioRGwFKhGLiIhYgWawIiKiErEVKMGKiAgmU2xyh/DaUYlYRETECjSDFRGRV75RusSnBCsiIrwhdy5NUioRi4iIWIFmsCIiohKxFSjBioiISsRWoBKxiIiIFWgGKyIi2irRCpRgRUREOzlZgUrEIiIiVqAZrIiIaJGTFSjBioiILtOxApWIRURErEAzWBERUYnYCpRgRUREl+lYgUrEIiIiVqAZrIiIqERsBUqwIiKiVcRWoBKxiIiIFWgGKyIiKhFbgRKsiIhoFbEVqEQsIiJiBZrBioiI7qZjBUqwIiKiErEVqEQsIiJiBZrBioiIVhFbgRKsiIjoHKwVqEQsIiJiBZrBioiISsRWoAQrIiJKsFagErGIiIgVaAYrIiJa4mQFdibVBURERAynErGIiIgVKMGKiIhYgRKsiIiIFSjBioiIWIESrIiIiBUowYqIiFiBEqyIiIgVKMGKiIhYgRKsiIiIFSjBioiIWIESrIiIiBUowYqIiFiBEqyIiIgVKMFKov37779s3LjR/NzHxwd/f/9kjEhSsgMHDlC4cGEuX74MwK1bt1i9erW5vXnz5vTr1y+5whN5abpdnSRa8+bNyZUrF6NHjwbg5s2bpEmThnTp0iVzZJISRUdHc+fOHbJkyYK9vT39+/fn8uXLLF68GIDbt29jb29PhgwZkjlSkcTRDdfllWXJkiW5Q5AUzNHRkWzZspmf//ff/G+99VYSRyRiDJWIU5DChQvzww8/0LJlS9zc3KhYsSJTp0616PPbb7/RsGFD3NzcqFatGpMmTSI6OtrcfvPmTXr06EGZMmXw8vLCz8+PFi1amEu8sbGxzJo1ixo1alCiRAlKly5NmzZt+Oeff4C42evBgwf58ccf8fHxAf5XIr506RJFihRh165dFjH179+fzz//HIibrYwbN45KlSrh4eHBp59+yu+//26135m8usKFC7N06VI+/fRTSpYsSd26ddm+fbtFn507d/Lpp5/i4eFBxYoVGTVqFJGRkeb2Xbt20bBhQ9zd3Slfvjz9+vXjzp07gGWJuF+/fvz4448cPHiQwoULA/8rET948AAPDw+WLVtm8d5Tp07F29ub2NhYTCYTc+bMoUqVKri7u1O/fn3Wr19v5d+QyDOYJMUoVKiQqUyZMqZ169aZ/vnnH9OMGTNMhQoVMh08eNBkMplMu3btMrm5uZmWL19uunjxomnPnj2m6tWrm7p162YymUymx48fmz755BOTr6+v6ejRo6YTJ06YmjZtaipcuLBpypQpJpPJZJo/f77J09PTtGPHDtPly5dNf/zxh6lKlSqmjh07mkwmk+nWrVumzz77zNS9e3fTjRs3TCaTyVS5cmXz65s1a2bq3bu3OebIyEhT6dKlTatWrTKZTCZTz549TfXr1zft37/fFBISYpo3b56pePHipt9++y1JfoeSeIUKFTKVKlXKtGTJEtO5c+dM48aNMxUpUsR0+PBhk8lkMm3dutVUpEgR07Rp00znz583bdu2zVSxYkXz38yNGzdMJUqUMC1ZssR0+fJlU2BgoMnHx8f07bffmkwmk2n//v2mQoUKmS5dumS6e/euqXv37qbPPvvMFB4ebjKZ4v6mvvnmG5PJZDL169fP9Pnnn1vEV716ddOECRNMJpPJNH78eFPlypVNv/32m+nixYumH374weTh4WFasmRJkvyuRP4vlYhTmAYNGlC/fn0AOnToQEBAAEeOHMHT05OZM2fy6aef0qRJEwDy5MnD0KFD+fLLL7l8+TKXL1/m+PHjbN68mQIFCgAwadIk80z0yWvGjBlD5cqVAciVKxc1a9bkl19+AeLKdQ4ODjg5OT21NNywYUOGDRtGREQEadOmZceOHTx+/JhatWpx8eJFfv75Z9atW0fRokUBaNWqFcHBwQQEBODt7W2135u8moYNG9K0aVMAevfuzcGDB1myZAmlS5dm9uzZVKtWjU6dOgGQP39+TCYTnTt35uzZs8TExBAdHc3bb79Nrly5yJUrFzNnzuTx48fx3idDhgw4OTnh4OBgUTZ+wtfXlxYtWnDlyhVy5crF8ePHuXDhAg0bNuThw4csWLCACRMmmP+W8uTJw5UrVwgICDDHL5JUlGBTmIIFC1o8z5AhAzExMQCcPHmS48eP88MPP5jbTf//fNa5c+c4d+4cmTJlMidXgKxZs5I/f37zcx8fH44dO8bkyZMJCQkhJCSEs2fPkiNHjgTFV6NGDYYNG8b27dupU6cO69evp2rVqqRPn549e/YA8MUXX1i8JiYmhowZMybityBJzcvLy+K5h4cHe/fuBeD06dN8/PHHFu1ly5Y1t9WuXZs6derQoUMHsmXLRoUKFfD29qZatWqJjsPT05PcuXPz888/0759e9avX0/p0qXJmzcvx48fJyoqil69epEq1f/Ofj169Ijo6GgiIyNxcnJK9HuKvCwl2BTG0dEx3rEnSTQ2NpY2bdrg6+sbr0+2bNm4cOECsbGxzx1/9uzZTJs2DV9fX8qXL0/Lli3Zvn27xWU5z+Ps7EzNmjXZsGEDFStWZM+ePcyePdsizqVLl8Zbcfx/vxDF9qRObflV8fjxY/N/M9NTLkR48nf25HXjx4+nc+fO7N69mz/++IM+ffrw/vvvs3DhwkTFYWdnR4MGDdiwYQNt2rRh8+bNfP311xZxTJo0yeIfkU887f87Itakb7XXyHvvvUdISAh58+Y1P0JDQxk7diwPHjygSJEi3Lt3j3Pnzplfc+vWLS5evGh+PnPmTDp37syQIUP47LPPKFWqFBcuXHjql+izNGrUiL1797Ju3TqyZs1KuXLlzPEBXLt2zSLGtWvXsnbtWoN+C2INf/31l8Xzo0ePUrx4cSBuEdSRI0cs2gMDA4G4isuxY8cYOXIkBQoUoGXLlsyePZuRI0eyf/9+bty4Ee+97OzsnhuLr68vZ8+eZcWKFTx48IBatWoBUKBAAVKnTs2///5r8fe1a9cuAgIC9I84SXL6i3uNtG3bli1btjB16lRCQkLYt28f/fv35969e2TLlg0vLy/c3d3p27cvf/75J8HBwfTu3ZuIiAjzl1rOnDnZu3cvZ8+e5fz580ycOJGtW7darEROly4dV65cITQ09KlxlClThpw5czJlyhTq169v/mJ77733qFy5MoMHD2bHjh1cunSJOXPmMGvWLPLkyWP9X5C8tIULF7JhwwZCQkIYM2YMp06d4ssvvwSgTZs2bN26lenTpxMSEsJvv/3G8OHDqVy5MgULFiR9+vQsW7aMcePGcfHiRU6fPs2mTZvIly8fmTNnjvdezs7OhIeHc+nSpafGkitXLry8vBg/frz59APEnS5p0qQJkydP5qeffuLSpUv88MMPjBs3juzZs1vvlyPyDEqwr5GaNWsyceJEtm3bRt26denTp0+8S3n8/f1xdXWlZcuWfPnll7i5ufH222/j4OAAwNixY4mMjKRRo0Y0a9aM06dPM3ToUG7cuMG///4LQJMmTTh9+jT16tV76kIViJtlPHjwgIYNG1ocnzhxItWrV2fQoEHUrl2bdevWMWLEiKeWtcV2NGnShAULFlCvXj0CAwMJCAigSJEiQNx59wkTJrB582bq1q3L4MGD+fjjj5k0aRIQN4v19/dn//79NGjQgM8//xx7e3vmzJnz1FllgwYNiIiIoE6dOoSFhT01noYNGz7176t///60aNGCyZMnU6tWLWbNmkW3bt3o3Lmzsb8QkQTQTk5vkJs3b3Ls2DEqVqxoTqjR0dF4eXkxePBgGjRokLwBik0qXLgwo0aNipfMROT5tMjpDZI6dWp69OhBkyZN+Pzzz4mJiSEgIABHR0c+/PDD5A5PROS1ohLxGyRjxozMnDmTP//8kwYNGvDZZ59x/fp1Fi1apO0ORUQMphKxiIiIFWgGKyIiYgVKsCIiIlagBCsiImIFSrAiiaRlCyKSEEqwkqSaN29O4cKFLR4lSpTA29uboUOHmu8Rag1r164133cU4jbdeHLP0YQIDQ2lXbt2XLly5ZVjuXz5MoULF37uFpGJje9V3iuhmjdvTvPmzV95HJE3ga6DlSRXrFgxBg8ebH4eExPD33//zYQJEwgKCmL58uUv3I/WCI0bN6ZSpUoJ7v/HH3/Eu5m8iMizKMFKkkufPj2lSpWyOObp6cmDBw+YMmUKx44di9duDa6urri6ulr9fUTkzaQSsdiMEiVKAJj3PG7evDm9e/emW7dulCpVilatWgEQFRXF2LFj+eijjyhRogR169Zl06ZNFmPFxsYyffp0vL29cXd3p1OnTvHKz08rwa5btw5fX1/c3d3x9vZm/PjxREdHs3btWvr37w9AlSpV6Nevn/k1q1ev5uOPPzaXuv39/ePt0bx161bq1auHm5sbvr6+BAcHG/Abi3Po0CFat26Np6cnJUqUwMfHB39//3i3JgwLC6N9+/a4ubnx0UcfMWXKlHhxJuSziEjCaAYrNiMkJASAd955x3xs8+bN1KtXjxkzZhAbG4vJZKJz584cOXKEbt26UbBgQX799Vd69OhBdHS0eT/lcePGsWjRIjp27Ii7uzubN29m/Pjxz33/pUuXMmzYMBo3bkzPnj25dOkSY8eO5c6dO3z99dd07NiRGTNmMHXqVHNinjVrFhMnTqRZs2b079+foKAg/P39uXr1KiNHjgRgx44ddOvWzXwDhqCgIPr06WPI7yw4OJiWLVuab/RgMpnYsGEDU6dOpUCBAhY3Qvf396dBgwZMmzaNo0ePMnPmTO7fv8+3336b4M8iIgmnBCtJzmQy8ejRI/PzO3fucPDgQWbMmIGHh4d5Jgvg4ODA0KFDzTfL3rt3L3v27GHixInUrl0bgEqVKhEREYGfnx916tTh4cOHLF68mFatWtGlSxdzn/DwcPbs2fPUmGJjY5k2bRpVq1bl+++/Nx+PiIhg48aNZMiQwXxLvaJFi5I7d27u3bvH9OnT+eyzzxg4cCAAFStW5K233mLgwIG0atWK9957j2nTpuHm5sa4cePMsQAvTPgJERwczAcffMC4cePMd6apUKECO3bs4MCBAxYJtlKlSuZEWalSJe7fv8+yZcvo1KkT9vb2CfosIpJwSrCS5A4dOmS+WfcTqVKl4oMPPmDYsGEWC5wKFChgTq4A+/btw87Ojo8++sgiSfv4+LB+/XrOnDnDtWvXiImJoXLlyhbvUatWrWcm2JCQEG7cuEG1atUsjrdu3ZrWrVs/9TVHjx4lMjISHx+feLFA3D8G3nnnHf7++2+6d+8eLxYjEmyDBg1o0KABUVFRhISEcPHiRYKCgnj8+DExMTHx3vP/ql69OgsXLuTYsWPY2dm98LMowYokjhKsJLnixYszdOhQAOzs7EiTJg05c+Y03zj7/0qXLp3F89u3b2MymShduvRTxw4PD+fu3bsA8W7mnS1btmfGdPv2bQBcXFwS/DmevKZdu3bPjOXOnTuYTKZ4sRh1A/DIyEiGDx/OTz/9xKNHj8idOzceHh6kTp063vW6//38T27w8H/PTT/vs4hI4ijBSpJLly4dJUuWfKnXZsiQAWdnZxYtWvTU9rx583L8+HEAbty4QYECBcxtTxLi02TMmBGIu2fu/3Xr1i1OnjyJh4fHM1/j5+dHvnz54rVnzZqVt956i1SpUnH9+nWLtufFkhgjRoxgy5YtTJo0iQ8++ABnZ2cAypcvH6/vfxd5PYnJxcXFPNt93mcRkcTRKmJJUcqWLcvDhw8xmUyULFnS/Dh9+jTTpk3j0aNHeHh44OTkxC+//GLx2t9+++2Z4xYoUIDMmTPH6/PTTz/Rrl07YmJizOc4n3B3d8fBwYGwsDCLWFKnTs2ECRO4fPkyadKkwcPDg61bt1rMKHfs2GHAbwMOHz6Ml5cXVatWNSfXEydOcPPmzXiriHfu3GnxfOPGjaRNmxZ3d/cEfRYRSRzNYCVF+eijj/D09KRTp0506tSJggULcvz4caZMmUKlSpXMZc9OnToxadIk0qZNS7ly5di1a9dzE6y9vT1du3Zl2LBhuLi44OPjQ0hICFOmTKFp06ZkypTJPGP99ddf+fDDDylYsCBt2rRh8uTJ3L9/Hy8vL8LCwpg8eTJ2dnYUKVIEgJ49e/Lll1/SpUsXPvvsM0JCQpg5c2aCP/OCBQviHcuYMSMNGzbEzc2NzZs3s3z5cgoWLEhwcDAzZszAzs6OiIgIi9ds3bqVHDly8MEHH/D777+zcuVKunfvbi7NJ+SziEjCKcFKipIqVSpmz57N5MmTmTVrFjdu3CBHjhy0atWKzp07m/u1b98eZ2dnFi5cyMKFC/Hw8OCbb75hyJAhzxy7adOmODs7ExAQwMqVK3F1daVt27a0bdsWAC8vLz744APGjx/Pvn37mD17Nl9//TXZsmVj2bJlzJ07l0yZMlG+fHl69uxJhgwZAChTpgxz5sxhwoQJdOnShdy5czNy5Eg6dOiQoM88atSoeMfy5MlDw4YN6devHzExMUyaNIno6Ghy585Nx44dOXv2LDt27LC4hnXAgAFs3LiRBQsWkC1bNr799ltatGhhbk/IZxGRhNMN10VERKxA52BFRESsQAlWRETECpRgRURErEAJVkRExAqUYEVERKxACVZERMQKlGBFRESsQAlWRETECpRgRURErEAJVkRExAqUYEVERKxACVZERMQK/h/2EVxM+PxIFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of False Positive =  496\n",
      "Number of False Negative =  21\n",
      "Total cost =  15460\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15460"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators= 15 , max_depth=6, n_jobs= -1, class_weight = 'balanced')\n",
    "clf.fit(train_mean_imputed,y_train)\n",
    "y_predd = clf.predict(test_mean_imputed)\n",
    "cm =confusion_matrix (y_test, y_pred)\n",
    "result(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5710e007",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98     15625\n",
      "           1       0.40      0.94      0.56       375\n",
      "\n",
      "    accuracy                           0.97     16000\n",
      "   macro avg       0.70      0.95      0.77     16000\n",
      "weighted avg       0.98      0.97      0.97     16000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predd))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38e79e5",
   "metadata": {},
   "source": [
    "### Adaboost(Decision Tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e18dd85",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc = DecisionTreeClassifier(max_depth=1)\n",
    "ada = AdaBoostClassifier(base_estimator=dtc, n_estimators=50, learning_rate=1.0, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91612134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   random_state=42)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.fit(train_mean_imputed, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1552e6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = ada.predict(test_mean_imputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8d04e468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99     15625\n",
      "           1       0.84      0.62      0.71       375\n",
      "\n",
      "    accuracy                           0.99     16000\n",
      "   macro avg       0.92      0.81      0.85     16000\n",
      "weighted avg       0.99      0.99      0.99     16000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741267f3",
   "metadata": {},
   "source": [
    "### Gradient boosting classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0bf216d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "gbc = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2c4c2c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(random_state=42)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.fit(train_mean_imputed, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "03e272a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predy = gbc.predict(test_mean_imputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "031bdfb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00     15625\n",
      "           1       0.89      0.70      0.78       375\n",
      "\n",
      "    accuracy                           0.99     16000\n",
      "   macro avg       0.94      0.85      0.89     16000\n",
      "weighted avg       0.99      0.99      0.99     16000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fede78dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC score: 0.8482453333333333\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_predy)\n",
    "roc_auc = roc_auc_score(y_test, y_predy)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "\n",
    "# Print the AUC-ROC score\n",
    "print('AUC-ROC score:', roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad26f70",
   "metadata": {},
   "source": [
    "### xg boost classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a40be52a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 18 candidates, totalling 54 fits\n",
      "Best HyperParameter:  {'max_depth': 6, 'n_estimators': 15}\n"
     ]
    }
   ],
   "source": [
    "# model fitting and hyper parameter tuning to find the best parameter\n",
    "clf=XGBClassifier(scale_pos_weight=59)\n",
    "\n",
    "depth=list(range(3, 10 ,3))\n",
    "nEstimator = list(range(3, 20 ,3))\n",
    "tuned_parameters = [{'max_depth':depth,'n_estimators':nEstimator}]\n",
    "\n",
    "gsv = GridSearchCV(clf,tuned_parameters,cv=3,verbose=1,scoring='roc_auc', return_train_score=True, n_jobs=-1)\n",
    "gsv.fit(train_mean_imputed,y_train)\n",
    "print(\"Best HyperParameter: \",gsv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7f89a9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of False Positive =  492\n",
      "Number of False Negative =  15\n",
      "Total cost =  12420\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "12420"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = XGBClassifier(max_depth= 3,  n_estimators= 18, n_jobs= -1, scale_pos_weight=40, min_child_weight=44)\n",
    "clf.fit(train_mean_imputed,y_train)\n",
    "y_pred = clf.predict(test_mean_imputed)\n",
    "cm =confusion_matrix (y_test, y_pred)\n",
    "result(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6d2754cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98     15625\n",
      "           1       0.42      0.96      0.59       375\n",
      "\n",
      "    accuracy                           0.97     16000\n",
      "   macro avg       0.71      0.96      0.79     16000\n",
      "weighted avg       0.99      0.97      0.97     16000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a8217860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC score: 0.964256\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "\n",
    "# Print the AUC-ROC score\n",
    "print('AUC-ROC score:', roc_auc)"
   ]
  },
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    "#### We can conclude following by observing all the algorithms\n",
    " * as it is clear that mean imputation is used to fill nan values we can use median, mode also but me\n",
    " * as we can cleary see that the data was imbalanced as neg outputs were more compared to pos, so we used class_weight='balanced' to give minor class more priority and major less priority\n"
   ]
  }
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