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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "75c0974e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import numpy as np\n",
+ "from sklearn.model_selection import train_test_split, cross_val_score, KFold, cross_val_predict\n",
+ "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score, roc_curve, roc_auc_score, auc\n",
+ "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+ "from sklearn.compose import ColumnTransformer\n",
+ "from sklearn.decomposition import PCA\n",
+ "from imblearn.over_sampling import SMOTE, RandomOverSampler\n",
+ "from imblearn.under_sampling import RandomUnderSampler\n",
+ "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
+ "from sklearn import datasets, svm, metrics\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn.model_selection import StratifiedKFold"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6804c79",
+ "metadata": {},
+ "source": [
+ "https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+dataset\n",
+ "\n",
+ "https://www.kaggle.com/datasets/henrysue/online-shoppers-intention/data\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66e90b1f",
+ "metadata": {},
+ "source": [
+ "• Administrative: This is the number of pages of this type (administrative) that the user visited.<br>\n",
+ "• Administrative_Duration: This is the amount of time spent in this category of pages.<br>\n",
+ "• Informational: This is the number of pages of this type (informational) that the user visited.<br>\n",
+ "• Informational_Duration: This is the amount of time spent in this category of pages.<br>\n",
+ "• ProductRelated: This is the number of pages of this type (product related) that the user visited.<br>\n",
+ "• ProductRelated_Duration: This is the amount of time spent in this category of pages.<br>\n",
+ "• BounceRates: The percentage of visitors who enter the website through that page and exit without triggering any additional tasks.<br>\n",
+ "• ExitRates: The percentage of pageviews on the website that end at that specific page.<br>\n",
+ "• PageValues: The average value of the page averaged over the value of the target page and/or the completion of an eCommerce transaction.<br>\n",
+ "• SpecialDay: This value represents the closeness of the browsing date to special days or holidays (e.g., Mother's Day or Valentine's Day) in which the transaction is more likely to be finalized.<br>\n",
+ "• Month: Contains the month the pageview occurred, in string form.<br>\n",
+ "• OperatingSystems: An integer value representing the operating system that the user was on when viewing the page.<br>\n",
+ "• Browser: An integer value representing the browser that the user was using to view the page.<br>\n",
+ "• Region: An integer value representing which region the user is located in.<br>\n",
+ "• TrafficType: An integer value representing what type of traffic the user is categorized into.<br>\n",
+ "• VisitorType: A string representing whether a visitor is New Visitor, Returning Visitor, or Other.<br>\n",
+ "• Weekend: A boolean representing whether the session is on a weekend.<br>\n",
+ "Revenue: A boolean representing whether or not the user completed the purchase.<br>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8ac2733",
+ "metadata": {},
+ "source": [
+ "# FUNCTIONS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3475dee6",
+ "metadata": {},
+ "source": [
+ "All functions used in this project are in this section"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "046d5abe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Read dataset, check for null and duplicates\n",
+ "def read_dataset(dataset):\n",
+ " \n",
+ " data = pd.read_csv(dataset)\n",
+ " nrow = len(data.index)\n",
+ " ncol = len(data.columns)\n",
+ "\n",
+ " print(\"The dataset contains\", format(nrow, \",d\"), \"rows and\", ncol, \"columns.\")\n",
+ " \n",
+ " #Check for null values\n",
+ " if ((data.isna().sum()).sum()) > 0:\n",
+ " print(\"There are null items in the dataset\")\n",
+ " else:\n",
+ " print(\"There are no null items in the dataset\")\n",
+ " \n",
+ " #Check for duplicates\n",
+ " \n",
+ " #col_names = [\"Country\", \"Year\", \"Status\"]\n",
+ " #(data.duplicated(subset=col_names)).sum()\n",
+ "\n",
+ " if (data.duplicated().sum()) > 0:\n",
+ " print(\"There are duplicates in the dataset\")\n",
+ " else:\n",
+ " print(\"There are no duplicates in the dataset\")\n",
+ " \n",
+ " return data\n",
+ "\n",
+ "\n",
+ "\n",
+ "#Function to categorize data into numeric and categorical\n",
+ "def categorize_data(data):\n",
+ " \n",
+ " numeric=[]\n",
+ " categorical=[]\n",
+ " numeric_dtypes = [\"int64\", \"int32\", \"float64\", \"float32\"]\n",
+ "\n",
+ " for i in range (len(data.columns)):\n",
+ " if data[data.columns[i]].dtype in numeric_dtypes:\n",
+ " numeric.append(data.columns[i])\n",
+ " else:\n",
+ " categorical.append(data.columns[i])\n",
+ " \n",
+ " return numeric, categorical\n",
+ "\n",
+ " \n",
+ "#Function to check for outliers\n",
+ "def outliers_check(data, numeric_cols):\n",
+ " outliers_sum =[]\n",
+ "\n",
+ " for col in (numeric_cols):\n",
+ " Q1 = data[col].quantile(0.25)\n",
+ " Q3 = data[col].quantile(0.75)\n",
+ " IQR = Q3 - Q1\n",
+ " outliers = (data[col] < (Q1 - 5 * IQR)) | (data[col] > (Q3 + 5 * IQR))\n",
+ " print(col, \"\", outliers.sum())\n",
+ " outliers_sum.append(outliers.sum())\n",
+ "\n",
+ " return outliers.sum()\n",
+ "\n",
+ "\n",
+ "def remove_duplicates(data):\n",
+ " duplicated_sum = data.duplicated().sum()\n",
+ " if duplicated_sum == 0:\n",
+ " print(\"Number of duplicated rows in dataset =\", duplicated_sum)\n",
+ " return data\n",
+ " else:\n",
+ " print(\"Number of duplicated rows in dataset =\", duplicated_sum)\n",
+ " data = data[~data.duplicated()]\n",
+ " print(\"Duplicated rows have been removed\")\n",
+ " return data\n",
+ "\n",
+ " \n",
+ "def remove_outliers(data, numeric_cols):\n",
+ " \n",
+ " for col in (numeric_cols):\n",
+ " median_value = np.median(data[col])\n",
+ " Q1 = data[col].quantile(0.25)\n",
+ " Q3 = data[col].quantile(0.75)\n",
+ " IQR = Q3 - Q1\n",
+ " outliers = (data[col] < (Q1 - 5 * IQR)) | (data[col] > (Q3 + 5 * IQR))\n",
+ " #print(col, \"\", outliers.sum())\n",
+ " data.loc[outliers, col] = median_value\n",
+ " return data\n",
+ "\n",
+ "\n",
+ "# remove special characters from columns\n",
+ "def remove_spec_chars(data, categorical_cols):\n",
+ " for col in categorical_cols:\n",
+ " data[col] = data[col].str.replace(r'\\W+', '').str.strip() #replaces special characters with white sapaces and removes the white spaces\n",
+ " return data\n",
+ "\n",
+ "\n",
+ "def replace_unknown(data, categorical_cols):\n",
+ " for col in categorical_cols:\n",
+ " if \"other\" in data[col].values:\n",
+ " #source: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mode.html\n",
+ " mode = data[col].mode()[0]\n",
+ " data[col] = data[col].replace(\"unknown\", mode)\n",
+ " return data\n",
+ "\n",
+ " \n",
+ "def oneHotEncoding(data, categorical, drop_first):\n",
+ "\n",
+ " data_final = pd.get_dummies(data, columns=categorical, drop_first=drop_first)\n",
+ "\n",
+ " return data_final"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64d45954",
+ "metadata": {},
+ "source": [
+ "# EDA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "c6fafe71",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The dataset contains 12,330 rows and 18 columns.\n",
+ "There are no null items in the dataset\n",
+ "There are duplicates in the dataset\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = read_dataset(\"online_shoppers_intention.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "3de6a535",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "125"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.duplicated().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e6802474",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of duplicated rows in dataset = 125\n",
+ "Duplicated rows have been removed\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = remove_duplicates(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "bf44932c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.duplicated().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "c54135de",
+ "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>Administrative</th>\n",
+ " <th>Administrative_Duration</th>\n",
+ " <th>Informational</th>\n",
+ " <th>Informational_Duration</th>\n",
+ " <th>ProductRelated</th>\n",
+ " <th>ProductRelated_Duration</th>\n",
+ " <th>BounceRates</th>\n",
+ " <th>ExitRates</th>\n",
+ " <th>PageValues</th>\n",
+ " <th>SpecialDay</th>\n",
+ " <th>Month</th>\n",
+ " <th>OperatingSystems</th>\n",
+ " <th>Browser</th>\n",
+ " <th>Region</th>\n",
+ " <th>TrafficType</th>\n",
+ " <th>VisitorType</th>\n",
+ " <th>Weekend</th>\n",
+ " <th>Revenue</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>64.000000</td>\n",
+ " <td>0.00</td>\n",
+ " <td>0.10</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>9</td>\n",
+ " <td>3</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2.666667</td>\n",
+ " <td>0.05</td>\n",
+ " <td>0.14</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>10</td>\n",
+ " <td>627.500000</td>\n",
+ " <td>0.02</td>\n",
+ " <td>0.05</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>Feb</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>Returning_Visitor</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Administrative Administrative_Duration Informational \\\n",
+ "0 0 0.0 0 \n",
+ "1 0 0.0 0 \n",
+ "2 0 0.0 0 \n",
+ "3 0 0.0 0 \n",
+ "4 0 0.0 0 \n",
+ "\n",
+ " Informational_Duration ProductRelated ProductRelated_Duration \\\n",
+ "0 0.0 1 0.000000 \n",
+ "1 0.0 2 64.000000 \n",
+ "2 0.0 1 0.000000 \n",
+ "3 0.0 2 2.666667 \n",
+ "4 0.0 10 627.500000 \n",
+ "\n",
+ " BounceRates ExitRates PageValues SpecialDay Month OperatingSystems \\\n",
+ "0 0.20 0.20 0.0 0.0 Feb 1 \n",
+ "1 0.00 0.10 0.0 0.0 Feb 2 \n",
+ "2 0.20 0.20 0.0 0.0 Feb 4 \n",
+ "3 0.05 0.14 0.0 0.0 Feb 3 \n",
+ "4 0.02 0.05 0.0 0.0 Feb 3 \n",
+ "\n",
+ " Browser Region TrafficType VisitorType Weekend Revenue \n",
+ "0 1 1 1 Returning_Visitor False False \n",
+ "1 2 1 2 Returning_Visitor False False \n",
+ "2 1 9 3 Returning_Visitor False False \n",
+ "3 2 2 4 Returning_Visitor False False \n",
+ "4 3 1 4 Returning_Visitor True False "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.set_option('display.max_columns', None)\n",
+ "\n",
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "f6148ae5",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "Int64Index: 12205 entries, 0 to 12329\n",
+ "Data columns (total 18 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Administrative 12205 non-null int64 \n",
+ " 1 Administrative_Duration 12205 non-null float64\n",
+ " 2 Informational 12205 non-null int64 \n",
+ " 3 Informational_Duration 12205 non-null float64\n",
+ " 4 ProductRelated 12205 non-null int64 \n",
+ " 5 ProductRelated_Duration 12205 non-null float64\n",
+ " 6 BounceRates 12205 non-null float64\n",
+ " 7 ExitRates 12205 non-null float64\n",
+ " 8 PageValues 12205 non-null float64\n",
+ " 9 SpecialDay 12205 non-null float64\n",
+ " 10 Month 12205 non-null object \n",
+ " 11 OperatingSystems 12205 non-null int64 \n",
+ " 12 Browser 12205 non-null int64 \n",
+ " 13 Region 12205 non-null int64 \n",
+ " 14 TrafficType 12205 non-null int64 \n",
+ " 15 VisitorType 12205 non-null object \n",
+ " 16 Weekend 12205 non-null bool \n",
+ " 17 Revenue 12205 non-null bool \n",
+ "dtypes: bool(2), float64(7), int64(7), object(2)\n",
+ "memory usage: 1.6+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "d3639f64",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Administrative</th>\n",
+ " <th>Administrative_Duration</th>\n",
+ " <th>Informational</th>\n",
+ " <th>Informational_Duration</th>\n",
+ " <th>ProductRelated</th>\n",
+ " <th>ProductRelated_Duration</th>\n",
+ " <th>BounceRates</th>\n",
+ " <th>ExitRates</th>\n",
+ " <th>PageValues</th>\n",
+ " <th>SpecialDay</th>\n",
+ " <th>OperatingSystems</th>\n",
+ " <th>Browser</th>\n",
+ " <th>Region</th>\n",
+ " <th>TrafficType</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " <td>12205.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.338878</td>\n",
+ " <td>81.646331</td>\n",
+ " <td>0.508726</td>\n",
+ " <td>34.825454</td>\n",
+ " <td>32.045637</td>\n",
+ " <td>1206.982457</td>\n",
+ " <td>0.020370</td>\n",
+ " <td>0.041466</td>\n",
+ " <td>5.949574</td>\n",
+ " <td>0.061942</td>\n",
+ " <td>2.124211</td>\n",
+ " <td>2.357804</td>\n",
+ " <td>3.153298</td>\n",
+ " <td>4.073904</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>3.330436</td>\n",
+ " <td>177.491845</td>\n",
+ " <td>1.275617</td>\n",
+ " <td>141.424807</td>\n",
+ " <td>44.593649</td>\n",
+ " <td>1919.601400</td>\n",
+ " <td>0.045255</td>\n",
+ " <td>0.046163</td>\n",
+ " <td>18.653671</td>\n",
+ " <td>0.199666</td>\n",
+ " <td>0.906823</td>\n",
+ " <td>1.710114</td>\n",
+ " <td>2.402340</td>\n",
+ " <td>4.016654</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>8.000000</td>\n",
+ " <td>193.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.014231</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>18.000000</td>\n",
+ " <td>608.942857</td>\n",
+ " <td>0.002899</td>\n",
+ " <td>0.025000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>4.000000</td>\n",
+ " <td>94.700000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>38.000000</td>\n",
+ " <td>1477.154762</td>\n",
+ " <td>0.016667</td>\n",
+ " <td>0.048529</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>4.000000</td>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>27.000000</td>\n",
+ " <td>3398.750000</td>\n",
+ " <td>24.000000</td>\n",
+ " <td>2549.375000</td>\n",
+ " <td>705.000000</td>\n",
+ " <td>63973.522230</td>\n",
+ " <td>0.200000</td>\n",
+ " <td>0.200000</td>\n",
+ " <td>361.763742</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>8.000000</td>\n",
+ " <td>13.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>20.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Administrative Administrative_Duration Informational \\\n",
+ "count 12205.000000 12205.000000 12205.000000 \n",
+ "mean 2.338878 81.646331 0.508726 \n",
+ "std 3.330436 177.491845 1.275617 \n",
+ "min 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.000000 0.000000 \n",
+ "50% 1.000000 9.000000 0.000000 \n",
+ "75% 4.000000 94.700000 0.000000 \n",
+ "max 27.000000 3398.750000 24.000000 \n",
+ "\n",
+ " Informational_Duration ProductRelated ProductRelated_Duration \\\n",
+ "count 12205.000000 12205.000000 12205.000000 \n",
+ "mean 34.825454 32.045637 1206.982457 \n",
+ "std 141.424807 44.593649 1919.601400 \n",
+ "min 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 8.000000 193.000000 \n",
+ "50% 0.000000 18.000000 608.942857 \n",
+ "75% 0.000000 38.000000 1477.154762 \n",
+ "max 2549.375000 705.000000 63973.522230 \n",
+ "\n",
+ " BounceRates ExitRates PageValues SpecialDay \\\n",
+ "count 12205.000000 12205.000000 12205.000000 12205.000000 \n",
+ "mean 0.020370 0.041466 5.949574 0.061942 \n",
+ "std 0.045255 0.046163 18.653671 0.199666 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.014231 0.000000 0.000000 \n",
+ "50% 0.002899 0.025000 0.000000 0.000000 \n",
+ "75% 0.016667 0.048529 0.000000 0.000000 \n",
+ "max 0.200000 0.200000 361.763742 1.000000 \n",
+ "\n",
+ " OperatingSystems Browser Region TrafficType \n",
+ "count 12205.000000 12205.000000 12205.000000 12205.000000 \n",
+ "mean 2.124211 2.357804 3.153298 4.073904 \n",
+ "std 0.906823 1.710114 2.402340 4.016654 \n",
+ "min 1.000000 1.000000 1.000000 1.000000 \n",
+ "25% 2.000000 2.000000 1.000000 2.000000 \n",
+ "50% 2.000000 2.000000 3.000000 2.000000 \n",
+ "75% 3.000000 2.000000 4.000000 4.000000 \n",
+ "max 8.000000 13.000000 9.000000 20.000000 "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Summary statistics of the dataset\n",
+ "data.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "e33d1922",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False 10297\n",
+ "True 1908\n",
+ "Name: Revenue, dtype: int64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Class imbalance\n",
+ "data['Revenue'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "277920d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Categorize data into numeric and categorical\n",
+ "\n",
+ "data['Weekend'] = data['Weekend'].astype('str')\n",
+ "data['Revenue'] = data['Revenue'].astype('str')\n",
+ "\n",
+ "numeric_cols, categorical_cols = categorize_data(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "cf7263f0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dtype('O')"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[\"Weekend\"].dtype"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b9c4d65",
+ "metadata": {},
+ "source": [
+ "# Visualisations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "77615c11",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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EiIiahqKiIri7u9fqI0gZsLVUdVnYzs6OAUtE1MTV5lYhH3IiIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgG/KhEqjW9Xo+SkpJa11tZWcHGxkbGjoiIHl0MWKoVvV6PVh6eKLieX+tt7B0ccfnSRYYsETVJDFiqlZKSEhRcz8fAxduhtGl+3/oyfSG0S8ahpKSEAUtETRIDlupEadMcFnYOxm6DiOiRx4eciIiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpKB0QP2t99+w7hx4+Do6AgrKyt07doVqamp0nohBCIjI+Hm5gZLS0v06dMH6enpBvsoKytDeHg4nJycYG1tjaFDh+Lq1asGNQUFBQgNDYVKpYJKpUJoaCgKCwsfxhSJiKgJMmrAFhQUoHfv3jAzM8O+fftw9uxZfPDBB2jevLlUs3z5csTExCA2NhYpKSlQq9UICgpCcXGxVBMREYGEhATEx8fjyJEj0Ov1CAkJQUVFhVQzduxYpKWlQavVQqvVIi0tDaGhoQ9zukRE1ISYGvPg77//Ptzd3bF582ZpzNPTU/qzEAKrVq3CokWLMGLECADAli1b4OLigp07d2LKlCnQ6XTYtGkTtm3bhv79+wMAtm/fDnd3dxw8eBADBgxARkYGtFotkpOTERAQAADYuHEjNBoNMjMz4ePj8/AmTURETYJRz2C/+uor9OjRAyNHjoSzszO6deuGjRs3SuuzsrKQk5OD4OBgaUypVCIwMBBHjx4FAKSmpuLWrVsGNW5ubvD19ZVqjh07BpVKJYUrAPTs2RMqlUqquVNZWRmKiooMFiIiotoyasBeuHABa9euhbe3N/bv34+pU6di5syZ2Lp1KwAgJycHAODi4mKwnYuLi7QuJycH5ubmsLe3v2eNs7NzteM7OztLNXeKjo6W7teqVCq4u7s/2GSJiKhJMWrAVlZW4sknn0RUVBS6deuGKVOmYNKkSVi7dq1BnUKhMHgthKg2dqc7a2qqv9d+Fi5cCJ1OJy1Xrlyp7bSIiIiMG7Curq7o2LGjwViHDh1w+fJlAIBarQaAameZeXl50lmtWq1GeXk5CgoK7lmTm5tb7fjXrl2rdnZcRalUws7OzmAhIiKqLaMGbO/evZGZmWkwdu7cOXh4eAAAvLy8oFarkZiYKK0vLy9HUlISevXqBQDo3r07zMzMDGqys7Nx5swZqUaj0UCn0+HEiRNSzfHjx6HT6aQaIiKihmTUp4hnz56NXr16ISoqCqNGjcKJEyewYcMGbNiwAcCfl3UjIiIQFRUFb29veHt7IyoqClZWVhg7diwAQKVSYeLEiZg7dy4cHR3h4OCAefPmwc/PT3qquEOHDhg4cCAmTZqE9evXAwAmT56MkJAQPkFMRESyMGrA+vv7IyEhAQsXLsTSpUvh5eWFVatW4ZVXXpFqFixYgNLSUkybNg0FBQUICAjAgQMHYGtrK9WsXLkSpqamGDVqFEpLS9GvXz/ExcXBxMREqtmxYwdmzpwpPW08dOhQxMbGPrzJEhFRk6IQQghjN9EYFBUVQaVSQafTNcn7sVX3tIet2AsLO4f71t8suo4v54cgNze3xie4iYgao7pkgdE/KpGIiOhxxIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpKBUQM2MjISCoXCYFGr1dJ6IQQiIyPh5uYGS0tL9OnTB+np6Qb7KCsrQ3h4OJycnGBtbY2hQ4fi6tWrBjUFBQUIDQ2FSqWCSqVCaGgoCgsLH8YUiYioiTL6GWynTp2QnZ0tLadPn5bWLV++HDExMYiNjUVKSgrUajWCgoJQXFws1URERCAhIQHx8fE4cuQI9Ho9QkJCUFFRIdWMHTsWaWlp0Gq10Gq1SEtLQ2ho6EOdJxERNS2mRm/A1NTgrLWKEAKrVq3CokWLMGLECADAli1b4OLigp07d2LKlCnQ6XTYtGkTtm3bhv79+wMAtm/fDnd3dxw8eBADBgxARkYGtFotkpOTERAQAADYuHEjNBoNMjMz4ePjU2NfZWVlKCsrk14XFRU19NSJiOgxZvQz2F9++QVubm7w8vLC6NGjceHCBQBAVlYWcnJyEBwcLNUqlUoEBgbi6NGjAIDU1FTcunXLoMbNzQ2+vr5SzbFjx6BSqaRwBYCePXtCpVJJNTWJjo6WLimrVCq4u7s36LyJiOjxZtSADQgIwNatW7F//35s3LgROTk56NWrF/Lz85GTkwMAcHFxMdjGxcVFWpeTkwNzc3PY29vfs8bZ2bnasZ2dnaWamixcuBA6nU5arly58kBzJSKipsWol4gHDRok/dnPzw8ajQZt2rTBli1b0LNnTwCAQqEw2EYIUW3sTnfW1FR/v/0olUoolcpazYOIiOhORr9E/FfW1tbw8/PDL7/8It2XvfMsMy8vTzqrVavVKC8vR0FBwT1rcnNzqx3r2rVr1c6OiYiIGsojFbBlZWXIyMiAq6srvLy8oFarkZiYKK0vLy9HUlISevXqBQDo3r07zMzMDGqys7Nx5swZqUaj0UCn0+HEiRNSzfHjx6HT6aQaIiKihmbUS8Tz5s3DkCFD0KpVK+Tl5eHdd99FUVERwsLCoFAoEBERgaioKHh7e8Pb2xtRUVGwsrLC2LFjAQAqlQoTJ07E3Llz4ejoCAcHB8ybNw9+fn7SU8UdOnTAwIEDMWnSJKxfvx4AMHnyZISEhNz1CWIiIqIHZdSAvXr1KsaMGYM//vgDLVq0QM+ePZGcnAwPDw8AwIIFC1BaWopp06ahoKAAAQEBOHDgAGxtbaV9rFy5Eqamphg1ahRKS0vRr18/xMXFwcTERKrZsWMHZs6cKT1tPHToUMTGxj7cyRIRUZOiEEIIYzfRGBQVFUGlUkGn08HOzs7Y7Tx0Vfe1h63YCws7h/vW3yy6ji/nhyA3N7fGp7iJiBqjumTBI3UPloiI6HHBgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZPDIBGx0dDQUCgUiIiKkMSEEIiMj4ebmBktLS/Tp0wfp6ekG25WVlSE8PBxOTk6wtrbG0KFDcfXqVYOagoIChIaGQqVSQaVSITQ0FIWFhQ9hVkRE1FQ9EgGbkpKCDRs2oHPnzgbjy5cvR0xMDGJjY5GSkgK1Wo2goCAUFxdLNREREUhISEB8fDyOHDkCvV6PkJAQVFRUSDVjx45FWloatFottFot0tLSEBoa+tDmR0RETY/RA1av1+OVV17Bxo0bYW9vL40LIbBq1SosWrQII0aMgK+vL7Zs2YKSkhLs3LkTAKDT6bBp0yZ88MEH6N+/P7p164bt27fj9OnTOHjwIAAgIyMDWq0W//rXv6DRaKDRaLBx40bs3bsXmZmZRpkzERE9/owesNOnT8fgwYPRv39/g/GsrCzk5OQgODhYGlMqlQgMDMTRo0cBAKmpqbh165ZBjZubG3x9faWaY8eOQaVSISAgQKrp2bMnVCqVVFOTsrIyFBUVGSxERES1ZWrMg8fHx+Onn35CSkpKtXU5OTkAABcXF4NxFxcXXLp0SaoxNzc3OPOtqqnaPicnB87OztX27+zsLNXUJDo6GkuWLKnbhIiIiP7HaGewV65cwaxZs7B9+3ZYWFjctU6hUBi8FkJUG7vTnTU11d9vPwsXLoROp5OWK1eu3POYREREf2W0gE1NTUVeXh66d+8OU1NTmJqaIikpCR999BFMTU2lM9c7zzLz8vKkdWq1GuXl5SgoKLhnTW5ubrXjX7t2rdrZ8V8plUrY2dkZLERERLVltIDt168fTp8+jbS0NGnp0aMHXnnlFaSlpaF169ZQq9VITEyUtikvL0dSUhJ69eoFAOjevTvMzMwMarKzs3HmzBmpRqPRQKfT4cSJE1LN8ePHodPppBoiIqKGZrR7sLa2tvD19TUYs7a2hqOjozQeERGBqKgoeHt7w9vbG1FRUbCyssLYsWMBACqVChMnTsTcuXPh6OgIBwcHzJs3D35+ftJDUx06dMDAgQMxadIkrF+/HgAwefJkhISEwMfH5yHOmIiImhKjPuR0PwsWLEBpaSmmTZuGgoICBAQE4MCBA7C1tZVqVq5cCVNTU4waNQqlpaXo168f4uLiYGJiItXs2LEDM2fOlJ42Hjp0KGJjYx/6fIiIqOlQCCGEsZtoDIqKiqBSqaDT6Zrk/diq+9rDVuyFhZ3DfetvFl3Hl/NDkJubW+NT3EREjVFdssDovwdLRET0OKpXwLZu3Rr5+fnVxgsLC9G6desHboqIiKixq1fAXrx40eCzfquUlZXht99+e+CmiIiIGrs6PeT01VdfSX/ev38/VCqV9LqiogKHDh2Cp6dngzVHRETUWNUpYIcPHw7gz09GCgsLM1hnZmYGT09PfPDBBw3WHBERUWNVp4CtrKwEAHh5eSElJQVOTk6yNEVERNTY1ev3YLOyshq6DyIiosdKvT9o4tChQzh06BDy8vKkM9sqn3zyyQM3RkRE1JjVK2CXLFmCpUuXokePHnB1db3vt9sQERE1NfUK2HXr1iEuLg6hoaEN3Q8REdFjoV6/B1teXs5voiEiIrqHegXs66+/jp07dzZ0L0RERI+Nel0ivnnzJjZs2ICDBw+ic+fOMDMzM1gfExPTIM0RERE1VvUK2FOnTqFr164AgDNnzhis4wNPRERE9QzYw4cPN3QfREREjxV+XR0REZEM6nUG27dv33teCv7mm2/q3RAREdHjoF4BW3X/tcqtW7eQlpaGM2fOVPsSACIioqaoXgG7cuXKGscjIyOh1+sfqCEiIqLHQYPegx03bhw/h5iIiAgNHLDHjh2DhYVFQ+6SiIioUarXJeIRI0YYvBZCIDs7Gz/++CP+/ve/N0hjREREjVm9AlalUhm8btasGXx8fLB06VIEBwc3SGNERESNWb0CdvPmzQ3dBxER0WOl3l+4DgCpqanIyMiAQqFAx44d0a1bt4bqi4iIqFGrV8Dm5eVh9OjR+Pbbb9G8eXMIIaDT6dC3b1/Ex8ejRYsWDd0nERFRo1Kvp4jDw8NRVFSE9PR0XL9+HQUFBThz5gyKioowc+bMhu6RiIio0anXGaxWq8XBgwfRoUMHaaxjx474+OOP+ZATERER6nkGW1lZWe07YAHAzMwMlZWVD9wUERFRY1evgH3uuecwa9Ys/P7779LYb7/9htmzZ6Nfv34N1hwREVFjVa+AjY2NRXFxMTw9PdGmTRu0bdsWXl5eKC4uxurVqxu6RyIiokanXvdg3d3d8dNPPyExMRH//e9/IYRAx44d0b9//4buj4iIqFGq0xnsN998g44dO6KoqAgAEBQUhPDwcMycORP+/v7o1KkTvv/+e1kaJSIiakzqFLCrVq3CpEmTYGdnV22dSqXClClTEBMT02DNERERNVZ1Ctiff/4ZAwcOvOv64OBgpKam1np/a9euRefOnWFnZwc7OztoNBrs27dPWi+EQGRkJNzc3GBpaYk+ffogPT3dYB9lZWUIDw+Hk5MTrK2tMXToUFy9etWgpqCgAKGhoVCpVFCpVAgNDUVhYWGt+yQiIqqrOgVsbm5ujb+eU8XU1BTXrl2r9f5atmyJ9957Dz/++CN+/PFHPPfccxg2bJgUosuXL0dMTAxiY2ORkpICtVqNoKAgFBcXS/uIiIhAQkIC4uPjceTIEej1eoSEhKCiokKqGTt2LNLS0qDVaqHVapGWlobQ0NC6TJ2IiKhO6vSQ0xNPPIHTp0+jbdu2Na4/deoUXF1da72/IUOGGLxetmwZ1q5di+TkZHTs2BGrVq3CokWLpK/H27JlC1xcXLBz505MmTIFOp0OmzZtwrZt26QHrLZv3w53d3ccPHgQAwYMQEZGBrRaLZKTkxEQEAAA2LhxIzQaDTIzM+Hj41OXt4CIiKhW6nQG+/zzz+Mf//gHbt68WW1daWkpFi9ejJCQkHo1UlFRgfj4eNy4cQMajQZZWVnIyckx+GQopVKJwMBAHD16FMCfXzZw69Ytgxo3Nzf4+vpKNceOHYNKpZLCFQB69uwJlUol1dSkrKwMRUVFBgsREVFt1ekM9u2338bu3bvRrl07zJgxAz4+PlAoFMjIyMDHH3+MiooKLFq0qE4NnD59GhqNBjdv3oSNjQ0SEhLQsWNHKfxcXFwM6l1cXHDp0iUAQE5ODszNzWFvb1+tJicnR6pxdnaudlxnZ2eppibR0dFYsmRJneZCRERUpU4B6+LigqNHj+Jvf/sbFi5cCCEEAEChUGDAgAFYs2ZNtUC8Hx8fH6SlpaGwsBBffPEFwsLCkJSUJK1XKBQG9UKIamN3urOmpvr77WfhwoWYM2eO9LqoqAju7u73nQ8RERFQjw+a8PDwwNdff42CggKcP38eQgh4e3tXO4usLXNzc+mebo8ePZCSkoIPP/wQb7zxBoA/z0D/el83Ly9PCnG1Wo3y8nIUFBQYHD8vLw+9evWSanJzc6sd99q1a/f8YUCpVEKpVNZrTkRERPX6qEQAsLe3h7+/P5566ql6h2tNhBAoKyuDl5cX1Go1EhMTpXXl5eVISkqSwrN79+4wMzMzqMnOzsaZM2ekGo1GA51OhxMnTkg1x48fh06nk2qIiIgaWr0+KrGhvPXWWxg0aBDc3d1RXFyM+Ph4fPvtt9BqtVAoFIiIiEBUVBS8vb3h7e2NqKgoWFlZYezYsQD+/HCLiRMnYu7cuXB0dISDgwPmzZsHPz8/6aniDh06YODAgZg0aRLWr18PAJg8eTJCQkL4BDEREcnGqAGbm5uL0NBQZGdnQ6VSoXPnztBqtQgKCgIALFiwAKWlpZg2bRoKCgoQEBCAAwcOwNbWVtrHypUrYWpqilGjRqG0tBT9+vVDXFwcTExMpJodO3Zg5syZ0tPGQ4cORWxs7MOdLBERNSkKUfWkEt1TUVERVCoVdDpdjR8V+biruvc9bMVeWNg53Lf+ZtF1fDk/BLm5uTU+xU1E1BjVJQvqfQ+WiIiI7o4BS0REJAMGLBERkQwYsERERDJgwBIREcmAAUtERCQDBiwREZEMGLBEREQyYMASERHJgAFLREQkAwYsERGRDBiwREREMjDqt+k0RXq9HiUlJbWut7Kygo2NjYwdERGRHBiwD5Fer0crD08UXM+v9Tb2Do64fOkiQ/YB8QcbInrYGLAPUUlJCQqu52Pg4u1Q2jS/b32ZvhDaJeNQUlLCf+wfAH+wISJjYMAagdKmea2+U5UaBn+wISJjYMBSk8EfbIjoYeJTxERERDJgwBIREcmAAUtERCQDBiwREZEMGLBEREQyYMASERHJgAFLREQkAwYsERGRDBiwREREMmDAEhERyYABS0REJAMGLBERkQwYsERERDJgwBIREcmAAUtERCQDBiwREZEMGLBEREQyMGrARkdHw9/fH7a2tnB2dsbw4cORmZlpUCOEQGRkJNzc3GBpaYk+ffogPT3doKasrAzh4eFwcnKCtbU1hg4diqtXrxrUFBQUIDQ0FCqVCiqVCqGhoSgsLJR7ikRE1EQZNWCTkpIwffp0JCcnIzExEbdv30ZwcDBu3Lgh1SxfvhwxMTGIjY1FSkoK1Go1goKCUFxcLNVEREQgISEB8fHxOHLkCPR6PUJCQlBRUSHVjB07FmlpadBqtdBqtUhLS0NoaOhDnS8RETUdpsY8uFarNXi9efNmODs7IzU1Fc8++yyEEFi1ahUWLVqEESNGAAC2bNkCFxcX7Ny5E1OmTIFOp8OmTZuwbds29O/fHwCwfft2uLu74+DBgxgwYAAyMjKg1WqRnJyMgIAAAMDGjRuh0WiQmZkJHx+fhztxIiJ67D1S92B1Oh0AwMHBAQCQlZWFnJwcBAcHSzVKpRKBgYE4evQoACA1NRW3bt0yqHFzc4Ovr69Uc+zYMahUKilcAaBnz55QqVRSzZ3KyspQVFRksBAREdXWIxOwQgjMmTMHTz/9NHx9fQEAOTk5AAAXFxeDWhcXF2ldTk4OzM3NYW9vf88aZ2fnasd0dnaWau4UHR0t3a9VqVRwd3d/sAkSEVGT8sgE7IwZM3Dq1Cns2rWr2jqFQmHwWghRbexOd9bUVH+v/SxcuBA6nU5arly5UptpEBERAXhEAjY8PBxfffUVDh8+jJYtW0rjarUaAKqdZebl5UlntWq1GuXl5SgoKLhnTW5ubrXjXrt2rdrZcRWlUgk7OzuDhYiIqLaMGrBCCMyYMQO7d+/GN998Ay8vL4P1Xl5eUKvVSExMlMbKy8uRlJSEXr16AQC6d+8OMzMzg5rs7GycOXNGqtFoNNDpdDhx4oRUc/z4ceh0OqmGiIioIRn1KeLp06dj586d+PLLL2FrayudqapUKlhaWkKhUCAiIgJRUVHw9vaGt7c3oqKiYGVlhbFjx0q1EydOxNy5c+Ho6AgHBwfMmzcPfn5+0lPFHTp0wMCBAzFp0iSsX78eADB58mSEhITwCWIiIpKFUQN27dq1AIA+ffoYjG/evBnjx48HACxYsAClpaWYNm0aCgoKEBAQgAMHDsDW1laqX7lyJUxNTTFq1CiUlpaiX79+iIuLg4mJiVSzY8cOzJw5U3raeOjQoYiNjZV3gkRE1GQZNWCFEPetUSgUiIyMRGRk5F1rLCwssHr1aqxevfquNQ4ODti+fXt92iQiIqqzR+IhJyIioscNA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAZGDdjvvvsOQ4YMgZubGxQKBfbs2WOwXgiByMhIuLm5wdLSEn369EF6erpBTVlZGcLDw+Hk5ARra2sMHToUV69eNagpKChAaGgoVCoVVCoVQkNDUVhYKPPsiIioKTNqwN64cQNdunRBbGxsjeuXL1+OmJgYxMbGIiUlBWq1GkFBQSguLpZqIiIikJCQgPj4eBw5cgR6vR4hISGoqKiQasaOHYu0tDRotVpotVqkpaUhNDRU9vkREVHTZWrMgw8aNAiDBg2qcZ0QAqtWrcKiRYswYsQIAMCWLVvg4uKCnTt3YsqUKdDpdNi0aRO2bduG/v37AwC2b98Od3d3HDx4EAMGDEBGRga0Wi2Sk5MREBAAANi4cSM0Gg0yMzPh4+PzcCZLRERNyiN7DzYrKws5OTkIDg6WxpRKJQIDA3H06FEAQGpqKm7dumVQ4+bmBl9fX6nm2LFjUKlUUrgCQM+ePaFSqaSampSVlaGoqMhgISIiqq1HNmBzcnIAAC4uLgbjLi4u0rqcnByYm5vD3t7+njXOzs7V9u/s7CzV1CQ6Olq6Z6tSqeDu7v5A8yEioqblkQ3YKgqFwuC1EKLa2J3urKmp/n77WbhwIXQ6nbRcuXKljp0TEVFT9sgGrFqtBoBqZ5l5eXnSWa1arUZ5eTkKCgruWZObm1tt/9euXat2dvxXSqUSdnZ2BgsREVFtPbIB6+XlBbVajcTERGmsvLwcSUlJ6NWrFwCge/fuMDMzM6jJzs7GmTNnpBqNRgOdTocTJ05INcePH4dOp5NqiIiIGppRnyLW6/U4f/689DorKwtpaWlwcHBAq1atEBERgaioKHh7e8Pb2xtRUVGwsrLC2LFjAQAqlQoTJ07E3Llz4ejoCAcHB8ybNw9+fn7SU8UdOnTAwIEDMWnSJKxfvx4AMHnyZISEhPAJYiIiko1RA/bHH39E3759pddz5swBAISFhSEuLg4LFixAaWkppk2bhoKCAgQEBODAgQOwtbWVtlm5ciVMTU0xatQolJaWol+/foiLi4OJiYlUs2PHDsycOVN62njo0KF3/d1bIiKihmDUgO3Tpw+EEHddr1AoEBkZicjIyLvWWFhYYPXq1Vi9evVdaxwcHLB9+/YHaZWIiKhOHtl7sERERI0ZA5aIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZGPX7YIno/vR6PUpKSmpdb2VlBRsbGxk7IqLaYMASPcL0ej1aeXii4Hp+rbexd3DE5UsXGbJERsaAJXqElZSUoOB6PgYu3g6lTfP71pfpC6FdMg4lJSUMWCIjY8ASNQJKm+awsHMwdhtEVAd8yImIiEgGDFgiIiIZMGCJiIhkwIAlIiKSAQOWiIhIBgxYIiIiGTBgiYiIZMCAJSIikgEDloiISAYMWCIiIhkwYImIiGTAgCUiIpIBA5aIiEgGDFgiIiIZ8OvqiIio0dLr9SgpKal1vZWV1UP7ruQmFbBr1qzBihUrkJ2djU6dOmHVqlV45plnjN0WERHVg16vRysPTxRcz6/1NvYOjrh86eJDCdkmE7CffvopIiIisGbNGvTu3Rvr16/HoEGDcPbsWbRq1crY7RERUR2VlJSg4Ho+Bi7eDqVN8/vWl+kLoV0yDiUlJQzYhhQTE4OJEyfi9ddfBwCsWrUK+/fvx9q1axEdHW3k7ogIeLQv990PezcepU1zWNg5GLuNappEwJaXlyM1NRVvvvmmwXhwcDCOHj1a4zZlZWUoKyuTXut0OgBAUVFRvfsoLi4GANzI/x23bt7/P+byG4UAgKysLGlbY/njjz8AsPeHrTH3Xlc3btzAs4F9oCssqPU2qub2+C7pW1hbW8vY2f2xd+Oo7/8fxcXFsLCwqNcxqzJACHH/YtEE/PbbbwKA+OGHHwzGly1bJtq1a1fjNosXLxYAuHDhwoULl2rLlStX7ps9TeIMtopCoTB4LYSoNlZl4cKFmDNnjvS6srIS169fh6Oj4123uZ+ioiK4u7vjypUrsLOzq9c+HhWPy1w4j0fP4zIXzuPR0xBzEUKguLgYbm5u961tEgHr5OQEExMT5OTkGIzn5eXBxcWlxm2USiWUSqXBWPPmzRukHzs7u0b/H2qVx2UunMej53GZC+fx6HnQuahUqlrVNYkPmjA3N0f37t2RmJhoMJ6YmIhevXoZqSsiInqcNYkzWACYM2cOQkND0aNHD2g0GmzYsAGXL1/G1KlTjd0aERE9hppMwL788svIz8/H0qVLkZ2dDV9fX3z99dfw8PB4aD0olUosXry42qXnxuhxmQvn8eh5XObCeTx6HvZcFELU5lljIiIiqosmcQ+WiIjoYWPAEhERyYABS0REJAMGLBERkQwYsA/Jd999hyFDhsDNzQ0KhQJ79uwxdkt1Fh0dDX9/f9ja2sLZ2RnDhw9HZmamsduql7Vr16Jz587SL5xrNBrs27fP2G09sOjoaCgUCkRERBi7lTqJjIyEQqEwWNRqtbHbqrfffvsN48aNg6OjI6ysrNC1a1ekpqYau6068fT0rPZ3olAoMH36dGO3Vie3b9/G22+/DS8vL1haWqJ169ZYunQpKisrZT92k/k1HWO7ceMGunTpgtdeew0vvviisdupl6SkJEyfPh3+/v64ffs2Fi1ahODgYJw9e9boH/pdVy1btsR7772Htm3bAgC2bNmCYcOG4eTJk+jUqZORu6uflJQUbNiwAZ07dzZ2K/XSqVMnHDx4UHptYmJixG7qr6CgAL1790bfvn2xb98+ODs749dff22wT4J7WFJSUlBRUSG9PnPmDIKCgjBy5EgjdlV377//PtatW4ctW7agU6dO+PHHH/Haa69BpVJh1qxZ8h78gT9Jn+oMgEhISDB2Gw8sLy9PABBJSUnGbqVB2Nvbi3/961/GbqNeiouLhbe3t0hMTBSBgYFi1qxZxm6pThYvXiy6dOli7DYaxBtvvCGefvppY7fR4GbNmiXatGkjKisrjd1KnQwePFhMmDDBYGzEiBFi3Lhxsh+bl4ip3qq+ws/B4dH7Hsa6qKioQHx8PG7cuAGNRmPsdupl+vTpGDx4MPr372/sVurtl19+gZubG7y8vDB69GhcuHDB2C3Vy1dffYUePXpg5MiRcHZ2Rrdu3bBx40Zjt/VAysvLsX37dkyYMKHeX3ZiLE8//TQOHTqEc+fOAQB+/vlnHDlyBM8//7zsx+YlYqoXIQTmzJmDp59+Gr6+vsZup15Onz4NjUaDmzdvwsbGBgkJCejYsaOx26qz+Ph4/PTTT0hJSTF2K/UWEBCArVu3ol27dsjNzcW7776LXr16IT09HY6OjsZur04uXLiAtWvXYs6cOXjrrbdw4sQJzJw5E0qlEq+++qqx26uXPXv2oLCwEOPHjzd2K3X2xhtvQKfToX379jAxMUFFRQWWLVuGMWPGyH9w2c+RqRo8BpeIp02bJjw8PGr1nYiPqrKyMvHLL7+IlJQU8eabbwonJyeRnp5u7Lbq5PLly8LZ2VmkpaVJY43xEvGd9Hq9cHFxER988IGxW6kzMzMzodFoDMbCw8NFz549jdTRgwsODhYhISHGbqNedu3aJVq2bCl27dolTp06JbZu3SocHBxEXFyc7MdmwBpBYw/YGTNmiJYtW4oLFy4Yu5UG1a9fPzF58mRjt1EnCQkJAoAwMTGRFgBCoVAIExMTcfv2bWO3WG/9+/cXU6dONXYbddaqVSsxceJEg7E1a9YINzc3I3X0YC5evCiaNWsm9uzZY+xW6qVly5YiNjbWYOydd94RPj4+sh+bl4ip1oQQCA8PR0JCAr799lt4eXkZu6UGJYRAWVmZsduok379+uH06dMGY6+99hrat2+PN954o9E+iVtWVoaMjAw888wzxm6lznr37l3t19fOnTv3UL9YpCFt3rwZzs7OGDx4sLFbqZeSkhI0a2b4uJGJiQl/Tedxotfrcf78eel1VlYW0tLS4ODggFatWhmxs9qbPn06du7ciS+//BK2trbSF9irVCpYWloaubu6eeuttzBo0CC4u7ujuLgY8fHx+Pbbb6HVao3dWp3Y2tpWuwdubW0NR0fHRnVvfN68eRgyZAhatWqFvLw8vPvuuygqKkJYWJixW6uz2bNno1evXoiKisKoUaNw4sQJbNiwARs2bDB2a3VWWVmJzZs3IywsDKamjTMuhgwZgmXLlqFVq1bo1KkTTp48iZiYGEyYMEH+g8t+jkxCCCEOHz4sAFRbwsLCjN1ardXUPwCxefNmY7dWZxMmTBAeHh7C3NxctGjRQvTr108cOHDA2G01iMZ4D/bll18Wrq6uwszMTLi5uYkRI0Y0uvvhf/Wf//xH+Pr6CqVSKdq3by82bNhg7JbqZf/+/QKAyMzMNHYr9VZUVCRmzZolWrVqJSwsLETr1q3FokWLRFlZmezH5tfVERERyYC/B0tERCQDBiwREZEMGLBEREQyYMASERHJgAFLREQkAwYsERGRDBiwREREMmDAEhERyYABS9TEjB8/HsOHDzd2G0SPPQYsUT1duXIFEydOhJubG8zNzeHh4YFZs2YhPz/f2K0BAC5evAiFQoG0tDSD8Q8//BBxcXENfrzDhw+jb9++cHBwgJWVFby9vREWFobbt283yP4VCgX27NnTIPsiehgYsET1cOHCBfTo0QPnzp3Drl27cP78eaxbtw6HDh2CRqPB9evXZTv2rVu3Hmh7lUqF5s2bN0wz/5Oeno5BgwbB398f3333HU6fPo3Vq1fDzMzsoXxrCdEjSfZPOyZ6DA0cOFC0bNlSlJSUGIxnZ2cLKysr6XtMPTw8xNKlS8WYMWOEtbW1cHV1FR999JHBNoWFhWLSpEmiRYsWwtbWVvTt29fgC9QXL14sunTpIjZt2iS8vLyEQqEQlZWVYt++faJ3795CpVIJBwcHMXjwYHH+/HlpO9zxpQyBgYFCCCHCwsLEsGHDpLrAwEARHh4u5s+fL+zt7YWLi4tYvHixQY8ZGRmid+/eQqlUig4dOojExESD7zVeuXKl8PT0vOv7pdfrha2trfj8888Nxr/66ithZWUlioqKRFlZmZg+fbpQq9VCqVQKDw8PERUVJb2Pf52Lh4eHwT6efPJJoVQqhZeXl4iMjBS3bt0yeB/WrVsnBg8eLCwtLUX79u3F0aNHxS+//CICAwOFlZWV6Nmzp8F7l5aWJvr06SNsbGyEra2tePLJJ0VKSspd50dUEwYsUR3l5+cLhUIh/eN/p0mTJgl7e3tRWVkpPDw8hK2trYiOjhaZmZnio48+EiYmJtI391RWVorevXuLIUOGiJSUFHHu3Dkxd+5c4ejoKPLz84UQfwastbW1GDBggPjpp5/Ezz//LCorK8W///1v8cUXX4hz586JkydPiiFDhgg/Pz9RUVEhhBDixIkTAoA4ePCgyM7OlvZXU8Da2dmJyMhIce7cObFlyxahUCikHisqKoSPj48ICgoSaWlp4vvvvxdPPfWUQcDu2rVLKJVKkZSUdNf3bdKkSeL55583GHvhhRfEq6++KoQQYsWKFcLd3V1899134uLFi+L7778XO3fuFEIIkZeXJ31zU3Z2tsjLyxNCCKHVaoWdnZ2Ii4sTv/76qzhw4IDw9PQUkZGR0jEAiCeeeEJ8+umnIjMzUwwfPlx4enqK5557Tmi1WnH27FnRs2dPMXDgQGmbTp06iXHjxomMjAxx7tw58dlnnxn80ENUGwxYojpKTk42CJc7xcTECAAiNzdXeHh4GPzDLcSfX802aNAgIYQQhw4dEnZ2duLmzZsGNW3atBHr168XQvwZsGZmZlKo3E1VCJ0+fVoIIURWVpYAIE6ePGlQV1PAPv300wY1/v7+4o033hBCCLFv3z5hamoqsrOzpfV3nsHevn1bjB8/XgAQarVaDB8+XKxevVrodDppm+PHjwsTExPx22+/CSGEuHbtmjAzMxPffvutEEKI8PBw8dxzz4nKysoa51fTe/7MM89U+0Fn27ZtwtXV1WC7t99+W3p97NgxAUBs2rRJGtu1a5ewsLCQXtva2oq4uLga+yCqLd6DJWpg4n/fAKlQKAAAGo3GYL1Go0FGRgYAIDU1FXq9Ho6OjrCxsZGWrKws/Prrr9I2Hh4eaNGihcF+fv31V4wdOxatW7eGnZ0dvLy8AACXL1+uc8+dO3c2eO3q6oq8vDwAQGZmJtzd3aFWq6X1Tz31lEG9iYkJNm/ejKtXr2L58uVwc3PDsmXL0KlTJ2RnZ0vbdOrUCVu3bgUAbNu2Da1atcKzzz4L4M+nm9PS0uDj44OZM2fiwIED9+07NTUVS5cuNXjvJk2ahOzsbJSUlNQ4PxcXFwCAn5+fwdjNmzdRVFQEAJgzZw5ef/119O/fH++9957B3wVRbTFgieqobdu2UCgUOHv2bI3r//vf/8Le3h5OTk533UdV+FZWVsLV1RVpaWkGS2ZmJubPny/VW1tbV9vHkCFDkJ+fj40bN+L48eM4fvw4AKC8vLzOczIzM6vWX9XDSUIIqd/7eeKJJxAaGoqPP/4YZ8+exc2bN7Fu3Tpp/euvv47NmzcDADZv3ozXXntN2veTTz6JrKwsvPPOOygtLcWoUaPw0ksv3fN4lZWVWLJkicF7d/r0afzyyy+wsLCocX5Vx6tprGrOkZGRSE9Px+DBg/HNN9+gY8eOSEhIqNV7QFTF1NgNEDU2jo6OCAoKwpo1azB79mxYWlpK63JycrBjxw68+uqr0j/aycnJBtsnJyejffv2AP4MlZycHJiamsLT07PWPeTn5yMjIwPr16/HM888AwA4cuSIQY25uTkAoKKios5z/Kv27dvj8uXLyM3Nlc7+UlJS7rudvb09XF1dcePGDWls3LhxWLBgAT766COkp6cjLCzMYBs7Ozu8/PLLePnll/HSSy9h4MCBuH79OhwcHGBmZlZtLk8++SQyMzPRtm3bB5pjTdq1a4d27dph9uzZGDNmDDZv3owXXnihwY9Djy8GLFE9xMbGolevXhgwYADeffddeHl5IT09HfPnz8cTTzyBZcuWSbU//PADli9fjuHDhyMxMRGff/45/u///g8A0L9/f2g0GgwfPhzvv/8+fHx88Pvvv+Prr7/G8OHD0aNHjxqPb29vD0dHR2zYsAGurq64fPky3nzzTYMaZ2dnWFpaQqvVomXLlrCwsIBKparzXIOCgtCmTRuEhYVh+fLlKC4uxqJFiwD8/zO/9evXIy0tDS+88ALatGmDmzdvYuvWrUhPT8fq1asN+h4xYgTmz5+P4OBgtGzZUlq3cuVKuLq6omvXrmjWrBk+//xzqNVq6VeKPD09cejQIfTu3RtKpRL29vb4xz/+gZCQELi7u2PkyJFo1qwZTp06hdOnT+Pdd9+t81wBoLS0FPPnz8dLL70ELy8vXL16FSkpKXjxxRfrtT9quniJmKgevL298eOPP6JNmzZ4+eWX0aZNG0yePBl9+/bFsWPH4ODgINXOnTsXqamp6NatG9555x188MEHGDBgAIA/A+rrr7/Gs88+iwkTJqBdu3YYPXo0Ll68KJ0t1qRZs2aIj49HamoqfH19MXv2bKxYscKgxtTUFB999BHWr18PNzc3DBs2rF5zNTExwZ49e6DX6+Hv74/XX38db7/9NgBIl2Gfeuop6PV6TJ06FZ06dUJgYCCSk5OxZ88eBAYGGuxv4sSJKC8vx4QJEwzGbWxs8P7776NHjx7w9/fHxYsX8fXXX6NZsz//mfrggw+QmJgId3d3dOvWDQAwYMAA7N27F4mJifD390fPnj0RExMDDw+Pes21ar75+fl49dVX0a5dO4waNQqDBg3CkiVL6r1PapoUouqJDCJqcJ6enoiIiEBERISxW2lQP/zwA55++mmcP38ebdq0qdO2O3bswKxZs/D7779Ll7GJHke8RExE95WQkAAbGxt4e3vj/PnzmDVrFnr37l2ncC0pKUFWVhaio6MxZcoUhis99niJmIjuq7i4GNOmTUP79u0xfvx4+Pv748svv6zTPpYvX46uXbvCxcUFCxculKlTokcHLxETERHJgGewREREMmDAEhERyYABS0REJAMGLBERkQwYsERERDJgwBIREcmAAUtERCQDBiwREZEM/h8pTpNB8EbtFQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Check distributions of numeric columns\n",
+ "for i in range (len(numeric_cols)):\n",
+ " plt.figure(figsize=(5,4))\n",
+ " sns.histplot(data[numeric_cols[i]], bins=30)\n",
+ " plt.title(numeric_cols[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "a64255ad",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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lUjz44IMN9hdFEZMmTYqePXtG586dY+jQofHyyy9XZrBtyMbm/dRTT/3K+v/mN79ZmcG2EVdeeWUccsghUVVVFbvsskuMGjUq/vOf/zR4jPVefpl5L8d6b1XRcN9998X5558fv/rVr2LevHnxrW99K4YPHx4LFy6s9NDatP322y8WL15cf5s/f36lh9TmfPrpp3HQQQfFjTfe2Oj+q6++OqZMmRI33nhjzJ07N7p37x5HH310/d94oXk2Nu8REccee2yD9f+3v/1tM46w7Xn88cfjJz/5STzzzDMxc+bM+PLLL2PYsGHx6aef1j/Gei+/zLxHlGG9F63IoYceWkyYMKHBtr333rv4xS9+UaERtX0TJ04sDjrooEoPY6sSEcUDDzxQf7+urq7o3r17cdVVV9VvW7lyZVFTU1NMnTq1AiNsm9ad96IoivHjxxcjR46syHi2FkuXLi0ionj88ceLorDeN5d1570oyrPeW82Zhs8//zz+8Y9/xLBhwxpsHzZsWMyZM6dCo9o6LFiwIHr27Bn9+vWLU045Jd58881KD2mr8tZbb8WSJUsarP2OHTvGEUccYe1vBrNnz45ddtklvv71r8cZZ5wRS5curfSQ2pTly5dHRMSOO+4YEdb75rLuvK/R0vXeaqLhgw8+iNWrV0e3bt0abO/WrVssWbKkQqNq+wYNGhR33XVXPPLII3HLLbfEkiVLYsiQIbFs2bJKD22rsWZ9W/ub3/Dhw2PatGnx2GOPxbXXXhtz586NI488MlatWlXpobUJRVHEhRdeGIcffnjsv//+EWG9bw6NzXtEedZ7s//K5aay7p/ZLoqirH96m4aGDx9e//UBBxwQgwcPjj322CPuvPPOuPDCCys4sq2Ptb/5jR49uv7r/fffPwYOHBh9+/aNv/71r3HiiSdWcGRtwznnnBMvvfRSPPXUU1/ZZ71vOuub93Ks91ZzpmGnnXaK9u3bf6U0ly5d+pUiZdPp0qVLHHDAAbFgwYJKD2Wrsea3Vaz9yuvRo0f07dvX+i+Dc889Nx566KGYNWtW9OrVq3679b5prW/eG9Oc9d5qomHbbbeNgw8+OGbOnNlg+8yZM2PIkCEVGtXWZ9WqVfHqq69Gjx49Kj2UrUa/fv2ie/fuDdb+559/Ho8//ri1v5ktW7YsFi1aZP23QFEUcc4558SMGTPisccei379+jXYb71vGhub98Y0a7236DLKMrv33nuLDh06FLfddlvxyiuvFOeff37RpUuX4u2336700Nqsiy66qJg9e3bx5ptvFs8880xx/PHHF1VVVea8zFasWFHMmzevmDdvXhERxZQpU4p58+YV77zzTlEURXHVVVcVNTU1xYwZM4r58+cX3//+94sePXoUtbW1FR75lm1D875ixYrioosuKubMmVO89dZbxaxZs4rBgwcXu+66q3lvgbPOOquoqakpZs+eXSxevLj+9tlnn9U/xnovv43Ne7nWe6uKhqIoit/97ndF3759i2233bYYMGBAg18XofxGjx5d9OjRo+jQoUPRs2fP4sQTTyxefvnlSg+rzZk1a1YREV+5jR8/viiK//0a2sSJE4vu3bsXHTt2LL797W8X8+fPr+yg24ANzftnn31WDBs2rNh5552LDh06FH369CnGjx9fLFy4sNLD3qI1Nt8RUdx+++31j7Hey29j816u9e5PYwMAKa3mmgYAoHUTDQBAimgAAFJEAwCQIhoAgBTRAACkiAYAIEU0AAApogFaoSVLlsTRRx8dXbp0ie23377Sw0m74447KjLeU089NUaNGrXZnxe2NqIBNoOmvqhdd911sXjx4njxxRfjtdde23QDa4Hddtstfvvb3zbYNnr06FY7XqDltqn0AICveuONN+Lggw+O/v37N/sYX3zxRXTo0KGMo9q4zp07R+fOnTfrcwKbjzMNsJkNHTo0fvrTn8Yll1wSO+64Y3Tv3j0mTZpUv3+33XaL+++/P+66664olUpx6qmnRkTEwoULY+TIkdG1a9eorq6Ok08+Od5///3675s0aVJ84xvfiD/84Q+x++67R8eOHaMoiiiVSnHzzTfH8ccfH9ttt13ss88+8fTTT8frr78eQ4cOjS5dusTgwYPjjTfeqD/WG2+8ESNHjoxu3bpF165d45BDDolHH320wc/wzjvvxAUXXBClUilKpVJENP7xxE033RR77LFHbLvttrHXXnvF3Xff3WB/qVSKW2+9NU444YTYbrvton///vHQQw/V71+9enWcfvrp0a9fv+jcuXPstddecf3117f0nwFoBtEAFXDnnXdGly5d4tlnn42rr746Jk+eHDNnzoyIiLlz58axxx4bJ598cixevDiuv/76KIoiRo0aFR9++GE8/vjjMXPmzHjjjTdi9OjRDY77+uuvx/Tp0+P++++PF198sX77FVdcEePGjYsXX3wx9t577xgzZkyceeaZcemll8bzzz8fERHnnHNO/eM/+eSTOO644+LRRx+NefPmxTHHHBMjRoyIhQsXRkTEjBkzolevXjF58uRYvHhxLF68uNGf84EHHojzzjsvLrroovjXv/4VZ555Zpx22mkxa9asBo/7v//7vzj55JPjpZdeiuOOOy7Gjh0bH374YURE1NXVRa9evWL69OnxyiuvxOWXXx6//OUvY/r06S37RwCarsx/nRNoxPjx44uRI0cWRVEURxxxRHH44Yc32H/IIYcUP//5z+vvjxw5sv7PZhdFUfz9738v2rdv3+DP2L788stFRBTPPfdcURRFMXHixKJDhw7F0qVLGxw7Iopf//rX9feffvrpIiKK2267rX7bn/70p6JTp04b/Bn23Xff4oYbbqi/37dv3+K6665r8Jjbb7+9qKmpqb8/ZMiQ4owzzmjwmO9973vFcccdt97xffLJJ0WpVCoefvjh9Y7l7LPPLr773e/W3197foFNx5kGqIADDzywwf0ePXrE0qVL1/v4V199NXr37h29e/eu37bvvvvG9ttvH6+++mr9tr59+8bOO++8wefr1q1bREQccMABDbatXLkyamtrIyLi008/jUsuuaT+Obp27Rr//ve/6880ZL366qtx2GGHNdh22GGHNRjzuuPr0qVLVFVVNZiPqVOnxsCBA2PnnXeOrl27xi233NLksQAt50JIqIB1L1AslUpRV1e33scX///ahI1t79Kly0afb83jG9u2ZgwXX3xxPPLII/Gb3/wm9txzz+jcuXOcdNJJ8fnnn2/sR/uKdcfd2M+yofmYPn16XHDBBXHttdfG4MGDo6qqKq655pp49tlnmzwWoGVEA2wB9t1331i4cGEsWrSo/mzDK6+8EsuXL4999tmn7M/35JNPxqmnnhonnHBCRPzvGoe33367wWO23XbbWL169QaPs88++8RTTz0V48aNq982Z86cJo35ySefjCFDhsTZZ59dv23tizaBzcfHE7AFOOqoo+LAAw+MsWPHxgsvvBDPPfdcjBs3Lo444ogYOHBg2Z9vzz33jBkzZsSLL74Y//znP2PMmDFfOROy2267xRNPPBH//e9/44MPPmj0OBdffHHccccdMXXq1FiwYEFMmTIlZsyYET/72c+aNJbnn38+HnnkkXjttdfisssui7lz57bo5wOaRzTAFqBUKsWDDz4YO+ywQ3z729+Oo446Knbfffe47777NsnzXXfddbHDDjvEkCFDYsSIEXHMMcfEgAEDGjxm8uTJ8fbbb8cee+zR6HUUERGjRo2K66+/Pq655prYb7/94uabb47bb789hg4dmh7LhAkT4sQTT4zRo0fHoEGDYtmyZQ3OOgCbT6koiqLSgwAAWj9nGgCAFNEAAKSIBgAgRTQAACmiAQBIEQ0AQIpoAABSRAMAkCIaAIAU0QAApIgGACDl/wFNR0tDm+vLkgAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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XUf6/G+Unq9x/qglGAwBAeGj2gRBf9H/enxP+PacJRwIAQPjgLQYAAOBolmcQYmNjlZ+fL0kyM5WVlUmS/H6/fD5fyPUBAEDDaZaB4PP5FAgEvNstWrRowtEAABB+eIsBAAA4CAQAAOAgEAAAgINAAAAADgIBAAA4CAQAAOAgEAAAgINAAAAADgIBAAA4CAQAAOAgEAAAgINAAAAADgIBAAA4CAQAAOAgEAAAgINAAAAADgIBAAA4CAQAAOAgEAAAgINAAAAADgIBAAA4CAQAAOAgEAAAgINAAAAADgIBAAA4CAQAAOAgEAAAgINAAAAADgIBAAA4CAQAAOAgEAAAgINAAAAADgIBAAA4ouq7oZlJkkpKShpsMAAAoHFVvm5Xvo7XpN6BcPToUUlSenp6fXcBAACayNGjR5WUlFTjcp+dKSFqUFFRoS+++EIJCQny+Xz1HmB1JSUlSk9P1+7du5WYmNhg+z0fMDehMS81Y25CY15qxtyEdj7Ni5np6NGjSk1NVUREzVca1PsMQkREhNLS0uq7+RklJiZ+6/8SGgtzExrzUjPmJjTmpWbMTWjny7zUduagEhcpAgAAB4EAAAAczS4Q/H6/8vLy5Pf7m3oozQ5zExrzUjPmJjTmpWbMTWjhOC/1vkgRAACcv5rdGQQAAND0CAQAAOAgEAAAgKPZBcLLL7+szp07KzY2Vn379tWKFSuaekj1tnz5ct12221KTU2Vz+fTW2+9FbTczDR+/HilpqYqEAgoKytLmzZtClqnrKxMDz30kFJSUhQXF6fbb79dn3/+edA6hw4d0n333aekpCQlJSXpvvvu0+HDh4PWKS4u1m233aa4uDilpKTo4Ycf1okTJxrjsM9o0qRJ6t+/vxISEtS2bVsNHjxYW7duDVonXOdm6tSp6tmzp/dZ68zMTOXn53vLw3Veqps0aZJ8Pp9Gjx7t3ReuczN+/Hj5fL6gn/bt23vLw3VeJGnPnj2699571bp1a7Vo0UK9e/fWunXrvOXhPDd1Ys3InDlzLDo62qZNm2abN2+23Nxci4uLs88++6yph1Yv//znP+3Xv/61zZs3zyTZggULgpZPnjzZEhISbN68eVZUVGRDhgyxDh06WElJibfOyJEj7YILLrDFixdbQUGBZWdnW69evezUqVPeOjfddJNlZGTYypUrbeXKlZaRkWEDBw70lp86dcoyMjIsOzvbCgoKbPHixZaammqjRo1q9DkI5cYbb7SZM2faxo0brbCw0G699Va78MIL7dixY9464To3b7/9tr377ru2detW27p1qz355JMWHR1tGzduNLPwnZeq1qxZY506dbKePXtabm6ud3+4zk1eXp595zvfsb1793o/+/fv95aH67wcPHjQOnbsaCNGjLDVq1fbzp07bcmSJfbJJ59464Tr3NRVswqE7373uzZy5Mig+y677DL71a9+1UQjajjVA6GiosLat29vkydP9u4rLS21pKQke+WVV8zM7PDhwxYdHW1z5szx1tmzZ49FRETYe++9Z2ZmmzdvNkn24YcfeuusWrXKJNnHH39sZqdDJSIiwvbs2eOt87e//c38fr8dOXKkUY73bOzfv98k2bJly8yMuamuZcuW9uqrrzIvZnb06FG7+OKLbfHixXbNNdd4gRDOc5OXl2e9evUKuSyc5+WXv/ylfe9736txeTjPTV01m7cYTpw4oXXr1umGG24Iuv+GG27QypUrm2hUjWfnzp3at29f0PH6/X5dc8013vGuW7dOJ0+eDFonNTVVGRkZ3jqrVq1SUlKSrrjiCm+dK6+8UklJSUHrZGRkKDU11VvnxhtvVFlZWdDptqZy5MgRSVKrVq0kMTeVysvLNWfOHH399dfKzMxkXiT94he/0K233qrrr78+6P5wn5vt27crNTVVnTt31g9/+EPt2LFDUnjPy9tvv61+/frp7rvvVtu2bXX55Zdr2rRp3vJwnpu6ajaB8NVXX6m8vFzt2rULur9du3bat29fE42q8VQeU23Hu2/fPsXExKhly5a1rtO2bVtn/23btg1ap/rjtGzZUjExMU0+t2amMWPG6Hvf+54yMjIkMTdFRUWKj4+X3+/XyJEjtWDBAnXv3j3s52XOnDkqKCjQpEmTnGXhPDdXXHGF3njjDS1cuFDTpk3Tvn37NGDAAB04cCCs52XHjh2aOnWqLr74Yi1cuFAjR47Uww8/rDfeeMMbrxSec1NX9f6ypsZS/ZshzaxBvy2yuanP8VZfJ9T69VmnKYwaNUobNmzQBx984CwL17m59NJLVVhYqMOHD2vevHkaPny4li1b5i0Px3nZvXu3cnNztWjRIsXGxta4XjjOzc033+z9uUePHsrMzNRFF12k119/XVdeeaWk8JyXiooK9evXT88++6wk6fLLL9emTZs0depUDRs2zFsvHOemrprNGYSUlBRFRkY6NbV//36nvM4HlVcZ13a87du314kTJ3To0KFa1/nPf/7j7P/LL78MWqf64xw6dEgnT55s0rl96KGH9Pbbb+v9998P+mbQcJ+bmJgYde3aVf369dOkSZPUq1cvTZkyJaznZd26ddq/f7/69u2rqKgoRUVFadmyZXrppZcUFRXljSkc56a6uLg49ejRQ9u3bw/r50yHDh3UvXv3oPu6deum4uJiSfw7UxfNJhBiYmLUt29fLV68OOj+xYsXa8CAAU00qsbTuXNntW/fPuh4T5w4oWXLlnnH27dvX0VHRwets3fvXm3cuNFbJzMzU0eOHNGaNWu8dVavXq0jR44ErbNx40bt3bvXW2fRokXy+/3q27dvox5nKGamUaNGaf78+frXv/6lzp07By0P57kJxcxUVlYW1vNy3XXXqaioSIWFhd5Pv379lJOTo8LCQnXp0iVs56a6srIybdmyRR06dAjr58xVV13lfHx627Zt6tixoyT+namTc3MtZN1Ufsxx+vTptnnzZhs9erTFxcXZrl27mnpo9XL06FFbv369rV+/3iTZCy+8YOvXr/c+tjl58mRLSkqy+fPnW1FRkf3oRz8K+RGbtLQ0W7JkiRUUFNi1114b8iM2PXv2tFWrVtmqVausR48eIT9ic91111lBQYEtWbLE0tLSmuwjNg888IAlJSXZ0qVLgz6a9d///tdbJ1zn5oknnrDly5fbzp07bcOGDfbkk09aRESELVq0yMzCd15CqfopBrPwnZtHH33Uli5dajt27LAPP/zQBg4caAkJCd6/m+E6L2vWrLGoqCh75plnbPv27TZr1ixr0aKFvfnmm9464To3ddWsAsHM7M9//rN17NjRYmJirE+fPt5H376N3n//fZPk/AwfPtzMTn/MJi8vz9q3b29+v9+uvvpqKyoqCtrH8ePHbdSoUdaqVSsLBAI2cOBAKy4uDlrnwIEDlpOTYwkJCZaQkGA5OTl26NChoHU+++wzu/XWWy0QCFirVq1s1KhRVlpa2piHX6NQcyLJZs6c6a0TrnPzk5/8xHv+t2nTxq677jovDszCd15CqR4I4To3lZ/dj46OttTUVLvzzjtt06ZN3vJwnRczs3/84x+WkZFhfr/fLrvsMvvLX/4StDyc56Yu+DZHAADgaDbXIAAAgOaDQAAAAA4CAQAAOAgEAADgIBAAAICDQAAAAA4CAQAAOAgEAADgIBCAOho/frx69+7d1MOok127dsnn86mwsLBJHn/EiBEaPHhwkzx2Q3rttdeUnJzc1MMAmgSBgG+9ESNGyOfzyefzKTo6Wl26dNFjjz2mr7/+uqmHVqulS5fK5/Pp8OHDQfdXPZ6oqChdeOGFeuCBB5xvlGtoTfWiXv3vr127dvr+97+vGTNmqKKi4pyNo1OnTnrxxReD7hsyZIi2bdt2zsYANCcEAs4LN910k/bu3asdO3bo6aef1ssvv6zHHnvMWe/kyZNNMLqzV3k8u3bt0quvvqp//OMfevDBB5t6WI2m6vHm5+crOztbubm5GjhwoE6dOlXv/ZrZN9o+EAiobdu29d4e+DYjEHBe8Pv9at++vdLT0zV06FDl5OTorbfe8t4WmDFjhrp06SK/3y8zU3FxsQYNGqT4+HglJibqnnvucb7TffLkyWrXrp0SEhJ0//33q7S0NGh5VlaWRo8eHXTf4MGDNWLECO92WVmZxo4dq/T0dPn9fl188cWaPn26du3apezsbElSy5Yt5fP5grarPJ60tDTdcMMNGjJkiBYtWhT0WDNnzlS3bt0UGxuryy67TC+//HKN81NeXq77779fnTt3ViAQ0KWXXqopU6Z4y8ePH6/XX39df//7373/zS9dulSStGfPHg0ZMkQtW7ZU69atNWjQIO3atSto32PGjFFycrJat26tsWPH6my/4qXyeC+44AL16dNHTz75pP7+978rPz9fr732mqTQb5scPnw4aKyVZ2UWLlyofv36ye/3a8WKFfr00081aNAgtWvXTvHx8erfv7+WLFni7ScrK0ufffaZHnnkEe/4pdBvMUydOlUXXXSRYmJidOmll+qvf/1r0HKfz6dXX31Vd9xxh1q0aKGLL75Yb7/99lnNB9AcEAg4LwUCAe9swSeffKK5c+dq3rx53ovL4MGDdfDgQS1btkyLFy/Wp59+qiFDhnjbz507V3l5eXrmmWe0du1adejQodYX4JoMGzZMc+bM0UsvvaQtW7bolVdeUXx8vNLT0zVv3jxJ0tatW7V3796gF+yqduzYoffee0/R0dHefdOmTdOvf/1rPfPMM9qyZYueffZZjRs3Tq+//nrIfVRUVCgtLU1z587V5s2b9dvf/lZPPvmk5s6dK0l67LHHdM8993j/k9+7d68GDBig//73v8rOzlZ8fLyWL1+uDz74QPHx8brpppt04sQJSdLzzz+vGTNmaPr06frggw908OBBLViw4Kznqrprr71WvXr10vz5889627Fjx2rSpEnasmWLevbsqWPHjumWW27RkiVLtH79et1444267bbbVFxcLEmaP3++0tLSNGHCBO/4Q1mwYIFyc3P16KOPauPGjfr5z3+uH//4x3r//feD1nvqqad0zz33aMOGDbrllluUk5OjgwcPnv0kAE2pSb9LEmgAw4cPt0GDBnm3V69eba1bt7Z77rnH8vLyLDo62vbv3+8tX7RokUVGRgZ9ZeumTZtMkq1Zs8bMzDIzM23kyJFBj3PFFVdYr169vNvVv27YzGzQoEHe13lv3brVJNnixYtDjrvy68Crfy3s8OHDLTIy0uLi4iw2Ntb7OuwXXnjBWyc9Pd1mz54dtN3EiRMtMzPTzMx27txpkmz9+vUhH9vM7MEHH7S77ror6HGrzqOZ2fTp0+3SSy+1iooK776ysjILBAK2cOFCMzPr0KGDTZ482Vt+8uRJS0tLc/ZVk1CPW2nIkCHWrVu3Go/p0KFDJsnef/99M/vfnL711ltnfNzu3bvbH//4R+92x44d7Q9/+EPQOjNnzrSkpCTv9oABA+xnP/tZ0Dp333233XLLLd5tSfab3/zGu33s2DHz+XyWn59/xjEBzQlnEHBeeOeddxQfH6/Y2FhlZmbq6quv1h//+EdJUseOHdWmTRtv3S1btig9PV3p6enefd27d1dycrK2bNnirZOZmRn0GNVvn0lhYaEiIyN1zTXXnPXxZGdnq7CwUKtXr9ZDDz2kG2+8UQ899JAk6csvv9Tu3bt1//33Kz4+3vt5+umn9emnn9a4z1deeUX9+vVTmzZtFB8fr2nTpnn/g67JunXr9MknnyghIcF7nFatWqm0tFSffvqpjhw5or179wbNTVRUlPr163fWxxyKmXmn+89G9cf/+uuvNXbsWO/vOT4+Xh9//PEZj7+6LVu26Kqrrgq676qrrvKeN5V69uzp/TkuLk4JCQnav3//WR4F0LSimnoAQEPIzs7W1KlTFR0drdTU1KDT8XFxcUHr1vSic7YvRhEREc577VUvggwEAnXeV3VxcXHq2rWrJOmll15Sdna2nnrqKU2cONG7sn/atGm64oorgraLjIwMub+5c+fqkUce0fPPP6/MzEwlJCTo97//vVavXl3rOCoqKtS3b1/NmjXLWVY1uhrLli1b1LlzZ0mn51tS0JzXdNFp9b/zxx9/XAsXLtRzzz2nrl27KhAI6Ac/+IH3NsnZqP4cCfW8qfr8q9zmXH4iA2gInEHAeaHyBbVjx47OP87Vde/eXcXFxdq9e7d33+bNm3XkyBF169ZNktStWzd9+OGHQdtVv92mTZug96rLy8u1ceNG73aPHj1UUVGhZcuWhRxHTEyMt92Z5OXl6bnnntMXX3yhdu3a6YILLtCOHTvUtWvXoJ/KF9PqVqxYoQEDBujBBx/U5Zdfrq5duzpnG2JiYpyx9OnTR9u3b1fbtm2dx0pKSlJSUpI6dOgQNDenTp3SunXrznhMZ/Kvf/1LRUVFuuuuuyT9L0iqznldf8/DihUrNGLECN1xxx3q0aOH2rdvH3ShpRT6+Kvr1q2bPvjgg6D7Vq5c6T1vgPMJgYCwc/3116tnz57KyclRQUGB1qxZo2HDhumaa67xTk3n5uZqxowZmjFjhrZt26a8vDxt2rQpaD/XXnut3n33Xb377rv6+OOP9eCDDwb9ToNOnTpp+PDh+slPfqK33npLO3fu1NKlS70LAzt27Cifz6d33nlHX375pY4dO1bjmLOysvSd73xHzz77rKTTnzqYNGmSpkyZom3btqmoqEgzZ87UCy+8EHL7rl27au3atVq4cKG2bdumcePG6aOPPgpap1OnTtqwYYO2bt2qr776SidPnlROTo5SUlI0aNAgrVixQjt37tSyZcuUm5urzz//3JuryZMna8GCBSHnoS7Kysq0b98+7dmzRwUFBXr22Wc1aNAgDRw4UMOGDZN0+ozMlVdeqcmTJ2vz5s1avny5fvOb39Rp/127dtX8+fNVWFiof//73xo6dKjzP/pOnTpp+fLl2rNnj7766quQ+3n88cf12muv6ZVXXtH27dv1wgsvaP78+SE/Ugt86zXlBRBAQ6jtIre8vLygCwsrffbZZ3b77bdbXFycJSQk2N1332379u0LWueZZ56xlJQUi4+Pt+HDh9vYsWOD9nXixAl74IEHrFWrVta2bVubNGlS0EWKZmbHjx+3Rx55xDp06GAxMTHWtWtXmzFjhrd8woQJ1r59e/P5fN52NR3PrFmzLCYmxru4ctasWda7d2+LiYmxli1b2tVXX23z5883M/eCvtLSUhsxYoQlJSVZcnKyPfDAA/arX/0q6Hj2799v3//+9y0+Pj7owr+9e/fasGHDLCUlxfx+v3Xp0sV+9rOf2ZEjR8zs9EWJubm5lpiYaMnJyTZmzBgbNmzYWV2kqP9/IWZUVJS1adPGrr/+epsxY4aVl5cHrbt582a78sorLRAIWO/evW3RokUhL1KsfuHnzp07LTs72wKBgKWnp9uf/vQn5yLTVatWWc+ePc3v91vlP43VL1I0M3v55ZetS5cuFh0dbZdccom98cYbQcsl2YIFC4LuS0pKspkzZ9ZpPoDmwmd2lh9YBgAA5z3eYgAAAA4CAUCjKS4uDvooZvWfs/2YIYBzh7cYADSaU6dOOZ8WqKpTp06KiuLT1kBzRCAAAAAHbzEAAAAHgQAAABwEAgAAcBAIAADAQSAAAAAHgQAAABwEAgAAcBAIAADA8f8AFx6n+e8nuLkAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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OHbvB24wZMybtf/fdd8fo0aOjR48e7bbWrqwtc4746MzBiSeeGDfccIPHDzdRa2ddX18fTz/9dDz55JPNl9NOOy1GjBgRTz75ZOy///6VWnqX0paf6QMOOCAaGhrif//7X/N1y5cvj27dusWgQYPadb1dVVvm/M4770S3bi0PJd27d4+I//8XLl9chxwL2+3pjxvx8Utorr322rJs2bLyq1/9qvTp06c8//zzpZRSJk+eXH74wx827//xSzt+/etfl2XLlpVrr73Wyxw3QWvnfMMNN5Tq6upyxRVXlJdffrn58uabb3bUt9BltHbWn+ZVDJumtXNubGwsgwYNKt/5znfKv/71r7J48eKyxx57lJNPPrmjvoUuobVznjNnTqmuri5XXnllWbFiRbn//vvL6NGjy3777ddR30KX0NjYWJYuXVqWLl1aIqJceumlZenSpc0vJ+0Mx8KKB0IppVxxxRVlyJAhpWfPnmXfffctixcvbt52wgknlIMPPrjF/osWLSpf/epXS8+ePcuuu+5arrrqqgqvuGtqzZwPPvjgEhHpcsIJJ1R+4V1Qa3+mP0kgbLrWzvnZZ58thx12WOnVq1cZNGhQ+c1vflPeeeedCq+662ntnGfOnFn23HPP0qtXrzJgwIBy/PHHl9WrV1d41V3L3//+98/8N7czHAu93TMAkHgvBgAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBCAiPnpHuAULFmzy/uedd17ss88+7bYeoGMJBOgEXnvttTj11FNjl112iZqamujfv38cccQR8dBDD1VsDS+//HKMHz++zbc/77zzoqqqKqqqqqK6ujq23377+PrXvx5//OMfY/369ZtxpUAlVPTtnoENmzhxYrz//vvx5z//OYYOHRqvvvpq3HvvvfHGG29UbA39+/f/wvex1157xd/+9rdoamqK119/PRYtWhQXXnhh/OUvf4lFixZFXV3dZlgpUAnOIEAHe/PNN+P++++Piy++OL7xjW/EkCFDYr/99ospU6Y0v+12VVVVXHXVVTF+/Pjo1atX7LbbbnHLLbe0uJ///ve/MWnSpNhmm21iu+22iwkTJsTzzz/fYp/Zs2fHXnvtFTU1NTFgwIA4/fTTm7d9+iGGs88+O4YPHx69e/eOoUOHxrRp0+L999//zO+luro6+vfvHwMHDowvf/nLccYZZ8TixYvjmWeeiYsvvrh5v+uuuy5Gjx4ddXV10b9//zjuuOPitddei4iP3iJ42LBh8Yc//KHFfT/zzDPRrVu3WLFixSbPFmg7gQAdrG/fvtG3b99YsGDBZ56KnzZtWkycODGeeuqp+MEPfhDf//7349lnn42IiHfeeSe+8Y1vRN++feO+++6L+++/P/r27Rvf+ta34r333ouIiKuuuip+8YtfxCmnnBJPP/10LFy4MIYNG7bRr1dXVxdz586NZcuWxeWXXx7XXHNNXHbZZa3+/kaOHBnjx4+PW2+9tfm69957Ly644IJ46qmnYsGCBbFq1ao48cQTI+KjUDnppJNizpw5Le5n9uzZcdBBB8Xuu+/e6jUAbdCu7xUJbJJ58+aVbbbZptTW1paxY8eWKVOmlKeeeqp5e0SU0047rcVt9t9///Kzn/2slFLKtddeW0aMGFGampqat69fv7706tWr3HXXXaWUUgYOHFimTp260TVERJk/f/5Gt8+YMaOMGjWq+fNPv031Z71t9dlnn1169eq10ft+9NFHS0SUxsbGUkopDQ0NpXv37uWRRx4ppZTy3nvvlR122KHMnTt3o/cBbF7OIEAnMHHixGhoaIiFCxfGEUccEYsWLYp999035s6d27zPmDFjWtxmzJgxzWcQHn/88fjPf/4TdXV1zWcktt1221i3bl2sWLEiXnvttWhoaIhvfvObm7ymefPmxYEHHhj9+/ePvn37xrRp0+LFF19s0/dXSomqqqrmz5cuXRoTJkyIIUOGRF1dXRxyyCEREc33P2DAgDjqqKNi9uzZERFx2223xbp16+K73/1um74+0HoCATqJ2traOPzww+Pcc8+NBx98ME488cSYPn36Z97m44NuU1NTjBo1Kp588skWl+XLl8dxxx0XvXr1atVaHn744fje974X48ePj9tuuy2WLl0aU6dObX64orWeffbZ2G233SIi4u23345x48ZF375947rrrot//OMfMX/+/IiIFvd/8sknx0033RTvvvtuzJkzJyZNmhS9e/du09cHWk8gQCe15557xttvv938+cMPP9xi+8MPPxwjR46MiIh99903nnvuudhxxx1j2LBhLS79+vWLurq62HXXXePee+/dpK/9wAMPxJAhQ2Lq1KkxevTo2GOPPeKFF15o0/fx73//O+68886YOHFi8+dr1qyJ//u//4uDDjooRo4c2fwExU868sgjo0+fPnHVVVfFHXfcESeddFKbvj7QNgIBOtjrr78ehx56aFx33XXxz3/+M1atWhW33HJLzJgxIyZMmNC83y233BKzZ8+O5cuXx/Tp0+PRRx9tfhXC8ccfH9tvv31MmDAhlixZEqtWrYrFixfHmWeeGatXr46Ij/6fgksuuSRmzpwZzz33XDzxxBMxa9asDa5p2LBh8eKLL8ZNN90UK1asiJkzZzb/lf9ZPvjgg3jllVeioaEhnn766Zg1a1YcfPDBsc8++8RZZ50VERG77LJL9OzZM2bNmhUrV66MhQsXxgUXXJDuq3v37nHiiSfGlClTYtiwYekhFqCddfSTIGBrt27dujJ58uSy7777ln79+pXevXuXESNGlHPOOae88847pZSPnkB4xRVXlMMPP7zU1NSUIUOGlBtvvLHF/bz88svlRz/6Udl+++1LTU1NGTp0aPnpT39a3nrrreZ9rr766jJixIjSo0ePMmDAgHLGGWc0b4tPPUnxrLPOKtttt13p27dvmTRpUrnssstKv379mrdv6EmKEVEionTv3r1su+225cADDyyXXXZZWbduXYu13nDDDWXXXXctNTU1ZcyYMWXhwoUlIsrSpUtb7LdixYoSEWXGjBltnC7QVlWllNKRgQJ8vqqqqpg/f34cc8wxHb2UinrggQfikEMOidWrV8dOO+3U0cuBrYr/SRHodNavXx8vvfRSTJs2LY499lhxAB3AcxCATufGG2+MESNGxFtvvRUzZszo6OXAVslDDABA4gwCAJAIBAAgEQgAQCIQAIBEIAAAiUAAABKBAAAkAgEASP4fnHn4Wd9pYhMAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Box plots for numeric columns\n",
+ "for i in range (len(numeric_cols)):\n",
+ " plt.figure()\n",
+ " sns.boxplot(x=data[numeric_cols[i]], data=data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "51443624",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Bar plots for categorical columns\n",
+ "for i in range (len(categorical_cols)):\n",
+ " plt.figure(figsize=(5,5))\n",
+ " sns.countplot(x=data[categorical_cols[i]], data=data)\n",
+ " plt.xticks(rotation=65) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "dd840803",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Administrative 2\n",
+ "Administrative_Duration 273\n",
+ "Informational 2631\n",
+ "Informational_Duration 2405\n",
+ "ProductRelated 173\n",
+ "ProductRelated_Duration 166\n",
+ "BounceRates 691\n",
+ "ExitRates 0\n",
+ "PageValues 2730\n",
+ "SpecialDay 1249\n",
+ "OperatingSystems 0\n",
+ "Browser 4322\n",
+ "Region 0\n",
+ "TrafficType 261\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Check for outliers\n",
+ "outliers = outliers_check(data, numeric_cols)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f49629db",
+ "metadata": {},
+ "source": [
+ "# Correlation Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "dc3d0c9c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\amych\\AppData\\Local\\Temp\\ipykernel_5032\\3699474598.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+ " correlation_matrix = data.corr()\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "correlation_matrix = data.corr()\n",
+ "\n",
+ "plt.figure(figsize=(8, 4))\n",
+ "sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', linewidths=0.5)\n",
+ "plt.title('Correlation Matrix Heatmap')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0c36206",
+ "metadata": {},
+ "source": [
+ "# Preprocessing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "435c0457",
+ "metadata": {},
+ "source": [
+ "## Remove Outliers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "ae17e493",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Remove outliers\n",
+ "\n",
+ "removed_outliers = remove_outliers(data, numeric_cols)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35862085",
+ "metadata": {},
+ "source": [
+ "## One Hot Encoding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "10caf523",
+ "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>Administrative</th>\n",
+ " <th>Administrative_Duration</th>\n",
+ " <th>Informational</th>\n",
+ " <th>Informational_Duration</th>\n",
+ " <th>ProductRelated</th>\n",
+ " <th>ProductRelated_Duration</th>\n",
+ " <th>BounceRates</th>\n",
+ " <th>ExitRates</th>\n",
+ " <th>PageValues</th>\n",
+ " <th>SpecialDay</th>\n",
+ " <th>OperatingSystems</th>\n",
+ " <th>Browser</th>\n",
+ " <th>Region</th>\n",
+ " <th>TrafficType</th>\n",
+ " <th>Month_Dec</th>\n",
+ " <th>Month_Feb</th>\n",
+ " <th>Month_Jul</th>\n",
+ " <th>Month_June</th>\n",
+ " <th>Month_Mar</th>\n",
+ " <th>Month_May</th>\n",
+ " <th>Month_Nov</th>\n",
+ " <th>Month_Oct</th>\n",
+ " <th>Month_Sep</th>\n",
+ " <th>VisitorType_Other</th>\n",
+ " <th>VisitorType_Returning_Visitor</th>\n",
+ " <th>Weekend_True</th>\n",
+ " <th>Revenue_True</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.002899</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>64.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.10</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.002899</td>\n",
+ " <td>0.20</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>9</td>\n",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2.666667</td>\n",
+ " <td>0.050000</td>\n",
+ " <td>0.14</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>10</td>\n",
+ " <td>627.500000</td>\n",
+ " <td>0.020000</td>\n",
+ " <td>0.05</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Administrative Administrative_Duration Informational \\\n",
+ "0 0 0.0 0 \n",
+ "1 0 0.0 0 \n",
+ "2 0 0.0 0 \n",
+ "3 0 0.0 0 \n",
+ "4 0 0.0 0 \n",
+ "\n",
+ " Informational_Duration ProductRelated ProductRelated_Duration \\\n",
+ "0 0.0 1 0.000000 \n",
+ "1 0.0 2 64.000000 \n",
+ "2 0.0 1 0.000000 \n",
+ "3 0.0 2 2.666667 \n",
+ "4 0.0 10 627.500000 \n",
+ "\n",
+ " BounceRates ExitRates PageValues SpecialDay OperatingSystems Browser \\\n",
+ "0 0.002899 0.20 0.0 0.0 1 2 \n",
+ "1 0.000000 0.10 0.0 0.0 2 2 \n",
+ "2 0.002899 0.20 0.0 0.0 4 2 \n",
+ "3 0.050000 0.14 0.0 0.0 3 2 \n",
+ "4 0.020000 0.05 0.0 0.0 3 2 \n",
+ "\n",
+ " Region TrafficType Month_Dec Month_Feb Month_Jul Month_June \\\n",
+ "0 1 1 0 1 0 0 \n",
+ "1 1 2 0 1 0 0 \n",
+ "2 9 3 0 1 0 0 \n",
+ "3 2 4 0 1 0 0 \n",
+ "4 1 4 0 1 0 0 \n",
+ "\n",
+ " Month_Mar Month_May Month_Nov Month_Oct Month_Sep VisitorType_Other \\\n",
+ "0 0 0 0 0 0 0 \n",
+ "1 0 0 0 0 0 0 \n",
+ "2 0 0 0 0 0 0 \n",
+ "3 0 0 0 0 0 0 \n",
+ "4 0 0 0 0 0 0 \n",
+ "\n",
+ " VisitorType_Returning_Visitor Weekend_True Revenue_True \n",
+ "0 1 0 0 \n",
+ "1 1 0 0 \n",
+ "2 1 0 0 \n",
+ "3 1 0 0 \n",
+ "4 1 1 0 "
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#One hot encoding\n",
+ "\n",
+ "data_encoded = oneHotEncoding(removed_outliers, categorical_cols, True)\n",
+ "\n",
+ "data_encoded.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "706be55f",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(12205, 27)"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_encoded.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "4560bf4e",
+ "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>Administrative</th>\n",
+ " <th>Administrative_Duration</th>\n",
+ " <th>Informational</th>\n",
+ " <th>Informational_Duration</th>\n",
+ " <th>ProductRelated</th>\n",
+ " <th>ProductRelated_Duration</th>\n",
+ " <th>BounceRates</th>\n",
+ " <th>ExitRates</th>\n",
+ " <th>PageValues</th>\n",
+ " <th>SpecialDay</th>\n",
+ " <th>OperatingSystems</th>\n",
+ " <th>Browser</th>\n",
+ " <th>Region</th>\n",
+ " <th>TrafficType</th>\n",
+ " <th>Month_Dec</th>\n",
+ " <th>Month_Feb</th>\n",
+ " <th>Month_Jul</th>\n",
+ " <th>Month_June</th>\n",
+ " <th>Month_Mar</th>\n",
+ " <th>Month_May</th>\n",
+ " <th>Month_Nov</th>\n",
+ " <th>Month_Oct</th>\n",
+ " <th>Month_Sep</th>\n",
+ " <th>VisitorType_Other</th>\n",
+ " <th>VisitorType_Returning_Visitor</th>\n",
+ " <th>Weekend_True</th>\n",
+ " <th>Revenue_True</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>6045</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>19</td>\n",
+ " <td>1138.793636</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.007018</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7750</th>\n",
+ " <td>3</td>\n",
+ " <td>273.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>11</td>\n",
+ " <td>385.740000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.007692</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>203</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3</td>\n",
+ " <td>133.500000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.088889</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>8</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10452</th>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>27</td>\n",
+ " <td>630.750000</td>\n",
+ " <td>0.016</td>\n",
+ " <td>0.020000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1177</th>\n",
+ " <td>2</td>\n",
+ " <td>52.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>18</td>\n",
+ " <td>1262.000000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.009524</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Administrative Administrative_Duration Informational \\\n",
+ "6045 0 0.0 0 \n",
+ "7750 3 273.0 0 \n",
+ "203 0 0.0 0 \n",
+ "10452 0 0.0 0 \n",
+ "1177 2 52.0 0 \n",
+ "\n",
+ " Informational_Duration ProductRelated ProductRelated_Duration \\\n",
+ "6045 0.0 19 1138.793636 \n",
+ "7750 0.0 11 385.740000 \n",
+ "203 0.0 3 133.500000 \n",
+ "10452 0.0 27 630.750000 \n",
+ "1177 0.0 18 1262.000000 \n",
+ "\n",
+ " BounceRates ExitRates PageValues SpecialDay OperatingSystems \\\n",
+ "6045 0.000 0.007018 0.0 0.0 2 \n",
+ "7750 0.000 0.007692 0.0 0.0 2 \n",
+ "203 0.000 0.088889 0.0 0.0 1 \n",
+ "10452 0.016 0.020000 0.0 0.0 2 \n",
+ "1177 0.000 0.009524 0.0 0.0 2 \n",
+ "\n",
+ " Browser Region TrafficType Month_Dec Month_Feb Month_Jul \\\n",
+ "6045 2 3 2 0 0 0 \n",
+ "7750 2 1 2 0 0 0 \n",
+ "203 2 1 8 0 0 0 \n",
+ "10452 2 3 1 0 0 0 \n",
+ "1177 2 1 2 0 0 0 \n",
+ "\n",
+ " Month_June Month_Mar Month_May Month_Nov Month_Oct Month_Sep \\\n",
+ "6045 0 0 0 0 0 0 \n",
+ "7750 0 0 0 0 1 0 \n",
+ "203 0 1 0 0 0 0 \n",
+ "10452 0 0 0 1 0 0 \n",
+ "1177 0 1 0 0 0 0 \n",
+ "\n",
+ " VisitorType_Other VisitorType_Returning_Visitor Weekend_True \\\n",
+ "6045 0 1 0 \n",
+ "7750 0 1 0 \n",
+ "203 0 1 0 \n",
+ "10452 0 1 1 \n",
+ "1177 0 0 0 \n",
+ "\n",
+ " Revenue_True \n",
+ "6045 0 \n",
+ "7750 1 \n",
+ "203 0 \n",
+ "10452 0 \n",
+ "1177 0 "
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Randomise the dataset\n",
+ "\n",
+ "data_sampled = data_encoded.sample(frac=1, random_state=42)\n",
+ "\n",
+ "#All features except the target are assigned to 'X'\n",
+ "X = data_sampled.drop(\"Revenue_True\", axis=1)\n",
+ "\n",
+ "#Target feature is assigned to 'Y'\n",
+ "Y = data_sampled[\"Revenue_True\"]\n",
+ "\n",
+ "data_sampled.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca7697d9",
+ "metadata": {},
+ "source": [
+ "# Dataset Rebalancing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1389cec9",
+ "metadata": {},
+ "source": [
+ "Undersampling and Oversampling sampling methods were used for modelling. Results for both methods will be compared to the result of modelling without any sampling method.\n",
+ "\n",
+ "Rebalancing is done on the training set only."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "ad90f3bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#split into train and test\n",
+ "\n",
+ "x, xtest, y, ytest = train_test_split(X, Y, test_size=0.2, random_state=None)\n",
+ "xtrain, xval, ytrain, yval = train_test_split(x, y, test_size=0.3, random_state=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "90c10eaf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2106, 26)"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Undersampling\n",
+ "\n",
+ "undersampling = RandomUnderSampler(random_state=42)\n",
+ "\n",
+ "xtrain_undersampled, ytrain_undersampled = undersampling.fit_resample(xtrain, ytrain)\n",
+ "\n",
+ "xtrain_undersampled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "7f76dc3e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(11562, 26)"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Oversampling\n",
+ "\n",
+ "oversampling = RandomOverSampler(random_state=42)\n",
+ "\n",
+ "xtrain_oversampled, ytrain_oversampled = oversampling.fit_resample(xtrain, ytrain)\n",
+ "\n",
+ "xtrain_oversampled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "33486b2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled:\n",
+ " 0 1053\n",
+ "1 1053\n",
+ "Name: Revenue_True, dtype: int64\n",
+ "\n",
+ "Oversampled:\n",
+ " 0 5781\n",
+ "1 5781\n",
+ "Name: Revenue_True, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Target column is now balanced\n",
+ "\n",
+ "print(\"Undersampled:\\n\", ytrain_undersampled.value_counts())\n",
+ "\n",
+ "print(\"\\nOversampled:\\n\", ytrain_oversampled.value_counts())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35a04fc0",
+ "metadata": {},
+ "source": [
+ "# Dataset Scaling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "667b8bfb",
+ "metadata": {},
+ "source": [
+ "Both oversampled and undersampled datasets are scaled and PCA will be applied to them"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "f515c607",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(11562, 26)"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Scaling the data - oversampled dataset & undersampled\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "xtrain_oversampled[numeric_cols] = scaler.fit_transform(xtrain_oversampled[numeric_cols])\n",
+ "xtrain_undersampled[numeric_cols] = scaler.fit_transform(xtrain_undersampled[numeric_cols])\n",
+ "\n",
+ "\n",
+ "xtest[numeric_cols] = scaler.transform(xtest[numeric_cols])\n",
+ "xval[numeric_cols] = scaler.transform(xval[numeric_cols])\n",
+ "\n",
+ "\n",
+ "\n",
+ "xtrain_oversampled.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "05988265",
+ "metadata": {},
+ "source": [
+ "# Dimensionality Reduction Using Principal Component Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "cfd07695",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#PCA - oversampled dataset\n",
+ "\n",
+ "pca_undersampled = PCA(n_components=24)\n",
+ "\n",
+ "# Fit PCA on the training data\n",
+ "pca_undersampled.fit(xtrain_undersampled)\n",
+ "\n",
+ "# Transform both the training and testing data\n",
+ "xtrain_pca_undersampled = pca_undersampled.transform(xtrain_undersampled)\n",
+ "xval_pca_undersampled = pca_undersampled.transform(xval)\n",
+ "xtest_pca_undersampled = pca_undersampled.transform(xtest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "98a3d312",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of xtrain_pca_undersampled: (11562, 24)\n",
+ "Shape of ytrain: (11562,)\n",
+ "Unique labels in ytrain: [0 1]\n",
+ "Number of unique labels: 2\n"
+ ]
+ }
+ ],
+ "source": [
+ "#PCA - oversampled dataset\n",
+ "\n",
+ "# Initialize PCA with desired number of components\n",
+ "pca = PCA(n_components=24)\n",
+ "\n",
+ "# Fit PCA on the training data\n",
+ "pca.fit(xtrain_oversampled)\n",
+ "\n",
+ "# Transform both training and testing data\n",
+ "xtrain_pca_oversampled = pca.transform(xtrain_oversampled)\n",
+ "xval_pca_oversampled = pca.transform(xval)\n",
+ "xtest_pca_oversampled = pca.transform(xtest)\n",
+ "\n",
+ "print(\"Shape of xtrain_pca_undersampled:\", xtrain_pca_oversampled.shape)\n",
+ "print(\"Shape of ytrain:\", ytrain_oversampled.shape)\n",
+ "\n",
+ "# Print unique labels and their counts\n",
+ "print(\"Unique labels in ytrain:\", np.unique(ytrain))\n",
+ "print(\"Number of unique labels:\", len(np.unique(ytrain)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "722202d4",
+ "metadata": {},
+ "source": [
+ "# Modelling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "626080bd",
+ "metadata": {},
+ "source": [
+ "## Decision Tree"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b0d35a9",
+ "metadata": {},
+ "source": [
+ "Modelling will be done on the dataset after scaling, and the dataset after PCA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 201,
+ "id": "5498f414",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DT_models = []\n",
+ "DT_name = []\n",
+ "x_val_list = []\n",
+ "x_test_list = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 207,
+ "id": "6dbb2b77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ccp_alpha = 0.005\n",
+ "txt = \"ccp_alpha: 0.005\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 208,
+ "id": "5d824f45",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-85 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-85 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-85 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-85 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-85 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-85 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-85 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-85 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-85 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-85 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-85 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-85 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-85 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-85 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-85 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-85 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-85 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-85\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" checked><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "DecisionTreeClassifier()"
+ ]
+ },
+ "execution_count": 208,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#DecsionTree model - undersampled dataset\n",
+ "\n",
+ "# Initialize the decision tree classifier\n",
+ "clf_oversampled = DecisionTreeClassifier()\n",
+ "DT_models.append(clf_oversampled)\n",
+ "DT_name.append(f\"Oversampled dataset(No PCA), {txt}\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "# Fit the classifier to the training data\n",
+ "clf_oversampled.fit(xtrain_oversampled, ytrain_oversampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 166,
+ "id": "9ec369bf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-64 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-64 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-64 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-64 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-64 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-64 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-64 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-64 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-64 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-64 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-64 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-64 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-64 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-64 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-64 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-64 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-64 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-64\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" checked><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "DecisionTreeClassifier()"
+ ]
+ },
+ "execution_count": 166,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#DecsionTree model - oversampled dataset\n",
+ "\n",
+ "# Initialize the decision tree classifier\n",
+ "clf_undersampled = DecisionTreeClassifier()\n",
+ "\n",
+ "DT_models.append(clf_undersampled)\n",
+ "DT_name.append(f\"Undersampled dataset(No PCA), {txt}\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "clf_undersampled.fit(xtrain_undersampled, ytrain_undersampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 209,
+ "id": "1064c58f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-86 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-86 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-86 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-86 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-86 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-86 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-86 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-86 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-86 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-86 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-86 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-86 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-86 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-86 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-86 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-86 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-86 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-86\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" checked><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "DecisionTreeClassifier()"
+ ]
+ },
+ "execution_count": 209,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#DecsionTree model - oversampled dataset\n",
+ "\n",
+ "# Initialize the decision tree classifier\n",
+ "clf_oversampled_pca = DecisionTreeClassifier()\n",
+ "\n",
+ "DT_models.append(clf_oversampled_pca)\n",
+ "DT_name.append(f\"Oversampled dataset(PCA), {txt}\")\n",
+ "x_val_list.append(xval_pca_oversampled)\n",
+ "x_test_list.append(xtest_pca_oversampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "clf_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "id": "3bf79310",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-66 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-66 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-66 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-66 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-66 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-66 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-66 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-66 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-66 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-66 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-66 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-66 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-66 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-66 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-66 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-66 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-66 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-66\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" checked><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "DecisionTreeClassifier()"
+ ]
+ },
+ "execution_count": 168,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#DecsionTree model - undersampled dataset\n",
+ "\n",
+ "# Initialize the decision tree classifier\n",
+ "clf_undersampled_pca = DecisionTreeClassifier()\n",
+ "\n",
+ "DT_models.append(clf_undersampled_pca)\n",
+ "DT_name.append(f\"Undersampled dataset(PCA), {txt}\")\n",
+ "x_val_list.append(xval_pca_undersampled)\n",
+ "x_test_list.append(xtest_pca_undersampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "clf_undersampled_pca.fit(xtrain_pca_undersampled, ytrain_undersampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 131,
+ "id": "0828f0bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cv_scores = cross_val_score(clf_undersampled, xtrain, ytrain, cv=10, scoring='accuracy')\n",
+ "y_pred = cross_val_predict(clf_undersampled, xtrain, ytrain, cv=10)\n",
+ "accuracy = accuracy_score(ytrain, y_pred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 179,
+ "id": "dc6f68ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict(models, names, x_list, Y, eval_type):\n",
+ " \n",
+ " print(f\"\\033[1mEvaluating {eval_type} data\\033[0m \\n\")\n",
+ " \n",
+ " fpr_list, tpr_list, roc_auc_list = [], [], []\n",
+ " \n",
+ " for model, name, X in zip(models, names, x_list):\n",
+ " print(f\"\\033[1mEvaluating {name}...\\033[0m\")\n",
+ " y_pred = model.predict(X)\n",
+ " accuracy = accuracy_score(Y, y_pred)\n",
+ " report = classification_report(Y, y_pred)\n",
+ " print(f\"{name} Accuracy:\", accuracy)\n",
+ " \n",
+ " \n",
+ " conf_matrix = confusion_matrix(Y, y_pred)\n",
+ " plt.figure(figsize=(3, 2))\n",
+ " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
+ " plt.title('Confusion Matrix')\n",
+ " plt.xlabel('Predicted')\n",
+ " plt.ylabel('Actual')\n",
+ " plt.show()\n",
+ " print(f\"{name} Classification report:\\n\", report)\n",
+ "\n",
+ " \n",
+ " y_prob = model.predict_proba(X)[:, 1]\n",
+ " fpr, tpr, _ = roc_curve(Y, y_prob)\n",
+ " fpr_list.append(fpr)\n",
+ " tpr_list.append(tpr)\n",
+ " \n",
+ " roc_auc = auc(fpr, tpr)\n",
+ " #print(f\"{name} AUC = {roc_auc}\")\n",
+ " roc_auc_list.append(roc_auc)\n",
+ " \n",
+ " cmap = plt.colormaps['tab10']\n",
+ " colors = cmap.colors[:len(names)]\n",
+ " plt.figure(figsize=(9,5))\n",
+ " for i, (fpr, tpr, roc_auc, name) in enumerate(zip(fpr_list, tpr_list, roc_auc_list, names)):\n",
+ " plt.plot(fpr, tpr, color=colors[i], lw=2, label=f'{name} (area = {roc_auc:.2f})')\n",
+ " plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
+ " plt.xlim([0.0, 1.0])\n",
+ " plt.ylim([0.0, 1.05])\n",
+ " plt.xlabel('False Positive Rate')\n",
+ " plt.ylabel('True Positive Rate')\n",
+ " plt.title('Receiver Operating Characteristic (ROC). SVM on Dataset 1')\n",
+ " plt.legend(loc='lower right')\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "2bc9298e",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating validation data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA)...\u001b[0m\n",
+ "Oversampled dataset(No PCA) Accuracy: 0.774061433447099\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.87 0.87 0.87 2481\n",
+ " 1 0.26 0.26 0.26 449\n",
+ "\n",
+ " accuracy 0.77 2930\n",
+ " macro avg 0.56 0.56 0.56 2930\n",
+ "weighted avg 0.77 0.77 0.77 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA)...\u001b[0m\n",
+ "Undersampled dataset(No PCA) Accuracy: 0.6170648464163823\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.62 0.73 2481\n",
+ " 1 0.22 0.59 0.32 449\n",
+ "\n",
+ " accuracy 0.62 2930\n",
+ " macro avg 0.56 0.60 0.53 2930\n",
+ "weighted avg 0.79 0.62 0.67 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA)...\u001b[0m\n",
+ "Oversampled dataset(PCA) Accuracy: 0.7771331058020478\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.87 0.87 0.87 2481\n",
+ " 1 0.27 0.27 0.27 449\n",
+ "\n",
+ " accuracy 0.78 2930\n",
+ " macro avg 0.57 0.57 0.57 2930\n",
+ "weighted avg 0.78 0.78 0.78 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA)...\u001b[0m\n",
+ "Undersampled dataset(PCA) Accuracy: 0.6095563139931741\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA) Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.88 0.62 0.73 2481\n",
+ " 1 0.21 0.55 0.30 449\n",
+ "\n",
+ " accuracy 0.61 2930\n",
+ " macro avg 0.55 0.59 0.52 2930\n",
+ "weighted avg 0.78 0.61 0.66 2930\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 700x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(DT_models, DT_name, x_val_list, yval, \"validation\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 210,
+ "id": "10b082c7",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating testing data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), ccp_alpha: 0.0005...\u001b[0m\n",
+ "Oversampled dataset(No PCA), ccp_alpha: 0.0005 Accuracy: 0.7689471528062269\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), ccp_alpha: 0.0005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.86 0.87 0.86 2035\n",
+ " 1 0.29 0.27 0.28 406\n",
+ "\n",
+ " accuracy 0.77 2441\n",
+ " macro avg 0.57 0.57 0.57 2441\n",
+ "weighted avg 0.76 0.77 0.77 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), ccp_alpha: 0.0005...\u001b[0m\n",
+ "Oversampled dataset(PCA), ccp_alpha: 0.0005 Accuracy: 0.7722244981564932\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), ccp_alpha: 0.0005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.85 0.88 0.87 2035\n",
+ " 1 0.28 0.23 0.25 406\n",
+ "\n",
+ " accuracy 0.77 2441\n",
+ " macro avg 0.57 0.56 0.56 2441\n",
+ "weighted avg 0.76 0.77 0.76 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Oversampled dataset(No PCA), ccp_alpha: 0.005 Accuracy: 0.7734535026628431\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.86 0.87 0.86 2035\n",
+ " 1 0.31 0.30 0.30 406\n",
+ "\n",
+ " accuracy 0.77 2441\n",
+ " macro avg 0.59 0.58 0.58 2441\n",
+ "weighted avg 0.77 0.77 0.77 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), ccp_alpha: 0.005...\u001b[0m\n",
+ "Oversampled dataset(PCA), ccp_alpha: 0.005 Accuracy: 0.7730438344940598\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.85 0.88 0.87 2035\n",
+ " 1 0.28 0.24 0.26 406\n",
+ "\n",
+ " accuracy 0.77 2441\n",
+ " macro avg 0.57 0.56 0.56 2441\n",
+ "weighted avg 0.76 0.77 0.77 2441\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 900x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(DT_models, DT_name, x_test_list, ytest, \"testing\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cbf362dc",
+ "metadata": {},
+ "source": [
+ "### Using the oversampled dataset with PCA for hyperparameter tuning because of its high performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "id": "fa5b7114-aee5-4e45-b0cb-676f2ee19102",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy Score on train data: 0.6292163985469642\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x1000 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Create a DecisionTreeClassifier with specified hyperparameters\n",
+ "clf_oversampled_pca = DecisionTreeClassifier(random_state=0, ccp_alpha=0.010)\n",
+ "\n",
+ "# Fit the classifier on the PCA-transformed oversampled training data\n",
+ "clf_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled)\n",
+ "\n",
+ "# Evaluate the model on the PCA-transformed test data\n",
+ "y_pred_test_dt_pca = clf_oversampled_pca.predict(xtest_pca_oversampled)\n",
+ "\n",
+ "print('Accuracy Score on train data: ', accuracy_score(y_true=ytrain_oversampled, y_pred=clf_oversampled_pca.predict(xtrain_pca_oversampled)))\n",
+ "\n",
+ "DT_models.append(clf_oversampled_pca)\n",
+ "DT_name.append(\"Oversampled Dataset ccp_alpha: 0.010\")\n",
+ "x_test_list.append(xtest_pca_oversampled)\n",
+ "\n",
+ "plt.figure(figsize=(10,10))\n",
+ "plot_tree(clf_oversampled_pca, filled=True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "id": "c21986e4",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy Score on train data: 0.6292163985469642\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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ubm54/PgxOnfu/MdxEiIOlFxIhSOso/13Ot+LG0VWlqWL8vLykJSUJFJZTU3NEvt0iprDoqOjYWxszLcvKioKBgYGvx0fANSuXRsARI6TkL+BkgupMHR1daGgoIBPnz4J7Pv48aMEIgIePXoktj4XBwcHAMCzZ8/QrFkz3vaCggIEBgaiU6dOZYoxNDQUQOHfj5CKgpILqTCkpaXRoUMHXLp0Cd++feM1byUkJODYsWMSiUmcfS5WVlawsbHB3r17MWHCBF4tx9vbG8nJyejfvz+vbFZWFsLDw6Gtrc3rP0pKSoKmpibfOTMyMrB+/XqoqqrCycnpdy6NkHJFyYVUKP/88w9u3ryJFi1aYPz48ZCVlcW+fftQp04dJCcn//VnU8TZ5wIAGzduROfOndG2bVuMGDEC4eHh2LBhA5ydndGnTx9euWfPnqFNmzZYunQpli1bBgDYunUrfHx80K5dOxgaGiI6Ohqenp4ICwvD3r17oaysLLY4CflTlFxIhWJnZwcfHx/MmjUL//zzD/T19TFx4kQoKCjg5cuXYLPZkg7xj3To0AFXr17FkiVLMHXqVKiqqmL06NFYtWpVqYmzefPmePz4Mfbv34/ExEQoKiqiUaNG2LlzJ7p06fKXroAQ0bCYsvRykr8uMDAQDg4OCAgIgL29vaTD+eumTp2KvXv3Ij09HTIydE9UHVX3z0BlQ9O/kArn1+c94uPj4eXlhRYtWlBiIaSSoE8qqXDq1KmD4cOHw8LCAlFRUdi/fz/S09OxZMkSSYdGCBERJRdS4XTr1g1nz55FTEwMb94tLy8v3pP2hJCKj5ILqXAOHjwo6RAIIX+I+lwIIYSIHSUXQgghYkfJhVR6hw4dElhIjBAiWZRcCKkAfHx8MGbMGNja2kJGRqbEh0VXrlyJ3r17w8DAACwWC+PGjRNarmi1TWE/BQUFAuXT09Mxd+5cmJiYgM1mo2bNmujXrx+ysrJ4ZZ4/f45JkyahXr16UFJSgqGhIQYMGCCxud9IxUUd+oRUAMeOHcOJEydgZ2eHOnXq4Pv378WWXbhwIXR1ddG4cWNcvXq11HNv375dYJ2XX2eOTk1NRevWrREREQEPDw+YmZkhPj4eDx48QG5uLhQVFQEAa9asgb+/P/r3748GDRogJiYG27dvh729PR4/foz69euX4epJVUTJhZAKYOXKldi7dy/k5OTg5uaGEydOFFv2y5cvvCn7RZlrzcXFBfr6+iWWmT9/Pr59+4bAwEC+5QDmzp3LV27GjBk4duwY39ICAwcOhI2NDVauXInjx4+XGg+pHqhZjPCkp6dj+vTpMDY2hry8PHR0dODs7Ix79+7xyjx48AADBgyAkZER5OXlUaNGDbi7uwusJVLUJBMSEgJXV1eoq6tDS0sLs2fPBpfLRWxsLAYMGAB1dXVoa2tj6dKlfMeHhYWBxWJh9erV2LZtG4yNjcFms+Ho6IjHjx+LdD3Pnz9H165doaamBkVFRbRo0QJ379797Wv+GwwMDEpcC+Znv64FUxqGYZCWlgYulyt0f0pKCjw9PTF27FgYGxsjLy9P6IJmQOEyy7/GaW5uDhsbGwQHB/9WXKRqo5oL4Rk3bhzOnDmDiRMnol69ekhOTsbTp0/x6tUr3jolp0+fRnp6Ojw8PKCjo4OgoCDs378fb9++xaNHjwTupAcPHgwLCwusWrUKvr6+WL9+PdTU1HDq1Ck0atQIq1atwtmzZ7F8+XLY2tqib9++fMcfO3YMycnJmDBhArhcLnbs2IEOHTrgxYsXsLKyKvZa/Pz80KlTJ9jZ2WHJkiWQkZHBkSNH0LFjR/j6+vKuR5RrFobL5Yq8OJeKiorQpY3/FgsLC2RkZEBRURHdu3fHhg0bUKtWLd7+hw8fIicnB2ZmZujXrx8uXLgALpeLpk2bYufOnaUuJcAwDGJjY0v89yDVEEMqhYCAAAYAExAQUG6voa6uzkycOLHEMpmZmQLbjh49ygBgHjx4wNu2dOlSBgAzatQo3jYul8uYmpoyLBaLWbBgAW97dnY2o6WlxXTr1o237evXrwwARk5Ojvn69Stv+4cPHxgZGRlm0KBBvG2enp4MAF45LpfLWFpaMu3atWO4XC6vXE5ODmNpack0bdr0t65ZmKL4RPnx9PT8rXO7uroy8vLyIpUFwHh4eAjdt3nzZmbSpEmMt7c3c/bsWWbGjBmMrKwsY2hoyCQkJPDKbdy4kQHAaGlpMU2aNGGOHj3K7Ny5k9HT02M0NDSYyMjIEmM4fPgwA4DZt2+f6BdZBn/jM0DEh2ouhEdVVRXPnz9HdHQ0b0neXxV17DIMg/T0dOTl5fFWVQwICECLFi34yru7u/P+n8VioXHjxggNDeXbzmazYWdnhy9fvgi8Xs+ePVGnTh3e7xYWFujUqRNu3LgBhmGE9jm8fv0aHz58wJw5c5CYmMi3r3379ti1axeysrKgqKgo0jULo6+vL/IiYvXq1RP5vOI0depUvt/79u2LZs2aoV+/fti0aRNWrFgBoHDBMaDw3+f27du8dWHs7e3h5OSEbdu2YdWqVUJf4+PHj5g8eTKaNm2KkSNHluPVkMqGkgvhWb9+PVxdXVGrVi00atQInTt3xuDBg/maOyIiIjB79mxcu3YN6enpfMenpqYKnLNoffciRaOWft2urq6Od+/eCRxvbm4usM3CwgJXr15Famoq1NXVBfYXDYsdPXp0MVcK3nooolyzMGw2W6yLiP0tLi4uqFOnDm7dusVLLgoKCgCAHj168C045ujoCGNjYzx8+FDouWJjY9GlSxeoq6vjzJkzAiPQSPVGyYXw9O/fH61atcKFCxfg4+ODTZs2YeXKlfD09MSwYcPA5XLRsWNHxMfHY/78+bC2toaSkhK4XC46d+4stMO4uC8cYdsZIUsLCauZFJUrbqRUURyrV6/mrVv/Kx0dHZGuuTgcDgfx8fHF7v+Zmpoa7wu8IqhduzZiYmJ4vxsYGAAA9PT0BMrq6uoiJSVFYHtqaio6d+6M1NRUPHz4kHcOQopQciF89PT04OHhAQ8PD6SkpMDJyQn//PMPhg0bhqCgIISEhODQoUNwdXXlHfPp06dyi0fYw3mfP3+Guro6VFVVhR5jamoKoLAjXZTaRUnXXJyIiAiRR215enrCzc1NpLJ/w5cvX/iaGosScGRkpEDZqKgogdpjTk4OevbsiU+fPuH27dvUkU+EouRCABTeiWdkZPA9bKeurg5jY2M8f/4cAHgLdf1aw1i/fn25xXX58mWEh4fD0NAQQGFiuXHjBvr161dszcXBwQFmZmbYuHEjhg8fDhUVFb798fHx0NHREemai1MZ+lySkpKgqanJt23fvn2IjIyEh4cHb5ulpSVsbW1x6dIlvmNu3bqFiIgITJgwgVeWw+Fg4MCBePToES5dugRHR8e/czGk0qHkQgAUPu9Rs2ZNuLi4wNbWFqqqqvD398eNGzcwceJEAICVlRXMzc0xc+ZMfP/+HZqamrh+/XqJT5P/KQsLC7Ro0QITJkwAwzDYvn075OXlBZ6L+ZmUlBQOHDiAzp07o27duhg1ahRq1aqFqKgoPHjwABwOB3fv3hXpmosj7j6XoKAgXLp0iff/HA6H1ydia2uLHj168Mp6eXnh27dvvN8DAwN5ZYcPHw4jIyMAhTU4FxcX1K1bF2w2G/7+/jh+/Disra0FOvs3bdqEDh06oHnz5hg7dizS0tKwceNGmJmZ8SWXmTNn4tKlS+jZsycSExPh7e3Nd56SanukmpHkUDUiuvIehpmbm8vMnj2bsbOzY1RVVRlFRUWmXr16zPr165n8/HxeuZCQEKZTp06Mqqoqo6GhwQwePJiJiYlhADBLly7llSsaihwdHc33Oh4eHoywt52Liwujp6fH+71oqO+qVauYrVu3MnXq1GHk5eWZJk2aMP7+/nzH/joUucjr16+Zfv36Mdra2oycnBxjaGjIuLi4MNevX/+ta/4biq5B2I+rqytf2datWxdb9u7du7xy7u7uTL169RgVFRVGVlaWMTU1ZWbMmMEkJycLjcHX15dxcnJi2Gw2o6GhwQwdOpSJiooS+bXL++uEhiJXLiyGEdKLSiqcwMBAODg4ICAgAPb29pIOp9yFhYXB2NgYq1atwrx58yQdDqkAqttnoLKj6V8IIYSIHSUXQgghYkfJhRBCiNjRaDFSIdWpU0foQ5WEkMqBai6EEELEjpILIYQQsaPkQop17949sFisv75wlrgVLTxW9FPSKo/k75o0aRLv34XNZks6HCJGlFxItTF27Fh4eXmhadOmvG1FCVTYz61bt/iOL64ci8USmH8rOTkZs2fPhrm5ORQUFGBoaIhhw4YJzJUWHR2NefPmoV27dlBTUxNb8isuzkWLFvGVc3Z2LvG6fp5vjMPhYOfOnbCzs4OKigp0dXXRvn17gb8TULhsc+/evWFgYAAWi4Vx48YJjdPV1RVeXl5o2bLlH18zqVioQ59UG02bNi12epKJEyfCycmJb5uNjQ3f715eXgLHffjwAStWrECnTp142zgcDtq1a4cPHz5g3LhxsLa2RlhYGHbu3Ilr167h3bt3vLVjPnz4gDVr1sDExAR2dna4f//+n14mT9u2bQXWWKlfvz7f7wsXLsSYMWP4tnE4HLi7u8PCwgI1a9bkbZ8+fTq2bduGwYMHY/z48UhPT8eBAwfQsWNHXLlyBV27duU7r66uLho3boyrV68WG2Pjxo3RuHFj3Lp1C8+ePfuTyyUVDCUXQgC0aNECgwYNKrGMsMQ0d+5cgX0PHz7Ey5cvsXXrVkyePJm3vUmTJujVqxcuXLiA8ePHAyicZDMhIQFaWlq4d+8e2rRpI47LAVC4Fk5pc3116NBBYNv169eRn5/Pd2xeXh727t2Lvn374tixY7ztbm5uMDAwwOHDh/mSy5cvX3izRhc3wSip2qhZrIo4ffo0byXBX504cQIsFgt+fn4ACidGdHNzg6mpKdhsNrS1tTFo0CCEh4eX+jrOzs5C15Z3c3Pjm8YdKJw9edu2bahfvz7YbDZ0dXUxevRoJCQklOkay1tGRgby8/NFLs8wDI4fPw4zMzO+Wk/RImq/rmxZ9HvRap5A4bIAWlpafxJ2iXJycpCdnf1bx3h7e4PFYmHIkCG8bXl5ecjNzRW4Jk1NTbDZbL5rAiDycgSk6qLkUkV0794dysrKQtvrT548CQMDA167tq+vLz59+gRXV1ds27YNY8aMwY0bN+Ds7IysrCyxxTR+/HjMmDEDjo6O2LJlC9zd3XH69Gm0adMGOTk5JR6bn5+PhIQEkX5+JyEUx93dHSoqKmCz2WjZsiUePXpU6jH37t1DREQEhg4dyre9SZMmUFRUxKJFi3Dr1i1ERkbC398fkyZNgqWlJfr16/fH8YrC29sbioqKUFRUhKWlJTw9PUs9JjMzExcvXkSrVq14yxwAgLKyMhwcHODp6YkjR44gPDwc7969w6hRo8AwjMAsy4TQrMiVhCgzwg4ZMoTR1NRk8vLyeNtSU1MZNpvNTJs2jbctMzNT4Fh/f38GAOPl5cXbdvfuXYGZdlu3bs20bt1a4HhXV1fGyMhI4HyHDx/mK3fv3j0GALNnz56SLpf32qL8/ByfMEUzLHt6egq9bhcXF2b//v3MxYsXmTVr1jC6urqMrKws8/DhwxLPO3r0aAYA8+nTJ4F9ly5dYmrUqMEXZ/PmzZn4+PhSr/n48eMlvq4omjVrxmzevJm5ePEis3v3bsbOzo4BwKxdu7bE47y8vBgAzP79+wX2ffjwgbG3t+e7JgMDA+bFixclnhMA4+HhUWIZV1dXRl5evsQyNCty5UJ9LlXIoEGDcOzYMfj6+vLavy9evIicnBy+/oSfmzAyMjKQm5sLCwsLqKurIyAgQCxrcpw6dQrKysro3LkzXzNYvXr1oKWlhbt372Ls2LHFHm9rayvyYly2trZljrNZs2Zo1qwZ7/eePXti0KBBqFu3LubOnVvs+vG5ubk4e/YsnJycYGZmJrBfR0cH9evXh4eHB+zs7PDhwwesXr0affr0gY+PT7kve+zv78/3+6hRo+Do6Ihly5Zh7NixfAuk/czb2xtsNlto7UpVVRVWVlZwcHBA586dkZycjI0bN6J79+64f/++wIg5Ur1RcqlCOnXqBA0NDZw4cYKXXE6cOAFjY2O+FQOTk5Mxb948nDlzBklJSXznSE1NFUssHz9+REZGhtB12QEgLi6uxOM1NDTEuhjX7zA0NET//v1x5MgRZGVlCfQnAIUrZKakpAhNxKGhoWjTpg0OHjyIwYMH87Y3btwYbdu2xf79+/k6+v8GWVlZTJ06FW5ubnj8+DE6d+4sUCY2Nha3bt1Cnz59BJJPQUEB2rVrhxYtWmDPnj287b1794a5uTnmzZuHs2fPlvt1kMqDkksVIicnhz59+uDMmTO8jlxfX1/MnDmTr9zAgQPx4MEDzJo1Cw0bNoSKigpYLBYGDRoELpdb4muwWCyhc35xOBy+37lcLrS0tIp9ZkNDQ6PE18nLyxNIfMXR1NSEnJycSGVFVbt2bXC5XKSkpAhNLt7e3pCVlcXAgQMF9h0+fBi5ubno1asX3/Y2bdrwVrv828kFKLwmAMX+XY8fPw4OhyM0Yd6/fx/BwcECS1praWmhRYsWAjUlQii5VDGDBg3CwYMHce3aNSQnJyM/P5+vSSw5ORm+vr5YtmwZ31LBOTk5SE5OLvX86urq+Pr1q8D2n5fdBQqX2PX19YWTkxOUlZV/+zoePXok8rDcu3fvCh3B9idCQ0MhLS0tsAY9UPg3vH79Ojp37gxtbW2B/bGxsWAYRiDhFm0rKCgQa6yiCg0NBQDo6uoK3X/06FFoaWnxDSkuEhsbCwBCYy8oKJDYNZGKi5JLFdO2bVvo6uri5MmTSE5OhpWVFV+fhIxM4T/5r7WPTZs2lVprAQqTxvXr1xEfHw8dHR0AwOvXr+Hv78+7MwYKa0c7d+7E8uXLsXbtWr5zcDgcpKWllVh7+Vt9LklJSQIJ5O3btzhz5gxat24tdEqSU6dOIS8vr9i+KUtLSwDAsWPH4OHhwdt+6dIlZGZmlvsqisKuKSMjA+vXr4eqqqrAw6JA4cOcL168wPjx4yErKyuw/+dr6tGjB2/79+/f8eDBA75ZDwgBKLlUOdLS0ujXrx88PT2Rl5eHxYsX8+1XUVGBs7Mz1q5di7y8PBgZGeHhw4fw8/MT6XmL0aNHY9OmTejUqRNGjx6NuLg47N69G/Xq1UNaWhqvXKtWrTBx4kSsW7cOQUFB6NSpE+Tl5fH582ecPXsW//zzD9zc3Ip9nb/V5zJw4ECoqKjAwcEBurq6CAkJwZ49eyArKyvQBFTE29sbqqqq6Nmzp9D9bm5uWL9+PSZNmoTXr1/zOvR37twJfX19uLu785VfsWIFAPBqhOfPn8fnz58BgG+6lmXLluGff/4ptaa2detW+Pj4oF27djA0NER0dDQ8PT0RFhaGvXv3Cq1Jent7AxD+oCgA2Nvbo1OnTjhx4gTS09PRtWtXpKSkYOfOncjOzsbChQv5ynt5efHVZgMDA3nXOXz4cBgZGRUbP6kiJDtYjYjqd4ZhPnjwgDdUNCQkRGB/VFQU079/f0ZTU5NRUVFhunXrxnz69IkxMjJiXF1deeWEDUVmGIbx9vZmTExMGDk5OcbOzo65efOmwFDkIgcOHGAaN27MKCgoMCoqKky9evWYmTNnMt++ffvdP0GZlTQUecuWLYyjoyOjqanJyMjIMHp6eszgwYOZ4OBgoecKCwtjWCwWM3LkyBJfMyoqihk7dixjamrKyMnJMTo6OszgwYOZL1++CJRFCcOsfzZz5kyGxWIx79+/L/G1fXx8mI4dOzL6+vqMrKwso6amxrRr1465du1asceYmJgwJiYmJZ43OzubWblyJWNjY8MoKSkxKioqTNu2bYUOBW/duvVvDR2nochVD4thaEWmyiAwMBAODg4ICAgo92aVqiYsLAzGxsbYtm0bBg0aBFVVVbEPAPgbmjRpAiMjI5w+fVrSoYhNZmYmsrOzMXnyZJw/f77Eh2vpM1C50BP6pNqYPHkydHR0cO7cOUmH8tvS0tLw+vVrLF++XNKhiNXcuXOho6NDyyBUQdTnQqo8fX19vsEBv84MXBmoqqoiNzdX0mGI3eTJk9G7d28Ahf2FpOqg5EKqPDabLbEHMknJLC0teSPRSNVCzWKEEELEjpILIYQQsaPkQgghROwouRBCCBE7Si6EEELEjkaLVTLv37+XdAiESAS99ysXSi6VhLa2NhQVFcWykBchlZWioqLQmahJxUPTv1Qi4eHhfKs6kh/u3r2LWbNmYe/evXBwcJB0OGXi7u6OzMxMHD16FCwWS9LhVEja2towNDSUdBhEBJRcSKXH5XJhb28PTU1N3LlzR9LhlJmfnx+cnZ1x/vx53lPrhFRWlFxIpXfu3Dm4uLjAz88PrVq1knQ4f6Rt27ZISkpCYGAgpKRovA2pvCi5kEqNy+XCzs4OOjo6uH37tqTD+WP3799H69atcfbsWfTt21fS4RBSZpRcSKV25swZ9O/fHw8ePECLFi0kHY5YtGvXDvHx8Xj16hXVXkilRcmFVFpcLhe2trYCsx5Xdg8fPkTLli1x5swZuLi4SDocQsqEkguptE6fPo0BAwbA398fzZo1k3Q4YtWhQwfExMTg9evXVHshlRIlF1IpcblcNGjQADVr1sTNmzclHY7Y+fv7o0WLFjh16hT69+8v6XAI+W2UXEildPLkSQwaNAiPHj1C06ZNJR1OuejYsSOioqIQFBREtRdS6VByIZUOh8NB/fr1YWhoiBs3bkg6nHLz+PFjNGvWDCdOnMDAgQMlHQ4hv4WSC6l0Tpw4gcGDB+PJkydwdHSUdDjlqnPnzoiIiEBQUBAtA0wqFUoupFLhcDiwsbGBsbExrl27Julwyt3Tp0/h5OSE48ePY9CgQZIOhxCRUXIhlcqxY8cwdOhQPH36FE2aNJF0OH9F165d8fXrV7x9+5ZqL6TSoORCKg0Oh4N69erBzMwMV65ckXQ4f82zZ8/g6OiIo0ePYsiQIZIOhxCRUHIhlYa3tzeGDx+OZ8+eoXHjxpIO56/q1q0bQkND8e7dO6q9kEqBkgupFAoKClC3bl1YWlri8uXLkg7nr3v+/DmaNGkCLy8vWtOHVAqUXEil4OXlhREjRuDFixeVdr2WP9WjRw98/PgR7969g4wMrfNHKjZKLqTCKygogLW1NerWrYuLFy9KOhyJCQgIQKNGjXDkyBEMHz5c0uEQUiJKLqTCO3z4MNzc3BAYGIiGDRtKOhyJ6tWrF4KDg/H+/XuqvZAKjZILqdAKCgpgZWWF+vXr4/z585IOR+JevnwJe3t7HDp0CK6urpIOh5BiUXIhFZqnpydGjRqFly9fws7OTtLhVAi9e/fG27dvERISQrUXUmFRciEVVn5+PqysrGBra4tz585JOpwK49WrV2jYsCE8PT3h5uYm6XAIEYqSC6mwDh48iNGjR+P169do0KCBpMOpUPr27YvXr18jJCQEsrKykg6HEAGUXEiFlJ+fD0tLS9jb2+PMmTOSDqfCCQoKgq2tLQ4cOIBRo0ZJOhxCBFByIRXS/v374e7ujqCgINSvX1/S4VRI/fr1Q2BgID58+EC1F1LhUHIhFU5eXh4sLCzQuHFjnD59WtLhVFhFtZd9+/ZhzJgxkg6HED6UXEiFs3fvXowbNw5BQUGwsbGRdDgVWv/+/fH8+XN8/PgRcnJykg6HEB5KLqRCycvLg7m5OZycnHDy5ElJh1PhvX37Fg0aNMCePXvg7u4u6XAI4aHkQiqUPXv2YPz48Xj79i3q1q0r6XAqhYEDB+LJkyf49OkT1V5IhUHJhVQYubm5MDc3R/PmzXH8+HFJh1NpBAcHw8bGBrt27YKHh4ekwyEEACUXUoHs2rULEydOxLt372BtbS3pcCqVwYMHw9/fH58+fYK8vLykwyGEkgupGHJzc2FmZoaWLVvi2LFjkg6n0imqvezYsQPjx4+XdDiEUHIhFcOOHTswZcoUvHv3DlZWVpIOp1IaMmQIHjx4gM+fP1PthUgcJRcicTk5OTAzM4OzszO8vb0lHU6lFRISgnr16mHbtm2YMGGCpMMh1RwlFyJx27dvx9SpU/H+/XtYWFhIOpxKbdiwYbh37x4+f/4MNpst6XBINUbJhUhUTk4OTE1N0a5dOxw5ckTS4VR6Hz9+hLW1NbZs2YJJkyZJOhxSjVFyIRK1detWTJ8+HSEhITA3N5d0OFXCiBEjcPv2bYSGhlLthUiMlKQDINVXdnY2Vq1ahWHDhlFiEaNFixYhJiYGe/fulXQopBqj5EIkZs+ePYiPj8fixYslHUqVYmFhgWHDhmHVqlXIzs6WdDikmqLkQiQiOzsba9aswfDhw2FmZibpcKqcxYsXIz4+nmovRGIouRCJ2L17N+Lj47Fo0SJJh1IlmZmZYfjw4Vi9ejXVXohEUHIhf11WVhbWrFkDV1dXmJqaSjqcKmvRokWIj4/H7t27JR0KqYYouZC/bteuXUhMTMTChQslHUqVZmpqCldXV6xZswZZWVmSDodUM5RcyF+VmZnJq7WYmJhIOpwqb+HChUhMTMSuXbskHQqpZii5kL9q165dSE5Opr6Wv8TExASurq5Yu3YtMjMzJR0OqUYouZC/JjMzE2vXrsXIkSNRp04dSYdTbSxatAhJSUlUeyF/FSUX8tfs2LEDKSkp1Nfyl9WpUwcjR46k2gv5qyi5kL8iIyMD69atw6hRo2BkZCTpcKqdhQsXIiUlBTt27JB0KKSaoORC/ort27cjNTUVCxYskHQo1ZKRkRFGjRqFdevWISMjQ9LhkGqAkgspd+np6Vi3bh3GjBkDQ0NDSYdTbS1YsACpqanYvn27pEMh1QAlF1Lutm/fjoyMDMyfP1/SoVRrhoaGGD16NNavX4/09HRJh0OqOEoupFylpaVh/fr1GDNmDGrXri3pcKq9BQsWID09nWovpNxRciHlatu2bVRrqUBq166NMWPGYP369UhLS5N0OKQKo+RCyk1qaio2bNiAsWPHolatWpIOh/zf/PnzkZGRgW3btkk6FFKFUXIh5Wbr1q3IysqiWksFU6tWLYwdOxYbNmxAamqqpMMhVRQlF1IuUlJSsHHjRnh4eMDAwEDS4ZBfzJs3D1lZWdi6daukQyFVFCUXUi62bt2KnJwczJ07V9KhECFq1qyJsWPHYuPGjVR7IeWCkgsRO6q1VA7z5s1DTk4OtmzZIulQSBVEyYWI3ebNm5Gbm0u1lgrOwMAAHh4e2LhxI1JSUiQdDqliKLkQsUpOTsamTZswfvx41KhRQ9LhkFLMnTsXubm52Lx5s6RDIVUMJRciVps2bUJ+fj7VWiqJGjVqYPz48di0aROSk5MlHQ6pQii5ELFJSkrC5s2bMWHCBOjp6Uk6HCKiOXPmID8/H5s2bZJ0KKQKoeRCxGbTpk0oKCjA7NmzJR0K+Q36+voYP348tmzZgqSkJEmHQ6oISi5ELJKSkrBlyxZMnDiRai2VENVeiLhRciFisWHDBnA4HKq1VFJ6enqYOHEi1V6I2FByIX8sISEBW7duxaRJk6CrqyvpcEgZzZ49GxwOBxs2bJB0KKQKoORC/tiGDRvAMAzVWio5XV1dTJo0CVu3bkVCQoKkwyGVHCUX8kfi4+Oxbds2TJ48Gdra2pIOh/yh2bNng2EYqr2QP0bJhfyRDRs2gMViYebMmZIOhYiBtrY2Jk+ejG3btlHthfwRSi6kzOLj47F9+3aqtVQxM2fOBIvFwvr16yUdCqnEKLmQMlu3bh2kpKSo1lLFFNVetm/fjvj4eEmHQyopSi6kTOLi4rBjxw5MmTIFWlpakg6HiNnMmTMhJSWFdevWSToUUklRciFlsnbtWsjIyGDGjBmSDoWUAy0tLUyZMgU7duxAXFycpMMhlRAlF/LbYmJisHPnTkydOhWampqSDoeUkxkzZkBGRgZr166VdCikEqLkQn7bunXrICsri+nTp0s6FFKONDU1MXXqVOzcuROxsbGSDodUMpRcyG+JiYnBrl27MG3aNGhoaEg6HFLOpk+fDllZWaq9kN9GyYX8ljVr1kBOTo5qLdWEhoYGpk2bhl27diEmJkbS4ZBKhJILEVl0dDR2796N6dOnQ11dXdLhkL9k+vTpkJOTw5o1ayQdCqlEKLkQka1evRpsNhvTpk2TdCjkL1JXV8f06dOxe/duREdHSzocUklQciEiiYqKwp49ezBjxgyoqalJOhzyl02bNg1sNptqL0RklFyISFavXg1FRUVMmTJF0qEQCVBTU8OMGTOwe/duREVFSTocUglQciGlioyMxN69e6nWUs1NmTIFCgoKWL16taRDIZUAJRdSqlWrVkFJSYlqLdWcmpoaZs6cib179yIyMlLS4ZAKjpILKVFERAT27duHmTNnQlVVVdLhEAmbMmUKlJSUsGrVKkmHQio4Si6kRKtWrYKKigomT54s6VBIBaCqqoqZM2di3759iIiIkHQ4pAKj5EKKFRERgf3792PWrFlQUVGRdDikgpg8eTJUVFSo74WUiJILKdbKlSuhpqaGSZMmSToUUoGoqKhg1qxZ2L9/P9VeSLEouRChvn37hgMHDmD27NlQVlaWdDikgpk0aRJUVFSwcuVKSYdCKihKLkSoolrLhAkTJB0KqYCUlZUxe/ZsHDhwAN++fZN0OKQCouRCBISFheHgwYOYM2cO1VpIsSZOnAg1NTWqvRChKLkQAf/99x80NTWp1kJKpKysjDlz5uDgwYMICwuTdDikgqHkQvh8/foVhw4dwpw5c6CkpCTpcEgFN2HCBGhqalLthQig5EL4/Pfff9DS0sL48eMlHQqpBJSUlDBnzhx4enri69evkg6HVCCUXAjPly9fcOjQIcydOxeKioqSDodUEuPHj4empib+++8/SYdCKhBKLoRnxYoV0NbWhoeHh6RDIZWIoqIi5s6di0OHDuHLly+SDodUEJRcqimGYRAQEMD7/fPnzzhy5AjmzZtHtRby28aNGwdtbW2sWLGCty0mJgbfv3+XYFREkii5VFOvXr1Co0aNeKN8VqxYAV1dXaq1kDJRVFTEvHnzcOTIEXz+/BkAMG/ePEycOFHCkRFJoeRSTWVmZgIAcnJy8PnzZ3h7e2PevHlQUFCQcGSksvLw8ICuri6v7yU7OxtZWVkSjopICiWXao7FYuHff/+Fnp4exo4di4KCAgQFBUk6LFKJxMfHIyIiAgoKCpg3bx68vLzw+fNnsFgsSYdGJIiSSzXFMAyAwudavL29MX/+fCQmJqJdu3Zo3Lgxr2ZDSGm2bNkCCwsL7N+/H+7u7tDV1cW///4L4Mf7jFQ/MpIOgEhG0Yd+165dqFGjBgwNDWFnZwd5eXncunWLHqAkIlu4cCHi4+Ph7u6OO3fuYPr06Zg3bx66dOlCyaUaYzH0r18t3b9/H61btwaLxUL79u3h6+uLLl264MiRI9DW1pZ0eKQSOnHiBNzd3aGvr4/U1FQoKSnBxMQEt2/flnRoRAKoWayaKrqnkJaWxp07d7B27VpcuXKFEgsps0GDBiEwMBDKyspITk5GWFgYNa9WY5RcqqnExEQAhQs/PXjwALNnz4aUFL0dyJ8xNzfH48ePMWbMGACgxcSqMepzqabatGkDFxcXbN++Hfr6+pIOh1QhbDYbu3btgoWFBbhcrqTDIRJCfS6EEELEjtpBCCGEiN0fN4uFh4cjISFBHLEQUmFoa2vD0NBQYDu930l1V9xn41d/lFzCw8NhbWWFrOzsPzkNIRWOooIC3oeE8H2IwsPDYW1tTVOakGpNUVER79+/LzXB/FFySUhIQFZ2NnaObg2LGup/cipCKoyP0SmYcMAPCQkJfB+ghIQEZGVlYdlWT9Qxt5JghIRIRtinECybMlLgsyGMWEaLWdRQRwMjej6CVA91zK1gVb+hpMMgpEKjDn1CCCFiR8mFEEKI2FFyIYQQInaUXAghhIgdJZef9F53Fb3XXS3TsWsvBULX/YCYIyLk7xvfrwPG9+tQpmP3bfgXTrXYYo6IVEY0t1gVcPfdd6y9GIh335OgoiCHXo2MsaBPIyizZUs8LjIpA8cefsStNxH4EpcGaSkWrAw0ML2bHVrXrSlQPvBrPNZfDkRQeCIycvJRW1MZ/ZuaYWz7emDLFr6VwhPS0Wj+qWJfs5W1Ac7M6PJnF0yIEE/9fLF3/b/4HBwEJVU1tOvugvHzlkNRSfm3zvM9LBRD2tkjLzcX+y/6wcbBkW//u5fPcWDTf/jw9hWyMtKhX8sQXVyGYuDoSZBnFybWqIgw9G1a/HD1xi3bYtvxa79/kZUIJZefnJ1Z9i+9Wd0bYkY3O/EFI6IH76MwZKsP7I118O9AJ0QmZWC371t8iErGmRldSlxq9sarcGy/GYQudkYY0MwcHA4Xpx5/Rv9NN7DZtSWGtLDglQ34Eoeea6/CRFcVEzrWh5K8LPw/RGHFuRd4G5GIvWPbAgC0VNjYMbq1wGs9+xyLw34haFOvlvj/CESstp+8UeZjR09fiJFT54sxGtG88L+LGSN6o27Dxpj2zzrEREbgxN6t+PoxGNtOXP+tJZc3/zMH0tIyAHIF9r0NfIZxLu1Q29gMQzymQVFJCQGP7mPnqkX4+O41Vuz0AgBoaOlg6ZaDAscHvXiM81774NS6bDXDyoSSy0+k/2DKeSkpFqTw99cMX3zqKUz11HB+VlfIyUgDAEx0VTHl0ANcf/UNXRvWKfbY5pY1ELh6ELRUfjRjuLa2Rtvl57HqQgAGNzfnfSgP+4UAAC7O6QZNZfb/y1ohr+AWLr0IwybXfCjJy0JJXhb9ncwEXsvndTikWCy4OJqI69JJOZGWli7zsVJSUhJZumHzsjmobWKOnad8ICsnBwCobWyGFTPG4v7Ny2jduadI53l81wdP/XwxbPwMeG5ZLbD/vPc+AMDus7egpqEFAOgzzB35eXm4c+UsstfvhoKiEhQUldDFZYjA8Q9vXYOUlBQ69h5Y1kutNKpFn8uTTzHovPISao8/BPu5J7DtRhCO+3+ErvsBhCek88r92ucSnpAOXfcD2Hr9Nbzuh6DxglOoNd4THVdcxMuv8XyvIYk+l0/RKQj+noThrSx5iQUA+jmZQU1RDheffy3xeKuaGnyJBQDkZaXRrn5txKZmITUrj7c9Iycf8rLSUFeU5yuvp6YIGWkW5Er4QsrIyYNPUDiaW9WAvjotnywpr575Y1T3lmhlqobejubw2rkBV04ehlMtNqIiwnjlfu1ziYoIg1MtNo5sX4cLRw/Apbk1WpqoYmS35gh+9YLvNSTR5xL2+QM+v3+D3kNH8xILAHTuOwQqauq4dfm0SOcpyM/H5mWzMGDUJNQ0En4TlJWRATl5NlTUNPi2a+vpQ1pGBrKyckKPA4DMjHQ89L0K+2atoaNvIFJMlVmVr7m8jUjEwM03oKHExozudpCVkYL3/Q9QlBf90i+++IrUrDyMaGUJKSkWdt58g5G7buPZyv58X+qiyC/gIi07r/SCAFQV5CArU3z+DwovXPCrYR0dvu0y0lJoYKTN2/+74lKzwZaVhpL8jz6b5pY1cCUwDNOPPOA1iz38EI0Tjz5iUqcGJcZ5OSAM2Xkc9Hc0LVM85M99Cg7C1CHdoaahiZFT50NWVg4Xjx2EgqLoyf7WlbPISEtB76FjICUlhaO7N2Ge+0Cc9X/P96UuioL8fGSkp4pUVllFDTKyxfcffnjzEgBQ164R33YZGRlY1m+ID29eifQ6x/dtRVpqCkZNnYe71y8ILWPftBXuXjuPlbPHYajHdCgoKSHgkR+unDyCYeNnlBjn3avnkJuTjc59B4sUT2VX5ZPL2ouB4HKBy3O7obaWCgBgcDMLOC0S7W4GKKzBPFnRn3eXb6anhhE7buHuu0h0si19dtCfPQuNRZ/1onXknZ/VFc0taxS7PyalcAJFPTVFgX36aooI/BIvsL00X2JTcTUwDN0d6vAlDNfWVvgUkwKv+x9w3P8TAIDFAub3csC0UvqazjwNhYKcNLo71PnteIh47F2/HAzDxZ7zd1CjlhEAoPvAEejf0kbkc0RHhOH0g7dQ1yyc6snQ1AJzRvXDEz9ftOzQ7bfief38ESYO6CRS2R2nbsKhmWA/XpGE2GgAgLau4KJ3Ono1EPzyeamvkRAbDc+tqzF1yRooqagWW67PcHd8+/wBF44dwJWTRwAALBYLHnOWwW3y3BJf48b5E5BnK6BN1z6lxlMVVOnkwuFy4fc+Eh1ta/MSC1DY6eziaIqDd9+LdJ6eDsZ8zUdO5oVv4p+b1ERVr5YmTk/vLHLZkuTkFwAA5GUFaw1sWWneflFl5xXAfe9dKMhJY4lLY759MtJSMNRWQTNLffRtYgpltixuvg7HqosBUGbLYky7ekLPGZOSCf+QaPRqZAxl9u/d3RLx4HA4eHb/Nlq078ZLLACgrqmNTr0H4czh3SKdp223vrzEAgB2TZoDAKLCw347JvO6DbD1uGjD/s3rNihxf25O4azssnLyAvvk5Nm8/SXZ/t8C1DIyQY9BbiWWk5GRQQ3DOrBv2godew2AorIqHvpewZ61y6CopIIBoyYIPS4+JgqBj/zQrocLlJRVhJapaqp0cklIy0F2HgcmumoC+0x0i787+VUtLf6hjOpKhW/ilEzB0SSlUVeSFzrMtyyKhv/m5gsuJZuTz+HtFwWHy4XH3rv4EJWM41M6oYYGf3PJ5quvsPf2OzxZ0R+qioVJooeDMQBg+dnn6N3EBNoqCgLnPfv0C7gMg35O1CQmKckJccjNyUZtY8F/g9rGgoMviqNfi7+Wrqpe2O+Qnpr82zGpqmugSct2v32cMPLswvddfp7g5zEvN4e3vzhBLx7j5vkT2HHqZqmDEQ5tW4OTB3bg9P03UFYt/F5p262wJrLjvwXo0Ks/NLR0BI7zuXASXC4XnfsKdvJXVVU6uRQRNgrxd4YmSksJL1uW9aHzCjhIFjEpaSjJl9ino6dW+KGJTc2CkQ7/3VBMahb01QWby4oz48hD+ARFYI+7M1paC3Y2Hr4fguaWNXiJpUhnOyOcevwZQd8S0dZGcJjxmSefoa3CpiHIFYHwD4LIh0tLCX8vlmWl9Py8PKSlJIlUVlVds8Q+Ha3/N4clxMXAwNCYb198bDS09YpvWgYKay12TZpDR78mIr6GAgCSEwqblOOiIxH57Quvg/+81z7YN23FSyxFWnbsgWtnjiIk6CWatuko8Bo3zh2HhpYOHKvBEOQiVTq5aKuyoSAnjS+xaQL7QmNF60wUt+ehcWLrc7H9/zIHL8Pi0cRMj7e9gMPFm28JIn+hLzv9DMf9P2HV4Kbo1Vj4KJn4tGwUcAW/RDgcLu81f/UhKhnvvidhTNu6kJGuFgMTKyQNbV3IsxXw/f9fnD+L+PJJAhEV1hbE1edi1cAeABD86gUaNGrK215QUICPb17B0bl9ieePi45EzPdw9G8p2LS7YNwQKCgq4e7HwsExSQlx4BQINjdzOAV8//3Z14/v8Sk4CP1HToCMTJX+yuVTpa9UWkoKra1r4ubrcEQkpvP6XRLTc3DuqeAH7W8QZ5+LeQ11WNfUgNf9DxjpbM2r5Zx58hkpWXno0ejHXVxWbgEikzKgqczm6z/afjMIO33eYHpXW4xuW7fY1zLVU8PDkCjEpWVDV/VHM8O5Z1/AYgE2tbUEjjn95DOAwqHRRHKkpaXRpFU7PPC9gujv33j9LilJCfC5cFIiMYmzz6WOmSVMLesVDpMe4cGr5dw4dwxpqclo282FVzYnOwsxkRFQ19Ti9R/NWbkNOVmZfOcMfHwfZw7vxvh5y1HH7MeT9oYmFgh4dA+J8bHQ0vlxQ+d74RRYLBYshMR6/exRAKg2o8SKVOnkAgCze9rj7rtI9FhzFW6trSDz/6HItbVVkBKe+DutAmIhzj4XAPhngCMGbb6JvhuuY0BTM0QmZmCX71s0t6yBbg1/dN6+DItHn/XXMKtHQ8zpWXindzUwDMvPPIeJrirMaqjzkkGR1nVr8hLJ1C62GLf/Hjr/dxGura2hrCALn9fhuPsuEsNaWsBAk7+PhmEYnHsaClM9NdgbC7ZBk7/LfeZiPPXzhUeftug7YixkZGRx8dhB6Nc2Qlpq8m81E4uDOPtcAGDK0jWYPqwnJg7shK4uQxETFYHje7bAvmkrOHfpxSv37uVzTBzQCaOnL4T7zMUAgGZtBWtQWVkZAACHpq35pn9xnTQbSya5YnT3Fugz3B1Kyqp46HsVT/x80XPwSOga8LcWMAwDnwsnYWhijnoN+QfJVHVVPrnUN9TCqemdsOz0M6y/8hK6qgoY2aYuFGSl8SY8EfK/0eldETnXrYljUzpizcUALDrxBMpsWQxpYYFFfRuV+oXx7nthm/eXuDRMPOAnsP/8rK685NLX0RTaqgrYcu0V9tx6i9SsPBjpqGBhn0aY2Km+wLFPPsXge1ImL5ERybKoZ4stR69g27/zcWDTf9DS0YOL6zjIsxWw8e0ryMlX7skmHVu1x4bDF7Bv/XJsWjoLiiqq6DHIDePn/yvWxNmx90BoaOvg8La1OLF/O9JTk1HT0Bjj5y3H0HEzBMq/evoQMZERvERWnbCYsvTG/V9gYCAcHBxwa1GvSrfM8cITj+F1/wO+bBtB/QGET9C3BLRfcREBAQGwt/+RHIve74euP64yyxxvXDITF48ewO0PCdWqP4CUTcibl3Dr0lTgsyFMtfhWzc7j72RLSM/G6cef0cRMjxILqTZysvmf90hOjMeNs8fQoEkzSixE7KrFO8ph3kn0b2oGUz01xKRk4eiDD8jIzces7lXj7pMQUfRpaokuLkNgaGyG+NhoXDp+CJmZ6Rg9bYGkQyNVULVILh0a1MbVgDDEpWVDWkoKtkZa2DG6NZwsBKeLIKSqat6uM+5eO4+kuFhI/3/erWVbDsDOsYWkQyNVULVILlvcWkk6BEIkbtGGvZIOgVQj1OFACCFE7Ci5EEIIETtKLuXghJCFyAipbq6cOiKwEBmpPqpFnwv5PVm5BTj+6CNuvPqG95HJyMwpgLGuCoa3ssKIVpZ8y0F/ik7BMf+PuPcuEmHx6VBiy6CBoRbm9LSHXR3BJ/PvvvuOLddeIyQqGfkcLuroqGJEKysMb1m4EFuR7LwC7PF9izNPQxGRmA5VBXk0rKONGd3thJ6XkPJQUFAAr53rcfWUF2KjIqCprYuOvQdh9PSFYCv8mAYp7PMHXD5xGM/u30Lkty9QUFKGpY0d3GcuhrWtA985eztZIOZ7uNDXk5aRgX9YhtB96akpGNCqPpIT4/HvjiPo0GuA+C60HFByIQK+JaRhwfHHaGllgHEdbKDClsO94EjMPfoIL0LjsGP0j0kEvR9+wLGHH9Hdvg5GtrFGWnYejviFoMuqyzg+tROcf5rq5kpgGEbtug0HEx3M6t4QUlIs3HgVjtne/ghPSMfin9aQGbv3Lm69icDQFpbwaF8PCek5OOwXgh5rruL6gh5C5zIjRNyWTx0Fn4un0LXfUNg4TENoyFsc27MJXz68w4bD53nlLh33xOUTh+DcpTdcRoxFRnoaLnjvx5ierbDR6yIcW/2YPHP6svXIyuRPIGkpydi0dCacSpg1ee/65cjJzhL/RZYTSi5EgK6qIvyW9oVVzR/rhLu2tsLUQ/dx3P8TpnW1hXkNdQBA3yammN3DHsrsH8u7DmlugeaLz2LNxQC+5HLgTjD01RVxYVY3yMsWTrI50tkaHf+7hOP+H3nJJSIxHTdfh2NcBxssH/BjXqeOtoZo8895nHsaSsmFlLvgVy/gc/EUho2fiUkL/+NtNzQ2x6Zls+B/+zqat+sCAOjYawDGzFgERaUfaz/1GOSKga1tsW/9v3zJpXXnngKvdfZI4Ui+4ia3DA15h3NeezF62gLsXb9cLNdX3ip0csnIycOai4G4/vIbYlKzoMKWg6WBOmb3tOdNRf/kYwz23wlG4Nc4xKVlQ11JHh0b1MZilybQUPqxMt3aS4FYf/kl/Je7YMv117jxKhzSUiwMbm6BJS6NkZCRgwXHHuNecCRkpFkY6VwXc3v9mN4gPCEdjeafwqK+jaAoJ4Pdvm8Rm5qNerU18e9ARzQ21ROI/1cvv8Zj7aVAPAuNRQGHi/qG2pjXyx4trH6snyLKNZc3LRX+mZOLdG1YB8f9P+FjdAovudgKmfZHU5kNJwt93H8fybc9Iycf6opyvMQCFK6ro6PKRnJGDl85QHD55qL1axTkKvTbVuwyM9Kxb/1y+N28hITYaCgpq8LYwhpjZiziTUX/6ulDnPbchXcvnyExPhaq6ppo3q4LJi74D2oaP2bX3rfhXxzY9B9O3HuNI9vX4r7PFUhJSaPHQFdMXPgfkhPjsXHxDDy9fwvS0jLo5+oB91lLeMdHRYShb1MrTJj3L9iKSji+bwsS42JgZt0A0/9Zh/oOTqVeT/CrF9i34V8EvXiMgvx8WNa3g8espXBo7vxb11zeXj3zBwB06jOQb3unvoOwadks3Dx/gpdciqb9/5mahhbsHFvg+cM7pb7WjXPHoKisgpYdewjdv3HJDDh37gXb/6/+WRlU6E/pLK9HuBL4FSOdrWFloIGUrDwEfo3D24hE3hftpYCvyMjNx4hWVtBSYSP4ezKOPvyA95HJuDavh8CkdR777sJUTw0L+zaCX3Akdvq8gaqCLC6++Aq7OtpY2LcRrgSGYcOVl6hXWxPd7evwHX/2aShSs/Iw0tkaXIbBwbvv0X/TDfgu7MX7whXm0YdoDNx8Eza1NTGze0PISLFw6vFnDNh8A6end+FdjyjXLAyXy4i8CJkyW5bvC15UcamFVXJhiUdYWS1l/nLNLPWxy+ctVpx7jiHNLSAtJYVrL8Nw910k1g5txitnqqcGfXVF7PR5A2NdFdgZ6SAhPRurLwZAR1UBw1pa/nbsldmaeZNw99p5uLiOg4mFNdJTU/Du5XN8Cg7ifdHevnIOmRnp6D1sDNQ1tfH5/VtcPu6JLx+Cse/iPYHPwZKJI1DbxBzj5/2LZ/dv4+ieTVBSVcXty2dhbWuP8fP+xd1r53Fg80qY1W2ANl178x1/88JJpKcmw8XVA1wuF2cP78GUwd3gee0R6pgV/+8T+Pg+pg3rAfO6thg1bQFkpGVw7exRTBnaHVuPXeVdjyjXLAyXyxV5ETJFJRXIyQsujVykIC8PACDP5r/JYSsU/v7hzctSXyMxPgYamiXPuxgV/hVvXjxB1/7D+Ppxity6fAZvA57i+L1XiI74VuprVhQVOrncfhuBYS0t8e/A4u+GFvVtDEV5/stoZKqL8fvv4ennWN5690UaGGpjs1tLAIBbays4LjyNNZcCMbWLLRb0aQQAGNzcHHZzTuD4//sSfhYam4pH//aDoXbh2jA9HYzRctlZrL/8EnvGthEaI8MwmOXtjybmejgzvTPvg+7a2hptlp/Hf+df4Nq8HiJfszDfkzLQaP4pkcpudWuJQc0tfuv8eQUc7PZ9C0MtZTQy0S2x7OOP0XgeGofpXW35ts/uYY+opExsv/EGW68HAQDkZKSwybUlBjUz55WTk5HGwXHtMPGgH9x23uZtt6ihjmvzeggswVzVPb57E70Gj8K0pWuLLTNxwQrel16R+g6OWDrZDa+fP+Ktd1/E0sYOCzfsAQD0He6O/i3qYd/65RgxaTbGzy1sduk+YAR6NjbFlZOHBZJL+JePOOkXBIPadQAA7bq7YHDbhjiwaQX+3eElNEaGYbBm3iQ0aNwM245f430O+gx3x/COjbF7zVLsu3hP5GsWJiYyHH2bWpVeEMCijXvRfcCIYvfXNil8T75+9hCGJj/WJAp8/AAAEB8TVeL5Xz55gDcvnsBtytwSy904fwIA0MVFcAnknOwsbF8xH4M9psKgdh1KLuKiwpbFq7AExKZkQa+YJXuLEgvDMMjIyUdeAReNTQu//IK+JQgkl5/velksFhrW0UFYfDrfdrasDOrV1sI3IUOJO9ka8hILAJjqq6FN3Vq48+47GIYROr3324gkfI5JxaRODZCUwV+7aGVtgEP3QpCVWwBFeRmRrlkYXTUFkRchszLQKL3QLxYcf4JPMak4NqVjiZN9JqRnY8IBPxjpqGByF/7kIi8jjTo6qujYoDZ6NDKGrLQUzj4NxYwjD6DClkW3nxK5ulJhc2BnO0M4mukhOiUL264HYcjWm7g4pxu0VUpeF70qUVJWQfDrACSUsGRvUWJhGAZZGenIz89D/UaFNygfgl4KJJdeQ0bx/p/FYsHazgHfv33h2y7PZsO8XgNEhn8VeL2WHbrzEgsAGJqYw6l1Bzy551vs5+BTcBC+hX7EsPEzkJqcyLevcYu2OOe1FznZWWArKIp0zcJo6eiLvAiZiUXxi+MBQPN2XaBfyxA7Vi4CW1EJNvZNEBryDusWTIGMrCxyc7KLPTY5MR7LpoxETSNjjJg4u8TXuXn+BHT0a8KhmbPAvsPb16KgoKDUc1REFTq5LOvviMmefrCdcwJ2dbTRpl4t9G1iwtf8FJmUgX/OPMOtN995bfVF0rLz8auavyxqpaIgJ3S7moIcPkQlCxxvoqsmuE1PFb5vIpCWnQc1RcFq9pf/L6k87fCDYq4USM7MgaK8skjXLAxbVkasi5D9bPvNIBy5H4L5vR3Qvn7tYstl5uZj2DZfZOTk4/Kc7nyd/AAw79gjPPsci7tL+/ASVO/GJui17irmHnuEjg0MISsjhdSsXPRcexUe7W0w9afaj7N1TbRadg5brwfxdfRXdZMXr8byaWPQs7EprBo4oKlzB3ToPZCv+Sk2KgLbVizAozs3kJXBf1OUkS64pPevi1opqxS+r/UM+P99VVTV8fXje4HjaxubCm4zMYf/7evISEuFipq6wP7w/y+p/N+sccVcKZCanAi2gqJI1yyMPJsttkXI5OTlsenIRSyaMByLJwwHAEhJSWGQ+xS8evIQEWGfhR6XnZWJma59kZWZgT3n7vB18v/q/esAfPv8AUM9pkNKiv+mLSr8K47t3ow5q7eVeI6KqkInl56NjOFkoY/rL7/hXnAk9tx6iy3XX2GLWyv0dzIDl8tgwKYbSMzIwdQutjCvoQZFeVlwuQwGbbkJrpClan5+luJn0lKCd+PCVropad0hFoTvLIpjUd9GQjvAgR/9GKVdc3E4XC4S0nOK3f8zVQU5kTvFT/h/xL9nn2NUG2tM72ZXbLm8Ag5G7ryN4O9JODWtM99Is6L9xx99xLj2NgI1n862hlh6+hnC4tNgXkMdVwO/ISE9B11+WkkTKKwlmumr4fnnWJFiryradXeBnWML3L95CU/9buP4/m04tH0tFm3Yiy4uQ8DlcjFlSHekJCbAddJs1DGzAltREQyXwbRhPcDlcgXOKS0tvM9N2HahSz4J+yD8v1xxi3Mx/49jwvwVsGogfEZydU0dka65OBwOBymJ8cXu/5mSiprQPo6fGVtY4+itF/j6KQQpifEwNDGHlq4+ujY0Qm1jc4Hy+Xl5mOc+EKHv32DLsSswsSy5dnTj3HEAQGcXwVFie9b9A50aNVHfoSkivhYuy17UFJcUH4eIr6GoaWQskJQqigqdXABAV1UBrq2t4NraCqlZueiy6jLWX36J/k5mePc9CZ9iUrF1ZCu+NvuimkJ5CBVy7q9xaVBTlIOKgqyQI4A6OqoACjvSRaldlHTNxYlMyhR7n8uNV98w/chD9LA3xspBTYstx+UymHTQDw9ConBwfDuhs00nZ+SigMOggCv4RVW0reD/Xz7xaYXNDQUcwS9FDlf4Oao6LR099Bnmjj7D3JGemoIxPVvhwKb/0MVlCD6/f4Nvnz9g8aZ96NZ/OO+Y8C/C76zFIULIuSPCQqGipg4lFVWhx9Q0MgEAKCqriFS7KOmaixMbFSG2PpefGZtbAeaF5/38/i2S4mPRa8hIvjJcLhf/TBuNFw/vYtXeE6XONs3hcHDr8hmYWtnAvG4Dgf1x0ZH4HhaKAa1sBPZtWjYLm5bNwo2g71AvZcCApFTY5MLhcpGZUwBVRTneNjVFeRhqq+BVWOGdiYz0/++Qfrmz2uHzptzi8nkdge+JGailVVhN/RKXhjvvvqOHvXGxd2y2Rtow1lXFbt+3GNDUDMpsOb79CenZ0FZREOmaiyPuPpfHH6Mxdu9dNLPQx84xrYut8QHA/OOPceH5V2wY3hxd7IyEltFWZUNNUQ7XXn7D/N4OvJoTh8vFpRdfoSQvC1O9wqYZU/3C/557Foq6tX4Mow36loDPMakY2uL3BiNUZhwOB9mZGVBW/dEcq6Kmjhq16+D96wAAgLT0j37Hnx3bs6nc4np46ypiIsOhX9MQABDxNRRP7vmgbbc+xX4OrBrYo1YdU5zYuwVdXIZASVmFb39yYjw0tHREuubiiLPPRRgOh4PtK+ZDUUkZfYaO4du3YfF03Lp0GvPW7ECrTsKHFP/s+YM7SIyLwaAxk4Xud5+5GCmJCXzbvnwMxv6NKzBozGQ0aNQUSsrCE3lFUGGTS0ZOPmxnn0A3+zqoV1sTKmxZPPschztvv2NUG2sAgLm+Okx0VbH09DNEJWdCXUked95+R1RyZrnFZaKnih5rr2CkszUYBjhwNxhyMtKY1aP4hcekpFjY7NoCg7bcRIsl5zC4uQUMNBQRk5KFJ59jweUyOD+rq0jXXBxx9rlEJKZj+PZbYLGA7g7GuPiCv0O3bi1N1Pv/l/6eW2/hee89Gpnqgi0ng9NP+O9ouzY0gpK8LKSlpDC5cwOsOPcCnVdewsBm5pCRYuH8sy94/S0B83s7QE6msEmmUwNDWNfUwNbrQYhMyoSTuT6ikzNx4G4w2HLSGN9R8E6uqsrKSEePRiZo07U3zOo2gJKyCoJePMaTez7o51rYd2FkZonaxmbYunwe4qIjoaquicd3byI+OrKUs5ddbWNzePRpC5cRHmDA4IznbsjJyWP09EXFHiMlJYWF63dj2rCeGNzGDj0GukKnRk0kxEbj1TN/cDkc7DztI9I1F0ecfS4AsHDcUGjq6MHE0ho52dm4ef4EPr59hWVbPfn6rU7s34azh/egvoMT5NkKuH72GN95nLv0goIif7/ujXPHICUlhU69+Z+jKWLfVHCpELVHhQ8P12vYGG279/3TyytXFTa5KMjJYGQba/gFR+LG62/gcBgYaitjWf8mGNuuHgBARloK3pM7YOGJJ9jh8wbSLBba2tTCiWmdYTPzWCmvUDYujqYCD1Hu92hbaod7U4sauDa/JzZeeYlD994jPScPuqoKsKujwxupJso1/w3hCRlIyy4c4z/36COB/bN6NOQll7fhhc8UvAiNw4vQOIGyL1YNgJJ8YXPhlC62MNJWwZ7b77Dl2mtk5RbATF8N64c3x4hWP5oyZGWkcGlON2y+9ho+r8NxNTAM8rLScDLXx9xe9jDTVxf3JVdYbAVFuLiOw/MHt3Hf5wo4BQUwMKyDyYtXY+DoSQAAGRkZrD90DpuWzMTR3ZsgLSUNJ+cOWOB9Cd0aCq9J/qlOvQcKPET5356jpXa4N3Rqif2X7sNzyyqcPbIXmRlp0NLRg7VtI/QcPFLka/5brBrY4+ppL1w67glZOTnYNGyCnWd8BUbffXz3GgDwJuAJ3gQ8ETiPbZMQvuSSk52F+zcvo6FTS4HBFVUFixHaWyeawMBAODg44NaiXmhQTEd1VfHzE/pTfhliS6qWoG8JaL/iIgICAmBv/+PJ66L3+6Hrj2FVv3oukf3zE/ojJlW+4bHkz4S8eQm3Lk0FPhvCVMxhBoQQQio1Si6EEELEjpILIYQQsauwHfoVjaG2CuL2jZZ0GIRIlEHtOnjyXbSHdUn1RjUXQgghYkfJhRBCiNhVquTi/yEauu4H4P8hWtKh/JHwhHTouh/g/Zx/FirpkKq0V2HxlfbvHfDID0612Ah45CfpUP5IVEQYnGqxeT++F0WbqoiUv/ULp/H+XVqZCk7MW1bU5yJBw1tZwslcH41Mf6yPkpGTjx033+BVWDxehSUgMSOnxGdrzj8LxZ5b7/A5JhVSLMCshjo82tVDr8YmfOUS0rOx8vwL3HwdgbSsPBjrqmBsexuBhbdiU7Kw9/a7wtf/loD07HzscXdGnyaCs+CKKi0rD4tOPkHg13hEp2SCywWMdFQwqJk5RrWx5j2ZX+RhSBTWX36JV2EJkJZmwdFMD0tcmvBNhln03FFxWlkb4MyMwlUCjXRUsWN0a3yKTsHma6/LfB3kz/UeOhq2TZrDxuHHrNYhb17i0NY1+PD2JZLiYqGgpIw65pYYMnaa0GlUfC+ewvF92xD+5SOkWFIwMrPEwDGT0L5HP75yyYnx2LV6CR7euoaMtBTUqmOKgaMn8S0rUCQzIx27Vi/BnavnkJmWCrO6DTB21mI4lrCmfWmcaglfVM9tylyMm/MP7/en92/h1IEd+BQchJSkBKioacDcuj5cJ89BQ6eWfMdyOByc996Pi0cPIPLbF8izFWBqbYMRE2eVODPBq2f+GNe3cP/VwDBo6f6Y/69r/2GoZ98EF48dQPCrF2W+3l9RcpGgxia6ApNRJmXkYMOVlzDQUIKNoSb8gotfkGiXzxssPf0MbW1qYWHfRijgcHH2aSjc995FcmYu3JwLp4zJyMlDjzVXEZ2ciTHt6qKmpjJuBUVgxpGHSMnKxaROPybN+xybim03gmCkowKb2lp4/DHmj68zPScPobGp6GRriJqaSpBisfA8NA5LTz/F89BYHBj340Nx+00Ehm33hZWBBub3cUBuPgee996jx9oruLmgJ0z+P/+YlgobO0YLrkj47HMsDvuFoE29H089ayjJo7+TGfw/RFNykTAbB0eBiSejvn1FXm4Oug8YAW29GsjOysS96xcwZ3R/zFqxGf3cfkz5cmzvFmxdPhdOzh0xft6/KMjPx83zJ7Bo/DCkpSSj73B3AIXJwqNPW8RFR2LAqAnQq2mIR7evY9WcCUhPTcGw8TN452QYBnNH98ebF08waOwU6NesjWunj2LGiN7YcuwKGjUXvgigKBo1d0a3XybHNLPmn74o7NMHyMrJw8XVAxpaOkhLScbN8ycwcUAnrDlwGi07dOOV3bxsNk577kTHXgPQd4Q7sjIycOnEIUwd0h0bDp1Hs3aC8wtyuVxsXDwDCopKyM4SnBqrrl0j1LVrhOcP71Byqcr01BQRtG4Q9NWVSr07P3AnGHZG2jg+pSNvssDhrSzReP5pnHj0iZdcDvmFIDQ2FcendES7/6/HMtLZGq47bmHdpUAMambOW3zL1kgLIZuGQlOZDf8P0eiz/tofX1NNTWVcncd/B+rmbA1VBTkcuBuMyKQM1NQsnAh02elnqKmhhGvze/Amt+zvZIami8/gv/MveIlISV5W6CzRPq/DIcViwcXRRGAfqZjadu8rME9W/5ET4NalKU7s28qXXE577oS1rQM2eV3kved7Dx0Nl+bWuHLyCC+5nPfah/Avn7DxyEU0a9sJAOAyYizmjh6AfRv+RbcBw6GhVTi9v9+NS3jhfw9LNx9Al35DAQDd+o/AsA6NsOWfufDyeVbma6ttbFbiLM4AMHD0RAwcPZFvWz+3cejbzBonD2znJZf8vDxcPHYAzl16Y/mOI7yy3QYMR3cHY1w94y00uZz33o/YqO/oOXgkTh7YXuZr+V3l1udy6cVX6LofwP33gnfe55+FQtf9AB79v+/k3fckTD54H40XnELt8YdgNd0bY/fewffEjFJfp/e6q+i9TnAW1MkH78Nh3km+bQzDYP/td2i97Bxqjz+EujOOYtqhB0gUcR2Uv0FeVhr66qIt45uRmw8dVQW+WWjZsjJQV+Rfr+Xpp1ioKcrxEkuRfk6myM7j4PrLH0unKrPloKksvDovbkUzS6f/f1G3lMxcfIhOQZeGRnzx19BQQnOLGvB5HSGwINzPMnLy4BMUjuZWNUT+G4rT7Stn4VSLjecP7wjs8714Ck612Ah8fB8A8Cn4DZZPHwOX5tZoZaqGTvVrYtGE4YiJDC/1dcb364Dx/QSba5ZPH4PeTvwzRjMMg1MHd2JoOwe0MlVDF9va+G+mB1KSEgSOr0ikpaWhW6MmMtLT+LZnZWRAU1uX7z0vz2ZDRU2db22WV8/8oaKmzkssRTr1GYTcnGz43bjE23br8hmoqmuiY59BvG1y8vLoNXQ0b/XMP5Gbk4Oc7OJXrRSGraAIdU0tZP50/fn5ecjLzYW2Lv+SFqrqmpCTZwtdmyY1OQl71/0D91lL+GaZ/hvKrebSoUFtKMnL4sLzULSyNuDbd+H5V+irK/KWIPYLjsSXuFQMbGoOPTUFhMWn47BfCAK/XsP9ZX15Sxn/qdnej3DM/wMGNjXHqDZ1EZmUgf13gvEyLB43F/YEW7b418kv4PImcyyNqoIcZGXKf6xEM4sauPbyG/bceovOtkbgcLnwfvABX+LSsKx/E165vAKO0MXBira9/paI4QJ7xS83n4OMnHxk5xUg8Gs8dtwMgqGWMm+q/dwCDl9cv8aaW8BBSGQyXx/Vzy4HhCE7j4P+jmXvH/oTzdt1haKSMnwvnkbjFm359t26dAY6ega8NT6ePbiNiK+f0bXfMGjp6iPy21ec996H4FfPcex2IG/Z4j+1dv5kXDpxCF37DUO/keMQExmB0567EPzqBQ5e9Yc8u/gbiYL8fKGrWAqjrKIGGVnh6xmJKiszA3m5OUhLSYHfjYt4fPcm2vfsz1fGvmkr+N24iBP7t6FVx+4oKODg0nFPRHz9jMmLVv+IPS8PbLbg37Do7/rhzUvetg9vXsKqQUOBhdLqNWzM229kWrZlHm6cO44LRw+AYRgYmphjxMRZ6D7QVWjZjLRUFBTkIzkhHldOHsGXD8FwnTyHt19RSRlWDexx5dQR1G3YCPZNWyErIwPeuzcCDCN0Us8965ZBW1cffYaNwcHNK8t0DWVVbslFQU4GnWwNcTXwG9YMac77sk3PzsPdd9/h2tqKt0aIW2trTOhYn+/4TraG6L7mCq6+DCtxkSxRPfsciyP3Q7BtZCsM/GlhMee6NdF7/TWcevyZb2ZegeNDY0VuIjo/qyuaW4q+7ndZrR7aDKnZeVh88ikWn3wKAFBRkIXXpA5oa/Ojz8FUTw33giPxJS4NJro/1n8oqjnGpJTfEgU/O/3kM2Ycecj73d5YB5tdW/LeGzoqClBTlMOTT/z9PHkFHAR+LZxxObqEWM88DYWCnDS6O9QRf/AiYCsooEWHbrh3/SLmrNzK+7LNTE/Dk3s+6DPcnbdqoMuIsRjqMY3v+JYdu2Fs7za4e+1CqU0pogh68Rjnvfdjyeb96NpvGG+7Y6v2mNC/I66f8UbvYWOKPf7180eYOKBTsft/tuPUTTg0E+wD+x1r5k3CzfMnABTWXNp2d8GclVv5ysz6bzPS01KwedlsbF5WOHGmkooq1nueg5Pzj9pcbRNzPL1/CxFfQ/mWZA58UrjUeFzMjxaVhNho1HcQXDZbR6/wMxwfW7bRqfUbOaF9936oYVgHiXHROO+1HytmeiAlKZGvz6fIDNfeCHr+GAAgL8+Gi6sHxvyyhMHy7YexeOIILJ/2499NR88AO0/7wKIe/6CfT8FBuHj0ADZ6XSx25dHyVK59Ln2amODcs1D4vY/krb1+/dU35ORz0Pun0Uw/10wycvKRV8CBqZ4a1BTlEPQtQSzJpWhBqrY2tfiawSwNNKCpLA//kOgSk0u9WpoiL8ZV76cFrsqTopw0THRVoaPCRhc7I+QWcHDYLwSjd9/Bqemd0NhUDwAwrKUlDvuFwH3PHawY6AgDDWXcfhuBQ34hAIDsfM5fibd9/Vo4Pb0zUrPycC/4O96GJyElK5e3X0qKhRGtrLDtRhAWn3yCEa2skM/hYsOVl4hNLWxWyCkm1piUTPiHRKNXI2OBxdj+pg69BsDnwkk8u3+b1/59/+Zl5Obm8N2F/1wzycrMQH5eLgxNzKGipo4Pb16KJbncvnwWikrKcHLuyNcMZmJZF2oaWgh47FdicjGv20DkhbeEraT4u1wnzUG3AcOREBuNG+eOIzsrE9lZmXzNOWwFRdQ2NoOmlg5ade6J/NxcnPPehwUeg7Hl2BXUd3ACAPQaMgrnvfdh4fihmL5sHfQMauHRXR+c89oLAMjN+dFMlZuTDVk5eYF45OTZAmV/x74L9/h+7zHQDaN6tMT+jSvQe+hogWaq6cvWIy0lCTGREbhy8ggy09OQl5sDWbkf72clZRUYmVrAqn5DODl3RHpqMo7v24qZbn2x68wtGJr8+K7csHg6mrXrAsdW7csU/58q1+TSpl5NqCvK4fyzL7zkcuH5Fxhqq8DB5EfTRkpmLv499xxXAsKQnJnLdw5Rm6JKExqbiszcfNQrZp2XhPSS30DqSvJiW4xLXEbuug0WWDj1U9Lr3dgELZeew/xjj3FrcW8AhYt77XZ3xtyjj9BrXWHtS5kti/8GOWHqoQdQlv+z5gxR6asr8fpCejYyxtpLgRi05SaerugPPfXCL9u5veyRkpmLfbeDsefWOwBAA0MtTOhYH1uuv4YyW3isZ59+AZdh0M9JMk1iRZxad4CqmgZ8L53iJRffS6dhYFgHNvY/mirTUpKxc9Ui3Ll6HmkpSXznELUpqjThXz4hKzMDXe0Mhe5PTih5dVNVdQ2xLrxVGhPLurw157u4DMXEAZ0we1Q/eF715/WxzB87CCwWC1uOXuEd175nfwxu2xDrF03H4euFd/5m1jZYvv0I1i2YwuufUlRWwYx/1mPFTA8oKf1YBVOerYD8PP7vHQDIy83h7RcHGVlZDBw9Ef9Od8ebgKdo2qYj335rWwfe/3fuOwQjOjXB8unuWLO/sO+4oKAAkwZ1gW3jZpi3ZgevbOvOPdG/hQ12rlqE1fsKa34+F07ibeAzHLv9EpJSrslFTkYaXRvWweXAr8jJL0BOHgd+wVECKwm6772Dp59iMaFjfdgYakGZLQsWAI99d8EVXEadD4vFEljeFQA4DP+BXIaBprI89rgLH1aoriR45/KzvAKOQOIrjoaSvMCzG+IWFp8Gv+AorBnajG+7nIw02tnUwoG7wcjOK+D1X/RwMEZnWyMERyYhN5+DerU1EZVU2MRkqieZpVJ7NTLG+ssvceN1OFxbW/Hi3zCiBRb0aYRP0SlQVZRD3Vqa+Pfs8//HKrxT8syTz9BWYfMNQZYEWTk5tO7cE3eunUduTg5yc7Lx7MFtDBk7la/cognD8PqpP4aMmwaLenZQVFYGi8XC4gkjwC3lTV/ce57L4a/VMVwu1DS08O/OIwJlAUBVreTlrvPz8gQSX3FU1TX57rD/FIvFQtvuLli/cCrCv3yCkakFIr99wbP7tzH7l6YyWTk5NG3TCWcO7UJOdjavY7tttz5o1bE7Pr9/g7y8XJjXbYDYqO8ACpvNimjp6iMhTnDIfVFzWFHzmDjo/X9hsNL+rvJsNlp06I5jezbxrunV0wf4+vE9X98SAKhpaKFBk2YIevGYt23bigVo260vWCwWIr6G/v81kwEA0RHfUFCQDz0D/gE+4lbuQ5F7NzHBMf+PuPXmO1Izc5HP4fI1iaVk5sIvOAqzezTE7J4/Fp/JyS9ASmbptRZVBTmEJ6QLbP91pFkdHVX4BUfBwUS32LvfkjwPjatQfS7xaYU1LQ5H8IuogMsFwwAcLv8XkKyMFGx/WtTNL7hwGVxnCX0hFzVxCaudaqmwoaXyY1SMX3AkamkqwUxIcvkQlYx335Mwpm1dyEhLftKJ9r364/LJw3h05wbSU5NRkJ+PDr0G8PanpSTj2f3bGDNjEcbM+NGmnpuTg/TU5FLPr6KqjqiIMIHtv440q1nHBM8e3IaNvSMUlZR/+zqCXjz+q30uvypqjspIK6zJJSUU9rtxCgoEynI4BWAYBlwuf4KVkZWFVYMf3yvP7t8GADi2/lEjs2pgj+cP7oDD4fD1Tbx7WXhDYynGheG+h30BAGho65RaNjcnGwzDICszHWwFBSTF///6OUKuv6CA7+8SHxMJnwsn4XPhpEDZMb1aw8SyLo7dDizrZYik3JNLS6sa0FZh4+LzL0jJyoW5vhpsamv9CEC6sLr7633YHt934IqwSGYdXRXcefsdCenZvGc13kYk4tnnONTU/DEctXdjY3jee48NV15iab8mfOfgcLlIz84vsfZS0fpcTHTVIMVi4fzzLxjVpi5vcERGTj58XkfARFe1xCQal5aNbTeCUN9QCy2tyjcRJqbnQEtFcFSS94MPAAC7UlYxPfPkM4LCE7FioCPvOn92+slnAEA/MfTNiUOj5m2goa2LW5fPID01GUZmlnx9EtIyhR+7X2sfJ/ZvLbXWAhQmjcf3biI5MZ73rMan4CAEPX/Mt2Ru+x79cPbwHhzYtBKTF/GPFOJwOMhMT4OqevG1l7/V5/LzdRTJz8vDjbPHIM9WgLFF4fNatY3NICUlBd9Lp9DPbRxvcERWZgYe+l5FbWOzEpNoYnwsvHZugIWNHd+DkW279cWtS6fhc+Ekr68rLzcXF48egKmVTZlGiqUmJ0FNg/97ICszA8f2bIaSiips7H8MIBB2/anJSfC7cQn6NWtDU7uwC8Hw/3H4XDjB92BlXNR3vH7mzzfrwcrdgs3/t6+cxe0rZzF/7U7o1xLeVCpO5Z5cpKWk0MPBGCcefUReARczutnx7Vdmy6G5ZQ3suBmE/AIuamkp4+nnGDz+GANN5ZKbqgBgaAtL7PF9h4Gbb2JICwskpOXgsN97WBqo8z0T0dSiBka1scaOm28Q/D0JberVgpyMFL7GpeFqYBjm9LTHoObFv4n+Zp/LgTvBSM3KRer/7+gfhkSj4P81lDFt60FVUQ5aKmwMa2mJI/dD0HPdVfRqZIy8Ai6OPfyAqORMgafXmy8+gx6NjFFbUxlRyZnwevAB+QVc7Bzdmu+ZAQDYeKWwnTY8obD2d+3lN3yNKxxvP6P7j7u4tZcCsf7yy1JragfuBuPm63B0qF8bNTWVkJadjztvI/AgJBqdbA3R8qeh6qeffMblF1/hZKEPVQU5PPsci1OPP6Njg9oY3bauwLkZhsG5p6Ew1VODvXHpd4N/g7S0NNp264MrJ4+gID8PI6fO59uvpKwC+6at4L1rIwry86BfyxCvnz3CyycPoKahVcxZf+gxyA0n9m3F1KE90HOQG5IT4nDOez+MLeoiM+PHcxENnVqin+s4HN29EaHv38DRuQNk5eTxPSwU966dx5iZi9H9l6fHf/a3+lwWjR8GtoIibBwcoaWjh4TYaFw/ewwRXz9jypI1vIShrqn9/476/Rjn0g7tuvdDQX4eLp04hLjoSCzdcpDvvAOdbdGuW1/o1zJEXHQkLh49iPz8PPyz1ZPvPd+ma280dGqJ1XMm4FvoB+gb1MbVM96I/PYFm7wv8Z1z34Z/cWDTf6XW1E4d3IFn92+jUQtn6BnURmJcDK6cOoLoiG+Yt2YHXxJ07+UMqwb2sKhnCzUNTUR//4YrJ48gKT4WK3Z688pZ1W8Ip9Yd4HvxNLIyMtC0bWdkpKXg7OG9yM3Jhtvkubyyvz6UCgChHwr7MFu078o3/Ut5+StP6PdpYgLPe+95//+r3WOcsfDEExy5H4J8DhdNzfVxdmZX9N94vdRzW9RQx/ZRrbDmUiCWnnoKixrq2DG6Nc49/YJHH/mHEK4e0gwNDLVx2O89Vl14AWkpKdTSVEKPRsZoYWVQzCv8fTt93iDip2a9e8GRuPf/Jqx+TmZQVSxs214ztClsamvC+8EHrL4YgAIOF3VraeLg+Hbobl+H75z1DbVw6tEnxKVlQ01RHm3r1cLcXva8Bxl/tvoif3X54ouvuPjiKwD+5JKZmw8WC9BVLbnDs5W1AYK/J+Hk40+IT8uGjLQULPTV8e9AR4xuw58wTPXUkJadhy3XXiMrtwDGuipY2q8x3NvVg7SUYJPXk08x+J6UiTk/NalWBB16DcDZw3t4//+r5dsPY9PSWTjvfQAFBflo6NgC20/exOTBXUo9t7G5FZZsPoB9G5Zjy/I5MDa3xrItB3DzwkneQ5pFZv23GZb17XDeaz92r1kKaRkZ6NesjbbdXdCoubNYrvVPdXEZgmtnjuLUwZ1IS0mCkrIqrBo0xOTFq9GqY3e+srP+2wLzera4eOwg9q7/BwX5+TCv2wCr9p5Am669+cpa1rPFtTPeSIyPhYqaBpycO2DsrCXQr8l/185isbDe8yx2rV6Ci8c8kZWeBlMrG6w/dF4guWZnZYLFYpX65dygcVO8DXyKS8cPITU5EWwFRVg1sMfs/7YKPNjZe+ho+N24hOcP7iAjPRWq6pqo7+CIIWOn8p6LKrLmwGmc2L8VPudPYsd/C8CSkkJdWwcs23oQ9k1bifLn/mtYjLCeQREFBgbCwcEBtxb1QoNSmjbID0XTuqwa3BS9G5tARUG23AcAlIdO/11ELS1lvrnBKqICDhepWXl4FhoL1x23Sp2IM+hbAtqvuIiAgADY2/9IWkXv90PXH8NKjO3w1UFURBj6NrXCzH83oUOv/lBSVhXrAIC/ZVS3FtCvZYiVe4SPOq2MsrMykZuTjQ2Lp8PvxiXcDy1+tGLIm5dw69JU4LMhjOR7P6ux+ccfw3rGUVwNDJN0KL8tPTsP774nYW4vh9ILS9jbiERYzzgK1x23JB1Ktbdh8XR0blAL965fkHQovy0zPQ2f3gfBfdYSSYciVjv+W4jODWrB9+JpsZ6XJq6UAF01Bb7BAdZ/6aFLcVJRkMP3XSMlHYZIzPTVKv3fu7LT0tHnGxxgamVTQumKSUlFFQ++pJVesJLpP2oCWnUunFhWWkp8LSiUXCSALStT4R7IrMqU2XL095YweTb7rz6QSURnZGpR5rnTSkLNYoQQQsSOkgshhBCxo+RCCCFE7Ci5EEIIETtKLoQQQsROLKPFPkaniOM0hFQIpb2fwz6F/J1ACKlgfue9/0fJRVtbG4oKCphwwO9PTkNIhaOooABtbf5ZJ7S1taGoqIhlUyrH8z2ElAdFRUWBz4YwfzT9CwCEh4cjISGh9IKEVCLa2towNBScOZbe76S6K+6z8as/Ti6EEELIr6hDnxBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSIHSUXQgghYkfJhRBCiNhRciGEECJ2lFwIIYSI3f8A5sskIqIjzhEAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Create a DecisionTreeClassifier with specified hyperparameters\n",
+ "clf_oversampled_pca = DecisionTreeClassifier(random_state=0, ccp_alpha=0.010)\n",
+ "\n",
+ "# Fit the classifier on the PCA-transformed oversampled training data\n",
+ "clf_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled,)\n",
+ "\n",
+ "# Evaluate the model on the PCA-transformed test data\n",
+ "y_pred_test_dt_pca = clf_oversampled_pca.predict(xtest_pca_oversampled)\n",
+ "\n",
+ "print('Accuracy Score on train data: ', accuracy_score(y_true=ytrain_oversampled, y_pred=clf_oversampled_pca.predict(xtrain_pca_oversampled)))\n",
+ "\n",
+ "DT_models.append(clf_oversampled_pca)\n",
+ "DT_name.append(\"Oversampled Dataset (PCA) ccp_alpha: 0.010\")\n",
+ "x_test_list.append(xtest_pca_oversampled)\n",
+ "\n",
+ "plt.figure(figsize=(5,5))\n",
+ "plot_tree(clf_oversampled_pca, filled=True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "id": "112aa85c-38a5-46b8-9d8d-3e685be6596a",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating testing data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled Dataset (PCA) ccp_alpha: 0.001...\u001b[0m\n",
+ "Oversampled Dataset (PCA) ccp_alpha: 0.001 Accuracy: 0.6698074559606718\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled Dataset (PCA) ccp_alpha: 0.001 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.90 0.68 0.77 2035\n",
+ " 1 0.28 0.62 0.38 406\n",
+ "\n",
+ " accuracy 0.67 2441\n",
+ " macro avg 0.59 0.65 0.58 2441\n",
+ "weighted avg 0.80 0.67 0.71 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled Dataset (PCA) ccp_alpha: 0.002...\u001b[0m\n",
+ "Oversampled Dataset (PCA) ccp_alpha: 0.002 Accuracy: 0.5931995083981975\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled Dataset (PCA) ccp_alpha: 0.002 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.56 0.70 2035\n",
+ " 1 0.26 0.76 0.38 406\n",
+ "\n",
+ " accuracy 0.59 2441\n",
+ " macro avg 0.59 0.66 0.54 2441\n",
+ "weighted avg 0.81 0.59 0.64 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled Dataset (PCA) ccp_alpha: 0.005...\u001b[0m\n",
+ "Oversampled Dataset (PCA) ccp_alpha: 0.005 Accuracy: 0.4506349856616141\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled Dataset (PCA) ccp_alpha: 0.005 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.96 0.36 0.52 2035\n",
+ " 1 0.22 0.92 0.36 406\n",
+ "\n",
+ " accuracy 0.45 2441\n",
+ " macro avg 0.59 0.64 0.44 2441\n",
+ "weighted avg 0.84 0.45 0.49 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled Dataset (PCA) ccp_alpha: 0.010...\u001b[0m\n",
+ "Oversampled Dataset (PCA) ccp_alpha: 0.010 Accuracy: 0.4506349856616141\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled Dataset (PCA) ccp_alpha: 0.010 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.96 0.36 0.52 2035\n",
+ " 1 0.22 0.92 0.36 406\n",
+ "\n",
+ " accuracy 0.45 2441\n",
+ " macro avg 0.59 0.64 0.44 2441\n",
+ "weighted avg 0.84 0.45 0.49 2441\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 900x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(DT_models, DT_name, x_test_list, ytest, \"testing\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "bbb453fc-c18b-47e5-ba91-f1c6bf48c4a2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "clf = DecisionTreeClassifier(random_state=0)\n",
+ "path = clf.cost_complexity_pruning_path(xtrain, ytrain)\n",
+ "ccp_alphas, impurities = path.ccp_alphas, path.impurities\n",
+ "\n",
+ "clfs = []\n",
+ "for ccp_alpha in ccp_alphas:\n",
+ " clf_oversampled_pca = DecisionTreeClassifier(random_state=0, ccp_alpha=0.015)\n",
+ " clf_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled)\n",
+ " clfs.append(clf_oversampled_pca)\n",
+ " \n",
+ "clfs = clfs[:-1]\n",
+ "ccp_alphas = ccp_alphas[:-1]\n",
+ "\n",
+ "# Calculate training and testing scores for each classifier\n",
+ "train_scores = [clf.score(xtrain_pca_oversampled, ytrain_oversampled) for clf in clfs]\n",
+ "test_scores = [clf.score(xtest_pca_oversampled, ytest) for clf in clfs]\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.set_xlabel(\"alpha\")\n",
+ "ax.set_ylabel(\"accuracy\")\n",
+ "ax.set_title(\"Accuracy vs alpha for training and testing sets\")\n",
+ "ax.plot(ccp_alphas, train_scores, marker='o', label=\"train\",\n",
+ " drawstyle=\"steps-post\")\n",
+ "ax.plot(ccp_alphas, test_scores, marker='o', label=\"test\",\n",
+ " drawstyle=\"steps-post\")\n",
+ "ax.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f4b8bf6",
+ "metadata": {},
+ "source": [
+ "## Support Vector Machine (SVM)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 195,
+ "id": "cd2bb756",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "SVM_models = []\n",
+ "SVM_name = []\n",
+ "x_val_list = []\n",
+ "x_test_list = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "id": "ca8d52e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gamma=0.1\n",
+ "txt = \"gamma: 0.1\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 197,
+ "id": "d00c0de6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-79 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-79 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-79 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-79 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-79 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-79 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-79 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-79 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-79 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-79 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-79 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-79 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-79 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-79 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-79 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-79 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-79 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-79\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-79\" type=\"checkbox\" checked><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVC(gamma=0.1, probability=True, random_state=42)"
+ ]
+ },
+ "execution_count": 197,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#SVM model - undersampled dataset\n",
+ "\n",
+ "# Initialize the SVM classifier\n",
+ "svm_oversampled = svm.SVC(probability=True, random_state=42, gamma=gamma)\n",
+ "SVM_models.append(svm_oversampled)\n",
+ "SVM_name.append(f\"Oversampled dataset(No PCA), {txt}\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "svm_oversampled.fit(xtrain_oversampled, ytrain_oversampled)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 198,
+ "id": "83303917",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-80 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-80 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-80 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-80 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-80 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-80 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-80 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-80 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-80 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-80 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-80 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-80 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-80 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-80 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-80 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-80 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-80 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-80\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-80\" type=\"checkbox\" checked><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVC(gamma=0.1, probability=True, random_state=42)"
+ ]
+ },
+ "execution_count": 198,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#SVM model - oversampled dataset\n",
+ "\n",
+ "# Initialize the SVM classifier\n",
+ "svm_undersampled = svm.SVC(probability=True, random_state=42, gamma=gamma)\n",
+ "\n",
+ "SVM_models.append(svm_undersampled)\n",
+ "SVM_name.append(f\"Undersampled dataset(No PCA), {txt}\")\n",
+ "x_val_list.append(xval)\n",
+ "x_test_list.append(xtest)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "svm_undersampled.fit(xtrain_undersampled, ytrain_undersampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 199,
+ "id": "0ec8bb49",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-81 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-81 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-81 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-81 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-81 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-81 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-81 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-81 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-81 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-81 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-81 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-81 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-81 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-81 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-81 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-81 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-81 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-81\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" checked><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVC(gamma=0.1, probability=True, random_state=42)"
+ ]
+ },
+ "execution_count": 199,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#SVM model - oversampled dataset\n",
+ "\n",
+ "# Initialize the SVM classifier\n",
+ "svm_oversampled_pca = svm.SVC(probability=True, random_state=42, gamma=gamma)\n",
+ "\n",
+ "SVM_models.append(svm_oversampled_pca)\n",
+ "SVM_name.append(f\"Oversampled dataset(PCA), {txt}\")\n",
+ "x_val_list.append(xval_pca_oversampled)\n",
+ "x_test_list.append(xtest_pca_oversampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "svm_oversampled_pca.fit(xtrain_pca_oversampled, ytrain_oversampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 200,
+ "id": "f77e3692",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-82 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-82 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-82 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-82 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-82 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-82 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–¾\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-82 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-82 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-82 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-82 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-82 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-82 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-82 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-82 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-82 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-82 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-82 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-82\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" checked><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(gamma=0.1, probability=True, random_state=42)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVC(gamma=0.1, probability=True, random_state=42)"
+ ]
+ },
+ "execution_count": 200,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#SVM model - oversampled dataset\n",
+ "\n",
+ "# Initialize the SVM classifier\n",
+ "svm_undersampled_pca = svm.SVC(probability=True, random_state=42, gamma=gamma)\n",
+ "\n",
+ "SVM_models.append(clf_undersampled_pca)\n",
+ "SVM_name.append(f\"Undersampled dataset(PCA), {txt}\")\n",
+ "x_val_list.append(xval_pca_undersampled)\n",
+ "x_test_list.append(xtest_pca_undersampled)\n",
+ "\n",
+ "# Fit the classifier to the training data\n",
+ "svm_undersampled_pca.fit(xtrain_pca_undersampled, ytrain_undersampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "2d706ad2",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating SVM validation data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), gamma: 1.5...\u001b[0m\n",
+ "Oversampled dataset(No PCA), gamma: 1.5 Accuracy: 0.8153583617747441\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), gamma: 1.5 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.85 0.94 0.90 2481\n",
+ " 1 0.26 0.11 0.16 449\n",
+ "\n",
+ " accuracy 0.82 2930\n",
+ " macro avg 0.56 0.53 0.53 2930\n",
+ "weighted avg 0.76 0.82 0.78 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA), gamma: 1.5...\u001b[0m\n",
+ "Undersampled dataset(No PCA), gamma: 1.5 Accuracy: 0.668259385665529\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA), gamma: 1.5 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.69 0.78 2481\n",
+ " 1 0.24 0.53 0.33 449\n",
+ "\n",
+ " accuracy 0.67 2930\n",
+ " macro avg 0.56 0.61 0.55 2930\n",
+ "weighted avg 0.79 0.67 0.71 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), gamma: 1.5...\u001b[0m\n",
+ "Oversampled dataset(PCA), gamma: 1.5 Accuracy: 0.8153583617747441\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), gamma: 1.5 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.85 0.94 0.90 2481\n",
+ " 1 0.26 0.11 0.16 449\n",
+ "\n",
+ " accuracy 0.82 2930\n",
+ " macro avg 0.56 0.53 0.53 2930\n",
+ "weighted avg 0.76 0.82 0.78 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA), gamma: 1.5...\u001b[0m\n",
+ "Undersampled dataset(PCA), gamma: 1.5 Accuracy: 0.6095563139931741\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA), gamma: 1.5 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.88 0.62 0.73 2481\n",
+ " 1 0.21 0.55 0.30 449\n",
+ "\n",
+ " accuracy 0.61 2930\n",
+ " macro avg 0.55 0.59 0.52 2930\n",
+ "weighted avg 0.78 0.61 0.66 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), gamma: 0.5...\u001b[0m\n",
+ "Oversampled dataset(No PCA), gamma: 0.5 Accuracy: 0.7631399317406143\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), gamma: 0.5 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.88 0.84 0.86 2481\n",
+ " 1 0.29 0.37 0.32 449\n",
+ "\n",
+ " accuracy 0.76 2930\n",
+ " macro avg 0.58 0.60 0.59 2930\n",
+ "weighted avg 0.79 0.76 0.77 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA), gamma: 0.5...\u001b[0m\n",
+ "Undersampled dataset(No PCA), gamma: 0.5 Accuracy: 0.6450511945392492\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA), gamma: 0.5 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.64 0.75 2481\n",
+ " 1 0.25 0.68 0.37 449\n",
+ "\n",
+ " accuracy 0.65 2930\n",
+ " macro avg 0.59 0.66 0.56 2930\n",
+ "weighted avg 0.82 0.65 0.69 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), gamma: 0.5...\u001b[0m\n",
+ "Oversampled dataset(PCA), gamma: 0.5 Accuracy: 0.7631399317406143\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), gamma: 0.5 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.88 0.84 0.86 2481\n",
+ " 1 0.29 0.37 0.32 449\n",
+ "\n",
+ " accuracy 0.76 2930\n",
+ " macro avg 0.58 0.60 0.59 2930\n",
+ "weighted avg 0.79 0.76 0.77 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA), gamma: 0.5...\u001b[0m\n",
+ "Undersampled dataset(PCA), gamma: 0.5 Accuracy: 0.6095563139931741\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA), gamma: 0.5 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.88 0.62 0.73 2481\n",
+ " 1 0.21 0.55 0.30 449\n",
+ "\n",
+ " accuracy 0.61 2930\n",
+ " macro avg 0.55 0.59 0.52 2930\n",
+ "weighted avg 0.78 0.61 0.66 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), gamma: 0.1...\u001b[0m\n",
+ "Oversampled dataset(No PCA), gamma: 0.1 Accuracy: 0.669283276450512\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.67 0.77 2481\n",
+ " 1 0.27 0.69 0.39 449\n",
+ "\n",
+ " accuracy 0.67 2930\n",
+ " macro avg 0.60 0.68 0.58 2930\n",
+ "weighted avg 0.82 0.67 0.71 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA), gamma: 0.1...\u001b[0m\n",
+ "Undersampled dataset(No PCA), gamma: 0.1 Accuracy: 0.6399317406143344\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.93 0.62 0.75 2481\n",
+ " 1 0.26 0.74 0.39 449\n",
+ "\n",
+ " accuracy 0.64 2930\n",
+ " macro avg 0.60 0.68 0.57 2930\n",
+ "weighted avg 0.83 0.64 0.69 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), gamma: 0.1...\u001b[0m\n",
+ "Oversampled dataset(PCA), gamma: 0.1 Accuracy: 0.669283276450512\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.67 0.77 2481\n",
+ " 1 0.27 0.69 0.39 449\n",
+ "\n",
+ " accuracy 0.67 2930\n",
+ " macro avg 0.60 0.68 0.58 2930\n",
+ "weighted avg 0.82 0.67 0.71 2930\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA), gamma: 0.1...\u001b[0m\n",
+ "Undersampled dataset(PCA), gamma: 0.1 Accuracy: 0.6095563139931741\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA), gamma: 0.1 Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.88 0.62 0.73 2481\n",
+ " 1 0.21 0.55 0.30 449\n",
+ "\n",
+ " accuracy 0.61 2930\n",
+ " macro avg 0.55 0.59 0.52 2930\n",
+ "weighted avg 0.78 0.61 0.66 2930\n",
+ "\n"
+ ]
+ },
+ {
+ "ename": "IndexError",
+ "evalue": "tuple index out of range",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[83], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predict(SVM_models, SVM_name, x_val_list, yval, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSVM validation\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "Cell \u001b[1;32mIn[36], line 38\u001b[0m, in \u001b[0;36mpredict\u001b[1;34m(models, names, x_list, Y, eval_type)\u001b[0m\n\u001b[0;32m 36\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m5\u001b[39m))\n\u001b[0;32m 37\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, (fpr, tpr, roc_auc, name) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mzip\u001b[39m(fpr_list, tpr_list, roc_auc_list, names)):\n\u001b[1;32m---> 38\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(fpr, tpr, color\u001b[38;5;241m=\u001b[39mcolors[i], lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (area = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mroc_auc\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 39\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m], [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnavy\u001b[39m\u001b[38;5;124m'\u001b[39m, lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 40\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlim([\u001b[38;5;241m0.0\u001b[39m, \u001b[38;5;241m1.0\u001b[39m])\n",
+ "\u001b[1;31mIndexError\u001b[0m: tuple index out of range"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 700x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(SVM_models, SVM_name, x_val_list, yval, \"SVM validation\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 194,
+ "id": "baf45f6f",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mEvaluating SVM testing data\u001b[0m \n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(No PCA), ...\u001b[0m\n",
+ "Oversampled dataset(No PCA), Accuracy: 0.6759524784924211\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(No PCA), Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.67 0.78 2035\n",
+ " 1 0.30 0.70 0.42 406\n",
+ "\n",
+ " accuracy 0.68 2441\n",
+ " macro avg 0.61 0.69 0.60 2441\n",
+ "weighted avg 0.81 0.68 0.72 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(No PCA), ...\u001b[0m\n",
+ "Undersampled dataset(No PCA), Accuracy: 0.6497337156902908\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(No PCA), Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.64 0.75 2035\n",
+ " 1 0.28 0.71 0.40 406\n",
+ "\n",
+ " accuracy 0.65 2441\n",
+ " macro avg 0.60 0.68 0.58 2441\n",
+ "weighted avg 0.81 0.65 0.69 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Oversampled dataset(PCA), ...\u001b[0m\n",
+ "Oversampled dataset(PCA), Accuracy: 0.6808684965178206\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Oversampled dataset(PCA), Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.92 0.68 0.78 2035\n",
+ " 1 0.30 0.69 0.42 406\n",
+ "\n",
+ " accuracy 0.68 2441\n",
+ " macro avg 0.61 0.69 0.60 2441\n",
+ "weighted avg 0.81 0.68 0.72 2441\n",
+ "\n",
+ "\u001b[1mEvaluating Undersampled dataset(PCA), ...\u001b[0m\n",
+ "Undersampled dataset(PCA), Accuracy: 0.614092585006145\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x200 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled dataset(PCA), Classification report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.88 0.62 0.73 2035\n",
+ " 1 0.24 0.60 0.34 406\n",
+ "\n",
+ " accuracy 0.61 2441\n",
+ " macro avg 0.56 0.61 0.53 2441\n",
+ "weighted avg 0.78 0.61 0.66 2441\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 900x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "predict(SVM_models, SVM_name, x_test_list, ytest, \"SVM testing\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5ce57cea",
+ "metadata": {},
+ "source": [
+ "## Multi Layer Perceptron"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c2ddfd6",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99c5c469",
+ "metadata": {},
+ "source": [
+ "## Deep Reinforcement Learning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "44854afd",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a070cb47",
+ "metadata": {},
+ "source": [
+ "## ILP"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "807fd99d",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e4efbfeb",
+ "metadata": {},
+ "source": [
+ "# Cross Validation on Best Performing Datasets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 223,
+ "id": "7c9594de-ce70-4c61-80ef-75c0ac9883e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Decision Tree: 0.762413 (0.012656)\n",
+ "SVM: 0.750564 (0.009150)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n",
+ "C:\\Users\\amych\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MLP: 0.804361 (0.010574)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 700x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.neural_network import MLPClassifier\n",
+ "\n",
+ "# Evaluate each model in turn\n",
+ "models = [\n",
+ " ('Decision Tree', DecisionTreeClassifier(ccp_alpha=0.001)),\n",
+ " ('SVM', svm.SVC(probability=True, random_state=42, gamma=0.1)),\n",
+ " (\"MLP\", MLPClassifier(random_state=42))\n",
+ "]\n",
+ "\n",
+ "\n",
+ "models_list = []\n",
+ "x_val_list = []\n",
+ "x_test_list = []\n",
+ "\n",
+ "results = []\n",
+ "names = []\n",
+ "for name, model in models:\n",
+ " models_list.append(model)\n",
+ " x_test_list.append(xtest_pca_oversampled)\n",
+ " kfold = StratifiedKFold(n_splits=10, random_state=None)\n",
+ " cv_results = cross_val_score(model, xtrain_pca_oversampled, ytrain_oversampled, cv=kfold, scoring='accuracy')\n",
+ " results.append(cv_results)\n",
+ " names.append(name)\n",
+ " print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))\n",
+ "\n",
+ "# Compare Algorithms\n",
+ "plt.figure(figsize=(7,6))\n",
+ "plt.boxplot(results, labels=names)\n",
+ "plt.title('Comparison of Models on Dataset 1')\n",
+ "plt.xlabel('Model')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.show()"
+ ]
+ }
+ ],
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