diff --git "a/notebook.ipynb" "b/notebook.ipynb" new file mode 100644--- /dev/null +++ "b/notebook.ipynb" @@ -0,0 +1,133816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Insurance Claim Prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.0 Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hide_input": true + }, + "source": [ + "### 1.1 Business Understanding / Project Objective" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this project, we assume the role of a Lead Data Analyst whose objective is to build a predictive model to determine if a building will have an insurance claim during a certain period or not. \n", + "We are required to predict the probability of having at least one claim over the insured period of the building. The model will be based on the building characteristics. The target variable, Claim, is a:\n", + "- 1 if the building has at least a claim over the insured period.\n", + "- 0 if the building doesn’t have a claim over the insured period." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Data Understanding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 13 characteristic columns and 1 target column. The columns in the dataset are described below:\n", + "\n", + "- *Customer Id*: Identification number for the Policy holder\n", + "- *YearOfObservation*: year of observation for the insured policy\n", + "- *Insured_Period*: duration of insurance policy in Olusola Insurance. (Ex*: Full year insurance, Policy Duration = 1; 6 months = 0.5\n", + "- *Residential*: is the building a residential building or not\n", + "- *Building_Painted*: is the building painted or not (N-Painted, V-Not Painted)\n", + "- *Building_Fenced*: is the building fenced or not (N-Fenced, V-Not Fenced)\n", + "- *Garden*: building has garden or not (V-has garden; O-no garden)\n", + "- *Settlement*: Area where the building is located. (R- *rural area; U- *urban area)\n", + "- *Building Dimension*: Size of the insured building in m2\n", + "- *Building_Type*: The type of building (Type 1, 2, 3, 4)\n", + "- *Date_of_Occupancy*: date building was first occupied\n", + "- *NumberOfWindows*: number of windows in the building\n", + "- *Geo Code*: Geographical Code of the Insured building\n", + "- *Claim*: target variable. (0*: no claim, 1*: at least one claim over insured period)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.0 Toolbox Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "hide_input": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading complete. Warnings hidden.\n" + ] + } + ], + "source": [ + "# Data Manipulation\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import plotly.express as px\n", + "import seaborn as sns\n", + "\n", + "# Warnings\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\") # Hiding the warnings\n", + "\n", + "# Modelling\n", + "from imblearn.over_sampling import SMOTE\n", + "from sklearn import metrics\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *\n", + "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "import xgboost as xgb\n", + "from xgboost import *\n", + "import lightgbm as lgb\n", + "from catboost import CatBoostClassifier\n", + "\n", + "# Additional libraries\n", + "import os\n", + "import pickle\n", + "\n", + "\n", + "print(\"Loading complete.\", \"Warnings hidden.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Removing the restriction on columns to display\n", + "pd.set_option(\"display.max_columns\", None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.0 Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Customer IdYearOfObservationInsured_PeriodResidentialBuilding_PaintedBuilding_FencedGardenSettlementBuilding DimensionBuilding_TypeDate_of_OccupancyNumberOfWindowsGeo_CodeClaim
0H1466320131.0000000NVVU290.011960.0.10530
1H203720151.0000000VNOR490.011850.0410530
2H380220141.0000000NVVU595.011960.0.10530
3H383420131.0000000VVVU2840.011960.0.10530
4H505320141.0000000VNOR680.011800.0310530
.............................................
7155H529020121.0000001VVVUNaN12001.0.NaN0
7156H592620131.0000000VVVUNaN21980.0.NaN1
7157H620420160.0382510VVVUNaN11992.0.NaN0
7158H653720131.0000000VVVUNaN11972.0.NaN0
7159H747020141.0000000VVVUNaN12004.0.NaN0
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7160 rows × 14 columns

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" + ], + "text/plain": [ + " Customer Id YearOfObservation Insured_Period Residential \\\n", + "0 H14663 2013 1.000000 0 \n", + "1 H2037 2015 1.000000 0 \n", + "2 H3802 2014 1.000000 0 \n", + "3 H3834 2013 1.000000 0 \n", + "4 H5053 2014 1.000000 0 \n", + "... ... ... ... ... \n", + "7155 H5290 2012 1.000000 1 \n", + "7156 H5926 2013 1.000000 0 \n", + "7157 H6204 2016 0.038251 0 \n", + "7158 H6537 2013 1.000000 0 \n", + "7159 H7470 2014 1.000000 0 \n", + "\n", + " Building_Painted Building_Fenced Garden Settlement Building Dimension \\\n", + "0 N V V U 290.0 \n", + "1 V N O R 490.0 \n", + "2 N V V U 595.0 \n", + "3 V V V U 2840.0 \n", + "4 V N O R 680.0 \n", + "... ... ... ... ... ... \n", + "7155 V V V U NaN \n", + "7156 V V V U NaN \n", + "7157 V V V U NaN \n", + "7158 V V V U NaN \n", + "7159 V V V U NaN \n", + "\n", + " Building_Type Date_of_Occupancy NumberOfWindows Geo_Code Claim \n", + "0 1 1960.0 . 1053 0 \n", + "1 1 1850.0 4 1053 0 \n", + "2 1 1960.0 . 1053 0 \n", + "3 1 1960.0 . 1053 0 \n", + "4 1 1800.0 3 1053 0 \n", + "... ... ... ... ... ... \n", + "7155 1 2001.0 . NaN 0 \n", + "7156 2 1980.0 . NaN 1 \n", + "7157 1 1992.0 . NaN 0 \n", + "7158 1 1972.0 . NaN 0 \n", + "7159 1 2004.0 . NaN 0 \n", + "\n", + "[7160 rows x 14 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Loading the data\n", + "dataset = pd.read_csv(\"data/train_data.csv\")\n", + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop the \"Customer Id\" column\n", + "dataset.drop(columns=\"Customer Id\", inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 7160 entries, 0 to 7159\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 YearOfObservation 7160 non-null int64 \n", + " 1 Insured_Period 7160 non-null float64\n", + " 2 Residential 7160 non-null int64 \n", + " 3 Building_Painted 7160 non-null object \n", + " 4 Building_Fenced 7160 non-null object \n", + " 5 Garden 7153 non-null object \n", + " 6 Settlement 7160 non-null object \n", + " 7 Building Dimension 7054 non-null float64\n", + " 8 Building_Type 7160 non-null int64 \n", + " 9 Date_of_Occupancy 6652 non-null float64\n", + " 10 NumberOfWindows 7160 non-null object \n", + " 11 Geo_Code 7058 non-null object \n", + " 12 Claim 7160 non-null int64 \n", + "dtypes: float64(3), int64(4), object(6)\n", + "memory usage: 727.3+ KB\n" + ] + } + ], + "source": [ + "# Looking at information about the columns\n", + "dataset.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "YearOfObservation 0\n", + "Insured_Period 0\n", + "Residential 0\n", + "Building_Painted 0\n", + "Building_Fenced 0\n", + "Garden 7\n", + "Settlement 0\n", + "Building Dimension 106\n", + "Building_Type 0\n", + "Date_of_Occupancy 508\n", + "NumberOfWindows 0\n", + "Geo_Code 102\n", + "Claim 0\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check for missing values\n", + "dataset.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearOfObservationInsured_PeriodResidentialBuilding_PaintedBuilding_FencedGardenSettlementBuilding DimensionBuilding_TypeDate_of_OccupancyNumberOfWindowsGeo_CodeClaim
88120121.0000000NVVU2208.011980.0.130711
172020121.0000000NVVU2360.011980.0.210540
286620121.0000000VNOR4142.021969.0>=10382290
333220121.0000001VVVU450.021960.0.514540
420520121.0000001VNOR999.022008.03661300
420720130.5232881NVVU315.021988.0.661300
443320141.0000000VVVU7100.021980.0.682781
571520151.0000000VNOR2188.021974.05830690
706720131.0000000VVVUNaN21960.0.NaN0
707020121.0000000VVVUNaN22008.0.NaN0
709820131.0000000VVVUNaN21960.0.NaN0
710220121.0000001VVVUNaN21960.0.NaN0
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" + ], + "text/plain": [ + " YearOfObservation Insured_Period Residential Building_Painted \\\n", + "881 2012 1.000000 0 N \n", + "1720 2012 1.000000 0 N \n", + "2866 2012 1.000000 0 V \n", + "3332 2012 1.000000 1 V \n", + "4205 2012 1.000000 1 V \n", + "4207 2013 0.523288 1 N \n", + "4433 2014 1.000000 0 V \n", + "5715 2015 1.000000 0 V \n", + "7067 2013 1.000000 0 V \n", + "7070 2012 1.000000 0 V \n", + "7098 2013 1.000000 0 V \n", + "7102 2012 1.000000 1 V \n", + "\n", + " Building_Fenced Garden Settlement Building Dimension Building_Type \\\n", + "881 V V U 2208.0 1 \n", + "1720 V V U 2360.0 1 \n", + "2866 N O R 4142.0 2 \n", + "3332 V V U 450.0 2 \n", + "4205 N O R 999.0 2 \n", + "4207 V V U 315.0 2 \n", + "4433 V V U 7100.0 2 \n", + "5715 N O R 2188.0 2 \n", + "7067 V V U NaN 2 \n", + "7070 V V U NaN 2 \n", + "7098 V V U NaN 2 \n", + "7102 V V U NaN 2 \n", + "\n", + " Date_of_Occupancy NumberOfWindows Geo_Code Claim \n", + "881 1980.0 . 13071 1 \n", + "1720 1980.0 . 21054 0 \n", + "2866 1969.0 >=10 38229 0 \n", + "3332 1960.0 . 51454 0 \n", + "4205 2008.0 3 66130 0 \n", + "4207 1988.0 . 66130 0 \n", + "4433 1980.0 . 68278 1 \n", + "5715 1974.0 5 83069 0 \n", + "7067 1960.0 . NaN 0 \n", + "7070 2008.0 . NaN 0 \n", + "7098 1960.0 . NaN 0 \n", + "7102 1960.0 . NaN 0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking for duplicates\n", + "dataset[dataset.duplicated()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the dataset preview and the info above, we note the following:\n", + "- There are a total of 7160 observations in the dataset\n", + "- Four columns have missing values. The missing values for the numeric columns will be filled with their respective medians\n", + "- There are no duplicate observations in any of the columns\n", + "- There are 6 numeric columns" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Fill the missing values for \"Building Dimension\"\n", + "bd_imputer = SimpleImputer(strategy=\"median\", missing_values= np.NaN)\n", + "dataset[\"Building Dimension\"] = bd_imputer.fit_transform(dataset[\"Building Dimension\"].values.reshape(-1,1))[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Fill the missing values for \"Date of Occupancy\"\n", + "do_imputer = SimpleImputer(strategy=\"median\")\n", + "dataset[\"Date_of_Occupancy\"] = do_imputer.fit_transform(dataset[\"Date_of_Occupancy\"].values.reshape(-1,1))[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Fill the missing values for \"Garden\"\n", + "do_imputer = SimpleImputer(strategy=\"most_frequent\")\n", + "dataset[\"Garden\"] = do_imputer.fit_transform(dataset[\"Garden\"].values.reshape(-1,1))[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "YearOfObservation 0\n", + "Insured_Period 0\n", + "Residential 0\n", + "Building_Painted 0\n", + "Building_Fenced 0\n", + "Garden 0\n", + "Settlement 0\n", + "Building Dimension 0\n", + "Building_Type 0\n", + "Date_of_Occupancy 0\n", + "NumberOfWindows 0\n", + "Geo_Code 102\n", + "Claim 0\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check for missing values\n", + "dataset.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Exploration of Numeric Columns" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "YearOfObservation 5\n", + "Insured_Period 401\n", + "Residential 2\n", + "Building_Painted 2\n", + "Building_Fenced 2\n", + "Garden 2\n", + "Settlement 2\n", + "Building Dimension 2043\n", + "Building_Type 4\n", + "Date_of_Occupancy 134\n", + "NumberOfWindows 11\n", + "Geo_Code 1307\n", + "Claim 2\n", + "dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the number of unique values in each column\n", + "dataset.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary table of the Descriptive Statistics of Columns with Numeric Values\n" + ] + }, + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
YearOfObservation7160.02013.6695531.3837692012.02012.0000002013.02015.02016.0
Insured_Period7160.00.9097580.2397560.00.9972681.01.01.0
Building Dimension7160.01871.8731842263.2961861.0531.5000001083.02250.020940.0
Building_Type7160.02.1860340.9406321.02.0000002.03.04.0
Date_of_Occupancy7160.01964.84972134.7303471545.01960.0000001970.01980.02016.0
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" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "YearOfObservation 7160.0 2013.669553 1.383769 2012.0 2012.000000 \n", + "Insured_Period 7160.0 0.909758 0.239756 0.0 0.997268 \n", + "Building Dimension 7160.0 1871.873184 2263.296186 1.0 531.500000 \n", + "Building_Type 7160.0 2.186034 0.940632 1.0 2.000000 \n", + "Date_of_Occupancy 7160.0 1964.849721 34.730347 1545.0 1960.000000 \n", + "\n", + " 50% 75% max \n", + "YearOfObservation 2013.0 2015.0 2016.0 \n", + "Insured_Period 1.0 1.0 1.0 \n", + "Building Dimension 1083.0 2250.0 20940.0 \n", + "Building_Type 2.0 3.0 4.0 \n", + "Date_of_Occupancy 1970.0 1980.0 2016.0 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Looking at the descriptive statistics of the columns with numeric values\n", + "numerics = [column for column in dataset.columns if (dataset[column].dtype != \"O\") & (len(dataset[column].unique()) > 2)]\n", + "print(\"Summary table of the 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualizing the distribution of the columns with categorical values and their species\n", + "categoricals = [column for column in dataset.columns if (dataset[column].dtype == \"O\")]\n", + "\n", + "for column in dataset[categoricals].columns:\n", + " # Visualizing the distribution of the categories in the column\n", + " fig = px.histogram(dataset, x=dataset[column], text_auto=True, title=f\"Distribution of values in the {column} column\")\n", + " fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + " . 3551\n", + "4 939\n", + "3 844\n", + "5 639\n", + "2 363\n", + "6 306\n", + "7 211\n", + "8 116\n", + "1 75\n", + ">=10 67\n", + "9 49\n", + "Name: NumberOfWindows, dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Explore the \"number of windows\" column\n", + "dataset[\"NumberOfWindows\"].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following will be applied to the values in the \"number of windows\" column: \n", + "- change the \">=10\" value to 10\n", + "- fill the nulls with the mode\n", + "- convert dtype to int" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Replace the \">=10\" values\n", + "dataset[\"NumberOfWindows\"].replace(\">=10\", 10, inplace= True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Replace the \" .\" values\n", + "dataset[\"NumberOfWindows\"].replace(\" .\", 4, inplace= True)\n", + "dataset[\"NumberOfWindows\"] = dataset[\"NumberOfWindows\"].apply(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.0 Feature Engineering\n", + "### 4.1 Feature Encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "YearOfObservation 5\n", + "Insured_Period 401\n", + "Residential 2\n", + "Building_Painted 2\n", + "Building_Fenced 2\n", + "Garden 2\n", + "Settlement 2\n", + "Building Dimension 2043\n", + "Building_Type 4\n", + "Date_of_Occupancy 134\n", + "NumberOfWindows 10\n", + "Geo_Code 1307\n", + "Claim 2\n", + "dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Looking at the number of unique values in each column\n", + "dataset.nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Here, the target column will be encoded using label encoding\n", + "- Subject to the number of unique values per column , the categorical columns will be encoded using one-hot encoding\n", + "- The numeric columns will be scaled before modelling" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 7160 entries, 0 to 7159\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 YearOfObservation 7160 non-null int64 \n", + " 1 Insured_Period 7160 non-null float64\n", + " 2 Residential 7160 non-null int64 \n", + " 3 Building_Painted 7160 non-null object \n", + " 4 Building_Fenced 7160 non-null object \n", + " 5 Garden 7160 non-null object \n", + " 6 Settlement 7160 non-null object \n", + " 7 Building Dimension 7160 non-null float64\n", + " 8 Building_Type 7160 non-null int64 \n", + " 9 Date_of_Occupancy 7160 non-null float64\n", + " 10 NumberOfWindows 7160 non-null int64 \n", + " 11 Geo_Code 7058 non-null object \n", + " 12 Claim 7160 non-null int64 \n", + "dtypes: float64(3), int64(5), object(5)\n", + "memory usage: 727.3+ KB\n" + ] + } + ], + "source": [ + "# Take another look at the dataset info\n", + "dataset.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop the Geo_Code column\n", + "dataset.drop(columns = \"Geo_Code\", inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['YearOfObservation',\n", + " 'Insured_Period',\n", + " 'Residential',\n", + " 'Building Dimension',\n", + " 'Building_Type',\n", + " 'Date_of_Occupancy',\n", + " 'NumberOfWindows']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Redefine the list of numeric columns\n", + "numerics = [column for column in dataset.columns if (dataset[column].dtype != \"O\")]\n", + "\n", + "# Drop the Claim column from the numerics list\n", + "numerics.remove(\"Claim\")\n", + "numerics" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Building_Painted', 'Building_Fenced', 'Garden', 'Settlement']" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Redefine the list of categorical columns\n", + "categoricals = [column for column in dataset.columns if (dataset[column].dtype == \"O\")]\n", + "categoricals" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " YearOfObservation Insured_Period Residential Building Dimension \\\n", + "0 2013 1.000000 0 290.0 \n", + "1 2015 1.000000 0 490.0 \n", + "2 2014 1.000000 0 595.0 \n", + "3 2013 1.000000 0 2840.0 \n", + "4 2014 1.000000 0 680.0 \n", + "... ... ... ... ... \n", + "7155 2012 1.000000 1 1083.0 \n", + "7156 2013 1.000000 0 1083.0 \n", + "7157 2016 0.038251 0 1083.0 \n", + "7158 2013 1.000000 0 1083.0 \n", + "7159 2014 1.000000 0 1083.0 \n", + "\n", + " Building_Type Date_of_Occupancy NumberOfWindows Claim \\\n", + "0 1 1960.0 4 0 \n", + "1 1 1850.0 4 0 \n", + "2 1 1960.0 4 0 \n", + "3 1 1960.0 4 0 \n", + "4 1 1800.0 3 0 \n", + "... ... ... ... ... \n", + "7155 1 2001.0 4 0 \n", + "7156 2 1980.0 4 1 \n", + "7157 1 1992.0 4 0 \n", + "7158 1 1972.0 4 0 \n", + "7159 1 2004.0 4 0 \n", + "\n", + " Building_Painted_V Building_Fenced_V Garden_V Settlement_U \n", + "0 0.0 1.0 1.0 1.0 \n", + "1 1.0 0.0 0.0 0.0 \n", + "2 0.0 1.0 1.0 1.0 \n", + "3 1.0 1.0 1.0 1.0 \n", + "4 1.0 0.0 0.0 0.0 \n", + "... ... ... ... ... \n", + "7155 1.0 1.0 1.0 1.0 \n", + "7156 1.0 1.0 1.0 1.0 \n", + "7157 1.0 1.0 1.0 1.0 \n", + "7158 1.0 1.0 1.0 1.0 \n", + "7159 1.0 1.0 1.0 1.0 \n", + "\n", + "[7160 rows x 12 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Encode the categorical columns\n", + "encoder = OneHotEncoder(drop = \"first\", sparse = False)\n", + "encoder.fit(dataset[categoricals])\n", + "\n", + "encoded_categoricals = encoder.transform(dataset[categoricals])\n", + "encoded_categoricals = pd.DataFrame(encoded_categoricals, columns = encoder.get_feature_names_out().tolist())\n", + "\n", + "# Add the encoded categoricals to the DataFrame and dropping the original columns\n", + "dataset = dataset.join(encoded_categoricals)\n", + "dataset.drop(columns= categoricals, inplace= True)\n", + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Feature Correlation and Selection" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "coloraxis": "coloraxis", + "hovertemplate": "x: %{x}
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Looking at the correlation between the variables in the merged dataframe\n", + "correlation = pd.DataFrame(dataset.corr())\n", + "\n", + "# Defining a colourscale for the correlation plot\n", + "colorscale = [[0.0, \"rgb(255,255,255)\"], [0.2, \"rgb(255, 255, 153)\"],\n", + " [0.4, \"rgb(153, 255, 204)\"], [0.6, \"rgb(179, 217, 255)\"],\n", + " [0.8, \"rgb(240, 179, 255)\"], [1.0, \"rgb(255, 77, 148)\"]\n", + " ]\n", + "\n", + "# Plotting the Correlation Matrix\n", + "fig = px.imshow(correlation,\n", + " text_auto=\".3f\",\n", + " aspect=\"auto\",\n", + " labels={\"color\": \"Correlation Coefficient\"},\n", + " contrast_rescaling=\"minmax\",\n", + " color_continuous_scale=colorscale\n", + " )\n", + "fig.update_xaxes(side=\"top\")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.0 Modelling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Preview**\n", + "- Train_test_split: Modelling will be done normally with a basic train_test_split. The selected model will then be cross-validated and fine-tuned before completion.\n", + "- Balancing: the dataset will be checked for imbalance, and actions taken thereof" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 5526\n", + "1 1634\n", + "Name: Claim, dtype: int64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check for imbalance\n", + "dataset[\"Claim\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the target & predictor variables\n", + "X = dataset.drop(columns=[\"Claim\"])\n", + "y = dataset[\"Claim\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 5526\n", + "1 5526\n", + "Name: Claim, dtype: int64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Resample the training dataframe using SMOTE\n", + "smote = SMOTE(sampling_strategy=\"minority\", n_jobs=-1, random_state=24)\n", + "X, y = smote.fit_resample(X, y)\n", + "y.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 3868\n", + "0 3868\n", + "Name: Claim, dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Splitting the dataframe into train and test\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=24, stratify=y)\n", + "y_train.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Scale the numeric columns\n", + "scaler = MinMaxScaler()\n", + "X_train[numerics] = scaler.fit_transform(X_train[numerics])\n", + "X_test[numerics] = scaler.transform(X_test[numerics])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the models\n", + "log_reg_model = LogisticRegression(random_state=24)\n", + "dt_model = DecisionTreeClassifier(random_state=24)\n", + "rf_model = RandomForestClassifier(random_state=24)\n", + "xgb_model = XGBClassifier(random_state=24)\n", + "catb_model = CatBoostClassifier(metric_period=100, random_state=24)\n", + "lgb_model = lgb.LGBMClassifier(random_state=24)\n", + "\n", + "# Create a dictionary of the models\n", + "smote_models = {\n", + " \"Logistic Regressor\": log_reg_model,\n", + " \"Decision Tree\": dt_model,\n", + " \"Random Forest\": rf_model,\n", + " \"XGBoost\": xgb_model,\n", + " \"CatBoost\": catb_model,\n", + " \"LightGBM\": lgb_model\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Defining a helper function to fit models to data and score them\n", + "def classification_fit_and_score(models, X_train= X_train, X_test= X_test, y_train= y_train, y_test= y_test):\n", + " \n", + " # List to collect the results\n", + " results = []\n", + " \n", + " # Looping through the models to fit and score each\n", + " for name, model in models.items():\n", + "\n", + " # fitting to the training data\n", + " model.fit(X_train, y_train)\n", + "\n", + " # making predictions\n", + " y_pred = model.predict(X_test)\n", + " \n", + " # Append model performance results\n", + " results.append([\n", + " name,\n", + " precision_score(y_test, y_pred),\n", + " recall_score(y_test, y_pred),\n", + " f1_score(y_test, y_pred),\n", + " accuracy_score(y_test, y_pred),\n", + " roc_auc_score(y_test, y_pred)\n", + " ])\n", + "\n", + " # Print Classification Report\n", + " model_classification_report = classification_report(y_test, y_pred)\n", + " print(f\"{name} Model Classification Report\", \"\\n\", model_classification_report, \"\\n\")\n", + " \n", + " # Defining the Confusion Matrix\n", + " model_confusion_matrix = pd.DataFrame(confusion_matrix(y_test, y_pred)).reset_index(drop=True)\n", + " print(f\"{name} Confusion Matrix:\", \"\\n\", model_confusion_matrix, \"\\n\")\n", + " \n", + " # Visualizing the Confusion Matrix\n", + " # Display Confusion Matrix directly from predictions\n", + " ConfusionMatrixDisplay.from_predictions(y_test, y_pred)\n", + " plt.show()\n", + " print(\"\\n\")\n", + "\n", + " # Calculate and show the AUC and ROC\n", + " fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n", + " plt.plot(fpr, tpr)\n", + " plt.xlabel(\"False Positive Rate\")\n", + " plt.ylabel(\"True Positive Rate\")\n", + " plt.show()\n", + " print(\"\\n\")\n", + " \n", + " print(f\"{name} AUC score: {roc_auc_score(y_test, y_pred)}\")\n", + "\n", + " print(\"\\n\")\n", + " print(\"----- ----- \"*6)\n", + " print(\"\\n\")\n", + " print(\"----- ----- \"*6)\n", + " print(\"\\n\")\n", + " \n", + " # Put the results together\n", + " eval_df = pd.DataFrame(results, columns=[\"model\", \"precision\", \"recall\", \"f1_score\", \"accuracy\", \"auc\"])\n", + " eval_df.set_index(\"model\", inplace=True)\n", + " eval_df.sort_values(by = [\"auc\",\"f1_score\", \"accuracy\", \"recall\"], ascending = False, inplace = True)\n", + "\n", + " return eval_df" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logistic Regressor Model Classification Report \n", + " precision recall f1-score support\n", + "\n", + " 0 0.64 0.71 0.67 1658\n", + " 1 0.68 0.61 0.64 1658\n", + "\n", + " accuracy 0.66 3316\n", + " macro avg 0.66 0.66 0.66 3316\n", + "weighted avg 0.66 0.66 0.66 3316\n", + " \n", + "\n", + "Logistic Regressor Confusion Matrix: \n", + " 0 1\n", + "0 1175 483\n", + "1 654 1004 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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yh++pfhTv27fP1SYkJETt2rVr0Obk+SUlJYqLi2tw/bi4uNP+uD6dZksIYmNjFRgY2CDg0tLSBl/QSVarVVYrvzC9UV0VoAMF33+HJYUh+nJnmCLb1inu/FpVlAXq0DchOnyw/k+j8Mv6tu3iahUdV+c675uCEO3YFKHHln3V4D0+2xqu/34SoZ59KxUR5dCe7eFaNCNRvTPLFXc+CQHOruqqQO3bE+a278TxAB0r+37/pxsjNO7BYtlPBOhgUbAu7VOlgb8u0/OPJEqSEjrWqN/wo8pfH6nyI0GKTajVyN+Xyl4doM3vnHrgIc49vnraYWFhoaKiolz7vbkvNeVH8enanKp9Y67zY82WEISEhCg9PV1r167VL3/5S9f+tWvXasSIEc0VVqv3+afhmvrrC1yvF82oX5hl0MgjmjJ/vzatsenJP32//sCsezpLkn47qUSjp3yfvL29IkYxCbVK7+e+9oBU/2tr/WtttWxugmrtFsWdZ9eQUUd00/iDfvpUgHdm3dNJdzxQrPsW7FNkW4dKvwlR7uwOeuOvMZIke02AemRU6ZfjvlUbm0NHvw3Sjk0R+tOIC1R+mFkGZhMVFeWWEJyJhIQESfW/8Dt0+H6w6w9/FCckJMhut6usrMytSlBaWqq+ffu62hw82PDf1kOHDp32x/XpNGuXwaRJkzR69Gj16tVLffr00fPPP6/9+/fr7rvvbs6wWrWefSv19oHtpz2eefMRZd7802MC7phWrDumnXrVwZRLq/XUG1+caYiA3/0wKZakskPBbonwjx05GKwHR3fxd1jws3NppcLk5GQlJCRo7dq1SktLk1Q/2H79+vWaPXu2JCk9PV3BwcFau3atRo4cKUkqLi7Wzp07NWfOHElSnz59VF5ers2bN+tnP/uZJOnjjz9WeXm5K2lorGZNCG6++WYdPnxYjz76qIqLi9WjRw+9+eab6tSpU3OGBQBohXzVZdBYlZWV2rt3r+t1QUGBtm/frujoaHXs2FHZ2dnKyclRSkqKUlJSlJOTo/DwcI0aNUqSZLPZlJWVpcmTJysmJkbR0dGaMmWKUlNTXbMOunfvrmuvvVbjxo3TokWLJEl33nmnhg4d2qQZBtI5MKhw/PjxGj9+fHOHAQCAT23dulXXXHON6/XJQfFjxoxRbm6upk6dqurqao0fP15lZWXKyMjQmjVrFBn5/biUefPmKSgoSCNHjlR1dbUGDBig3NxcBQZ+v9rmiy++qIkTJ7pmIwwfPvy0ax94YjEMo8UOka2oqJDNZlPZ510UFck8YLROgxMva+4QAL+pM2r1vl5VeXm51/3yp3PyXjFsTZaCI0J++oTTqK2y6/XMJX6NtTk1e4UAAICz4Wx3GbQ0/KwGAABUCAAA5kCFwDMSAgCAKZAQeEaXAQAAoEIAADAHKgSekRAAAEzBkLx8uFHrRkIAADAFKgSeMYYAAABQIQAAmAMVAs9ICAAApkBC4BldBgAAgAoBAMAcqBB4RkIAADAFw7DI8OKm7s25LQFdBgAAgAoBAMAcnLJ4tTCRN+e2BCQEAABTYAyBZ3QZAAAAKgQAAHNgUKFnJAQAAFOgy8AzEgIAgClQIfCMMQQAAIAKAQDAHAwvuwxae4WAhAAAYAqGJMPw7vzWjC4DAABAhQAAYA5OWWRhpcLTIiEAAJgCsww8o8sAAABQIQAAmIPTsMjCwkSnRUIAADAFw/BylkErn2ZAlwEAAKBCAAAwBwYVekZCAAAwBRICz0gIAACmwKBCzxhDAAAAqBAAAMyBWQaekRAAAEyhPiHwZgyBD4M5B9FlAAAAqBAAAMyBWQaekRAAAEzB+G7z5vzWjC4DAABAhQAAYA50GXhGhQAAYA6GD7YmOnbsmLKzs9WpUyeFhYWpb9++2rJly/chGYZmzJihxMREhYWFqX///tq1a5fbNWpqajRhwgTFxsYqIiJCw4cPV1FRUdOD+QkkBAAAc/iuQnCmm86gQjB27FitXbtWS5cu1Y4dO5SZmamBAwfqm2++kSTNmTNHc+fO1YIFC7RlyxYlJCRo0KBBOnbsmOsa2dnZWrlypVasWKENGzaosrJSQ4cOlcPh8NlXI5EQAADgF9XV1frHP/6hOXPm6Oqrr9YFF1ygGTNmKDk5WQsXLpRhGJo/f76mT5+uG2+8UT169FBeXp6OHz+u5cuXS5LKy8u1ZMkSPfnkkxo4cKDS0tK0bNky7dixQ+vWrfNpvCQEAABTOLlSoTebJFVUVLhtNTU1p3y/uro6ORwOhYaGuu0PCwvThg0bVFBQoJKSEmVmZrqOWa1W9evXTxs3bpQk5efnq7a21q1NYmKievTo4WrjKyQEAABT8Ka74IcDEpOSkmSz2VzbrFmzTvl+kZGR6tOnjx577DEdOHBADodDy5Yt08cff6zi4mKVlJRIkuLj493Oi4+Pdx0rKSlRSEiI2rVrd9o2vsIsAwAAmqCwsFBRUVGu11ar9bRtly5dqjvuuEPnnXeeAgMDdfnll2vUqFH65JNPXG0sFvexCYZhNNj3Y41p01RUCAAA5nByYKA3m6SoqCi3zVNC0LVrV61fv16VlZUqLCzU5s2bVVtbq+TkZCUkJEhSg1/6paWlrqpBQkKC7Ha7ysrKTtvGV0gIAACm4KsxBGciIiJCHTp0UFlZmd5++22NGDHClRSsXbvW1c5ut2v9+vXq27evJCk9PV3BwcFubYqLi7Vz505XG1+hywAAAD95++23ZRiGunXrpr179+ree+9Vt27ddPvtt8tisSg7O1s5OTlKSUlRSkqKcnJyFB4erlGjRkmSbDabsrKyNHnyZMXExCg6OlpTpkxRamqqBg4c6NNYSQgAAObQDA8zKC8v17Rp01RUVKTo6Gj96le/0syZMxUcHCxJmjp1qqqrqzV+/HiVlZUpIyNDa9asUWRkpOsa8+bNU1BQkEaOHKnq6moNGDBAubm5CgwM9OLDNGQxjJb7hOeKigrZbDaVfd5FUZH0fqB1Gpx4WXOHAPhNnVGr9/WqysvL3Qbq+dLJe0XH5x9SQHjoT59wGs7jJ7T/zkf9GmtzalSF4Omnn270BSdOnHjGwQAAgObRqIRg3rx5jbqYxWIhIQAAnLtabE3c/xqVEBQUFPg7DgAA/IqnHXp2xh3vdrtde/bsUV1dnS/jAQDAP5rhaYctSZMTguPHjysrK0vh4eG65JJLtH//fkn1YweeeOIJnwcIAAD8r8kJwbRp0/Tpp5/q/fffd3tgw8CBA/XSSy/5NDgAAHzH4oOt9WryOgSrVq3SSy+9pN69e7uto3zxxRfryy+/9GlwAAD4TDOsQ9CSNLlCcOjQIcXFxTXYX1VV5fMHLQAAgLOjyQnBFVdcoX/961+u1yeTgMWLF6tPnz6+iwwAAF9iUKFHTe4ymDVrlq699lp99tlnqqur01NPPaVdu3bpo48+0vr16/0RIwAA3vvBEwvP+PxWrMkVgr59++rf//63jh8/rq5du2rNmjWKj4/XRx99pPT0dH/ECAAA/OyMHm6UmpqqvLw8X8cCAIDfePsI45b75J/GOaOEwOFwaOXKldq9e7csFou6d++uESNGKCiIhycCAM5RzDLwqMl38J07d2rEiBEqKSlRt27dJEmff/652rdvr9dee02pqak+DxIAAPhXk8cQjB07VpdccomKior0ySef6JNPPlFhYaEuvfRS3Xnnnf6IEQAA750cVOjN1oo1uULw6aefauvWrWrXrp1rX7t27TRz5kxdccUVPg0OAABfsRj1mzfnt2ZNrhB069ZNBw8ebLC/tLRUF1xwgU+CAgDA51iHwKNGJQQVFRWuLScnRxMnTtQrr7yioqIiFRUV6ZVXXlF2drZmz57t73gBAIAfNKrLoG3btm7LEhuGoZEjR7r2Gd/NxRg2bJgcDocfwgQAwEssTORRoxKC9957z99xAADgX0w79KhRCUG/fv38HQcAAGhGZ7yS0PHjx7V//37Z7Xa3/ZdeeqnXQQEA4HNUCDxqckJw6NAh3X777XrrrbdOeZwxBACAcxIJgUdNnnaYnZ2tsrIybdq0SWFhYVq9erXy8vKUkpKi1157zR8xAgAAP2tyheDdd9/Vq6++qiuuuEIBAQHq1KmTBg0apKioKM2aNUvXX3+9P+IEAMA7zDLwqMkVgqqqKsXFxUmSoqOjdejQIUn1T0D85JNPfBsdAAA+cnKlQm+21uyMVircs2ePJOmyyy7TokWL9M033+i5555Thw4dfB4gAADwvyZ3GWRnZ6u4uFiS9PDDD2vw4MF68cUXFRISotzcXF/HBwCAbzCo0KMmJwS33nqr67/T0tL09ddf67///a86duyo2NhYnwYHAADOjjNeh+Ck8PBwXX755b6IBQAAv7HIy6cd+iySc1OjEoJJkyY1+oJz584942AAAEDzaFRCsG3btkZd7IcPQDqbrp10m4KCQ5vlvQF/67ppd3OHAPiNvdIuDThLb8a0Q494uBEAwBwYVOhRk6cdAgCA1sfrQYUAALQIVAg8IiEAAJiCt6sNslIhAABo9agQAADMgS4Dj86oQrB06VJdeeWVSkxM1L59+yRJ8+fP16uvvurT4AAA8BnDB1sr1uSEYOHChZo0aZKuu+46HT16VA6HQ5LUtm1bzZ8/39fxAQCAs6DJCcEzzzyjxYsXa/r06QoMDHTt79Wrl3bs2OHT4AAA8BUef+xZk8cQFBQUKC0trcF+q9WqqqoqnwQFAIDPsVKhR02uECQnJ2v79u0N9r/11lu6+OKLfRETAAC+xxgCj5qcENx77736/e9/r5deekmGYWjz5s2aOXOmHnjgAd17773+iBEAgBanrq5O//M//6Pk5GSFhYWpS5cuevTRR+V0Ol1tDMPQjBkzlJiYqLCwMPXv31+7du1yu05NTY0mTJig2NhYRUREaPjw4SoqKvJ5vE3uMrj99ttVV1enqVOn6vjx4xo1apTOO+88PfXUU7rlllt8HiAAAL5wthcmmj17tp577jnl5eXpkksu0datW3X77bfLZrPpj3/8oyRpzpw5mjt3rnJzc3XhhRfq8ccf16BBg7Rnzx5FRkZKkrKzs/X6669rxYoViomJ0eTJkzV06FDl5+e7jeXz1hmtQzBu3DiNGzdO3377rZxOp+Li4nwWEAAAfuGjdQgqKircdlutVlmt1gbNP/roI40YMULXX3+9JKlz587629/+pq1bt9ZfzjA0f/58TZ8+XTfeeKMkKS8vT/Hx8Vq+fLnuuusulZeXa8mSJVq6dKkGDhwoSVq2bJmSkpK0bt06DR482IsP5M6rlQpjY2NJBgAAppKUlCSbzebaZs2adcp2P//5z/XOO+/o888/lyR9+umn2rBhg6677jpJ9YP0S0pKlJmZ6TrHarWqX79+2rhxoyQpPz9ftbW1bm0SExPVo0cPVxtfaXKFIDk5WRbL6UdafvXVV14FBACAX3g7dfC7cwsLCxUVFeXafarqgCTdd999Ki8v10UXXaTAwEA5HA7NnDlTv/nNbyRJJSUlkqT4+Hi38+Lj412L/pWUlCgkJETt2rVr0Obk+b7S5IQgOzvb7XVtba22bdum1atXM6gQAHDu8lGXQVRUlFtCcDovvfSSli1bpuXLl+uSSy7R9u3blZ2drcTERI0ZM8bV7sc/sg3D8PjDu7FtmqrJCcHJgRA/9pe//MXVLwIAgNnde++9uv/++10D7lNTU7Vv3z7NmjVLY8aMUUJCgqT6KkCHDh1c55WWlrqqBgkJCbLb7SorK3OrEpSWlqpv374+jddnTzscMmSI/vGPf/jqcgAA+NZZXofg+PHjCghwv80GBga6ph0mJycrISFBa9eudR232+1av36962afnp6u4OBgtzbFxcXauXOnzxMCnz3t8JVXXlF0dLSvLgcAgE+d7WmHw4YN08yZM9WxY0ddcskl2rZtm+bOnas77rij/noWi7Kzs5WTk6OUlBSlpKQoJydH4eHhGjVqlCTJZrMpKytLkydPVkxMjKKjozVlyhSlpqa6Zh34SpMTgrS0NLd+C8MwVFJSokOHDunZZ5/1aXAAALRUzzzzjB588EGNHz9epaWlSkxM1F133aWHHnrI1Wbq1Kmqrq7W+PHjVVZWpoyMDK1Zs8a1BoEkzZs3T0FBQRo5cqSqq6s1YMAA5ebm+nQNAkmyGIbRpJznkUcecXsdEBCg9u3bq3///rrooot8GtxPqaiokM1mU8bQRxUUHHpW3xs4W7rev7u5QwD8xl5p14oBL6q8vLxRA/XOxMl7RdcHchQYeub3CseJE/oy5wG/xtqcmlQhqKurU+fOnTV48GDXYAgAAFoEH80yaK2aNKgwKChI99xzj2pqavwVDwAAfsHjjz1r8iyDjIwMbdu2zR+xAACAZtLkQYXjx4/X5MmTVVRUpPT0dEVERLgdv/TSS30WHAAAPtXKf+V7o9EJwR133KH58+fr5ptvliRNnDjRdcxisbhWTXI4HL6PEgAAbzGGwKNGJwR5eXl64oknVFBQ4M94AABAM2h0QnBydmKnTp38FgwAAP5ythcmammaNIbA1w9SAADgrKHLwKMmJQQXXnjhTyYFR44c8SogAABw9jUpIXjkkUdks9n8FQsAAH5Dl4FnTUoIbrnlFsXFxfkrFgAA/IcuA48avTAR4wcAAGi9mjzLAACAFokKgUeNTgicTqc/4wAAwK8YQ+BZk5cuBgCgRaJC4FGTH24EAABaHyoEAABzoELgEQkBAMAUGEPgGV0GAACACgEAwCToMvCIhAAAYAp0GXhGlwEAAKBCAAAwCboMPCIhAACYAwmBR3QZAAAAKgQAAHOwfLd5c35rRkIAADAHugw8IiEAAJgC0w49YwwBAACgQgAAMAm6DDwiIQAAmEcrv6l7gy4DAABAhQAAYA4MKvSMhAAAYA6MIfCILgMAAECFAABgDnQZeEZCAAAwB7oMPKLLAAAAUCEAAJgDXQaekRAAAMyBLgOPSAgAAOZAQuARYwgAAAAJAQDAHE6OIfBma4rOnTvLYrE02H7/+99LkgzD0IwZM5SYmKiwsDD1799fu3btcrtGTU2NJkyYoNjYWEVERGj48OEqKiry1VfihoQAAGAOhg+2JtiyZYuKi4td29q1ayVJN910kyRpzpw5mjt3rhYsWKAtW7YoISFBgwYN0rFjx1zXyM7O1sqVK7VixQpt2LBBlZWVGjp0qBwOxxl/DadDQgAAgB+0b99eCQkJru2NN95Q165d1a9fPxmGofnz52v69Om68cYb1aNHD+Xl5en48eNavny5JKm8vFxLlizRk08+qYEDByotLU3Lli3Tjh07tG7dOp/HS0IAADAFi2F4vUlSRUWF21ZTU/OT722327Vs2TLdcccdslgsKigoUElJiTIzM11trFar+vXrp40bN0qS8vPzVVtb69YmMTFRPXr0cLXxJRICAIA5+KjLICkpSTabzbXNmjXrJ9961apVOnr0qG677TZJUklJiSQpPj7erV18fLzrWElJiUJCQtSuXbvTtvElph0CANAEhYWFioqKcr22Wq0/ec6SJUs0ZMgQJSYmuu23WCxurw3DaLDvxxrT5kxQIQAAmIKvZhlERUW5bT+VEOzbt0/r1q3T2LFjXfsSEhIkqcEv/dLSUlfVICEhQXa7XWVlZadt40skBAAAczjLswxOeuGFFxQXF6frr7/etS85OVkJCQmumQdS/TiD9evXq2/fvpKk9PR0BQcHu7UpLi7Wzp07XW18iS4DAAD8xOl06oUXXtCYMWMUFPT9LddisSg7O1s5OTlKSUlRSkqKcnJyFB4erlGjRkmSbDabsrKyNHnyZMXExCg6OlpTpkxRamqqBg4c6PNYSQgAAKbQHA83Wrdunfbv36877rijwbGpU6equrpa48ePV1lZmTIyMrRmzRpFRka62sybN09BQUEaOXKkqqurNWDAAOXm5iowMPDMP8hpWAzDaLGrM1dUVMhmsylj6KMKCg5t7nAAv+h6/+7mDgHwG3ulXSsGvKjy8nK3gXq+dPJecfktMxUYcub3Cof9hD5ZMd2vsTYnKgQAAFPg8ceeMagQAABQIQAAmASPP/aIhAAAYBqtvezvDboMAAAAFQIAgEkYRv3mzfmtGAkBAMAUmGXgGV0GAACACgEAwCSYZeARCQEAwBQszvrNm/NbM7oMAAAAFQJIsbYq3f3Lzcq4uFDWkDoVHrRp9rKr9Xlhe0nStNHva0ifL9zO2VUQp3v+34hTXM3QnN+vVu9LivTAokHa8Gln/38A4AdqttWpcpldtXuccn5rqN3sUIX1C3YdNwxDx/7XruOv1sp5zFDIxYGy3WtVcJfvHxZj2A1VPF2j6rV1MmoMhfQKVNupoQqMa/gbyrAbOpR1XHVfONX+r+EKvtD3D52Bj9Bl4BEJgcm1CavRX6a8pm2fJ2rqX65V2bEwJbavUGW11a3dpl3n64ml/Vyva+tOXVy66Rc7JcPi15gBT4xqKTglUOFDg1U27USD45VL7ar6m11tHwxVUMcAHXvBrsMTqxX3UoQCIur/dsvn1ejEhjq1eyxUATaLyp+u0eHJ1WqfGy5LoPvfd8WCGgXGWlT3RYO3wjmGWQaeNWuXwQcffKBhw4YpMTFRFotFq1atas5wTOnWzE9VWhahJ5b20+59cSo5EqlP9pynA9+6P8mrti5QRyrCXdux4w2fGNb1vMO6ecAOPbHs6rMVPtBAaN8gRd1tVdg1wQ2OGYahqpdq1ea2EIVdE6zgroFq91CojBOGqtfUSpKclYaOv14r20SrrD8LUnC3QLWbEaq6L52q2eJwu96JjXWq+dihqInWBu+Fc9DJdQi82VqxZq0QVFVVqWfPnrr99tv1q1/9qjlDMa0rL92nzZ+dr0fGrtNlKcU6dDRCqz64WG/8+yK3dpelFOvV2UtVeTxE27/ooMWvXaGjlWGu49bgOj18x7ua/1JfHakIP9sfA2gUxwFDzsOGQjO+/6fPEmKRNS1I9h0ORfxSqv2vQ6qTrD9oE9g+QEFdAmTf4VBo7/r9jsNOHZ11QtFzwmSxUhVDy9esCcGQIUM0ZMiQRrevqalRTU2N63VFRYU/wjKVDrHHNOLq3Xr5nVQtW32Zunc+pD/etFG1dQF6++MLJUkff5ak97Z10cHDbdQh9piyhm7V/Ox/adwTv1RtXX1/6YRff6SdX8Vrw386N+OnATxzHq7/hRcQ7X4DD4i2yFFSP4TccdiQgqWAKPc2gdEW1/mGYejoYycU8ctghXQPVN2BVj78vJWgy8CzFjWGYNasWXrkkUeaO4xWJcBiaM/+WC1+7QpJ0hdFsercoUwjrtrtSgjeze/qal9QHK09+9rr5cf/pj499uuD7cm6MnWfLu92QFmzbmyWzwA02Y9/0Bun2PfjJj9oU/VyrZxVhtqMCfFDcPAbBhV61KISgmnTpmnSpEmu1xUVFUpKSmrGiFq+w+Xh+rq4ndu+fSVt1S+t4PTnVITr4JE2Or99uSTp8m4HlBhboX/9Oc+t3WPj1uk/exP0x/lDfR84cAYCYurv6M7DhgJjv9/vLDNcVYPAGItUKzkrDLcqgbPMUMCl9a/t+Q7V7nKq+OpKt+sfuv24wgYHqd1DYQJamhaVEFitVlmtDN7xpR1fxSsp/qjbvqS4ch080ua050RFnFD7dlU6/N1YgRfX9NQb/+7m1ibvwX9owSu9tXFHR5/HDJypwESLAmIsOrG5TsHd6ru7jFpDNdvqFPX7+n9bgi8KlIKkms11ChtYPzDR8a1TdV85FfKH+nOiJlkVedf31QHHt4aO/LFa7R4LVUgPph2eq+gy8KxFJQTwvb+/m6pnp7yq3w7epvc+6aLunQ5p2M//qz8vv0qSFGat1e3X52v9tmQdLg9XQswx3Tlii8orQ/XB9s6S5Jp58GMHy9qo+HBUg/2APzmPG3IUfd+n7zhgqPZzhyxRFgUlBCji5mBV5tkVlBSgoKQAVebZZQm1KCyz/uYf0Mai8GHBKn+6RgE2iwKiLCp/pkZBXQNkvaL+Zh+U4D5ByxJW/35B5weccq0CnCN42qFHJAQm99997TV90SDdNWKLxly3TSWHI/XMK320dssFkiSH06IuiUc0OOMLtQmz63B5uLZ93kEzlgxQdQ39pzj31O526PDvq12vK56qH4gcdl19Kb/N6BAZNVL5/6upX5jokkDFPBXmWoNAkmzZVlkCpSPTq6UaKaRXoGL+HNZgDQKgNWnWhKCyslJ79+51vS4oKND27dsVHR2tjh0pNZ8tH+3spI92djrlMXttkKYsuK7J17x6/DhvwwLOiDU9SImbIk973GKxKGqcVVHjTt/9aLFaZJsSKtuUhuttnEpQYoDH98S5gS4Dz5o1Idi6dauuueYa1+uTAwbHjBmj3NzcZooKANAqMcvAo2ZNCPr37y+jlffJAADQEjCGAABgCnQZeEZCAAAwB6dRv3lzfitGQgAAMAfGEHjEhFkAAECFAABgDhZ5OYbAZ5Gcm0gIAADmwEqFHtFlAAAAqBAAAMyBaYeekRAAAMyBWQYe0WUAAACoEAAAzMFiGLJ4MTDQm3NbAhICAIA5OL/bvDm/FaPLAAAAUCEAAJgDXQaekRAAAMyBWQYekRAAAMyBlQo9YgwBAACgQgAAMAdWKvSMhAAAYA50GXhElwEAAH7yzTff6Le//a1iYmIUHh6uyy67TPn5+a7jhmFoxowZSkxMVFhYmPr3769du3a5XaOmpkYTJkxQbGysIiIiNHz4cBUVFfk8VhICAIApWJzeb01RVlamK6+8UsHBwXrrrbf02Wef6cknn1Tbtm1dbebMmaO5c+dqwYIF2rJlixISEjRo0CAdO3bM1SY7O1srV67UihUrtGHDBlVWVmro0KFyOBw++mbq0WUAADAHH3UZVFRUuO22Wq2yWq0Nms+ePVtJSUl64YUXXPs6d+78g8sZmj9/vqZPn64bb7xRkpSXl6f4+HgtX75cd911l8rLy7VkyRItXbpUAwcOlCQtW7ZMSUlJWrdunQYPHnzmn+dHqBAAANAESUlJstlsrm3WrFmnbPfaa6+pV69euummmxQXF6e0tDQtXrzYdbygoEAlJSXKzMx07bNarerXr582btwoScrPz1dtba1bm8TERPXo0cPVxleoEAAAzMFHCxMVFhYqKirKtftU1QFJ+uqrr7Rw4UJNmjRJDzzwgDZv3qyJEyfKarXqd7/7nUpKSiRJ8fHxbufFx8dr3759kqSSkhKFhISoXbt2DdqcPN9XSAgAAKbgq6WLo6Ki3BKC03E6nerVq5dycnIkSWlpadq1a5cWLlyo3/3ud99f12JxO88wjAb7fqwxbZqKLgMAAPygQ4cOuvjii932de/eXfv375ckJSQkSFKDX/qlpaWuqkFCQoLsdrvKyspO28ZXSAgAAOZwclChN1sTXHnlldqzZ4/bvs8//1ydOnWSJCUnJyshIUFr1651Hbfb7Vq/fr369u0rSUpPT1dwcLBbm+LiYu3cudPVxlfoMgAAmIMhqYlTBxuc3wR/+tOf1LdvX+Xk5GjkyJHavHmznn/+eT3//POS6rsKsrOzlZOTo5SUFKWkpCgnJ0fh4eEaNWqUJMlmsykrK0uTJ09WTEyMoqOjNWXKFKWmprpmHfgKCQEAwBTO9uOPr7jiCq1cuVLTpk3To48+quTkZM2fP1+33nqrq83UqVNVXV2t8ePHq6ysTBkZGVqzZo0iIyNdbebNm6egoCCNHDlS1dXVGjBggHJzcxUYGHjGn+VULIbRctdirKiokM1mU8bQRxUUHNrc4QB+0fX+3c0dAuA39kq7Vgx4UeXl5Y0aqHcmTt4rfpF2v4ICz/xeUec4oXe3PeHXWJsTFQIAgDkY8nJhIp9Fck4iIQAAmAMPN/KIWQYAAIAKAQDAJJySvFnLx5sZCi0ACQEAwBTO9iyDloYuAwAAQIUAAGASDCr0iIQAAGAOJAQe0WUAAACoEAAATIIKgUckBAAAc2DaoUckBAAAU2DaoWeMIQAAAFQIAAAmwRgCj0gIAADm4DQkixc3dWfrTgjoMgAAAFQIAAAmQZeBRyQEAACT8DIhUOtOCOgyAAAAVAgAACZBl4FHJAQAAHNwGvKq7M8sAwAA0NpRIQAAmIPhrN+8Ob8VIyEAAJgDYwg8IiEAAJgDYwg8YgwBAACgQgAAMAm6DDwiIQAAmIMhLxMCn0VyTqLLAAAAUCEAAJgEXQYekRAAAMzB6ZTkxVoCzta9DgFdBgAAgAoBAMAk6DLwiIQAAGAOJAQe0WUAAACoEAAATIKliz0iIQAAmIJhOGV48cRCb85tCUgIAADmYBje/cpnDAEAAGjtqBAAAMzB8HIMQSuvEJAQAADMwemULF6MA2jlYwjoMgAAACQEAACTOLkwkTdbE8yYMUMWi8VtS0hI+EE4hmbMmKHExESFhYWpf//+2rVrl9s1ampqNGHCBMXGxioiIkLDhw9XUVGRT76OHyMhAACYguF0er011SWXXKLi4mLXtmPHDtexOXPmaO7cuVqwYIG2bNmihIQEDRo0SMeOHXO1yc7O1sqVK7VixQpt2LBBlZWVGjp0qBwOh0++kx9iDAEAAH4SFBTkVhU4yTAMzZ8/X9OnT9eNN94oScrLy1N8fLyWL1+uu+66S+Xl5VqyZImWLl2qgQMHSpKWLVumpKQkrVu3ToMHD/ZprFQIAADm4KMug4qKCretpqbmtG/5xRdfKDExUcnJybrlllv01VdfSZIKCgpUUlKizMxMV1ur1ap+/fpp48aNkqT8/HzV1ta6tUlMTFSPHj1cbXyJhAAAYA5Ow/tNUlJSkmw2m2ubNWvWKd8uIyNDf/3rX/X2229r8eLFKikpUd++fXX48GGVlJRIkuLj493OiY+Pdx0rKSlRSEiI2rVrd9o2vkSXAQAATVBYWKioqCjXa6vVesp2Q4YMcf13amqq+vTpo65duyovL0+9e/eWJFksFrdzDMNosO/HGtPmTFAhAACYg2HUryVwxlt9hSAqKsptO11C8GMRERFKTU3VF1984RpX8ONf+qWlpa6qQUJCgux2u8rKyk7bxpdICAAApmA4Da83b9TU1Gj37t3q0KGDkpOTlZCQoLVr17qO2+12rV+/Xn379pUkpaenKzg42K1NcXGxdu7c6WrjS3QZAADMwXBKOnsrFU6ZMkXDhg1Tx44dVVpaqscff1wVFRUaM2aMLBaLsrOzlZOTo5SUFKWkpCgnJ0fh4eEaNWqUJMlmsykrK0uTJ09WTEyMoqOjNWXKFKWmprpmHfgSCQEAAH5QVFSk3/zmN/r222/Vvn179e7dW5s2bVKnTp0kSVOnTlV1dbXGjx+vsrIyZWRkaM2aNYqMjHRdY968eQoKCtLIkSNVXV2tAQMGKDc3V4GBgT6P12IYLfdpDRUVFbLZbMoY+qiCgkObOxzAL7rev7u5QwD8xl5p14oBL6q8vNxtoJ4vnbxX9Lf8UkGW4DO+Tp1Rq/eNlX6NtTlRIQAAmMNZ7jJoaVp0QnCyuFFXe6KZIwH8x15pb+4QAL+praqV9P2/5/5Up1qvnn5cp1rfBXMOatFdBkVFRUpKSmruMAAAXiosLNT555/vl2ufOHFCycnJPlnMJyEhQQUFBQoNbX3d1C06IXA6nTpw4IAiIyP9skgDGqqoqFBSUlKDhTmA1oC/77PPMAwdO3ZMiYmJCgjw30z4EydOyG73vtoWEhLSKpMBqYV3GQQEBPgto4RnJxfkAFoj/r7PLpvN5vf3CA0NbbU3cl9hYSIAAEBCAAAASAjQRFarVQ8//HCj1+4GWhL+vmFmLXpQIQAA8A0qBAAAgIQAAACQEAAAAJEQAAAAkRCgCZ599lklJycrNDRU6enp+vDDD5s7JMAnPvjgAw0bNkyJiYmyWCxatWpVc4cEnHUkBGiUl156SdnZ2Zo+fbq2bdumq666SkOGDNH+/fubOzTAa1VVVerZs6cWLFjQ3KEAzYZph2iUjIwMXX755Vq4cKFrX/fu3XXDDTdo1qxZzRgZ4FsWi0UrV67UDTfc0NyhAGcVFQL8JLvdrvz8fGVmZrrtz8zM1MaNG5spKgCAL5EQ4Cd9++23cjgcio+Pd9sfHx/vk8eJAgCaHwkBGu3Hj5g2DIPHTgNAK0FCgJ8UGxurwMDABtWA0tLSBlUDAEDLREKAnxQSEqL09HStXbvWbf/atWvVt2/fZooKAOBLQc0dAFqGSZMmafTo0erVq5f69Omj559/Xvv379fdd9/d3KEBXqusrNTevXtdrwsKCrR9+3ZFR0erY8eOzRgZcPYw7RCN9uyzz2rOnDkqLi5Wjx49NG/ePF199dXNHRbgtffff1/XXHNNg/1jxoxRbm7u2Q8IaAYkBAAAgDEEAACAhAAAAIiEAAAAiIQAAACIhAAAAIiEAAAAiIQAAACIhAAAAIiEAPDajBkzdNlll7le33bbbbrhhhvOehxff/21LBaLtm/ffto2nTt31vz58xt9zdzcXLVt29br2CwWi1atWuX1dQD4DwkBWqXbbrtNFotFFotFwcHB6tKli6ZMmaKqqiq/v/dTTz3V6OVuG3MTB4CzgYcbodW69tpr9cILL6i2tlYffvihxo4dq6qqKi1cuLBB29raWgUHB/vkfW02m0+uAwBnExUCtFpWq1UJCQlKSkrSqFGjdOutt7rK1ifL/P/3f/+nLl26yGq1yjAMlZeX684771RcXJyioqL0i1/8Qp9++qnbdZ944gnFx8crMjJSWVlZOnHihNvxH3cZOJ1OzZ49WxdccIGsVqs6duyomTNnSpKSk5MlSWlpabJYLOrfv7/rvBdeeEHdu3dXaGioLrroIj377LNu77N582alpaUpNDRUvXr10rZt25r8Hc2dO1epqamKiIhQUlKSxo8fr8rKygbtVq1apQsvvFChoaEaNGiQCgsL3Y6//vrrSk9PV2hoqLp06aJHHnlEdXV1TY4HQPMhIYBphIWFqba21vV67969evnll/WPf/zDVbK//vrrVVJSojfffFP5+fm6/PLLNWDAAB05ckSS9PLLL+vhhx/WzJkztXXrVnXo0KHBjfrHpk2bptmzZ+vBBx/UZ599puXLlys+Pl5S/U1dktatW6fi4mL985//lCQtXrxY06dP18yZM7V7927l5OTowQcfVF5eniSpqqpKQ4cOVbdu3ZSfn68ZM2ZoypQpTf5OAgIC9PTTT2vnzp3Ky8vTu+++q6lTp7q1OX78uGbOnKm8vDz9+9//VkVFhW655RbX8bffflu//e1vNXHiRH322WdatGiRcnNzXUkPgBbCAFqhMWPGGCNGjHC9/vjjj42YmBhj5MiRhmEYxsMPP2wEBwcbpaWlrjbvvPOOERUVZZw4ccLtWl27djUWLVpkGIZh9OnTx7j77rvdjmdkZBg9e/Y85XtXVFQYVqvVWLx48SnjLCgoMCQZ27Ztc9uflJRkLF++3G3fY489ZvTp08cwDMNYtGiRER0dbVRVVbmOL1y48JTX+qFOnToZ8+bNO+3xl19+2YiJiXG9fuGFFwxJxqZNm1z7du/ebUgyPv74Y8MwDOOqq64ycnJy3K6zdOlSo0OHDq7XkoyVK1ee9n0BND/GEKDVeuONN9SmTRvV1dWptrZWI0aM0DPPPOM63qlTJ7Vv3971Oj8/X5WVlYqJiXG7TnV1tb788ktJ0u7du3X33Xe7He/Tp4/ee++9U8awe/du1dTUaMCAAY2O+9ChQyosLFRWVpbGjRvn2l9XV+can7B792717NlT4eHhbnE01XvvvaecnBx99tlnqqioUF1dnU6cOKGqqipFRERIkoKCgtSrVy/XORdddJHatm2r3bt362c/+5ny8/O1ZcsWt4qAw+HQiRMndPz4cbcYAZy7SAjQal1zzTVauHChgoODlZiY2GDQ4Mkb3klOp1MdOnTQ+++/3+BaZzr1LiwsrMnnOJ1OSfXdBhkZGW7HAgMDJUmGYZxRPD+0b98+XXfddbr77rv12GOPKTo6Whs2bFBWVpZb14pUP23wx07uczqdeuSRR3TjjTc2aBMaGup1nADODhICtFoRERG64IILGt3+8ssvV0lJiYKCgtS5c+dTtunevbs2bdqk3/3ud659mzZtOu01U1JSFBYWpnfeeUdjx45tcDwkJERS/S/qk+Lj43Xeeefpq6++0q233nrK61588cVaunSpqqurXUmHpzhOZevWraqrq9OTTz6pgID64UQvv/xyg3Z1dXXaunWrfvazn0mS9uzZo6NHj+qiiy6SVP+97dmzp0nfNYBzDwkB8J2BAweqT58+uuGGGzR79mx169ZNBw4c0JtvvqkbbrhBvXr10h//+EeNGTNGvXr10s9//nO9+OKL2rVrl7p06XLKa4aGhuq+++7T1KlTFRISoiuvvFKHDh3Srl27lJWVpbi4OIWFhWn16tU6//zzFRoaKpvNphkzZmjixImKiorSkCFDVFNTo61bt6qsrEyTJk3SqFGjNH36dGVlZel//ud/9PXXX+vPf/5zkz5v165dVVdXp2eeeUbDhg3Tv//9bz333HMN2gUHB2vChAl6+umnFRwcrD/84Q/q3bu3K0F46KGHNHToUCUlJemmm25SQECA/vOf/2jHjh16/PHHm/4/AkCzYJYB8B2LxaI333xTV199te644w5deOGFuuWWW/T111+7ZgXcfPPNeuihh3TfffcpPT1d+/bt0z333OPxug8++KAmT56shx56SN27d9fNN9+s0tJSSfX9808//bQWLVqkxMREjRgxQpI0duxY/e///q9yc3OVmpqqfv36KTc31zVNsU2bNnr99df12WefKS0tTdOnT9fs2bOb9Hkvu+wyzZ07V7Nnz1aPHj304osvatasWQ3ahYeH67777tOoUaPUp08fhYWFacWKFa7jgwcP1htvvKG1a9fqiiuuUO/evTV37lx16tSpSfEAaF4WwxedkQAAoEWjQgAAAEgIAAAACQEAABAJAQAAEAkBAAAQCQEAABAJAQAAEAkBAAAQCQEAABAJAQAAEAkBAACQ9P8Bb8BiN2JkhpkAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Logistic Regressor AUC score: 0.6571170084439084\n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "Decision Tree Model Classification Report \n", + " precision recall f1-score support\n", + "\n", + " 0 0.79 0.73 0.76 1658\n", + " 1 0.75 0.80 0.77 1658\n", + "\n", + " accuracy 0.77 3316\n", + " macro avg 0.77 0.77 0.77 3316\n", + "weighted avg 0.77 0.77 0.77 3316\n", + " \n", + "\n", + "Decision Tree Confusion Matrix: \n", + " 0 1\n", + "0 1213 445\n", + "1 331 1327 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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77ruv3ddZsWIFli9fbtfayTNtO16AC5eqoZB7YVhEkEW/JW33LgwyREROQrJwU1paCqPRiJCQEIvtISEhKC4ubvOY+Ph4bN68GdOmTUNtbS0aGxsxZcoUvPPOO+2+zpIlS5CSkmK+3zJyQ2QLk0lg/d4cAMCf7r0Nc0dHSVwRERG1R/L116/9bVcI0e5vwKdPn8aCBQvw4osv4tixY/j666+Rk5OD5OTkdp9foVBApVJZ3IhstetsCbJLqxGolGPq7QzHRETOTLKRm+DgYHh7e7capSkpKWk1mtNixYoVSEhIwPPPPw8AGDJkCPz9/ZGYmIi//OUv6NWrl8PrJs+0dm82AGB6XG8unEdE5OQkG7nx9fVFTEwM0tPTLbanp6cjPj6+zWNqamrg5WVZsrd301LzEs6LJjeXWVCB/+aUQ+4lw+z4SKnLISKiG5D0a6mUlBSsW7cOH330Ec6cOYNFixYhLy/P/DXTkiVLMHPmTPP+kydPxvbt27FmzRpkZ2dj//79WLBgAe644w5oNBqp3ga5ubXNc20mD9Wgl9pP4mqIiOhGJB1fnzZtGsrKyvDyyy9Dp9Nh0KBBSEtLg1arBQDodDrk5eWZ9589ezYqKyvx7rvv4o9//COCgoJw99134+9//7tUb4HcXGHFFaRl6QAASYmcRExE5AokXedGCrZcJ0/0l///NNbty0FCv+7YnDRS6nKIiDyWLZ/fkl8tReSsDLUN2HKkaXHIpMQ+EldDRETWYrghasfWw/moqmvELT0DcFd0D6nLISIiKzHcELWhwWjCR/ubJhInJUZx9WEiIhfCcEPUhrQsHXT6WgQHKHD/sDCpyyEiIhsw3BBdQwhhXrRv1igtlD7eEldERES2YLghusah7HKcLDRA6eOFR0dqpS6HiIhsxHBDdI11zaM2D8WEo6u/r8TVEBGRrRhuiK5yvqQSO8+WQCYD5o3m5d9ERK6I4YboKuv3NV0hNa5/CKKC/SWuhoiIOoLhhqhZaVUdth0vBADMv5OjNkRErorhhqjZpoO5qG80YWhEEGK1XaUuh4iIOojhhghAbYMRmw7lAgDmc9E+IiKXxnBDBGDb8QKUV9cjvKsf7h0YKnU5RER0ExhuyOOZTALr9zZNJJ6bEAW5N/9aEBG5Mv4rTh5v19kSZJdWI1Apx9TbI6Quh4iIbhLDDXm8llYL0+N6I0Ahl7gaIiK6WQw35NEyCyrw35xyyL1kmB0fKXU5RERkBww35NHWNs+1mTJUg15qP4mrISIie2C4IY9VWHEFaVk6AEBSIhftIyJyFww35LFS9+XAaBJI6NcdAzQqqcshIiI7Ybghj2SobcCWI/kAOGpDRORuGG7II205nIequkbc0jMAd0X3kLocIiKyI4Yb8jgNRhNS918EAMxP7MNWC0REbobhhjxOWpYOOn0tggMUuH+4RupyiIjIzhhuyKMIIcyL9s0apYVC7i1xRUREZG8MN+RRDmWX42ShAUofLzw6Uit1OURE5AAMN+RRWkZtHooJR1d/X4mrISIiR2C4IY9xvqQSu86WQCYD5o3m5d9ERO6K4YY8xvp9Ta0WxvUPQVSwv8TVEBGRozDckEcorarDtuOFAID5d3LUhojInTHckEfYdDAX9Y0mDI0IQqy2q9TlEBGRAzHckNurbTBi06FcAMD8xCgu2kdE5OYYbsjtbTtegPLqeoR39cO9A0OlLoeIiBysQ+GmsbER3333HT744ANUVlYCAIqKilBVVWXX4ohulskksH5v00TiuQlRkHszzxMRuTu5rQfk5ubi3nvvRV5eHurq6jBu3DgEBgbi1VdfRW1tLd5//31H1EnUIbvOliC7tBqBSjmm3h4hdTlERNQJbP419tlnn0VsbCwuX74MPz8/8/YHH3wQO3futGtxRDerZdG+6XG9EaCwOcsTEZELsvlf+3379mH//v3w9bVc3VWr1aKwsNBuhRHdrMyCCvw3pxxyLxlmx0dKXQ4REXUSm0duTCYTjEZjq+0FBQUIDAy0S1FE9rC2ea7NlKEa9FL73WBvIiJyFzaHm3HjxmHVqlXm+zKZDFVVVVi2bBkmTZpkz9qIOqyw4grSsnQAgKRELtpHRORJbP5a6s0338TYsWMxYMAA1NbWYvr06Th37hyCg4PxySefOKJGIpul7suB0SSQ0K87BmhUUpdDRESdyOZwo9FocOLECWzZsgXHjh2DyWTCvHnzMGPGDIsJxkRSMdQ2YMuRfAActSEi8kQ2h5s9e/YgPj4ec+bMwZw5c8zbGxsbsWfPHtx55512LZDIVlsO56GqrhG39AzAXdE9pC6HiIg6mc1zbsaOHYvy8vJW2/V6PcaOHWuXoog6qsFoQur+iwCA+Yl92GqBiMgD2RxuhBBtfmCUlZXB39/fLkURdVRalg46fS2CAxS4f7hG6nKIiEgCVn8t9dvf/hZA09VRs2fPhkKhMD9mNBqRmZmJ+Ph4+1dIZCUhhHnRvlmjtFDIvSWuiIiIpGB1uFGr1QCaPkACAwMtJg/7+vpi5MiRmD9/vv0rJLLSwewynCw0QOnjhUdHaqUuh4iIJGJ1uElNTQUAREZG4rnnnuNXUOR01jUv2vdwTAS6+vveYG8iInJXNl8ttWzZMkfUQXRTzpdUYtfZEshkwLzRUVKXQ0REEupQJ8HPPvsMn376KfLy8lBfX2/x2PHjx+1SGJEt1u9rGrUZ1z8EkcEcVSQi8mQ2Xy319ttvY86cOejZsycyMjJwxx13oHv37sjOzsbEiRMdUSPRdZVW1WHb8aamrfPv5KJ9RESezuZws3r1anz44Yd499134evri8WLFyM9PR0LFiyAXq93RI1E17XxYC7qG00YGhGEWG1XqcshIiKJ2Rxu8vLyzJd8+/n5obKyEgDw2GOPsbcUdbraBiP+eSgXAPA4F+0jIiJ0INyEhoairKwMAKDVanHo0CEAQE5ODoQQ9q2O6Aa2HS9AeXU9wrv6YcLAEKnLISIiJ2BzuLn77rvxn//8BwAwb948LFq0COPGjcO0adPw4IMP2r1AovaYTALrmy//npsQBbm3zX+ciYjIDdl8tdSHH34Ik8kEAEhOTka3bt2wb98+TJ48GcnJyXYvkKg9u86WILu0GoFKOabeHiF1OURE5CRsDjdeXl7w8vr1N+SpU6di6tSpAIDCwkKEhYXZrzqi6/iwudXC9LjeCFB0aFUDIiJyQ3YZxy8uLsYf/vAH9OvXz+ZjV69ejaioKCiVSsTExGDv3r3X3b+urg5Lly6FVquFQqFA37598dFHH3W0dHJRmQUVOJxTDrmXDHPiuWgfERH9yupwU1FRgRkzZqBHjx7QaDR4++23YTKZ8OKLL6JPnz44dOiQzSFj69atWLhwIZYuXYqMjAwkJiZi4sSJyMvLa/eYqVOnYufOnVi/fj1++uknfPLJJ7jttttsel1yfWub59pMGapBqFopcTVERORMZMLKS5yeeuop/Oc//8G0adPw9ddf48yZM5gwYQJqa2uxbNkyjBkzxuYXj4uLw4gRI7BmzRrztv79++OBBx7AihUrWu3/9ddf45FHHkF2dja6detm1WvU1dWhrq7OfN9gMCAiIgJ6vR4qlcrmmkl6hRVXcOeru2E0CaQtSMQADX+ORETuzmAwQK1WW/X5bfXIzZdffonU1FS8/vrr+OKLLyCEQHR0NHbt2tWhYFNfX49jx45h/PjxFtvHjx+PAwcOtHnMF198gdjYWLz66qsICwtDdHQ0nnvuOVy5cqXd11mxYgXUarX5FhHBiaeuLnVfDowmgYR+3RlsiIioFatnYRYVFWHAgAEAgD59+kCpVCIpKanDL1xaWgqj0YiQEMu1SUJCQlBcXNzmMdnZ2di3bx+USiV27NiB0tJSPPXUUygvL2/3K7ElS5YgJSXFfL9l5IZck6G2AVuO5AMAkhLZaoGIiFqzOtyYTCb4+PiY73t7e8Pf/+YbFF67oqwQot1VZk0mE2QyGTZv3gy1Wg0AWLlyJR566CG899578PPza3WMQqGAQqG46TrJOWw5nIequkbc0jMAd0X3kLocIiJyQlaHGyEEZs+ebQ4KtbW1SE5ObhVwtm/fbtXzBQcHw9vbu9UoTUlJSavRnBa9evVCWFiYOdgATXN0hBAoKCjALbfcYu3bIRfUYDQhdf9FAMB8tlogIqJ2WD3nZtasWejZs6d57sqjjz4KjUZjMZ/l6tBxI76+voiJiUF6errF9vT0dHPvqmslJCSgqKgIVVVV5m0///wzvLy8EB4ebvVrk2tKy9JBp69FcIAC9w/XSF0OERE5KatHblJTU+3+4ikpKXjssccQGxuLUaNG4cMPP0ReXp55peMlS5agsLAQGzduBABMnz4df/7znzFnzhwsX74cpaWleP755zF37tw2v5Ii9yGEwNrmRftmjdJCIfeWuCIiInJWki7rOm3aNJSVleHll1+GTqfDoEGDkJaWBq1WCwDQ6XQWa94EBAQgPT0df/jDHxAbG4vu3btj6tSp+Mtf/iLVW6BOcjC7DCcLDVD6eOHRkVqpyyEiIidm9To37sKW6+TJeczdcAS7zpbgsZFa/PmBQVKXQ0REncwh69wQSeV8SSV2nS2BTAbMG81WC0REdH0MN+T01u9rarUwrn8IIoNvfvkBIiJybww35NRKq+qw7XghAGD+nVy0j4iIbqxD4WbTpk1ISEiARqNBbm4uAGDVqlX497//bdfiiDYezEV9owlDI4IQq+0qdTlEROQCbA43a9asQUpKCiZNmoSKigoYjUYAQFBQEFatWmXv+siD1TYY8c9DTeH5cS7aR0REVrI53LzzzjtYu3Ytli5dCm/vX9caiY2NRVZWll2LI8+27XgByqvrEd7VDxMGtr1qNRER0bVsDjc5OTkYPnx4q+0KhQLV1dV2KYrIZBJYv7dpIvHchCjIvTk9jIiIrGPzJ0ZUVBROnDjRavtXX31l7hpOdLN2ni1Bdmk1ApVyTL2dXdyJiMh6Nq9Q/Pzzz+Ppp59GbW0thBA4fPgwPvnkE6xYsQLr1q1zRI3kgVpaLcyI0yJAIelC2kRE5GJs/tSYM2cOGhsbsXjxYtTU1GD69OkICwvDW2+9hUceecQRNZKHySyowOGccsi9ZJgdHyl1OURE5GI69Cvx/PnzMX/+fJSWlsJkMqFnz572ros82NrmuTZThmoQqlZKXA0REbkam+fcLF++HBcuXAAABAcHM9iQXRVcrkFalg4AkJTIRfuIiMh2Noebbdu2ITo6GiNHjsS7776LS5cuOaIu8lCp+y/CaBJI6NcdAzRsbEpERLazOdxkZmYiMzMTd999N1auXImwsDBMmjQJH3/8MWpqahxRI3kIQ20Dth7JBwDM56gNERF1UIcWDxk4cCBeeeUVZGdnY/fu3YiKisLChQsRGhpq7/rIg2w5nIequkZEhwRgTHQPqcshIiIXddMro/n7+8PPzw++vr5oaGiwR03kgRqMJqTuvwgASBrNVgtERNRxHQo3OTk5+Otf/4oBAwYgNjYWx48fx0svvYTi4mJ710ceIi1LB52+FsEBCtw/XCN1OURE5MJsvhR81KhROHz4MAYPHow5c+aY17kh6ighBD7c07Ro36xRWijk3jc4goiIqH02h5uxY8di3bp1GDhwoCPqIQ90MLsMp4oMUPp44dGRWqnLISIiF2dzuHnllVccUQd5sHXNi/Y9HBOBrv6+EldDRESuzqpwk5KSgj//+c/w9/dHSkrKdfdduXKlXQojz3C+pBK7zpZAJgPmjY6SuhwiInIDVoWbjIwM85VQGRkZDi2IPMv6fU2jNuP6hyAy2F/iaoiIyB1YFW52797d5v8T3YxLlXXYdrwQADD/Ti7aR0RE9mHzpeBz585FZWVlq+3V1dWYO3euXYoiz7DpUC7qG00YFhGEWG1XqcshIiI3YXO4+cc//oErV6602n7lyhVs3LjRLkWR+6ttMOKfh3IBNLVa4KJ9RERkL1ZfLWUwGCCEgBAClZWVUCqV5seMRiPS0tLYIZystu14Acqr6xHe1Q8TBoZIXQ4REbkRq8NNUFAQZDIZZDIZoqOjWz0uk8mwfPlyuxZH7slkEljffPn33IQoyL1vugsIERGRmdXhZvfu3RBC4O6778a2bdvQrVs382O+vr7QarXQaLhsPt3YzrMlyC6tRqBSjqm3R0hdDhERuRmrw82YMWMANPWV6t27N+dIUIet3dvUamFGnBYBCpvXkSQiIrouqz5ZMjMzMWjQIHh5eUGv1yMrK6vdfYcMGWK34sj9ZBZU4HBOOeReMsyOj5S6HCIickNWhZthw4ahuLgYPXv2xLBhwyCTySCEaLWfTCaD0Wi0e5HkPtY2z7WZMlSDULXyBnsTERHZzqpwk5OTgx49epj/n6gjCi7XIC1LBwBISuSifURE5BhWhRutVtvm/xPZInX/RRhNAgn9umOARiV1OURE5KY6tIjfl19+ab6/ePFiBAUFIT4+Hrm5uXYtjtyHobYBW4/kA2hatI+IiMhRbA43r7zyCvz8/AAABw8exLvvvotXX30VwcHBWLRokd0LJPew5XAequoaER0SgDHRPaQuh4iI3JjN1+Hm5+ejX79+AIDPP/8cDz30EB5//HEkJCTgrrvusnd95AYajCak7r8IAEgazVYLRETkWDaP3AQEBKCsrAwA8O233+Kee+4BACiVyjZ7ThF9mamDTl+L4AAF7h/OhR6JiMixbB65GTduHJKSkjB8+HD8/PPPuO+++wAAp06dQmRkpL3rIxcnhDAv2jdrlBYKubfEFRERkbuzeeTmvffew6hRo3Dp0iVs27YN3bt3BwAcO3YMv//97+1eILm2g9llOFVkgNLHC4+O5JV2RETkeDaP3AQFBeHdd99ttZ1NM6kt65oX7Xs4JgJd/X0lroaIiDxBhxr7VFRUYP369Thz5gxkMhn69++PefPmQa1W27s+cmHnSyqx62wJZDJg3ugoqcshIiIPYfPXUkePHkXfvn3x5ptvory8HKWlpXjzzTfRt29fHD9+3BE1kotqGbUZ1z8EkcH+EldDRESewuaRm0WLFmHKlClYu3Yt5PKmwxsbG5GUlISFCxdiz549di+SXM+lyjpszygEADx+JxftIyKizmNzuDl69KhFsAEAuVyOxYsXIzY21q7FkevadCgX9Y0mDIsIQoy2q9TlEBGRB7H5aymVSoW8vLxW2/Pz8xEYGGiXosi11TYY8c9DTa045idy0T4iIupcNoebadOmYd68edi6dSvy8/NRUFCALVu2ICkpiZeCEwBg2/EClFfXI7yrHyYMDJG6HCIi8jA2fy31+uuvQyaTYebMmWhsbAQA+Pj44Mknn8Tf/vY3uxdIrsVkEljfPJF4bkIU5N4252ciIqKbIhNCiI4cWFNTgwsXLkAIgX79+qFLly72rs0hDAYD1Go19Ho9VCqV1OW4nfTTv2D+xqNQKeU4sOQ3CFB0aLUBIiIiC7Z8flv9a3VNTQ2efvpphIWFoWfPnkhKSkKvXr0wZMgQlwk25HgtrRamx2kZbIiISBJWh5tly5Zhw4YNuO+++/DII48gPT0dTz75pCNrIxeTWVCBwznlkHvJMDs+UupyiIjIQ1n9q/X27duxfv16PPLIIwCARx99FAkJCTAajfD2ZjNEAtY2z7WZMlSDULVS4mqIiMhTWT1yk5+fj8TERPP9O+64A3K5HEVFRQ4pjFxLweUapGXpAABJiVy0j4iIpGN1uDEajfD1tWx8KJfLzVdMkWdL3X8RRpPA6H7BGKDhRG0iIpKO1V9LCSEwe/ZsKBQK87ba2lokJyfD3//XvkHbt2+3b4Xk9Ay1Ddh6JB8AkJTIBplERCQtq8PNrFmzWm179NFH7VoMuaYth/NQVdeI6JAAjInuIXU5RETk4awON6mpqY6sg1xUg9GE1P0XAQBJo9lqgYiIpCf58rGrV69GVFQUlEolYmJisHfvXquO279/P+RyOYYNG+bYAum6vszUQaevRXCAAvcP10hdDhERkbThZuvWrVi4cCGWLl2KjIwMJCYmYuLEiW025ryaXq/HzJkz8Zvf/KaTKqW2CCHMi/bNjtdCIeeSAEREJD1Jw83KlSsxb948JCUloX///li1ahUiIiKwZs2a6x73xBNPYPr06Rg1alQnVUptOZhdhlNFBih9vDAjTit1OURERAAkDDf19fU4duwYxo8fb7F9/PjxOHDgQLvHpaam4sKFC1i2bJlVr1NXVweDwWBxI/tY17xo38MxEejq73uDvYmIiDqHZOGmtLQURqMRISEhFttDQkJQXFzc5jHnzp3DCy+8gM2bN0Mut24u9IoVK6BWq823iIiIm66dgPMlldh1tgQyGTBvNC//JiIi59GhcLNp0yYkJCRAo9EgNzcXALBq1Sr8+9//tvm5rr26RgjR5hU3RqMR06dPx/LlyxEdHW318y9ZsgR6vd58y8/Pt7lGaq1l1GZc/xBEBvvfYG8iIqLOY3O4WbNmDVJSUjBp0iRUVFTAaDQCAIKCgrBq1Sqrnyc4OBje3t6tRmlKSkpajeYAQGVlJY4ePYpnnnkGcrkccrkcL7/8Mn788UfI5XLs2rWrzddRKBRQqVQWN7o5lyrrsD2jEADw+J1stUBERM7F5nDzzjvvYO3atVi6dKlFw8zY2FhkZWVZ/Ty+vr6IiYlBenq6xfb09HTEx8e32l+lUiErKwsnTpww35KTk3HrrbfixIkTiIuLs/WtUAdtOpSL+kYThkUEIUbbVepyiIiILFi9iF+LnJwcDB8+vNV2hUKB6upqm54rJSUFjz32GGJjYzFq1Ch8+OGHyMvLQ3JyMoCmr5QKCwuxceNGeHl5YdCgQRbH9+zZE0qlstV2cpwr9Ub881DTV5HzE7loHxEROR+bw01UVBROnDgBrdby0t+vvvoKAwYMsOm5pk2bhrKyMrz88svQ6XQYNGgQ0tLSzM+t0+luuOYNda5txwtQXl2P8K5+mDCw9deHREREUpMJIYQtB6SmpuL//u//8MYbb2DevHlYt24dLly4gBUrVmDdunV45JFHHFWrXRgMBqjVauj1es6/sZHJJHDPyh+QXVqNF/+/AZjLq6SIiKiT2PL5bfPIzZw5c9DY2IjFixejpqYG06dPR1hYGN566y2nDzZ0c3aeLUF2aTVUSjmm3s5L6omIyDnZHG4AYP78+Zg/fz5KS0thMpnQs2dPe9dFTqil1cL0OC0CFB36o0NERORwN/UJFRwcbK86yMn9mF+BwznlkHvJMDs+UupyiIiI2tWhCcXXu0ImOzv7pgoi59QyajNlqAahaqXE1RAREbXP5nCzcOFCi/sNDQ3IyMjA119/jeeff95edZETKbhcg69ONi22mJTIRfuIiMi52Rxunn322Ta3v/feezh69OhNF0TOJ3X/RRhNAqP7BWOAhleYERGRc7Nb48yJEydi27Zt9no6chKG2gZsPdLUjyspkZd+ExGR87NbuPnss8/QrVs3ez0dOYkth/NQVdeI6JAAjInuIXU5REREN2Tz11LDhw+3mFAshEBxcTEuXbqE1atX27U4klaD0YTU/RcBAEmj2WqBiIhcg83h5oEHHrC47+XlhR49euCuu+7CbbfdZq+6yAl8mamDTl+L4AAF7h+ukbocIiIiq9gUbhobGxEZGYkJEyYgNDTUUTWRExBCmC//nh2vhULufYMjiIiInINNc27kcjmefPJJ1NXVOaoechIHs8twqsgApY8XZsRpb3wAERGRk7B5QnFcXBwyMjIcUQs5kXV7cwAAD8dEoKu/r8TVEBERWc/mOTdPPfUU/vjHP6KgoAAxMTHw9/e3eHzIkCF2K46kcb6kErvOlkAmA+ax8zcREbkYq8PN3LlzsWrVKkybNg0AsGDBAvNjMpkMQgjIZDIYjUb7V0mdqmXUZvyAEEQG+99gbyIiIudidbj5xz/+gb/97W/IyclxZD0ksUuVddieUQgAmM9WC0RE5IKsDjdCCACAVsvJpe5s06Fc1DeaMCwiCDHarlKXQ0REZDObJhRzETf3dqXeiH8eygXQNGrDnzcREbkimyYUR0dH3/ADr7y8/KYKIulsO16A8up6hHf1w4SBIVKXQ0RE1CE2hZvly5dDrVY7qhaSkMkk8NG+pvlU80ZHQe5tt7ZjREREncqmcPPII4+gZ8+ejqqFJLTzbAmyS6uhUsoxNTZC6nKIiIg6zOpfzzn/wr21tFqYHqeFv8Lm5Y+IiIichtXhpuVqKXI/P+ZX4HBOOeReMsyOj5S6HCIiopti9a/oJpPJkXWQhFpGbaYM1SBUrZS4GiIiopvDWaMeruByDb46WQwASOKifURE5AYYbjxc6v6LMJoERvcLxgCNSupyiIiIbhrDjQfTX2nAlsN5AICkRDbIJCIi98Bw48G2HM5Ddb0R0SEBGBPdQ+pyiIiI7ILhxkM1GE3YcOAiACBpNFstEBGR+2C48VBfZuqg09ciOECB+4drpC6HiIjIbhhuPJAQwnz59+x4LRRyb4krIiIish+GGw90MLsMp4oMUPp4YUacVupyiIiI7IrhxgOt3dM0avNwTAS6+vtKXA0REZF9Mdx4mPMlldj90yXIZE3dv4mIiNwNw42HWbc3BwAwfkAIIoP9Ja6GiIjI/hhuPMilyjpszygEAMxnqwUiInJTDDceZNOhXNQ3mjAsIggx2q5Sl0NEROQQDDce4kq9EZsOXgTQNGrDRfuIiMhdMdx4iG3HC3C5pgER3fwwYWCI1OUQERE5DMONBzCZBD7a1zSReG5CFOTe/LETEZH74qecB9h5tgTZpdVQKeWYGhshdTlEREQOxXDjAVpaLUyP08JfIZe4GiIiIsdiuHFzP+ZX4HBOOeReMsyOj5S6HCIiIodjuHFzLaM2U4ZpEKpWSlwNERGR4zHcuLGCyzX46mQxACBpNBftIyIiz8Bw48ZS91+E0SQwul8wBmhUUpdDRETUKRhu3JT+SgO2HM4DACQlskEmERF5DoYbN7XlcB6q642IDgnAmOgeUpdDRETUaRhu3FCD0YQNBy4CAJLYaoGIiDwMw40b+jJTB52+Fj0CFbh/mEbqcoiIiDoVw42bEUKYL/+eNUoLhdxb4oqIiIg6F8ONmzmYXYZTRQYofbwwI04rdTlERESdjuHGzazd0zRq83BMBLr6+0pcDRERUedjuHEj50sqsfunS5DJgHmjefk3ERF5JoYbN7Jubw4AYPyAEEQG+0tcDRERkTQYbtzEpco6bM8oBADMT2SrBSIi8lySh5vVq1cjKioKSqUSMTEx2Lt3b7v7bt++HePGjUOPHj2gUqkwatQofPPNN51YrfPadPAi6htNGBYRhBhtV6nLISIikoyk4Wbr1q1YuHAhli5dioyMDCQmJmLixInIy8trc/89e/Zg3LhxSEtLw7FjxzB27FhMnjwZGRkZnVy5c7lSb8SmQ7kAmkZtuGgfERF5MpkQQkj14nFxcRgxYgTWrFlj3ta/f3888MADWLFihVXPMXDgQEybNg0vvviiVfsbDAao1Wro9XqoVO7RTPKfh3Lxv5+fREQ3P+z+412Qe0s+IEdERGRXtnx+S/YpWF9fj2PHjmH8+PEW28ePH48DBw5Y9RwmkwmVlZXo1q1bu/vU1dXBYDBY3NyJySTw0b6micRzE6IYbIiIyONJ9klYWloKo9GIkJAQi+0hISEoLi626jneeOMNVFdXY+rUqe3us2LFCqjVavMtIiLipup2NjvPliC7tBoqpRxTY93rvREREXWE5L/mXzs/RAhh1ZyRTz75BC+99BK2bt2Knj17trvfkiVLoNfrzbf8/PybrtmZtCzaNz1OC3+FXOJqiIiIpCfZp2FwcDC8vb1bjdKUlJS0Gs251tatWzFv3jz861//wj333HPdfRUKBRQKxU3X64x+zK/A4YvlkHvJMDs+UupyiIiInIJkIze+vr6IiYlBenq6xfb09HTEx8e3e9wnn3yC2bNn4+OPP8Z9993n6DKdWkuDzCnDNAhVKyWuhoiIyDlI+j1GSkoKHnvsMcTGxmLUqFH48MMPkZeXh+TkZABNXykVFhZi48aNAJqCzcyZM/HWW29h5MiR5lEfPz8/qNVqyd6HFAou1+Crk03vP2k0F+0jIiJqIWm4mTZtGsrKyvDyyy9Dp9Nh0KBBSEtLg1bb1M1ap9NZrHnzwQcfoLGxEU8//TSefvpp8/ZZs2Zhw4YNnV2+pFL3X4TRJDC6XzAGaNzjknYiIiJ7kHSdGym4wzo3+isNiF+xE9X1RmyYczvuurX9CdVERETuwCXWuaGO23I4D9X1RkSHBGBMdA+pyyEiInIqDDcupsFowoYDFwEASWy1QERE1ArDjYv5MlMHnb4WPQIVuH+YRupyiIiInA7DjQsRQpgv/541SguF3FviioiIiJwPw40LOXihDKeKDFD6eGFGnFbqcoiIiJwSw40LaRm1mRobga7+vhJXQ0RE5JwYblzE+ZJK7P7pEmSypu7fRERE1DaGGxexbm8OAGD8gBBEBvtLXA0REZHzYrhxAZcq67A9oxAAMD+RrRaIiIiuh+HGBWw6eBH1jSYMiwhCjLar1OUQERE5NYYbJ3el3ohNh3IBAI/fyUX7iIiIboThxsltO16AyzUNiOjmhwkDQ6Uuh4iIyOkx3Dgxk0ngo31NE4nnJkTB24ujNkRERDfCcOPEdp4tQXZpNVRKOabGRkhdDhERkUtguHFia/c0Ldo3PU4Lf4Vc4mqIiIhcA8ONk/oxvwKHL5bDx1uG2fGRUpdDRETkMhhunFRLq4XJQzUIVSslroaIiMh1MNw4ofzyGnx1shgAkDSai/YRERHZguHGCaXuvwijSWB0v2AM0KikLoeIiMilMNw4Gf2VBmw9kgcASEpkg0wiIiJbMdw4mS2H81Bdb8StIYEYE91D6nKIiIhcDsONE2kwmrDhwEUAwLzEKLZaICIi6gCGGyfyZaYOOn0tegQqcP8wjdTlEBERuSSGGychhMCHzYv2zRqlhULuLXFFREREronhxkkcvFCG0zoDlD5emBGnlbocIiIil8Vw4yRaFu2bGhuBrv6+EldDRETkuhhunMD5kkrs/ukSZLKm7t9ERETUcQw3TmDd3hwAwPgBIYgM9pe4GiIiItfGcCOxS5V12H68EAAwP5GtFoiIiG4Ww43ENh28iHqjCcMighCj7Sp1OURERC6P4UZCV+qN2HQoFwDw+J19uGgfERGRHTDcSGjb8QJcrmlARDc/TBgYKnU5REREboHhRiImk8D6fU0TiecmRMHbi6M2RERE9sBwI5HvzvyCnNJqqJRyTI2NkLocIiIit8FwI5GWy7+nx2nhr5BLXA0REZH7YLiRwI/5FTh8sRw+3jLMjo+UuhwiIiK3wnAjgZZWC5OHahCqVkpcDRERkXthuOlk+eU1+OpkMQAgaTQX7SMiIrI3hptOlrr/IowmgdH9gjFAo5K6HCIiIrfDcNOJ9FcasPVIHgBg/p0ctSEiInIEhptOtOVwHqrrjbg1JBB33hIsdTlERERuieGmkzQYTdhw4CIAYF5iFFstEBEROQjDTSf5MlMHnb4WPQIVuH+YRupyiIiI3BbDTScQQuDDPU2Xf88apYVC7i1xRURERO6L4aYTHLxQhtM6A/x8vDEjTit1OURERG6N4aYTtCza93BsOLr6+0pcDRERkXtjuHGwc79UYvdPlyCTNXX/JiIiIsdiuHGwlgaZ4weEIDLYX+JqiIiI3B/DjQNdqqzDjoxCAMD8RC7aR0RE1BkYbhxo08GLqDeaMLx3EGK0XaUuh4iIyCMw3DjIlXojNh3KBdA0asNF+4iIiDoHw42DbDtegMs1DYjo5ocJA0OlLoeIiMhjMNw4gMkksH5f00TiuQlR8PbiqA0REVFnYbhxgO/O/IKc0mqolHJMjY2QuhwiIiKPwnDjAC2Xf88YqYW/Qi5xNURERJ6F4cbOfsyvwOGL5fDxlmF2fKTU5RAREXkcycPN6tWrERUVBaVSiZiYGOzdu/e6+//www+IiYmBUqlEnz598P7773dSpdZpabUweagGISqlxNUQERF5HknDzdatW7Fw4UIsXboUGRkZSExMxMSJE5GXl9fm/jk5OZg0aRISExORkZGB//mf/8GCBQuwbdu2Tq68bfnlNUjL0gEAkkZz0T4iIiIpyIQQQqoXj4uLw4gRI7BmzRrztv79++OBBx7AihUrWu3/pz/9CV988QXOnDlj3pacnIwff/wRBw8etOo1DQYD1Go19Ho9VCrVzb+Jq7z8n9P4aH8ORvcLxj+T4uz63ERERJ7Mls9vyUZu6uvrcezYMYwfP95i+/jx43HgwIE2jzl48GCr/SdMmICjR4+ioaGhzWPq6upgMBgsbo6gv9KArUeaRpzm38lRGyIiIqlIFm5KS0thNBoREhJisT0kJATFxcVtHlNcXNzm/o2NjSgtLW3zmBUrVkCtVptvERGOuTQ7v7wGPQIVuDUkEHfeEuyQ1yAiIqIbk3xC8bVtCYQQ121V0Nb+bW1vsWTJEuj1evMtPz//Jitu26AwNXb+8S6kzrmdrRaIiIgkJNkiLMHBwfD29m41SlNSUtJqdKZFaGhom/vL5XJ07969zWMUCgUUCoV9ir4Bby8ZNEF+nfJaRERE1DbJRm58fX0RExOD9PR0i+3p6emIj49v85hRo0a12v/bb79FbGwsfHx8HFYrERERuQ5Jv5ZKSUnBunXr8NFHH+HMmTNYtGgR8vLykJycDKDpK6WZM2ea909OTkZubi5SUlJw5swZfPTRR1i/fj2ee+45qd4CERERORlJewNMmzYNZWVlePnll6HT6TBo0CCkpaVBq9UCAHQ6ncWaN1FRUUhLS8OiRYvw3nvvQaPR4O2338bvfvc7qd4CERERORlJ17mRgiPXuSEiIiLHcIl1boiIiIgcgeGGiIiI3ArDDREREbkVhhsiIiJyKww3RERE5FYYboiIiMitMNwQERGRW2G4ISIiIrfCcENERERuRdL2C1JoWZDZYDBIXAkRERFZq+Vz25rGCh4XbiorKwEAEREREldCREREtqqsrIRarb7uPh7XW8pkMqGoqAiBgYGQyWR2fW6DwYCIiAjk5+ezb5UD8Tx3Dp7nzsHz3Hl4rjuHo86zEAKVlZXQaDTw8rr+rBqPG7nx8vJCeHi4Q19DpVLxL04n4HnuHDzPnYPnufPwXHcOR5znG43YtOCEYiIiInIrDDdERETkVhhu7EihUGDZsmVQKBRSl+LWeJ47B89z5+B57jw8153DGc6zx00oJiIiIvfGkRsiIiJyKww3RERE5FYYboiIiMitMNwQERGRW2G4sdHq1asRFRUFpVKJmJgY7N2797r7//DDD4iJiYFSqUSfPn3w/vvvd1Klrs2W87x9+3aMGzcOPXr0gEqlwqhRo/DNN990YrWuy9Y/zy32798PuVyOYcOGObZAN2Hrea6rq8PSpUuh1WqhUCjQt29ffPTRR51Ureuy9Txv3rwZQ4cORZcuXdCrVy/MmTMHZWVlnVSta9qzZw8mT54MjUYDmUyGzz///IbHSPI5KMhqW7ZsET4+PmLt2rXi9OnT4tlnnxX+/v4iNze3zf2zs7NFly5dxLPPPitOnz4t1q5dK3x8fMRnn33WyZW7FlvP87PPPiv+/ve/i8OHD4uff/5ZLFmyRPj4+Ijjx493cuWuxdbz3KKiokL06dNHjB8/XgwdOrRzinVhHTnPU6ZMEXFxcSI9PV3k5OSI//73v2L//v2dWLXrsfU87927V3h5eYm33npLZGdni71794qBAweKBx54oJMrdy1paWli6dKlYtu2bQKA2LFjx3X3l+pzkOHGBnfccYdITk622HbbbbeJF154oc39Fy9eLG677TaLbU888YQYOXKkw2p0B7ae57YMGDBALF++3N6luZWOnudp06aJ//3f/xXLli1juLGCref5q6++Emq1WpSVlXVGeW7D1vP82muviT59+lhse/vtt0V4eLjDanQ31oQbqT4H+bWUlerr63Hs2DGMHz/eYvv48eNx4MCBNo85ePBgq/0nTJiAo0ePoqGhwWG1urKOnOdrmUwmVFZWolu3bo4o0S109DynpqbiwoULWLZsmaNLdAsdOc9ffPEFYmNj8eqrryIsLAzR0dF47rnncOXKlc4o2SV15DzHx8ejoKAAaWlpEELgl19+wWeffYb77ruvM0r2GFJ9Dnpc48yOKi0thdFoREhIiMX2kJAQFBcXt3lMcXFxm/s3NjaitLQUvXr1cli9rqoj5/lab7zxBqqrqzF16lRHlOgWOnKez507hxdeeAF79+6FXM5/OqzRkfOcnZ2Nffv2QalUYseOHSgtLcVTTz2F8vJyzrtpR0fOc3x8PDZv3oxp06ahtrYWjY2NmDJlCt55553OKNljSPU5yJEbG8lkMov7QohW2260f1vbyZKt57nFJ598gpdeeglbt25Fz549HVWe27D2PBuNRkyfPh3Lly9HdHR0Z5XnNmz582wymSCTybB582bccccdmDRpElauXIkNGzZw9OYGbDnPp0+fxoIFC/Diiy/i2LFj+Prrr5GTk4Pk5OTOKNWjSPE5yF+/rBQcHAxvb+9WvwWUlJS0SqUtQkND29xfLpeje/fuDqvVlXXkPLfYunUr5s2bh3/961+45557HFmmy7P1PFdWVuLo0aPIyMjAM888A6DpQ1gIAblcjm+//RZ33313p9TuSjry57lXr14ICwuDWq02b+vfvz+EECgoKMAtt9zi0JpdUUfO84oVK5CQkIDnn38eADBkyBD4+/sjMTERf/nLXziybidSfQ5y5MZKvr6+iImJQXp6usX29PR0xMfHt3nMqFGjWu3/7bffIjY2Fj4+Pg6r1ZV15DwDTSM2s2fPxscff8zvzK1g63lWqVTIysrCiRMnzLfk5GTceuutOHHiBOLi4jqrdJfSkT/PCQkJKCoqQlVVlXnbzz//DC8vL4SHhzu0XlfVkfNcU1MDLy/Lj0Bvb28Av44s0M2T7HPQodOV3UzLpYbr168Xp0+fFgsXLhT+/v7i4sWLQgghXnjhBfHYY4+Z92+5BG7RokXi9OnTYv369bwU3Aq2nuePP/5YyOVy8d577wmdTme+VVRUSPUWXIKt5/lavFrKOrae58rKShEeHi4eeughcerUKfHDDz+IW265RSQlJUn1FlyCrec5NTVVyOVysXr1anHhwgWxb98+ERsbK+644w6p3oJLqKysFBkZGSIjI0MAECtXrhQZGRnmS+6d5XOQ4cZG7733ntBqtcLX11eMGDFC/PDDD+bHZs2aJcaMGWOx//fffy+GDx8ufH19RWRkpFizZk0nV+yabDnPY8aMEQBa3WbNmtX5hbsYW/88X43hxnq2nuczZ86Ie+65R/j5+Ynw8HCRkpIiampqOrlq12PreX777bfFgAEDhJ+fn+jVq5eYMWOGKCgo6OSqXcvu3buv+++ts3wOyoTg+BsRERG5D865ISIiIrfCcENERERuheGGiIiI3ArDDREREbkVhhsiIiJyKww3RERE5FYYboiIiMitMNwQERGRW2G4ISILGzZsQFBQkNRldFhkZCRWrVp13X1eeuklDBs2rFPqIaLOx3BD5IZmz54NmUzW6nb+/HmpS8OGDRssaurVqxemTp2KnJwcuzz/kSNH8Pjjj5vvy2QyfP755xb7PPfcc9i5c6ddXq89177PkJAQTJ48GadOnbL5eVw5bBJJgeGGyE3de++90Ol0FreoqCipywLQ1GVcp9OhqKgIH3/8MU6cOIEpU6bAaDTe9HP36NEDXbp0ue4+AQEB6N69+02/1o1c/T6//PJLVFdX47777kN9fb3DX5vIkzHcELkphUKB0NBQi5u3tzdWrlyJwYMHw9/fHxEREXjqqadQVVXV7vP8+OOPGDt2LAIDA6FSqRATE4OjR4+aHz9w4ADuvPNO+Pn5ISIiAgsWLEB1dfV1a5PJZAgNDUWvXr0wduxYLFu2DCdPnjSPLK1ZswZ9+/aFr68vbr31VmzatMni+Jdeegm9e/eGQqGARqPBggULzI9d/bVUZGQkAODBBx+ETCYz37/6a6lvvvkGSqUSFRUVFq+xYMECjBkzxm7vMzY2FosWLUJubi5++ukn8z7X+3l8//33mDNnDvR6vXkE6KWXXgIA1NfXY/HixQgLC4O/vz/i4uLw/fffX7ceIk/BcEPkYby8vPD222/j5MmT+Mc//oFdu3Zh8eLF7e4/Y8YMhIeH48iRIzh27BheeOEF+Pj4AACysrIwYcIE/Pa3v0VmZia2bt2Kffv24ZlnnrGpJj8/PwBAQ0MDduzYgWeffRZ//OMfcfLkSTzxxBOYM2cOdu/eDQD47LPP8Oabb+KDDz7AuXPn8Pnnn2Pw4MFtPu+RI0cAAKmpqdDpdOb7V7vnnnsQFBSEbdu2mbcZjUZ8+umnmDFjht3eZ0VFBT7++GMAMJ8/4Po/j/j4eKxatco8AqTT6fDcc88BAObMmYP9+/djy5YtyMzMxMMPP4x7770X586ds7omIrfl8L7jRNTpZs2aJby9vYW/v7/59tBDD7W576effiq6d+9uvp+amirUarX5fmBgoNiwYUObxz722GPi8ccft9i2d+9e4eXlJa5cudLmMdc+f35+vhg5cqQIDw8XdXV1Ij4+XsyfP9/imIcfflhMmjRJCCHEG2+8IaKjo0V9fX2bz6/VasWbb75pvg9A7Nixw2KfZcuWiaFDh5rvL1iwQNx9993m+998843w9fUV5eXlN/U+AQh/f3/RpUsXAUAAEFOmTGlz/xY3+nkIIcT58+eFTCYThYWFFtt/85vfiCVLllz3+Yk8gVzaaEVEjjJ27FisWbPGfN/f3x8AsHv3brzyyis4ffo0DAYDGhsbUVtbi+rqavM+V0tJSUFSUhI2bdqEe+65Bw8//DD69u0LADh27BjOnz+PzZs3m/cXQsBkMiEnJwf9+/dvsza9Xo+AgAAIIVBTU4MRI0Zg+/bt8PX1xZkzZywmBANAQkIC3nrrLQDAww8/jFWrVqFPnz649957MWnSJEyePBlyecf/OZsxYwZGjRqFoqIiaDQabN68GZMmTULXrl1v6n0GBgbi+PHjaGxsxA8//IDXXnsN77//vsU+tv48AOD48eMQQiA6Otpie11dXafMJSJydgw3RG7K398f/fr1s9iWm5uLSZMmITk5GX/+85/RrVs37Nu3D/PmzUNDQ0Obz/PSSy9h+vTp+PLLL/HVV19h2bJl2LJlCx588EGYTCY88cQTFnNeWvTu3bvd2lo+9L28vBASEtLqQ1wmk1ncF0KYt0VEROCnn35Ceno6vvvuOzz11FN47bXX8MMPP1h83WOLO+64A3379sWWLVvw5JNPYseOHUhNTTU/3tH36eXlZf4Z3HbbbSguLsa0adOwZ88eAB37ebTU4+3tjWPHjsHb29visYCAAJveO5E7Yrgh8iBHjx5FY2Mj3njjDXh5NU25+/TTT294XHR0NKKjo7Fo0SL8/ve/R2pqKh588EGMGDECp06dahWibuTqD/1r9e/fH/v27cPMmTPN2w4cOGAxOuLn54cpU6ZgypQpePrpp3HbbbchKysLI0aMaPV8Pj4+Vl2FNX36dGzevBnh4eHw8vLCfffdZ36so+/zWosWLcLKlSuxY8cOPPjgg1b9PHx9fVvVP3z4cBiNRpSUlCAxMfGmaiJyR5xQTORB+vbti8bGRrzzzjvIzs7Gpk2bWn1NcrUrV67gmWeewffff4/c3Fzs378fR44cMQeNP/3pTzh48CCefvppnDhxAufOncMXX3yBP/zhDx2u8fnnn8eGDRvw/vvv49y5c1i5ciW2b99unki7YcMGrF+/HidPnjS/Bz8/P2i12jafLzIyEjt37kRxcTEuX77c7uvOmDEDx48fx1//+lc89NBDUCqV5sfs9T5VKhWSkpKwbNkyCCGs+nlERkaiqqoKO3fuRGlpKWpqahAdHY0ZM2Zg5syZ2L59O3JycnDkyBH8/e9/R1pamk01EbklKSf8EJFjzJo1S9x///1tPrZy5UrRq1cv4efnJyZMmCA2btwoAIjLly8LISwnsNbV1YlHHnlERERECF9fX6HRaMQzzzxjMYn28OHDYty4cSIgIED4+/uLIUOGiL/+9a/t1tbWBNlrrV69WvTp00f4+PiI6OhosXHjRvNjO3bsEHFxcUKlUgl/f38xcuRI8d1335kfv3ZC8RdffCH69esn5HK50Gq1QojWE4pb3H777QKA2LVrV6vH7PU+c3NzhVwuF1u3bhVC3PjnIYQQycnJonv37gKAWLZsmRBCiPr6evHiiy+KyMhI4ePjI0JDQ8WDDz4oMjMz262JyFPIhBBC2nhFREREZD/8WoqIiIjcCsMNERERuRWGGyIiInIrDDdERETkVhhuiIiIyK0w3BAREZFbYbghIiIit8JwQ0RERG6F4YaIiIjcCsMNERERuRWGGyIiInIr/w8wJYwf/5+KlwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Decision Tree AUC score: 0.7659831121833535\n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "Random Forest Model Classification Report \n", + " precision recall f1-score support\n", + "\n", + " 0 0.80 0.84 0.82 1658\n", + " 1 0.83 0.79 0.81 1658\n", + "\n", + " accuracy 0.81 3316\n", + " macro avg 0.81 0.81 0.81 3316\n", + "weighted avg 0.81 0.81 0.81 3316\n", + " \n", + "\n", + "Random Forest Confusion Matrix: \n", + " 0 1\n", + "0 1388 270\n", + "1 348 1310 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Random Forest AUC score: 0.8136308805790109\n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "XGBoost Model Classification Report \n", + " precision recall f1-score support\n", + "\n", + " 0 0.79 0.86 0.83 1658\n", + " 1 0.85 0.78 0.81 1658\n", + "\n", + " accuracy 0.82 3316\n", + " macro avg 0.82 0.82 0.82 3316\n", + "weighted avg 0.82 0.82 0.82 3316\n", + " \n", + "\n", + "XGBoost Confusion Matrix: \n", + " 0 1\n", + "0 1428 230\n", + "1 373 1285 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "XGBoost AUC score: 0.8181544028950543\n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "Learning rate set to 0.024679\n", + "0:\tlearn: 0.6786875\ttotal: 171ms\tremaining: 2m 50s\n", + "100:\tlearn: 0.4683385\ttotal: 2.23s\tremaining: 19.9s\n", + "200:\tlearn: 0.4357294\ttotal: 4.59s\tremaining: 18.3s\n", + "300:\tlearn: 0.4171304\ttotal: 6.44s\tremaining: 15s\n", + "400:\tlearn: 0.3999295\ttotal: 8.08s\tremaining: 12.1s\n", + "500:\tlearn: 0.3846297\ttotal: 9.91s\tremaining: 9.87s\n", + "600:\tlearn: 0.3722118\ttotal: 11.7s\tremaining: 7.8s\n", + "700:\tlearn: 0.3609185\ttotal: 13.2s\tremaining: 5.65s\n", + "800:\tlearn: 0.3515386\ttotal: 15s\tremaining: 3.73s\n", + "900:\tlearn: 0.3430796\ttotal: 16.8s\tremaining: 1.85s\n", + "999:\tlearn: 0.3354195\ttotal: 19s\tremaining: 0us\n", + "CatBoost Model Classification Report \n", + " precision recall f1-score support\n", + "\n", + " 0 0.78 0.87 0.82 1658\n", + " 1 0.86 0.75 0.80 1658\n", + "\n", + " accuracy 0.81 3316\n", + " macro avg 0.82 0.81 0.81 3316\n", + "weighted avg 0.82 0.81 0.81 3316\n", + " \n", + "\n", + "CatBoost Confusion Matrix: \n", + " 0 1\n", + "0 1449 209\n", + "1 408 1250 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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GYe3cHKX9eyMkyZXU5h8LdRuZfyxEzeKr5gh8lRml0pNBGjv9iKwRTlkjHLrv8cMKDpZi45lH0Fgwh8CzBnt35eXlyszMdN17+ZSBAwdq48aNNZ5TVlamwsJCtw3+882XEVrz9xaaPO9AjXMDJOmzd2OU9Wm0Hnz60Fmv06ZDqSbP269/LY7XsIsu1x3dOqllq3I1a1HhVjUA6ttDaYfUtmOJ0sfVMJ/ljKkAFoukn78RFvwUov9+oI16DijUmm92aPXenYqMduqbLyPkdAT2t0aYR4N9Xfvhhx/kcDiqPa85ISHB9cSnM6Wnp+upp56qj/BMacfmJjr+Q4h+d2Un1z6nw6IlT9m1ZkkL/b8tu5X1abSOfB+mmy/t4nbuX+5ro849i/U//6p6stdvbz6u3958XPnHQhQe6ZTFIr3+Ygsltiqr1/cEnDLuvw8qZWChJt10kX44cnouy6mqVbP4Cv2Ud7pK0LR5pfKPnf4ncttH0fp9r46Kia2Uo9Ki4sJg/TNrl3JzmBfTWDjl47MMAnxSYYPXby1nfBU1DKPavlOmTZvmelqUJBUWFiopKalO4zOT/v/1k674zQm3fY+Naqd+/5Wvgbf9JKlqCdfgUT+6jXngt5fqgRmHdPXA6hWbZi2qyrHv/jNWoVanrriOCViob4YemnlIva4v0KO3XKyjOVa3o7kHwvTj0RBdcV2Rvt0ZKUkKCXWqy9VFWjrTXu1qhT9V/bPZ9ZoTatq8Upvei6n7twC/MHxcKWCQENSN5s2bKzg4uFo1IC8vr1rV4BSr1Sqr1VrjMdROSXGQDmef/h3m5oTp250Rim5aqfgLKxQT63AbHxIiNYuvVNLFVd/sY+Mra5xIGH9BhRJbnV6i+MY/muuyHsWKiHJq28fR+vtf7LrnscNqYnNUOxeoSw+nHVLfm/I14/dtVVIUpGYtqnr+xSeCVV4aJMmiNX9vodvHH9Wh76w6lB2mOybkqawkSB+sbuq6zsDbftKBb6wq+DFEHZNP6g9PH9LqF1vo4LfhDfPG4LX6ftphY9NgCUFYWJiSk5O1bt063XTTTa7969at0/DhwxsqrID39ReRmnLLxa7Xi2dcIEkaMPInTZ7nv9Ude7MitfzZRJUWB+nCi8s0YXaO+t+S77frA7U19O6qitZfX//Wbf9fU5O07tVYSdKrf2uhsHCnHk4/qGibQ19tj9S0O9qppPj0M+cvvKhUv592RNFNHTqaE6p/zk/Q6y82r783AtQxi1GbBzLXkVWrVmn06NF64YUXlJKSohdffFFLlizRrl271Lp16189v7CwUDabTflft1NMNLPVEJgG2bs1dAhAnak0KvSh3lBBQYFiYuqm/XLqs+Kmdb9XaNS5z/moKC7X6gEv1WmsDalB5xDcdttt+vHHH/X000/ryJEj6ty5s95+++1aJQMAAHiDloFnDT6pcNy4cRo3blxDhwEAgKk1eEIAAEB98PV5BCw7BAAgANAy8IyZeAAAgAoBAMAcqBB4RkIAADAFEgLPaBkAAAAqBAAAc6BC4BkJAQDAFAz5tnSwwW7rW09ICAAApkCFwDPmEAAAACoEAABzoELgGQkBAMAUSAg8o2UAAACoEAAAzIEKgWckBAAAUzAMiwwfPtR9ObcxoGUAAACoEAAAzMEpi083JvLl3MaAhAAAYArMIfCMlgEAAKBCAAAwByYVekZCAAAwBVoGnpEQAABMgQqBZ8whAAAAVAgAAOZg+NgyCPQKAQkBAMAUDEmG4dv5gYyWAQAAoEIAADAHpyyycKfCsyIhAACYAqsMPKNlAAAAqBAAAMzBaVhk4cZEZ0WFAABgCobh++aNjz/+WEOHDpXdbpfFYtGaNWvOiMfQjBkzZLfbFRERoT59+mjXrl1uY8rKyjR+/Hg1b95cUVFRGjZsmA4ePOg2Jj8/X6NHj5bNZpPNZtPo0aN1/Phxr38/JAQAANSB4uJide3aVQsXLqzx+OzZszVnzhwtXLhQn3/+uRITEzVgwACdOHHCNSY1NVWrV6/WypUrtWHDBhUVFWnIkCFyOByuMaNGjVJWVpbWrl2rtWvXKisrS6NHj/Y6XloGAABTqO9JhYMHD9bgwYPPci1D8+bN0/Tp03XzzTdLkpYtW6aEhAS98soreuCBB1RQUKClS5dq+fLl6t+/vyRpxYoVSkpK0vr16zVo0CDt2bNHa9eu1aZNm9SzZ09J0pIlS5SSkqK9e/eqQ4cOtY6XCgEAwBROJQS+bJJUWFjotpWVlXkdS3Z2tnJzczVw4EDXPqvVqt69e2vjxo2SpMzMTFVUVLiNsdvt6ty5s2vMZ599JpvN5koGJOnqq6+WzWZzjaktEgIAgCmcetqhL5skJSUlufr1NptN6enpXseSm5srSUpISHDbn5CQ4DqWm5ursLAwNWvWzOOY+Pj4atePj493jaktWgYAAHghJydHMTExrtdWq/Wcr2WxuLchDMOotu9MZ46paXxtrnMmKgQAAFPw1yqDmJgYt+1cEoLExERJqvYtPi8vz1U1SExMVHl5ufLz8z2OOXr0aLXrHzt2rFr14deQEAAATKHqQ92XOQT+i6Vt27ZKTEzUunXrXPvKy8v10UcfqVevXpKk5ORkhYaGuo05cuSIdu7c6RqTkpKigoICbdmyxTVm8+bNKigocI2pLVoGAADUgaKiIu3bt8/1Ojs7W1lZWYqNjVWrVq2UmpqqtLQ0tW/fXu3bt1daWpoiIyM1atQoSZLNZtPYsWM1adIkxcXFKTY2VpMnT1aXLl1cqw46duyo66+/Xvfdd58WL14sSbr//vs1ZMgQr1YYSCQEAACTqO9lh1u3blXfvn1drydOnChJGjNmjDIyMjRlyhSVlJRo3Lhxys/PV8+ePfXee+8pOjradc7cuXMVEhKikSNHqqSkRP369VNGRoaCg4NdY15++WVNmDDBtRph2LBhZ733gScWw/BnEaR+FRYWymazKf/rdoqJpvuBwDTI3q2hQwDqTKVRoQ/1hgoKCtwm6vnTqc+Ki5ZPU3Bk+Dlfx3GyVN+OTq/TWBsSn6IAAICWAQDAHHj8sWckBAAAczB+3nw5P4CREAAAzMHHCoECvELAHAIAAECFAABgDr+82+C5nh/ISAgAAKbApELPaBkAAAAqBAAAkzAsvk0MDPAKAQkBAMAUmEPgGS0DAABAhQAAYBLcmMgjEgIAgCmwysCzWiUE8+fPr/UFJ0yYcM7BAACAhlGrhGDu3Lm1upjFYiEhAACcvwK87O+LWiUE2dnZdR0HAAB1ipaBZ+e8yqC8vFx79+5VZWWlP+MBAKBuGH7YApjXCcHJkyc1duxYRUZGqlOnTjpw4ICkqrkDzzzzjN8DBAAAdc/rhGDatGn64osv9OGHHyo8PNy1v3///lq1apVfgwMAwH8sftgCl9fLDtesWaNVq1bp6quvlsVy+pdz2WWX6dtvv/VrcAAA+A33IfDI6wrBsWPHFB8fX21/cXGxW4IAAAAaD68TgiuvvFL/93//53p9KglYsmSJUlJS/BcZAAD+xKRCj7xuGaSnp+v666/X7t27VVlZqeeee067du3SZ599po8++qguYgQAwHc87dAjrysEvXr10qeffqqTJ0/qoosu0nvvvaeEhAR99tlnSk5OrosYAQBAHTunZxl06dJFy5Yt83csAADUGR5/7Nk5JQQOh0OrV6/Wnj17ZLFY1LFjRw0fPlwhITwrCQBwnmKVgUdef4Lv3LlTw4cPV25urjp06CBJ+vrrr9WiRQu9+eab6tKli9+DBAAAdcvrOQT33nuvOnXqpIMHD2rbtm3atm2bcnJydPnll+v++++vixgBAPDdqUmFvmwBzOsKwRdffKGtW7eqWbNmrn3NmjXTzJkzdeWVV/o1OAAA/MViVG2+nB/IvK4QdOjQQUePHq22Py8vTxdffLFfggIAwO+4D4FHtUoICgsLXVtaWpomTJig1157TQcPHtTBgwf12muvKTU1VbNmzarreAEAQB2oVcugadOmbrclNgxDI0eOdO0zfl6LMXToUDkcjjoIEwAAH3FjIo9qlRB88MEHdR0HAAB1i2WHHtUqIejdu3ddxwEAABrQOd9J6OTJkzpw4IDKy8vd9l9++eU+BwUAgN9RIfDI64Tg2LFj+v3vf6933nmnxuPMIQAAnJdICDzyetlhamqq8vPztWnTJkVERGjt2rVatmyZ2rdvrzfffLMuYgQAAHXM6wrBf/7zH73xxhu68sorFRQUpNatW2vAgAGKiYlRenq6brzxxrqIEwAA37DKwCOvKwTFxcWKj4+XJMXGxurYsWOSqp6AuG3bNv9GBwCAn5y6U6EvWyA7pzsV7t27V5LUrVs3LV68WIcOHdILL7ygli1b+j1AAABQ97xuGaSmpurIkSOSpCeffFKDBg3Syy+/rLCwMGVkZPg7PgAA/INJhR55nRDceeedrv/u3r27vv/+e3311Vdq1aqVmjdv7tfgAABA/Tjn+xCcEhkZqSuuuMIfsQAAUGcs8vFph36L5PxUq4Rg4sSJtb7gnDlzzjkYAADQMGqVEGzfvr1WF/vlA5Dq00133KaQkPAG+dlAXRu2+8OGDgGoMyVFlfrwynr6YSw79IiHGwEAzIFJhR55vewQAAAEHp8nFQIA0ChQIfCIhAAAYAq+3m2QOxUCAACvVVZW6s9//rPatm2riIgItWvXTk8//bScTqdrjGEYmjFjhux2uyIiItSnTx/t2rXL7TplZWUaP368mjdvrqioKA0bNkwHDx70e7wkBAAAczD8sHlh1qxZeuGFF7Rw4ULt2bNHs2fP1v/8z/9owYIFrjGzZ8/WnDlztHDhQn3++edKTEzUgAEDdOLECdeY1NRUrV69WitXrtSGDRtUVFSkIUOGyOFwnOtvokbnlBAsX75c11xzjex2u/bv3y9Jmjdvnt544w2/BgcAgN/Uc0Lw2Wefafjw4brxxhvVpk0b3XLLLRo4cKC2bt1aFY5haN68eZo+fbpuvvlmde7cWcuWLdPJkyf1yiuvSJIKCgq0dOlSPfvss+rfv7+6d++uFStWaMeOHVq/fr2vvxE3XicEixYt0sSJE3XDDTfo+PHjrgyladOmmjdvnl+DAwDgfFNYWOi2lZWV1Tju2muv1fvvv6+vv/5akvTFF19ow4YNuuGGGyRJ2dnZys3N1cCBA13nWK1W9e7dWxs3bpQkZWZmqqKiwm2M3W5X586dXWP8xeuEYMGCBVqyZImmT5+u4OBg1/4ePXpox44dfg0OAAB/8dfjj5OSkmSz2Vxbenp6jT9v6tSpuuOOO3TppZcqNDRU3bt3V2pqqu644w5JUm5uriQpISHB7byEhATXsdzcXIWFhalZs2ZnHeMvXq8yyM7OVvfu3avtt1qtKi4u9ktQAAD4nZ/uVJiTk6OYmBjXbqvVWuPwVatWacWKFXrllVfUqVMnZWVlKTU1VXa7XWPGjHGNO/Muv4Zh/Oqdf2szxlteJwRt27ZVVlaWWrdu7bb/nXfe0WWXXea3wAAA8Cs/3YcgJibGLSE4m0cffVR/+tOfdPvtt0uSunTpov379ys9PV1jxoxRYmKipKoqQMuWLV3n5eXluaoGiYmJKi8vV35+vluVIC8vT7169fLhzVTndcvg0Ucf1UMPPaRVq1bJMAxt2bJFM2fO1GOPPaZHH33Ur8EBANBYnTx5UkFB7h+zwcHBrmWHbdu2VWJiotatW+c6Xl5ero8++sj1YZ+cnKzQ0FC3MUeOHNHOnTv9nhB4XSH4/e9/r8rKSk2ZMkUnT57UqFGjdMEFF+i5555zZUEAAJxv6vvGREOHDtXMmTPVqlUrderUSdu3b9ecOXN0zz33VF3PYlFqaqrS0tLUvn17tW/fXmlpaYqMjNSoUaMkSTabTWPHjtWkSZMUFxen2NhYTZ48WV26dFH//v3P/c3U4JzuVHjffffpvvvu0w8//CCn06n4+Hi/BgUAgN/V862LFyxYoMcff1zjxo1TXl6e7Ha7HnjgAT3xxBOuMVOmTFFJSYnGjRun/Px89ezZU++9956io6NdY+bOnauQkBCNHDlSJSUl6tevnzIyMtwm9vuDxTCMRnszxsLCQtlsNvVJnsbjjxGwhmV82NAhAHWmpKhSU6/8RAUFBbXqy5+LU58V7Z5IU1D4uX9WOEtL9d3Tj9VprA3pnCYVeprZ+N133/kUEAAAdcLHlgEPNzpDamqq2+uKigpt375da9euZVIhAOD8xdMOPfI6IXjkkUdq3P+3v/3NdTtGAADQuPjt4UaDBw/Wv/71L39dDgAA/6rnZxk0Nue0yqAmr732mmJjY/11OQAA/Kq+lx02Nl4nBN27d3ebVGgYhnJzc3Xs2DE9//zzfg0OAADUD68TghEjRri9DgoKUosWLdSnTx9deuml/ooLAADUI68SgsrKSrVp00aDBg1y3YMZAIBGgVUGHnk1qTAkJER/+MMfzvrsZwAAzlf+evxxoPJ6lUHPnj21ffv2uogFAAA0EK/nEIwbN06TJk3SwYMHlZycrKioKLfjl19+ud+CAwDArwL8W74vap0Q3HPPPZo3b55uu+02SdKECRNcxywWiwzDkMVikcPh8H+UAAD4ijkEHtU6IVi2bJmeeeYZZWdn12U8AACgAdQ6ITj1UMTWrVvXWTAAANQVbkzkmVdzCDw95RAAgPMaLQOPvEoILrnkkl9NCn766SefAgIAAPXPq4Tgqaeeks1mq6tYAACoM7QMPPMqIbj99tsVHx9fV7EAAFB3aBl4VOsbEzF/AACAwOX1KgMAABolKgQe1TohcDqddRkHAAB1ijkEnnl962IAABolKgQeef1wIwAAEHioEAAAzIEKgUckBAAAU2AOgWe0DAAAABUCAIBJ0DLwiIQAAGAKtAw8o2UAAACoEAAATIKWgUckBAAAcyAh8IiWAQAAoEIAADAHy8+bL+cHMhICAIA50DLwiIQAAGAKLDv0jDkEAACACgEAwCRoGXhEQgAAMI8A/1D3BS0DAABAhQAAYA5MKvSMhAAAYA7MIfCIlgEAAKBCAAAwB1oGnpEQAADMgZaBR7QMAAAAFQIAgDnQMvCMhAAAYA60DDwiIQAAmAMJgUfMIQAAACQEAABzODWHwJfNW4cOHdLvfvc7xcXFKTIyUt26dVNmZqbruGEYmjFjhux2uyIiItSnTx/t2rXL7RplZWUaP368mjdvrqioKA0bNkwHDx709ddRDQkBAMAcDD9sXsjPz9c111yj0NBQvfPOO9q9e7eeffZZNW3a1DVm9uzZmjNnjhYuXKjPP/9ciYmJGjBggE6cOOEak5qaqtWrV2vlypXasGGDioqKNGTIEDkcjnP8RdSMOQQAAHihsLDQ7bXVapXVaq02btasWUpKStJLL73k2temTRvXfxuGoXnz5mn69Om6+eabJUnLli1TQkKCXnnlFT3wwAMqKCjQ0qVLtXz5cvXv31+StGLFCiUlJWn9+vUaNGiQ394XFQIAgClYDMPnTZKSkpJks9lcW3p6eo0/780331SPHj106623Kj4+Xt27d9eSJUtcx7Ozs5Wbm6uBAwe69lmtVvXu3VsbN26UJGVmZqqiosJtjN1uV+fOnV1j/IUKAQDAHPy0yiAnJ0cxMTGu3TVVByTpu+++06JFizRx4kQ99thj2rJliyZMmCCr1aq77rpLubm5kqSEhAS38xISErR//35JUm5ursLCwtSsWbNqY06d7y8kBAAAeCEmJsYtITgbp9OpHj16KC0tTZLUvXt37dq1S4sWLdJdd93lGmexWNzOMwyj2r4z1WaMt2gZAABMob5XGbRs2VKXXXaZ276OHTvqwIEDkqTExERJqvZNPy8vz1U1SExMVHl5ufLz8886xl9ICAAA5lDPqwyuueYa7d27123f119/rdatW0uS2rZtq8TERK1bt851vLy8XB999JF69eolSUpOTlZoaKjbmCNHjmjnzp2uMf5CywAAgDrwxz/+Ub169VJaWppGjhypLVu26MUXX9SLL74oqapVkJqaqrS0NLVv317t27dXWlqaIiMjNWrUKEmSzWbT2LFjNWnSJMXFxSk2NlaTJ09Wly5dXKsO/IWEAABgCvX9cKMrr7xSq1ev1rRp0/T000+rbdu2mjdvnu68807XmClTpqikpETjxo1Tfn6+evbsqffee0/R0dGuMXPnzlVISIhGjhypkpIS9evXTxkZGQoODj73N1MDi2EYjfbuzIWFhbLZbOqTPE0hIeENHQ5QJ4ZlfNjQIQB1pqSoUlOv/EQFBQW1mqh3Lk59Vlxx+0wFh537Z4WjvFTbVk6v01gbEhUCAIAp8Phjz5hUCAAAqBAAAEyCxx97REIAADCNQC/7+4KWAQAAoEIAADAJw6jafDk/gJEQAABMgVUGntEyAAAAVAgAACbBKgOPSAgAAKZgcVZtvpwfyGgZAAAAKgRwd9t/7dQ9o7O0+t+X6oWlPX7ea+h3t3+pGwbuU5Oocn31TZz+tvgq7c9p6jqvWdMS3Xv3Nl3R9YgiIyqUcyhGK1/rrA2ftW6Q9wHz+nFriPb9I0LHd4Wo7FiQrpxfqJb9KyRJzgrpq/mROvpxqE4eDFZIE0MtUip02cRihcefrgd/OiZGP34e6nZd++Ay9Xi2yPW6vMCinWlRyv2galxi3wp1mV6s0JgArys3ZrQMPCIhgMslF/+gGwZ+o++ym7rtH3nTbt087Cs9Oz9FBw/HaNStO5T+1PsaO26YSkqr/jGckvqpoiIrNCOtjwoKrep73fd6bPIGjZ8crW+zYxvg3cCsKk9aFNOhUkk3lWnrI9FuxxylFh3fHaxLHiyR7dJKlRcGaVd6pDY/FKPe/1vgNrb1raXq8PBJ1+vgM56Js+3RJio5GqSrXzwhSfriySht+1MT9Xz+RN28MfiMVQaeNWjL4OOPP9bQoUNlt9tlsVi0Zs2ahgzH1MLDKzT1j59q3t+u1onisF8cMTRi6B6t/N/O+nRTK+0/0FR/fa6XrNZK9b0u2zWqY4cf9MbbHbT3m+bKPRqtf/5vFxUXh+rii36q/zcDU0u4rkIdHymRfUB5tWOh0YZ6LT2hCwaXq0lbp2K7Vqrz9GIV7ArRycPu/xwGhxsKb3F6C40+/Wlw4ttg5W0IU7enixXbrVKx3SrV9eliHf0wTEXZdGLPW6fuQ+DLFsAa9C+3uLhYXbt21cKFCxsyDEh6+P7PtSXzAm3/sqXb/sSEIsXFlioz6/T+ispg7diZoMsu/cG1b9eeFup9zX5FNymTxWKo97XfKzTUqS93JtTbewDOReWJIMliVCv1H3zLqrW9mumDoTbtmh2pyuLTx/KzQhQS7VSzrpWufbFdKxUS7dRP291bDUBj0aAtg8GDB2vw4MG1Hl9WVqaysjLX68LCwroIy3R6X/u9Lr7oJ42fXP3/RWzTUklS/nH3eml+QbjiW5z+F3LmX3+j6ZM/0Wsr/leVlRaVlYXo6Wd660iue8kWOJ84yqTdcyN1wY3lCm1yOiG4cEiZIi9wytrCqRPfBGvP3EgV7A1Wr6VV7YDSH4Jkja3+bdEaa6jsB0u9xQ/v0DLwrFHNIUhPT9dTTz3V0GEElBbNi/WHe7fqsRn9VFERXOvzLJLbBJu77/xCTZqUa+oT/VRYGK6UnjmaPuVjTXpsoL7f38zvcQO+clZImZOiJad0+RPFbsda33r6i0dMe4eiWjv08a1NdXx3sJpe5qg6UNPnvnGW/Tg/MKnQo0aVEEybNk0TJ050vS4sLFRSUlIDRtT4XXzRT2rWtFQLn33btS842FCXy/I07Ia9GvvQMElSs6al+ik/0jWmqa1U+ccjJEktE09o+I17df/4Ia6VB99936zqGoO/1vwXetbfGwJqwVkhbZ0YrZOHgtTrpUK36kBNbJc5ZAkxVLy/KiEIb+5U2Y/VP/nL8i2yxgX4pwYCVqNKCKxWq6xWa0OHEVCyvkjU/ROGuO2bNH6jcg7Z9OrrnXQkt4l+/ClcV3Q74lotEBLiUJfOR7V0WXdJktVa1Ud1Gu7/QDqcFlmC+McR55dTyUDx/iD1yihUWNNf/xs9sS9YRqVF4S2q7kzTrFulKk8EKf/LEDW7vOrvP/+LEFWeCFJs94o6jR/njpaBZ40qIYD/lZSGav+Bpm77SstCdOKE1bV/zb876vZbdurQ4WgdOhKjO27ZqbKyEH3wcVtJUs5Bmw4djtYjf9isJRlXqPCEVb165uiKrkf0xMy+9fyOYHaVxVLxgdPtr5OHglWwx6lQm6HweKe2pkbr+J5g9Xz+hAyHVHqsKpENsxkKCpOKDwTp4FtWJVxXrrBmhk7sC9au/4mUrWOlYrtXffhHX+RQ/LXl+uLJKF0+o6rd8MWTUUroU7V6AecpnnboEQkBftWrqy9TmLVSDz+wRdFNyvXV1801bUY/1z0IHI4g/fkvfTX2ru16avqHigiv0OEj0frr/F76PPOCBo4eZnN8V4g23m1zvd41K0qSlDSiVB0eKlHuB1XLaj+6uanbeb0yCtT8qkoFhUo/bArVd8vD5ThpUXiiUwm9y9VhXIksv5hmc8XsIu1Ii9Kme6smzib0rdDlf3afiwA0Jg2aEBQVFWnfvn2u19nZ2crKylJsbKxatWrVgJGZ25Q/Dzxjj0UrVnbVipVdz3rO4SMx+sus3nUbGFALza+q1LDdP571uKdjkhTR0qlr/t+vr2AKa2ooeXbRr47D+YOWgWcNmhBs3bpVffueLimfmjA4ZswYZWRkNFBUAICAxCoDjxo0IejTp4+MAO/JAADQGDCHAABgCrQMPCMhAACYg9Oo2nw5P4CREAAAzIE5BB7xWC4AAECFAABgDhb5OIfAb5Gcn0gIAADmwJ0KPaJlAAAAqBAAAMyBZYeekRAAAMyBVQYe0TIAAABUCAAA5mAxDFl8mBjoy7mNAQkBAMAcnD9vvpwfwGgZAAAAKgQAAHOgZeAZCQEAwBxYZeARCQEAwBy4U6FHzCEAAABUCAAA5sCdCj0jIQAAmAMtA49oGQAAACoEAABzsDirNl/OD2QkBAAAc6Bl4BEtAwAAQIUAAGAS3JjIIyoEAABTOHXrYl+2c5Weni6LxaLU1FTXPsMwNGPGDNntdkVERKhPnz7atWuX23llZWUaP368mjdvrqioKA0bNkwHDx485zg8ISEAAKAOff7553rxxRd1+eWXu+2fPXu25syZo4ULF+rzzz9XYmKiBgwYoBMnTrjGpKamavXq1Vq5cqU2bNigoqIiDRkyRA6Hw+9xkhAAAMzh1KRCXzYvFRUV6c4779SSJUvUrFmzX4RiaN68eZo+fbpuvvlmde7cWcuWLdPJkyf1yiuvSJIKCgq0dOlSPfvss+rfv7+6d++uFStWaMeOHVq/fr3ffi2nkBAAAMzBkOT0Yfs5HygsLHTbysrKzvojH3roId14443q37+/2/7s7Gzl5uZq4MCBrn1Wq1W9e/fWxo0bJUmZmZmqqKhwG2O329W5c2fXGH8iIQAAmIK/5hAkJSXJZrO5tvT09Bp/3sqVK7Vt27Yaj+fm5kqSEhIS3PYnJCS4juXm5iosLMytsnDmGH9ilQEAAF7IyclRTEyM67XVaq1xzCOPPKL33ntP4eHhZ72WxWJxe20YRrV9Z6rNmHNBhQAAYA6GfJxDUHWZmJgYt62mhCAzM1N5eXlKTk5WSEiIQkJC9NFHH2n+/PkKCQlxVQbO/Kafl5fnOpaYmKjy8nLl5+efdYw/kRAAAMyhHicV9uvXTzt27FBWVpZr69Gjh+68805lZWWpXbt2SkxM1Lp161znlJeX66OPPlKvXr0kScnJyQoNDXUbc+TIEe3cudM1xp9oGQAA4GfR0dHq3Lmz276oqCjFxcW59qempiotLU3t27dX+/btlZaWpsjISI0aNUqSZLPZNHbsWE2aNElxcXGKjY3V5MmT1aVLl2qTFP2BhAAAYA5OSb603v38cKMpU6aopKRE48aNU35+vnr27Kn33ntP0dHRrjFz585VSEiIRo4cqZKSEvXr108ZGRkKDg72bzCSLIbReJ/WUFhYKJvNpj7J0xQScvZJG0BjNizjw4YOAagzJUWVmnrlJyooKHCbqOdPpz4r+nWeopDg6v3+2qp0lOn9nbPrNNaGxBwCAABAywAAYBI8/tgjEgIAgDmQEHhEywAAAFAhAACYBBUCj0gIAADmcJ4tOzzfkBAAAEzhlw8oOtfzAxlzCAAAABUCAIBJMIfAIxICAIA5OA3J4sOHujOwEwJaBgAAgAoBAMAkaBl4REIAADAJHxMCBXZCQMsAAABQIQAAmAQtA49ICAAA5uA05FPZn1UGAAAg0FEhAACYg+Gs2nw5P4CREAAAzIE5BB6REAAAzIE5BB4xhwAAAFAhAACYBC0Dj0gIAADmYMjHhMBvkZyXaBkAAAAqBAAAk6Bl4BEJAQDAHJxOST7cS8AZ2PchoGUAAACoEAAATIKWgUckBAAAcyAh8IiWAQAAoEIAADAJbl3sEQkBAMAUDMMpw4cnFvpybmNAQgAAMAfD8O1bPnMIAABAoKNCAAAwB8PHOQQBXiEgIQAAmIPTKVl8mAcQ4HMIaBkAAAAqBAAAk6Bl4BEJAQDAFAynU4YPLYNAX3ZIywAAAFAhAACYBC0Dj0gIAADm4DQkCwnB2dAyAAAAVAgAACZhGJJ8uQ9BYFcISAgAAKZgOA0ZPrQMDBICAAACgOGUbxUClh0CAIAAR4UAAGAKtAw8IyEAAJgDLQOPGnVCcCpbq3SUNXAkQN0pKaps6BCAOlP68993fXz7rlSFT/clqlSF/4I5D1mMRlwDOXjwoJKSkho6DACAj3JycnThhRfWybVLS0vVtm1b5ebm+nytxMREZWdnKzw83A+RnV8adULgdDp1+PBhRUdHy2KxNHQ4plBYWKikpCTl5OQoJiamocMB/Iq/7/pnGIZOnDghu92uoKC6m+deWlqq8vJyn68TFhYWkMmA1MhbBkFBQXWWUcKzmJgY/sFEwOLvu37ZbLY6/xnh4eEB+0HuLyw7BAAAJAQAAICEAF6yWq168sknZbVaGzoUwO/4+4aZNepJhQAAwD+oEAAAABICAABAQgAAAERCAAAAREIALzz//PNq27atwsPDlZycrE8++aShQwL84uOPP9bQoUNlt9tlsVi0Zs2ahg4JqHckBKiVVatWKTU1VdOnT9f27dv1m9/8RoMHD9aBAwcaOjTAZ8XFxeratasWLlzY0KEADYZlh6iVnj176oorrtCiRYtc+zp27KgRI0YoPT29ASMD/MtisWj16tUaMWJEQ4cC1CsqBPhV5eXlyszM1MCBA932Dxw4UBs3bmygqAAA/kRCgF/1ww8/yOFwKCEhwW1/QkKCXx4nCgBoeCQEqLUzHzFtGAaPnQaAAEFCgF/VvHlzBQcHV6sG5OXlVasaAAAaJxIC/KqwsDAlJydr3bp1bvvXrVunXr16NVBUAAB/CmnoANA4TJw4UaNHj1aPHj2UkpKiF198UQcOHNCDDz7Y0KEBPisqKtK+fftcr7Ozs5WVlaXY2Fi1atWqASMD6g/LDlFrzz//vGbPnq0jR46oc+fOmjt3rq677rqGDgvw2Ycffqi+fftW2z9mzBhlZGTUf0BAAyAhAAAAzCEAAAAkBAAAQCQEAABAJAQAAEAkBAAAQCQEAABAJAQAAEAkBAAAQCQEgM9mzJihbt26uV7ffffdGjFiRL3H8f3338tisSgrK+usY9q0aaN58+bV+poZGRlq2rSpz7FZLBatWbPG5+sAqDskBAhId999tywWiywWi0JDQ9WuXTtNnjxZxcXFdf6zn3vuuVrf7rY2H+IAUB94uBEC1vXXX6+XXnpJFRUV+uSTT3TvvfequLhYixYtqja2oqJCoaGhfvm5NpvNL9cBgPpEhQABy2q1KjExUUlJSRo1apTuvPNOV9n6VJn/H//4h9q1ayer1SrDMFRQUKD7779f8fHxiomJ0W9/+1t98cUXbtd95plnlJCQoOjoaI0dO1alpaVux89sGTidTs2aNUsXX3yxrFarWrVqpZkzZ0qS2rZtK0nq3r27LBaL+vTp4zrvpZdeUseOHRUeHq5LL71Uzz//vNvP2bJli7p3767w8HD16NFD27dv9/p3NGfOHHXp0kVRUVFKSkrSuHHjVFRUVG3cmjVrdMkllyg8PFwDBgxQTk6O2/F///vfSk5OVnh4uNq1a6ennnpKlZWVXscDoOGQEMA0IiIiVFFR4Xq9b98+vfrqq/rXv/7lKtnfeOONys3N1dtvv63MzExdccUV6tevn3766SdJ0quvvqonn3xSM2fO1NatW9WyZctqH9RnmjZtmmbNmqXHH39cu3fv1iuvvKKEhARJVR/qkrR+/XodOXJEr7/+uiRpyZIlmj59umbOnKk9e/YoLS1Njz/+uJYtWyZJKi4u1pAhQ9ShQwdlZmZqxowZmjx5ste/k6CgIM2fP187d+7UsmXL9J///EdTpkxxG3Py5EnNnDlTy5Yt06effqrCwkLdfvvtruPvvvuufve732nChAnavXu3Fi9erIyMDFfSA6CRMIAANGbMGGP48OGu15s3bzbi4uKMkSNHGoZhGE8++aQRGhpq5OXluca8//77RkxMjFFaWup2rYsuushYvHixYRiGkZKSYjz44INux3v27Gl07dq1xp9dWFhoWK1WY8mSJTXGmZ2dbUgytm/f7rY/KSnJeOWVV9z2/eUvfzFSUlIMwzCMxYsXG7GxsUZxcbHr+KJFi2q81i+1bt3amDt37lmPv/rqq0ZcXJzr9UsvvWRIMjZt2uTat2fPHkOSsXnzZsMwDOM3v/mNkZaW5nad5cuXGy1btnS9lmSsXr36rD8XQMNjDgEC1ltvvaUmTZqosrJSFRUVGj58uBYsWOA63rp1a7Vo0cL1OjMzU0VFRYqLi3O7TklJib799ltJ0p49e/Tggw+6HU9JSdEHH3xQYwx79uxRWVmZ+vXrV+u4jx07ppycHI0dO1b33Xefa39lZaVrfsKePXvUtWtXRUZGusXhrQ8++EBpaWnavXu3CgsLVVlZqdLSUhUXFysqKkqSFBISoh49erjOufTSS9W0aVPt2bNHV111lTIzM/X555+7VQQcDodKS0t18uRJtxgBnL9ICBCw+vbtq0WLFik0NFR2u73apMFTH3inOJ1OtWzZUh9++GG1a53r0ruIiAivz3E6nZKq2gY9e/Z0OxYcHCxJMgzjnOL5pf379+uGG27Qgw8+qL/85S+KjY3Vhg0bNHbsWLfWilS1bPBMp/Y5nU499dRTuvnmm6uNCQ8P9zlOAPWDhAABKyoqShdffHGtx19xxRXKzc1VSEiI2rRpU+OYjh07atOmTbrrrrtc+zZt2nTWa7Zv314RERF6//33de+991Y7HhYWJqnqG/UpCQkJuuCCC/Tdd9/pzjvvrPG6l112mZYvX66SkhJX0uEpjpps3bpVlZWVevbZZxUUVDWd6NVXX602rrKyUlu3btVVV10lSdq7d6+OHz+uSy+9VFLV723v3r1e/a4BnH9ICICf9e/fXykpKRoxYoRmzZqlDh066PDhw3r77bc1YsQI9ejRQ4888ojGjBmjHj166Nprr9XLL7+sXbt2qV27djVeMzw8XFOnTtWUKVMUFhama665RseOHdOuXbs0duxYxcfHKyIiQmvXrtWFF16o8PBw2Ww2zZgxQxMmTFBMTIwGDx6ssrIybd26Vfn5+Zo4caJGjRql6dOna+zYsfrzn/+s77//Xn/961+9er8XXXSRKisrtWDBAg0dOlSffvqpXnjhhWrjQkNDNX78eM2fP1+hoaF6+OGHdfXVV7sShCeeeEJDhgxRUlKSbr31VgUFBenLL7/Ujh079N///d/e/48A0CBYZQD8zGKx6O2339Z1112ne+65R5dccoluv/12ff/9965VAbfddpueeOIJTZ06VcnJydq/f7/+8Ic/eLzu448/rkmTJumJJ55Qx44dddtttykvL09SVX9+/vz5Wrx4sex2u4YPHy5Juvfee/X3v/9dGRkZ6tKli3r37q2MjAzXMsUmTZro3//+t3bv3q3u3btr+vTpmjVrllfvt1u3bpozZ45mzZqlzp076+WXX1Z6enq1cZGRkZo6dapGjRqllJQURUREaOXKla7jgwYN0ltvvaV169bpyiuv1NVXX605c+aodevWXsUDoGFZDH80IwEAQKNGhQAAAJAQAAAAEgIAACASAgAAIBICAAAgEgIAACASAgAAIBICAAAgEgIAACASAgAAIBICAAAg6f8DP1zIj3r+tXwAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "CatBoost AUC score: 0.8139324487334137\n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- \n", + "\n", + "\n", + "LightGBM Model Classification Report \n", + " precision recall f1-score support\n", + "\n", + " 0 0.78 0.86 0.82 1658\n", + " 1 0.85 0.76 0.80 1658\n", + "\n", + " accuracy 0.81 3316\n", + " macro avg 0.81 0.81 0.81 3316\n", + "weighted avg 0.81 0.81 0.81 3316\n", + " \n", + "\n", + "LightGBM Confusion Matrix: \n", + " 0 1\n", + "0 1427 231\n", + "1 398 1260 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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precisionrecallf1_scoreaccuracyauc
model
XGBoost0.8481850.7750300.8099590.8181540.818154
CatBoost0.8567510.7539200.8020530.8139320.813932
Random Forest0.8291140.7901090.8091410.8136310.813631
LightGBM0.8450700.7599520.8002540.8103140.810314
Decision Tree0.7488710.8003620.7737610.7659830.765983
Logistic Regressor0.6751850.6055490.6384740.6571170.657117
\n", + "
" + ], + "text/plain": [ + " precision recall f1_score accuracy auc\n", + "model \n", + "XGBoost 0.848185 0.775030 0.809959 0.818154 0.818154\n", + "CatBoost 0.856751 0.753920 0.802053 0.813932 0.813932\n", + "Random Forest 0.829114 0.790109 0.809141 0.813631 0.813631\n", + "LightGBM 0.845070 0.759952 0.800254 0.810314 0.810314\n", + "Decision Tree 0.748871 0.800362 0.773761 0.765983 0.765983\n", + "Logistic Regressor 0.675185 0.605549 0.638474 0.657117 0.657117" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit and evaluate the models\n", + "model_performances = classification_fit_and_score(smote_models)\n", + "model_performances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the *XGBoost* model has the highest AUC score, it will be used for prediction on the test data." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
+       "              predictor=None, random_state=24, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n", + " predictor=None, random_state=24, ...)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Finalize the XGBoost Model\n", + "xgb_model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.0 Evaluation on Test Data" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Customer IdYearOfObservationInsured_PeriodResidentialBuilding_PaintedBuilding_FencedGardenSettlementBuilding DimensionBuilding_TypeDate_of_OccupancyNumberOfWindowsGeo_Code
0H1192020131.0000000VNOR300.011960.033310
1H1192120160.9972680VNOR300.011960.033310
2H980520130.3698630VVVU790.011960.0.3310
3H749320141.0000000VNOR1405.012004.033321
4H749420161.0000000VNOR1405.012004.033321
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" + ], + "text/plain": [ + " Customer Id YearOfObservation Insured_Period Residential \\\n", + "0 H11920 2013 1.000000 0 \n", + "1 H11921 2016 0.997268 0 \n", + "2 H9805 2013 0.369863 0 \n", + "3 H7493 2014 1.000000 0 \n", + "4 H7494 2016 1.000000 0 \n", + "\n", + " Building_Painted Building_Fenced Garden Settlement Building Dimension \\\n", + "0 V N O R 300.0 \n", + "1 V N O R 300.0 \n", + "2 V V V U 790.0 \n", + "3 V N O R 1405.0 \n", + "4 V N O R 1405.0 \n", + "\n", + " Building_Type Date_of_Occupancy NumberOfWindows Geo_Code \n", + "0 1 1960.0 3 3310 \n", + "1 1 1960.0 3 3310 \n", + "2 1 1960.0 . 3310 \n", + "3 1 2004.0 3 3321 \n", + "4 1 2004.0 3 3321 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import test data and submission sample\n", + "test_df = pd.read_csv(\"data/test_data.csv\")\n", + "test_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3069, 13)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the shape of the test data\n", + "test_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 3069 entries, 0 to 3068\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Customer Id 3069 non-null object \n", + " 1 YearOfObservation 3069 non-null int64 \n", + " 2 Insured_Period 3069 non-null float64\n", + " 3 Residential 3069 non-null int64 \n", + " 4 Building_Painted 3069 non-null object \n", + " 5 Building_Fenced 3069 non-null object \n", + " 6 Garden 3065 non-null object \n", + " 7 Settlement 3069 non-null object \n", + " 8 Building Dimension 3056 non-null float64\n", + " 9 Building_Type 3069 non-null int64 \n", + " 10 Date_of_Occupancy 2341 non-null float64\n", + " 11 NumberOfWindows 3069 non-null object \n", + " 12 Geo_Code 3056 non-null object \n", + "dtypes: float64(3), int64(3), object(7)\n", + "memory usage: 311.8+ KB\n" + ] + } + ], + "source": [ + "# Check the info of the test data\n", + "test_df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop the columns that were not used\n", + "test_df.drop(columns = [\"Customer Id\", \"Geo_Code\"], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# Clean the Number of Windows column\n", + "# Replace the \">=10\" values\n", + "test_df[\"NumberOfWindows\"].replace(\">=10\", 10, inplace= True)\n", + "\n", + "# Replace the \" .\" values\n", + "test_df[\"NumberOfWindows\"].replace(\" .\", 4, inplace= True)\n", + "test_df[\"NumberOfWindows\"] = test_df[\"NumberOfWindows\"].apply(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 3069 entries, 0 to 3068\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 YearOfObservation 3069 non-null int64 \n", + " 1 Insured_Period 3069 non-null float64\n", + " 2 Residential 3069 non-null int64 \n", + " 3 Building_Painted 3069 non-null object \n", + " 4 Building_Fenced 3069 non-null object \n", + " 5 Garden 3065 non-null object \n", + " 6 Settlement 3069 non-null object \n", + " 7 Building Dimension 3056 non-null float64\n", + " 8 Building_Type 3069 non-null int64 \n", + " 9 Date_of_Occupancy 2341 non-null float64\n", + " 10 NumberOfWindows 3069 non-null int64 \n", + "dtypes: float64(3), int64(4), object(4)\n", + "memory usage: 263.9+ KB\n" + ] + } + ], + "source": [ + "test_df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1960., 2004., 1988., nan, 1980., 2005., 2006., 1974., 1984.,\n", + " 1977., 1850., 1973., 1998., 1968., 1900., 1931., 1950., 2001.,\n", + " 1975., 1972., 1930., 1920., 1971., 1890., 1945., 1970., 1989.,\n", + " 1979., 1955., 1922., 1910., 1940., 1994., 1990., 1965., 1995.,\n", + " 1956., 1856., 1957., 2002., 1954., 1982., 1981., 2003., 1993.,\n", + " 1953., 2007., 1999., 2008., 1958., 2000., 1951., 1962., 2012.,\n", + " 1996., 1961., 1925., 1928., 1820., 1870., 1875., 1895., 1987.,\n", + " 1985., 1991., 1903., 1934., 1983., 1963., 1935., 1800., 1884.,\n", + " 2011., 1803., 1976., 1967., 1906., 1978., 1854., 1948., 1964.,\n", + " 1992., 1750., 1938., 1997., 1913., 1932., 1986., 1959., 1905.])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df[\"Date_of_Occupancy\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Fill the missing values\n", + "test_df[\"Building Dimension\"] = bd_imputer.transform(test_df[\"Building Dimension\"].values.reshape(-1,1))[:,0]\n", + "test_df[\"Date_of_Occupancy\"].replace(\"O\", 1970.0, inplace=True)\n", + "test_df[\"Date_of_Occupancy\"].fillna(1970.0, inplace=True)\n", + "test_df[\"Garden\"] = do_imputer.transform(test_df[\"Garden\"].values.reshape(-1,1))[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['YearOfObservation',\n", + " 'Insured_Period',\n", + " 'Residential',\n", + " 'Building Dimension',\n", + " 'Building_Type',\n", + " 'Date_of_Occupancy',\n", + " 'NumberOfWindows']" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the list of numeric columns\n", + "numerics = [column for column in test_df.columns if (test_df[column].dtype != \"O\")]\n", + "numerics" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Building_Painted', 'Building_Fenced', 'Garden', 'Settlement']" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the list of categorical columns\n", + "categoricals = [column for column in test_df.columns if (test_df[column].dtype == \"O\")]\n", + "categoricals" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearOfObservationInsured_PeriodResidentialBuilding DimensionBuilding_TypeDate_of_OccupancyNumberOfWindowsBuilding_Painted_VBuilding_Fenced_VGarden_VSettlement_U
020131.0000000300.011960.031.00.00.00.0
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320141.00000001405.012004.031.00.00.00.0
420161.00000001405.012004.031.00.00.00.0
....................................
306420151.00000001083.041900.041.01.01.01.0
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3069 rows × 11 columns

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" + ], + "text/plain": [ + " YearOfObservation Insured_Period Residential Building Dimension \\\n", + "0 2013 1.000000 0 300.0 \n", + "1 2016 0.997268 0 300.0 \n", + "2 2013 0.369863 0 790.0 \n", + "3 2014 1.000000 0 1405.0 \n", + "4 2016 1.000000 0 1405.0 \n", + "... ... ... ... ... \n", + "3064 2015 1.000000 0 1083.0 \n", + "3065 2012 1.000000 0 1083.0 \n", + "3066 2012 1.000000 0 1083.0 \n", + "3067 2013 1.000000 0 1083.0 \n", + "3068 2012 1.000000 0 1083.0 \n", + "\n", + " Building_Type Date_of_Occupancy NumberOfWindows Building_Painted_V \\\n", + "0 1 1960.0 3 1.0 \n", + "1 1 1960.0 3 1.0 \n", + "2 1 1960.0 4 1.0 \n", + "3 1 2004.0 3 1.0 \n", + "4 1 2004.0 3 1.0 \n", + "... ... ... ... ... \n", + "3064 4 1900.0 4 1.0 \n", + "3065 2 1948.0 4 1.0 \n", + "3066 2 1993.0 4 1.0 \n", + "3067 1 1800.0 4 1.0 \n", + "3068 2 1950.0 4 1.0 \n", + "\n", + " Building_Fenced_V Garden_V Settlement_U \n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 1.0 1.0 1.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "... ... ... ... \n", + "3064 1.0 1.0 1.0 \n", + "3065 1.0 1.0 1.0 \n", + "3066 1.0 1.0 1.0 \n", + "3067 1.0 1.0 1.0 \n", + "3068 1.0 1.0 1.0 \n", + "\n", + "[3069 rows x 11 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Encode the categorical columns\n", + "encoded_test_categoricals = encoder.transform(test_df[categoricals])\n", + "encoded_test_categoricals = pd.DataFrame(encoded_test_categoricals, columns = encoder.get_feature_names_out().tolist())\n", + "\n", + "# Add the encoded categoricals to the DataFrame and dropping the original columns\n", + "test_df = test_df.join(encoded_test_categoricals)\n", + "test_df.drop(columns= categoricals, inplace= True)\n", + "test_df" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# # Create a column for the target variable\n", + "# test_df[\"Claim\"] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# Scale the numeric columns\n", + "test_df[numerics] = scaler.transform(test_df[numerics])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearOfObservationInsured_PeriodResidentialBuilding DimensionBuilding_TypeDate_of_OccupancyNumberOfWindowsBuilding_Painted_VBuilding_Fenced_VGarden_VSettlement_UClaim
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11.000.9972680.00.0142800.0000000.8653850.2222221.00.00.00.00
20.250.3698630.00.0376810.0000000.8653850.3333331.01.01.01.00
30.501.0000000.00.0670520.0000000.9711540.2222221.00.00.00.00
41.001.0000000.00.0670520.0000000.9711540.2222221.00.00.00.00
.......................................
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3069 rows × 12 columns

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" + ], + "text/plain": [ + " YearOfObservation Insured_Period Residential Building Dimension \\\n", + "0 0.25 1.000000 0.0 0.014280 \n", + "1 1.00 0.997268 0.0 0.014280 \n", + "2 0.25 0.369863 0.0 0.037681 \n", + "3 0.50 1.000000 0.0 0.067052 \n", + "4 1.00 1.000000 0.0 0.067052 \n", + "... ... ... ... ... \n", + "3064 0.75 1.000000 0.0 0.051674 \n", + "3065 0.00 1.000000 0.0 0.051674 \n", + "3066 0.00 1.000000 0.0 0.051674 \n", + "3067 0.25 1.000000 0.0 0.051674 \n", + "3068 0.00 1.000000 0.0 0.051674 \n", + "\n", + " Building_Type Date_of_Occupancy NumberOfWindows Building_Painted_V \\\n", + "0 0.000000 0.865385 0.222222 1.0 \n", + "1 0.000000 0.865385 0.222222 1.0 \n", + "2 0.000000 0.865385 0.333333 1.0 \n", + "3 0.000000 0.971154 0.222222 1.0 \n", + "4 0.000000 0.971154 0.222222 1.0 \n", + "... ... ... ... ... \n", + "3064 1.000000 0.721154 0.333333 1.0 \n", + "3065 0.333333 0.836538 0.333333 1.0 \n", + "3066 0.333333 0.944712 0.333333 1.0 \n", + "3067 0.000000 0.480769 0.333333 1.0 \n", + "3068 0.333333 0.841346 0.333333 1.0 \n", + "\n", + " Building_Fenced_V Garden_V Settlement_U Claim \n", + "0 0.0 0.0 0.0 0 \n", + "1 0.0 0.0 0.0 0 \n", + "2 1.0 1.0 1.0 0 \n", + "3 0.0 0.0 0.0 0 \n", + "4 0.0 0.0 0.0 0 \n", + "... ... ... ... ... \n", + "3064 1.0 1.0 1.0 0 \n", + "3065 1.0 1.0 1.0 1 \n", + "3066 1.0 1.0 1.0 0 \n", + "3067 1.0 1.0 1.0 1 \n", + "3068 1.0 1.0 1.0 0 \n", + "\n", + "[3069 rows x 12 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Predict on the unseen data - XGBoost model\n", + "test_df[\"Claim\"] = xgb_model.predict(test_df)\n", + "test_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.0 Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.0 Exporting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Per their confusion matrices, the Decision Tree model and the XGBoost model tie on the performance metrics. As a personal decision, the XGBoost is recommended for further optimization and deployment." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# Exporting the requirements\n", + "requirements = \"\\n\".join(f\"{m.__name__}=={m.__version__}\" for m in globals().values() if getattr(m, \"__version__\", None))\n", + "\n", + "with open(\"requirements.txt\", \"w\") as f:\n", + " f.write(requirements)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a dictionary of objects to export\n", + "exports = {\"encoder\": encoder,\n", + " \"scaler\": scaler,\n", + " \"model\": xgb_model}" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Exporting the dictionary with Pickle\n", + "with open(\"src/Streamlit_toolkit\", \"wb\") as file:\n", + " pickle.dump(exports, file)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "# Exporting the model\n", + "xgb_model.save_model(\"src/xgb_model.json\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "78px", + "width": "187px" + }, + "number_sections": false, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "541.6px", + "left": "278px", + "top": "110.325px", + "width": "239.819px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + }, + "vscode": { + "interpreter": { + "hash": "1a4ce4bc5f820c6c47c7565419227e532b3448deb4a621e77e51010fbe64b648" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}