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Phase 2.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "91c37298",
"metadata": {},
"source": [
"# Phase 2 \n",
"## UK Smokers Prediction ML Project (Predictive Analysis)\n",
"### 2 June 2024\n",
"\n",
"Wong Yi Wei (Ethan) S3966890"
]
},
{
"cell_type": "markdown",
"id": "8899f5a9",
"metadata": {},
"source": [
"# Table of Contents\n",
"\n",
"* [1.0 Introduction](#1)<br>\n",
" * [1.1 Phase 1 Summary](#1.1)<br>\n",
" * [1.2 Report Overview](#1.2)<br>\n",
" * [1.3 Overview of Methodology](#1.3)<br>\n",
"* [2.0 Data Preparation](#2.0)<br>\n",
" * [2.1 Data Import and Consistency Check](#2.1)<br>\n",
" * [2.2 Encoding Categorical Features](#2.2)<br>\n",
" * [2.2.1 Encoding Target Feature](#2.2.1)<br>\n",
" * [2.2.2 Encoding Categorical Descriptive Feature](#2.2.2)<br>\n",
" * [2.2.3 Feature Scaling](#2.2.3)<br>\n",
"* [3.0 Predictive Modeling](#3.0)<br>\n",
" * [3.1 Feature Selection](#3.1)<br>\n",
" * [3.1.1 Full Set of Features](#3.1.1)<br>\n",
" * [3.1.2 F-Score](#3.1.2)<br>\n",
" * [3.1.3 Random Forest Importance (RFI)](#3.1.3)<br>\n",
" * [3.1.4 spFSR](#3.1.4)<br>\n",
" * [3.1.5 Performance Comparison using Paired T-tests](#3.1.5)<br> \n",
" * [3.2 Model Fitting and Tuning](#3.2)<br>\n",
" * [3.2.1 Data Sampling & Train-Test Splitting](#3.2.1)<br>\n",
" * [3.2.2 K-Nearest Neighbors (KNN)](#3.2.2)<br>\n",
" * [3.2.3 Decision tree (DT)](#3.2.3)<br>\n",
" * [3.2.4 Gaussian Naive Bayes (NB)](#3.2.4)<br>\n",
" * [3.2.5 Model Comparison](#3.2.5)<br>\n",
"* [4.0 Critique and Limitations](#4.0)<br>\n",
"* [5.0 Summary and Conclusions](#5.0)<br>\n",
" * [5.1 Project Summary](#5.1)<br>\n",
" * [5.2 Summary of Findings](#5.2)<br>\n",
" * [5.3 Conclusion](#5.3)<br>\n",
"* [6.0 References](#6.0)<br>\n",
"\n",
"\n",
"***\n",
"# Setup"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "94a23419",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"from sklearn import preprocessing\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.model_selection import cross_val_score, RepeatedStratifiedKFold\n",
"import sklearn.metrics as metrics\n",
"from sklearn import feature_selection as fs\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.preprocessing import PowerTransformer\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout\n",
"from sklearn.metrics import accuracy_score, roc_auc_score\n",
"from tensorflow.keras.optimizers import SGD\n",
"from sklearn.metrics import confusion_matrix\n",
"\n",
"#Setting to view all contents\n",
"pd.set_option('display.max_columns', None)"
]
},
{
"cell_type": "markdown",
"id": "e9c32206",
"metadata": {},
"source": [
"***\n",
"# 1.0 Introduction <a class=\"anchor\" id=\"1\"></a>\n",
"\n",
"## 1.1 Phase 1 Summary <a class=\"anchor\" id=\"1.1\"></a>\n",
"\n",
"In Phase 1 of this project, we have obtained the dataset from kaggle, which was the UK smoking data accessed from: \n",
"[https://www.kaggle.com/datasets/mexwell/uk-smoking-data?resource=download] (MacQuarrie 2024). From the dataset in Phase 1, we have sucessfully obtained a clean and tidy dataset for analysis in Phase 2 of the report, which would be to perform predictive modelling to predict smokers in the United Kingdom based on various demographics within the data. Furthermore during Phase 1 of the project, we have also successfully dealt with missing values, and performed data exploration and visalisations. Through exploring the data, we have discovered that there were a large number of smokers within the data population, there were a higher number of young smokers within the population, and some smokers only smoked on weekdays, and on weekends, or vice versa. \n",
"\n",
"## 1.2 Report Overview <a class=\"anchor\" id=\"1.2\"></a>\n",
"\n",
"In the Phase 2 of this project in this report, we will first import the data from Phase 1 of the project and further prepare the data by using any encoding for the features as necessary. Then, we will select the best features in the dataset by using multiple feature selection methods and compare the methods using paired t-test for the best possible method. Then, we will fit the model with different algorithms and tune the algorithm as required. After fitting the model, we will then compare each algorithms using different metrics to identify the best model using a consistent training and test data. Furthermore, our goal in this phase of the project is to identify the best model for predictive analysis.\n",
"\n",
"## 1.3 Overview of Methodology <a class=\"anchor\" id=\"1.3\"></a>\n",
"\n",
"In Machine Learning, feature selection is important as it identifies the most importance features for model performance (*Feature Selection* 2024). We will be using the feature selection methods of full set of features, F-Score, Random Forest Importance (RFI), and spFSR. Full set of features selection method utilizes all descriptive features without any selection. Since this method allows for all features, hence no information can be lost. However, this method may be computationally demanding for large datasets as it includes all features within a dataset (Rosidi 2023). F-score feature selection is a filter-based feature selection which evaluates each features independently against the target variable based on the correlation. This method is also used to split the classification tree to accurately identify the importance of features (Yeung et al 2023). However, limitations of this method are that it does not indicate any of the combination of 2 features, also known as mutual information (Chen and Lin 2006). Random Forest Importance selection method computes the importance of each feature based on the node impurity decrease when splitting on the feature through information gain (Akmand 2022). This selection method tends to allow for more relevancies for features with higher importance scores. Simultaneous Perturbation Stochastic Approximation (spFSR) is a selection method which utilizes a stochastic optimization-based feature selection method to maximise the classification accuracy by pertubing the feature subset (Akmand 2022). This algorithm also searches for a local optimal set of features using error measures such as accuracy rate to identify the best feature.\n",
"\n",
"K-Nearest neighbours is an instance-based learning algorithm used for classification task to classify a new data point based on the majority class of its k-nearest neighbours in the feature space (*What is k-nearest neighbors (KNN) algorithm?* n.d.). This algorithm first calculates the distance such as Euclidean, Manhattan, and Minkowski and selects the k-nearest neighbours based on the distance. Decision Tree is an algorithm that is a non-parametric supervised learning algorithm, which contains branches starting with a root node, feeding into the decision nodes (*What is a decision tree?* n.d.). This algorithm selects the best features and split the data based on the impurity measure such as gini and entropy impurities. Gaussian Naïve Bayes is a classification technique based on a probabilistic approach which assumes each class follows a normal distribution, and each parameter can predict the output variable (Martins 2023). This algorithm calculates the prior probabilities of each class based on the training data using the variance of Laplace smoothing.\n",
"\n",
"Paired t-test is a statistical method to identify if there are any significant differences between the two groups (Gleichmann N 2020). If the p value within the test is greater than the test statistic of 0.05, then there are significant difference between the two groups. Confusion matrix is a matrix which defines the performance of a classification model by comparing the predicted values with the true values (Sharma et al. 2022). Within the matrix, there are true positives, which indicates that the prediction is correct and true, false positives, indicating that there are inaccurate results of true values within the prediction value, false negative, which indicates that the prediction is correct and false, and false positives, indicating that there are inaccurate results of false values within the prediction value. Classification report is a summary of the classification metrics for each class within the machine learning model (*What is the difference between a confusion matrix and a classification report?* n.d.). This includes the recall value, which are the number of true positives within the positive class, which measures how correctly the model identified the actual positive samples (Evidently n.d.). F-1 score within the classification report measures the harmonic mean of precision and recall values to evaluate the model performance (Sharma 2023).\n",
"\n",
"***\n",
"# 2.0 Data Preparation <a class=\"anchor\" id=\"2.0\"></a>\n",
"\n",
"## 2.1 Importing Data & Consistency Check <a class=\"anchor\" id=\"2.1\"></a>\n",
"\n",
"Before we do any predictive modelling, we will import the data from Phase 1, which has been exported into a csv file, and check the data structure and types to ensure consistencies with Phase 1 of the report."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a2ce621b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>marital_status</th>\n",
" <th>highest_qualification</th>\n",
" <th>nationality</th>\n",
" <th>ethnicity</th>\n",
" <th>gross_income</th>\n",
" <th>region</th>\n",
" <th>smoke</th>\n",
" <th>amt_weekends</th>\n",
" <th>amt_weekdays</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Male</td>\n",
" <td>Young</td>\n",
" <td>Divorced</td>\n",
" <td>No Qualification</td>\n",
" <td>British</td>\n",
" <td>White</td>\n",
" <td>2,600 to 5,200</td>\n",
" <td>The North</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Female</td>\n",
" <td>Middle-Aged</td>\n",
" <td>Single</td>\n",
" <td>No Qualification</td>\n",
" <td>British</td>\n",
" <td>White</td>\n",
" <td>Under 2,600</td>\n",
" <td>The North</td>\n",
" <td>True</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Male</td>\n",
" <td>Middle-Aged</td>\n",
" <td>Married</td>\n",
" <td>Degree</td>\n",
" <td>English</td>\n",
" <td>White</td>\n",
" <td>28,600 to 36,400</td>\n",
" <td>The North</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Female</td>\n",
" <td>Middle-Aged</td>\n",
" <td>Married</td>\n",
" <td>Degree</td>\n",
" <td>English</td>\n",
" <td>White</td>\n",
" <td>10,400 to 15,600</td>\n",
" <td>The North</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Female</td>\n",
" <td>Young</td>\n",
" <td>Married</td>\n",
" <td>GCSE/O Level</td>\n",
" <td>British</td>\n",
" <td>White</td>\n",
" <td>2,600 to 5,200</td>\n",
" <td>The North</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gender age marital_status highest_qualification nationality \\\n",
"0 Male Young Divorced No Qualification British \n",
"1 Female Middle-Aged Single No Qualification British \n",
"2 Male Middle-Aged Married Degree English \n",
"3 Female Middle-Aged Married Degree English \n",
"4 Female Young Married GCSE/O Level British \n",
"\n",
" ethnicity gross_income region smoke amt_weekends amt_weekdays \n",
"0 White 2,600 to 5,200 The North False 0 0 \n",
"1 White Under 2,600 The North True 12 12 \n",
"2 White 28,600 to 36,400 The North False 0 0 \n",
"3 White 10,400 to 15,600 The North False 0 0 \n",
"4 White 2,600 to 5,200 The North False 0 0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Import Data\n",
"smoke=pd.read_csv(\"Phase2.csv\")\n",
"smoke.iloc[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ceb346cd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1561, 11)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Shape\n",
"smoke.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "311785ae",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>marital_status</th>\n",
" <th>highest_qualification</th>\n",
" <th>nationality</th>\n",
" <th>ethnicity</th>\n",
" <th>gross_income</th>\n",
" <th>region</th>\n",
" <th>smoke</th>\n",
" <th>amt_weekends</th>\n",
" <th>amt_weekdays</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1561</td>\n",
" <td>1561</td>\n",
" <td>1561</td>\n",
" <td>1561</td>\n",
" <td>1561</td>\n",
" <td>1561</td>\n",
" <td>1561</td>\n",
" <td>1561</td>\n",
" <td>1561</td>\n",
" <td>1561.000000</td>\n",
" <td>1561.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>Female</td>\n",
" <td>Young</td>\n",
" <td>Married</td>\n",
" <td>No Qualification</td>\n",
" <td>English</td>\n",
" <td>White</td>\n",
" <td>5,200 to 10,400</td>\n",
" <td>The North</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>885</td>\n",
" <td>553</td>\n",
" <td>759</td>\n",
" <td>523</td>\n",
" <td>773</td>\n",
" <td>1457</td>\n",
" <td>394</td>\n",
" <td>400</td>\n",
" <td>1166</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.187060</td>\n",
" <td>3.487508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.757758</td>\n",
" <td>7.666444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.000000</td>\n",
" <td>55.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gender age marital_status highest_qualification nationality \\\n",
"count 1561 1561 1561 1561 1561 \n",
"unique 2 3 5 8 6 \n",
"top Female Young Married No Qualification English \n",
"freq 885 553 759 523 773 \n",
"mean NaN NaN NaN NaN NaN \n",
"std NaN NaN NaN NaN NaN \n",
"min NaN NaN NaN NaN NaN \n",
"25% NaN NaN NaN NaN NaN \n",
"50% NaN NaN NaN NaN NaN \n",
"75% NaN NaN NaN NaN NaN \n",
"max NaN NaN NaN NaN NaN \n",
"\n",
" ethnicity gross_income region smoke amt_weekends \\\n",
"count 1561 1561 1561 1561 1561.000000 \n",
"unique 5 8 7 2 NaN \n",
"top White 5,200 to 10,400 The North False NaN \n",
"freq 1457 394 400 1166 NaN \n",
"mean NaN NaN NaN NaN 4.187060 \n",
"std NaN NaN NaN NaN 8.757758 \n",
"min NaN NaN NaN NaN 0.000000 \n",
"25% NaN NaN NaN NaN 0.000000 \n",
"50% NaN NaN NaN NaN 0.000000 \n",
"75% NaN NaN NaN NaN 0.000000 \n",
"max NaN NaN NaN NaN 60.000000 \n",
"\n",
" amt_weekdays \n",
"count 1561.000000 \n",
"unique NaN \n",
"top NaN \n",
"freq NaN \n",
"mean 3.487508 \n",
"std 7.666444 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 55.000000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Summary Statistics\n",
"smoke.describe(include='all')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ab364e86",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gender \n",
" ['Male' 'Female'] \n",
"\n",
"age \n",
" ['Young' 'Middle-Aged' 'Old'] \n",
"\n",
"marital_status \n",
" ['Divorced' 'Single' 'Married' 'Widowed' 'Separated'] \n",
"\n",
"highest_qualification \n",
" ['No Qualification' 'Degree' 'GCSE/O Level' 'GCSE/CSE' 'Other/Sub Degree'\n",
" 'Higher/Sub Degree' 'ONC/BTEC' 'A Levels'] \n",
"\n",
"nationality \n",
" ['British' 'English' 'Scottish' 'Other' 'Welsh' 'Irish'] \n",
"\n",
"ethnicity \n",
" ['White' 'Mixed' 'Black' 'Asian' 'Chinese'] \n",
"\n",
"gross_income \n",
" ['2,600 to 5,200' 'Under 2,600' '28,600 to 36,400' '10,400 to 15,600'\n",
" '15,600 to 20,800' 'Above 36,400' '5,200 to 10,400' '20,800 to 28,600'] \n",
"\n",
"region \n",
" ['The North' 'Midlands & East Anglia' 'London' 'South East' 'South West'\n",
" 'Wales' 'Scotland'] \n",
"\n",
"smoke \n",
" [False True] \n",
"\n",
"amt_weekends \n",
" [ 0 12 6 8 15 5 20 25 4 30 10 40 9 7 2 50 16 35 18 1 3 60 24 45] \n",
"\n",
"amt_weekdays \n",
" [ 0 12 6 8 2 20 15 25 4 10 30 3 40 9 5 50 7 18 35 1 55 16 24 45] \n",
"\n"
]
}
],
"source": [
"#Unique Values\n",
"colnames=[\"gender\",\"age\",\"marital_status\",\"highest_qualification\",\n",
" \"nationality\",\"ethnicity\",\"gross_income\",\"region\",\"smoke\",\n",
" \"amt_weekends\",\"amt_weekdays\"]\n",
"\n",
"for i in colnames:\n",
" print(i,'\\n',smoke[i].unique(),'\\n')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "04d3f588",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing Values: \n",
" gender 0\n",
"age 0\n",
"marital_status 0\n",
"highest_qualification 0\n",
"nationality 0\n",
"ethnicity 0\n",
"gross_income 0\n",
"region 0\n",
"smoke 0\n",
"amt_weekends 0\n",
"amt_weekdays 0\n",
"dtype: int64\n"
]
}
],
"source": [
"#Missing Values\n",
"print(\"Missing Values:\",'\\n',smoke.isnull().sum())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b6bca579",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gender object\n",
"age object\n",
"marital_status object\n",
"highest_qualification object\n",
"nationality object\n",
"ethnicity object\n",
"gross_income object\n",
"region object\n",
"smoke bool\n",
"amt_weekends int64\n",
"amt_weekdays int64\n",
"dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Data Types\n",
"smoke.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "14cc1007",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gender object\n",
"age category\n",
"marital_status category\n",
"highest_qualification category\n",
"nationality category\n",
"ethnicity object\n",
"gross_income category\n",
"region category\n",
"smoke bool\n",
"amt_weekends int64\n",
"amt_weekdays int64\n",
"dtype: object"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Data Type Conversion\n",
"#String Conversion\n",
"smoke['gender']=smoke['gender'].astype('str')\n",
"\n",
"#Category Conversion\n",
"cat=['marital_status','nationality','region']\n",
"\n",
"for i in cat:\n",
" smoke[i]=smoke[i].astype('category')\n",
"\n",
"from pandas.api.types import CategoricalDtype\n",
"\n",
"age_cat=CategoricalDtype(categories=['Young','Middle-Aged',\"Old\"], ordered = True)\n",
"\n",
"smoke['age']=smoke['age'].astype(age_cat)\n",
"\n",
"quali=CategoricalDtype(categories=[\"No Qualification\",\"GCSE/CSE\",\"GCSE/O Level\",\"ONC/BTEC\",\"A Levels\",\n",
" \"Other/Sub Degree\",\"Degree\",\"Higher/Sub Degree\"], ordered = True)\n",
"\n",
"smoke['highest_qualification']=smoke['highest_qualification'].astype(quali)\n",
"\n",
"income=CategoricalDtype(categories=[\"Under 2,600\",\"2,600 to 5,200\",\"5,200 to 10,400\",\"10,400 to 15,600\",\n",
" \"15,600 to 20,800\",\"20,800 to 28,600\",\"28,600 to 36,400\",\n",
" \"Above 36,400\"], ordered = True)\n",
"\n",
"smoke['gross_income']=smoke['gross_income'].astype(income)\n",
"\n",
"#Check Conversion\n",
"smoke.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0edc65ac",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"English 49.520\n",
"British 32.735\n",
"Scottish 8.584\n",
"Other 4.228\n",
"Welsh 3.587\n",
"Irish 1.345\n",
"Name: nationality, dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Check value spread for nationality\n",
"smoke['nationality'].value_counts(normalize=True).mul(100).round(3)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "9fbcfa35",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"White 93.338\n",
"Asian 2.434\n",
"Black 2.050\n",
"Chinese 1.281\n",
"Mixed 0.897\n",
"Name: ethnicity, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Check values spread for ethnicity\n",
"smoke['ethnicity'].value_counts(normalize=True).mul(100).round(3)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c4755a3e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Midlands & East Anglia 25.625\n",
"The North 25.625\n",
"South East 14.798\n",
"London 11.019\n",
"South West 9.353\n",
"Scotland 9.033\n",
"Wales 4.548\n",
"Name: region, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Check value spread for region\n",
"smoke['region'].value_counts(normalize=True).mul(100).round(3)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "72db458a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['White', 'Other']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Change ethnicity values to 'Other'\n",
"smoke.loc[smoke['ethnicity'] != \"White\", 'ethnicity'] = 'Other'\n",
"smoke['ethnicity']=smoke['ethnicity'].astype('category')\n",
"smoke['ethnicity'].unique().tolist()"
]
},
{
"cell_type": "markdown",
"id": "d1b24508",
"metadata": {},
"source": [
"From the code cells above, we have successfully imported the data from the Phase 1 report labelled ```smoke.csv```. Data import was imported without any errors such as loss of data or missing values. However, upon checking the data types of all columns, columns ```gender```, ```age```, ```marital_status```, ```highest_qualification```, ```nationality```, ```ethnicity```, ```gross_income```, and ```region``` are incorrect and has been addressed appropriately.\n",
"\n",
"Additionally, since we have not checked the value spread for the categorical columns ```nationality```, ```ethnicity```, and ```region```, we have checked and found that for ```ethnicity```, more than 90% of the respondents were white, and this issue is addressed by replacing all other respondents as 'Other'.\n",
"\n",
"## 2.2 Encoding Categorical Features <a class=\"anchor\" id=\"2.2\"></a>\n",
"\n",
"Now that changes are made as appropriate, we will now encode all categorical features as it is essential to encode both the target and descriptive features into numerical features. That is, encoding the categorical values with numerical values. \n",
"\n",
"### 2.2.1 Encoding Target Feature <a class=\"anchor\" id=\"2.2.1\"></a>"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8be5b531",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False 1166\n",
"True 395\n",
"Name: smoke, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Encode target feature\n",
"Data = smoke.drop(columns='smoke')\n",
"target = smoke['smoke']\n",
"target.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "58d057e9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False 1166\n",
"True 395\n",
"Name: smoke, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Replace with binary values\n",
"target = target.replace({\"False\": 0, \"True\": 1})\n",
"target.value_counts()"
]
},
{
"cell_type": "markdown",
"id": "c55ebf30",
"metadata": {},
"source": [
"### 2.2.2 Encoding Categorical Descriptive Feature <a class=\"anchor\" id=\"2.2.2\"></a>\n",
"\n",
"In this section we will encode the categorical descriptive features by using one-hot encoding. Furthermore, we will also define the dummy variables for categorical descriptive variables with levels for feature selection."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8b253dc9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['gender']"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Show categorical columns\n",
"categorical_cols = Data.columns[Data.dtypes == object].tolist()\n",
"categorical_cols"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "13a7b971",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['gender', 'amt_weekends', 'amt_weekdays', 'age_Young',\n",
" 'age_Middle-Aged', 'age_Old', 'marital_status_Divorced',\n",
" 'marital_status_Married', 'marital_status_Separated',\n",
" 'marital_status_Single', 'marital_status_Widowed',\n",
" 'highest_qualification_No Qualification',\n",
" 'highest_qualification_GCSE/CSE', 'highest_qualification_GCSE/O Level',\n",
" 'highest_qualification_ONC/BTEC', 'highest_qualification_A Levels',\n",
" 'highest_qualification_Other/Sub Degree',\n",
" 'highest_qualification_Degree',\n",
" 'highest_qualification_Higher/Sub Degree', 'nationality_British',\n",
" 'nationality_English', 'nationality_Irish', 'nationality_Other',\n",
" 'nationality_Scottish', 'nationality_Welsh', 'ethnicity_Other',\n",
" 'ethnicity_White', 'gross_income_Under 2,600',\n",
" 'gross_income_2,600 to 5,200', 'gross_income_5,200 to 10,400',\n",
" 'gross_income_10,400 to 15,600', 'gross_income_15,600 to 20,800',\n",
" 'gross_income_20,800 to 28,600', 'gross_income_28,600 to 36,400',\n",
" 'gross_income_Above 36,400', 'region_London',\n",
" 'region_Midlands & East Anglia', 'region_Scotland', 'region_South East',\n",
" 'region_South West', 'region_The North', 'region_Wales'],\n",
" dtype='object')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#One-hot encoding\n",
"for col in categorical_cols:\n",
" if (Data[col].nunique() == 2):\n",
" Data[col] = pd.get_dummies(Data[col], drop_first = True)\n",
" \n",
"Data = pd.get_dummies(Data)\n",
"\n",
"Data.columns"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e280eb7d",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gender</th>\n",
" <th>amt_weekends</th>\n",
" <th>amt_weekdays</th>\n",
" <th>age_Young</th>\n",
" <th>age_Middle-Aged</th>\n",
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" <th>marital_status_Married</th>\n",
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" <th>marital_status_Single</th>\n",
" <th>marital_status_Widowed</th>\n",
" <th>highest_qualification_No Qualification</th>\n",
" <th>highest_qualification_GCSE/CSE</th>\n",
" <th>highest_qualification_GCSE/O Level</th>\n",
" <th>highest_qualification_ONC/BTEC</th>\n",
" <th>highest_qualification_A Levels</th>\n",
" <th>highest_qualification_Other/Sub Degree</th>\n",
" <th>highest_qualification_Degree</th>\n",
" <th>highest_qualification_Higher/Sub Degree</th>\n",
" <th>nationality_British</th>\n",
" <th>nationality_English</th>\n",
" <th>nationality_Irish</th>\n",
" <th>nationality_Other</th>\n",
" <th>nationality_Scottish</th>\n",
" <th>nationality_Welsh</th>\n",
" <th>ethnicity_Other</th>\n",
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" <th>gross_income_Under 2,600</th>\n",
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" <th>gross_income_5,200 to 10,400</th>\n",
" <th>gross_income_10,400 to 15,600</th>\n",
" <th>gross_income_15,600 to 20,800</th>\n",
" <th>gross_income_20,800 to 28,600</th>\n",
" <th>gross_income_28,600 to 36,400</th>\n",
" <th>gross_income_Above 36,400</th>\n",
" <th>region_London</th>\n",
" <th>region_Midlands & East Anglia</th>\n",
" <th>region_Scotland</th>\n",
" <th>region_South East</th>\n",
" <th>region_South West</th>\n",
" <th>region_The North</th>\n",
" <th>region_Wales</th>\n",
" </tr>\n",
" </thead>\n",
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],
"text/plain": [
" gender amt_weekends amt_weekdays age_Young age_Middle-Aged age_Old \\\n",
"770 1 30 30 0 0 1 \n",
"541 0 0 0 1 0 0 \n",
"1367 0 5 2 1 0 0 \n",
"571 1 0 0 1 0 0 \n",
"1050 0 5 5 1 0 0 \n",
"\n",
" marital_status_Divorced marital_status_Married \\\n",
"770 0 0 \n",
"541 0 1 \n",
"1367 0 1 \n",
"571 0 0 \n",
"1050 0 0 \n",
"\n",
" marital_status_Separated marital_status_Single marital_status_Widowed \\\n",
"770 0 0 1 \n",
"541 0 0 0 \n",
"1367 0 0 0 \n",
"571 0 1 0 \n",
"1050 0 1 0 \n",
"\n",
" highest_qualification_No Qualification highest_qualification_GCSE/CSE \\\n",
"770 1 0 \n",
"541 0 0 \n",
"1367 0 0 \n",
"571 1 0 \n",
"1050 0 0 \n",
"\n",
" highest_qualification_GCSE/O Level highest_qualification_ONC/BTEC \\\n",
"770 0 0 \n",
"541 0 0 \n",
"1367 0 0 \n",
"571 0 0 \n",
"1050 0 0 \n",
"\n",
" highest_qualification_A Levels highest_qualification_Other/Sub Degree \\\n",
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"571 0 0 \n",
"1050 1 0 \n",
"\n",
" highest_qualification_Degree highest_qualification_Higher/Sub Degree \\\n",
"770 0 0 \n",
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"1367 0 0 \n",
"571 0 0 \n",
"1050 0 0 \n",
"\n",
" nationality_British nationality_English nationality_Irish \\\n",
"770 0 1 0 \n",
"541 0 1 0 \n",
"1367 1 0 0 \n",
"571 0 1 0 \n",
"1050 1 0 0 \n",
"\n",
" nationality_Other nationality_Scottish nationality_Welsh \\\n",
"770 0 0 0 \n",
"541 0 0 0 \n",
"1367 0 0 0 \n",
"571 0 0 0 \n",
"1050 0 0 0 \n",
"\n",
" ethnicity_Other ethnicity_White gross_income_Under 2,600 \\\n",
"770 0 1 0 \n",
"541 0 1 0 \n",
"1367 0 1 0 \n",
"571 0 1 0 \n",
"1050 0 1 1 \n",
"\n",
" gross_income_2,600 to 5,200 gross_income_5,200 to 10,400 \\\n",
"770 0 0 \n",
"541 0 1 \n",
"1367 1 0 \n",
"571 0 1 \n",
"1050 0 0 \n",
"\n",
" gross_income_10,400 to 15,600 gross_income_15,600 to 20,800 \\\n",
"770 0 0 \n",
"541 0 0 \n",
"1367 0 0 \n",
"571 0 0 \n",
"1050 0 0 \n",
"\n",
" gross_income_20,800 to 28,600 gross_income_28,600 to 36,400 \\\n",
"770 1 0 \n",
"541 0 0 \n",
"1367 0 0 \n",
"571 0 0 \n",
"1050 0 0 \n",
"\n",
" gross_income_Above 36,400 region_London region_Midlands & East Anglia \\\n",
"770 0 0 1 \n",
"541 0 0 1 \n",
"1367 0 0 0 \n",
"571 0 0 1 \n",
"1050 0 0 0 \n",
"\n",
" region_Scotland region_South East region_South West region_The North \\\n",
"770 0 0 0 0 \n",
"541 0 0 0 0 \n",
"1367 0 0 0 0 \n",
"571 0 0 0 0 \n",
"1050 0 1 0 0 \n",
"\n",
" region_Wales \n",
"770 0 \n",
"541 0 \n",
"1367 1 \n",
"571 0 \n",
"1050 0 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Random sample for Data\n",
"Data.sample(5, random_state=999)"
]
},
{
"cell_type": "markdown",
"id": "3bbc9191",
"metadata": {},
"source": [
"### 2.2.3 Feature Scaling <a class=\"anchor\" id=\"2.2.3\"></a>\n",
"\n",
"In this section we will perform a min-max scaling of descriptive features, and making a copy of the data for future column name references as the data will be converted into ```numPy``` array."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "4ffb9fe1",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gender</th>\n",
" <th>amt_weekends</th>\n",
" <th>amt_weekdays</th>\n",
" <th>age_Young</th>\n",
" <th>age_Middle-Aged</th>\n",
" <th>age_Old</th>\n",
" <th>marital_status_Divorced</th>\n",
" <th>marital_status_Married</th>\n",
" <th>marital_status_Separated</th>\n",
" <th>marital_status_Single</th>\n",
" <th>marital_status_Widowed</th>\n",
" <th>highest_qualification_No Qualification</th>\n",
" <th>highest_qualification_GCSE/CSE</th>\n",
" <th>highest_qualification_GCSE/O Level</th>\n",
" <th>highest_qualification_ONC/BTEC</th>\n",
" <th>highest_qualification_A Levels</th>\n",
" <th>highest_qualification_Other/Sub Degree</th>\n",
" <th>highest_qualification_Degree</th>\n",
" <th>highest_qualification_Higher/Sub Degree</th>\n",
" <th>nationality_British</th>\n",
" <th>nationality_English</th>\n",
" <th>nationality_Irish</th>\n",
" <th>nationality_Other</th>\n",
" <th>nationality_Scottish</th>\n",
" <th>nationality_Welsh</th>\n",
" <th>ethnicity_Other</th>\n",
" <th>ethnicity_White</th>\n",
" <th>gross_income_Under 2,600</th>\n",
" <th>gross_income_2,600 to 5,200</th>\n",
" <th>gross_income_5,200 to 10,400</th>\n",
" <th>gross_income_10,400 to 15,600</th>\n",
" <th>gross_income_15,600 to 20,800</th>\n",
" <th>gross_income_20,800 to 28,600</th>\n",
" <th>gross_income_28,600 to 36,400</th>\n",
" <th>gross_income_Above 36,400</th>\n",
" <th>region_London</th>\n",
" <th>region_Midlands & East Anglia</th>\n",
" <th>region_Scotland</th>\n",
" <th>region_South East</th>\n",
" <th>region_South West</th>\n",
" <th>region_The North</th>\n",
" <th>region_Wales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>770</th>\n",
" <td>1.0</td>\n",
" <td>0.500000</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1367</th>\n",
" <td>0.0</td>\n",
" <td>0.083333</td>\n",
" <td>0.036364</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>571</th>\n",
" <td>1.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1050</th>\n",
" <td>0.0</td>\n",
" <td>0.083333</td>\n",
" <td>0.090909</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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"</table>\n",
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],
"text/plain": [
" gender amt_weekends amt_weekdays age_Young age_Middle-Aged age_Old \\\n",
"770 1.0 0.500000 0.545455 0.0 0.0 1.0 \n",
"541 0.0 0.000000 0.000000 1.0 0.0 0.0 \n",
"1367 0.0 0.083333 0.036364 1.0 0.0 0.0 \n",
"571 1.0 0.000000 0.000000 1.0 0.0 0.0 \n",
"1050 0.0 0.083333 0.090909 1.0 0.0 0.0 \n",
"\n",
" marital_status_Divorced marital_status_Married \\\n",
"770 0.0 0.0 \n",
"541 0.0 1.0 \n",
"1367 0.0 1.0 \n",
"571 0.0 0.0 \n",
"1050 0.0 0.0 \n",
"\n",
" marital_status_Separated marital_status_Single marital_status_Widowed \\\n",
"770 0.0 0.0 1.0 \n",
"541 0.0 0.0 0.0 \n",
"1367 0.0 0.0 0.0 \n",
"571 0.0 1.0 0.0 \n",
"1050 0.0 1.0 0.0 \n",
"\n",
" highest_qualification_No Qualification highest_qualification_GCSE/CSE \\\n",
"770 1.0 0.0 \n",
"541 0.0 0.0 \n",
"1367 0.0 0.0 \n",
"571 1.0 0.0 \n",
"1050 0.0 0.0 \n",
"\n",
" highest_qualification_GCSE/O Level highest_qualification_ONC/BTEC \\\n",
"770 0.0 0.0 \n",
"541 0.0 0.0 \n",
"1367 0.0 0.0 \n",
"571 0.0 0.0 \n",
"1050 0.0 0.0 \n",
"\n",
" highest_qualification_A Levels highest_qualification_Other/Sub Degree \\\n",
"770 0.0 0.0 \n",
"541 0.0 0.0 \n",
"1367 1.0 0.0 \n",
"571 0.0 0.0 \n",
"1050 1.0 0.0 \n",
"\n",
" highest_qualification_Degree highest_qualification_Higher/Sub Degree \\\n",
"770 0.0 0.0 \n",
"541 1.0 0.0 \n",
"1367 0.0 0.0 \n",
"571 0.0 0.0 \n",
"1050 0.0 0.0 \n",
"\n",
" nationality_British nationality_English nationality_Irish \\\n",
"770 0.0 1.0 0.0 \n",
"541 0.0 1.0 0.0 \n",
"1367 1.0 0.0 0.0 \n",
"571 0.0 1.0 0.0 \n",
"1050 1.0 0.0 0.0 \n",
"\n",
" nationality_Other nationality_Scottish nationality_Welsh \\\n",
"770 0.0 0.0 0.0 \n",
"541 0.0 0.0 0.0 \n",
"1367 0.0 0.0 0.0 \n",
"571 0.0 0.0 0.0 \n",
"1050 0.0 0.0 0.0 \n",
"\n",
" ethnicity_Other ethnicity_White gross_income_Under 2,600 \\\n",
"770 0.0 1.0 0.0 \n",
"541 0.0 1.0 0.0 \n",
"1367 0.0 1.0 0.0 \n",
"571 0.0 1.0 0.0 \n",
"1050 0.0 1.0 1.0 \n",
"\n",
" gross_income_2,600 to 5,200 gross_income_5,200 to 10,400 \\\n",
"770 0.0 0.0 \n",
"541 0.0 1.0 \n",
"1367 1.0 0.0 \n",
"571 0.0 1.0 \n",
"1050 0.0 0.0 \n",
"\n",
" gross_income_10,400 to 15,600 gross_income_15,600 to 20,800 \\\n",
"770 0.0 0.0 \n",
"541 0.0 0.0 \n",
"1367 0.0 0.0 \n",
"571 0.0 0.0 \n",
"1050 0.0 0.0 \n",
"\n",
" gross_income_20,800 to 28,600 gross_income_28,600 to 36,400 \\\n",
"770 1.0 0.0 \n",
"541 0.0 0.0 \n",
"1367 0.0 0.0 \n",
"571 0.0 0.0 \n",
"1050 0.0 0.0 \n",
"\n",
" gross_income_Above 36,400 region_London region_Midlands & East Anglia \\\n",
"770 0.0 0.0 1.0 \n",
"541 0.0 0.0 1.0 \n",
"1367 0.0 0.0 0.0 \n",
"571 0.0 0.0 1.0 \n",
"1050 0.0 0.0 0.0 \n",
"\n",
" region_Scotland region_South East region_South West region_The North \\\n",
"770 0.0 0.0 0.0 0.0 \n",
"541 0.0 0.0 0.0 0.0 \n",
"1367 0.0 0.0 0.0 0.0 \n",
"571 0.0 0.0 0.0 0.0 \n",
"1050 0.0 1.0 0.0 0.0 \n",
"\n",
" region_Wales \n",
"770 0.0 \n",
"541 0.0 \n",
"1367 1.0 \n",
"571 0.0 \n",
"1050 0.0 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Min-max scaling\n",
"Data_copy = Data.copy()\n",
"\n",
"Data_scaler = preprocessing.MinMaxScaler()\n",
"Data_scaler.fit(Data)\n",
"Data = Data_scaler.fit_transform(Data)\n",
"\n",
"pd.DataFrame(Data, columns=Data_copy.columns).sample(5, random_state=999)"
]
},
{
"cell_type": "markdown",
"id": "67e901ea",
"metadata": {},
"source": [
"***\n",
"# 3.0 Predictive Modeling <a class=\"anchor\" id=\"3.0\"></a>\n",
"\n",
"## 3.1 Feature Selection <a class=\"anchor\" id=\"3.1\"></a>\n",
"\n",
"Here we will select the best features within the dataset by assessing and comparing our performance of the classifiers using all features. We will compare the F-Score, Random Forest Importance (RFI), and spFSR feature selection methods and compare the performance amongst all methods to select the best features.\n",
"\n",
"### 3.1.1 Full Set of Features <a class=\"anchor\" id=\"3.1.1\"></a>\n",
"\n",
"Here we will assess the performance using all features within the dataset by using the stratified 5-fold cross-validation with 3 repetitions, and the random_state is set to 999 for future references for analysis."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "60512c39",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1. , 0.99679487, 1. , 1. , 1. ,\n",
" 0.99680511, 1. , 1. , 1. , 1. ,\n",
" 1. , 0.99679487, 1. , 1. , 1. ])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Performance with Full Set of Features\n",
"clf = DecisionTreeClassifier(random_state=999)\n",
"\n",
"cv_method = RepeatedStratifiedKFold(n_splits=5, \n",
" n_repeats=3,\n",
" random_state=999)\n",
"\n",
"scoring_metric = 'accuracy'\n",
"\n",
"cv_results_full = cross_val_score(estimator=clf,\n",
" X=Data,\n",
" y=target, \n",
" cv=cv_method, \n",
" scoring=scoring_metric)\n",
"\n",
"cv_results_full"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "5d3283ee",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.999"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#CV mean results\n",
"cv_results_full.mean().round(3)"
]
},
{
"cell_type": "markdown",
"id": "93cb12f5",
"metadata": {},
"source": [
"### 3.1.2 F-Score <a class=\"anchor\" id=\"3.1.2\"></a>\n",
"\n",
"In this section we will use the F-Score feature selection method as it filters the features and measures the relationship between each descriptive features and the target feature through the F-Score distribution."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "851acedd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 5, 9, 7, 3, 17, 13, 12, 6])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#F-score\n",
"num_features = 10\n",
"fs_fit_fscore = fs.SelectKBest(fs.f_classif, k=num_features)\n",
"fs_fit_fscore.fit_transform(Data, target)\n",
"fs_indices_fscore = np.argsort(np.nan_to_num(fs_fit_fscore.scores_))[::-1][0:num_features]\n",
"fs_indices_fscore"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "a22e4820",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['amt_weekends', 'amt_weekdays', 'age_Old', 'marital_status_Single',\n",
" 'marital_status_Married', 'age_Young',\n",
" 'highest_qualification_Degree',\n",
" 'highest_qualification_GCSE/O Level',\n",
" 'highest_qualification_GCSE/CSE', 'marital_status_Divorced'],\n",
" dtype=object)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Best features for F-score ranked\n",
"best_features_fscore = Data_copy.columns[fs_indices_fscore].values\n",
"best_features_fscore"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "425bbc76",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3240.4118294 , 2451.33850509, 58.21575874, 45.53830706,\n",
" 40.63115417, 37.99583642, 16.77980932, 16.0445205 ,\n",
" 10.09145764, 9.22759182])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Score of best features for F-score\n",
"feature_importances_fscore = fs_fit_fscore.scores_[fs_indices_fscore]\n",
"feature_importances_fscore"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "fcf03a35",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 459,
"width": 793
}
},
"output_type": "display_data"
}
],
"source": [
"#Visualize features\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline \n",
"%config InlineBackend.figure_format = 'retina'\n",
"plt.style.use(\"ggplot\")\n",
"\n",
"def plot_imp(best_features, scores, method_name): \n",
" plt.barh(best_features, scores)\n",
" plt.title(method_name + ' Feature Importances')\n",
" plt.xlabel(\"Importance\")\n",
" plt.ylabel(\"Features\")\n",
" plt.show()\n",
"\n",
"plot_imp(best_features_fscore, feature_importances_fscore, 'Figure 1: F-Score')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "6c0261b9",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(1561, 10)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Shape of F-score\n",
"Data[:, fs_indices_fscore].shape"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "3cb4cf0b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.999"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#CV mean results for F score\n",
"cv_results_fscore = cross_val_score(estimator=clf,\n",
" X=Data[:, fs_indices_fscore],\n",
" y=target, \n",
" cv=cv_method, \n",
" scoring=scoring_metric)\n",
"cv_results_fscore.mean().round(3)"
]
},
{
"cell_type": "markdown",
"id": "3f847eb5",
"metadata": {},
"source": [
"From ```Figure 1``` we can observe that for the F-Score Feature Selection, the most important feature is \"age\", and the least important feature is \"region\". Furthermore, we can observe that the average cross-validation is consistent with our performance with the full set of features.\n",
"\n",
"### 3.1.3 Random Forest Importance (RFI) <a class=\"anchor\" id=\"3.1.3\"></a>\n",
"\n",
"Here we will select the best features using the Random Forest Importance (RFI), which adds additional randomness to the model and searches for the best features amongst the subset of features."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "c8ab4625",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['amt_weekends', 'amt_weekdays', 'age_Old', 'marital_status_Single',\n",
" 'marital_status_Married', 'age_Young',\n",
" 'highest_qualification_GCSE/O Level', 'gender',\n",
" 'highest_qualification_Degree', 'gross_income_5,200 to 10,400'],\n",
" dtype=object)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Random Forest Importance\n",
"num_features = 10 \n",
"model_rfi = RandomForestClassifier(n_estimators=100)\n",
"model_rfi.fit(Data, target)\n",
"fs_indices_rfi = np.argsort(model_rfi.feature_importances_)[::-1][0:num_features]\n",
"\n",
"#Best features for RFI ranked\n",
"best_features_rfi = Data_copy.columns[fs_indices_rfi].values\n",
"best_features_rfi"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "4974ae75",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.51768691, 0.39560584, 0.00977245, 0.00670413, 0.00559715,\n",
" 0.00460095, 0.00340457, 0.00327924, 0.00298661, 0.00258061])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Scores for RFI features\n",
"feature_importances_rfi = model_rfi.feature_importances_[fs_indices_rfi]\n",
"feature_importances_rfi"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "a2347ee1",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 459,
"width": 793
}
},
"output_type": "display_data"
}
],
"source": [
"#Visualize features\n",
"plot_imp(best_features_rfi, feature_importances_rfi, 'Figure 2: Random Forest')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "9739ee84",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.999"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#CV mean results for RFI\n",
"cv_results_rfi = cross_val_score(estimator=clf,\n",
" X=Data[:, fs_indices_rfi],\n",
" y=target, \n",
" cv=cv_method, \n",
" scoring=scoring_metric)\n",
"cv_results_rfi.mean().round(3)"
]
},
{
"cell_type": "markdown",
"id": "e0a7d1f5",
"metadata": {},
"source": [
"In ```Figure 2``` we can observe that the most important feature with the highest significant importance is amt_weekends and amt_weekdays. Interestingly, the other features weren't as significant.\n",
"\n",
"### 3.1.4 spFSR <a class=\"anchor\" id=\"3.1.4\"></a>\n",
"\n",
"spSFR is a feature selection method using the binary stochastic approximation to select the best features in the dataset."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "30885172",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"SpFSR-INFO: Wrapper: DecisionTreeClassifier(random_state=999)\n",
"SpFSR-INFO: Hot start: True\n",
"SpFSR-INFO: Hot start range: 0.2\n",
"SpFSR-INFO: Feature weighting: False\n",
"SpFSR-INFO: Scoring metric: accuracy\n",
"SpFSR-INFO: Number of jobs: 1\n",
"SpFSR-INFO: Number of observations in the dataset: 1561\n",
"SpFSR-INFO: Number of observations used: 1561\n",
"SpFSR-INFO: Number of features available: 42\n",
"SpFSR-INFO: Number of features to select: 10\n",
"SpFSR-INFO: iter_no: 0, num_ft: 10, value: 0.999, st_dev: 0.001, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 0, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 1, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 2, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 3, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 4, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 5, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 6, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 7, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 8, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 9, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 10, num_ft: 10, value: 0.999, st_dev: 0.001, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 10, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 11, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 12, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 13, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 14, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 15, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 16, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 17, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 18, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 19, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 20, num_ft: 10, value: 0.999, st_dev: 0.001, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 20, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 21, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 22, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 23, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 24, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 25, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 26, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 27, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 28, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 29, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 30, num_ft: 10, value: 0.999, st_dev: 0.001, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 30, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 31, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 32, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 33, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 34, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 35, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 36, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 37, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 38, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 39, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 40, num_ft: 10, value: 0.999, st_dev: 0.001, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 40, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 41, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 42, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 43, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 44, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 45, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 46, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 47, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 48, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 49, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 50, num_ft: 10, value: 0.999, st_dev: 0.002, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 50, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 51, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 52, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 53, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 54, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 55, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 56, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 57, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 58, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 59, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 60, num_ft: 10, value: 0.999, st_dev: 0.001, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 60, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 61, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 62, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 63, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 64, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 65, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 66, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 67, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 68, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 69, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 70, num_ft: 10, value: 0.999, st_dev: 0.003, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 70, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 71, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 72, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 73, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 74, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 75, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 76, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 77, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 78, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 79, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 80, num_ft: 10, value: 0.999, st_dev: 0.003, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 80, same feature stall limit reached, initializing search...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"SpFSR-INFO: ===> iter_no: 81, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 82, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 83, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 84, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 85, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 86, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 87, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 88, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 89, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 90, num_ft: 10, value: 0.999, st_dev: 0.001, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 90, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 91, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 92, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 93, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 94, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 95, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 96, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 97, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 98, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: ===> iter_no: 99, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: iter_no: 100, num_ft: 10, value: 0.999, st_dev: 0.002, best: 0.999 @ iter_no 0\n",
"SpFSR-INFO: ===> iter_no: 100, same feature stall limit reached, initializing search...\n",
"SpFSR-INFO: SpFSR completed in 0.07 minutes.\n",
"SpFSR-INFO: Best value = 0.999 with 10 features and 100 total iterations.\n",
"\n"
]
}
],
"source": [
"#spFSR\n",
"from spFSR import SpFSR\n",
"\n",
"sp_engine = SpFSR(x=Data, y=target, pred_type='c', wrapper=clf, scoring='accuracy')\n",
"\n",
"np.random.seed(999)\n",
"sp_output = sp_engine.run(num_features=num_features).results"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "78328440",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 5, 9, 7, 3, 17, 6, 4, 13]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Best features for spFSR ranked\n",
"fs_indices_spfsr = sp_output.get('selected_features')\n",
"fs_indices_spfsr"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "d63e0f4a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['amt_weekends', 'amt_weekdays', 'age_Old', 'marital_status_Single',\n",
" 'marital_status_Married', 'age_Young',\n",
" 'highest_qualification_Degree', 'marital_status_Divorced',\n",
" 'age_Middle-Aged', 'highest_qualification_GCSE/O Level'],\n",
" dtype=object)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Best features for spFSR ranked cont.\n",
"best_features_spfsr = Data_copy.columns[fs_indices_spfsr].values\n",
"best_features_spfsr"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "59706c92",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.1 , 0.08665648, -0.09739153, -0.09761024, -0.09778658,\n",
" -0.09834239, -0.09895688, -0.09909979, -0.09910913, -0.09922988])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Scores of features for spFSR\n",
"feature_importances_spfsr = sp_output.get('selected_ft_importance')\n",
"feature_importances_spfsr"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "3d099118",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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4eXmhsbERycnJSE1NFcvm5+fjgQcewLlz57QOYVdQUICpU6cqXGvZMY0YMQKenp6wsbFBSUkJLl68qPA7U5P2vN9+9913WLNmjcJ7VlZWCAoKQu/evWFtbY3q6mrcvHkTKSkp4v2HqF11ahqFiIiIiKiTdHaPjEceeUShvIeHh7B7925BIpEolCstLRVeeuklsVzLJ241kS/XUU8G29vbi08q7tq1SxwSS6a+vl7Iy8sTl+vq6pSGw5g0aZIQHR2tcn/79u0TPD09Fcr//PPPOh+boVRXVwspKSnC5s2bhSFDhijE06NHD52e2hWE9u+RMWrUKIX6X3/9daG4uFht+cuXLwsff/yx4Ofnp3OPDGdnZ/H1Qw89JOTn5yuUlUgkwu7duwV3d3eF7e69916NsbdXj4EXX3xR6bw7OzsL//73v4UzZ84ofQdbq7U9Mh5++GGF7e666y6DxNNSe5xfqVQqzJ49W6HeoUOHCn/99ZfKJ8AjIiKEgIAAhfJr1qzRuI/FixcLQ4YMET7//HONQ0ZlZGQIzz77rMIT+u7u7mqHNsvIyBAyMjKETz/9VCGeX3/9VVzX8qeoqEipHt53W6etPTJk96E5c+ao7A23c+dOwcrKSixvYmIixMTECD179hQACOPGjVN5zztx4oTg4uKisK9Dhw5pjE2+R4KdnZ1gYWEhABBGjBghJCQkKJWPj48Xhg8frrCP3r17ax1S6IknnlDYxtHRUfj222+FhoYGhXIVFRXC0qVLlYYG+u6773Q+Dmtra7Fn36xZs4TLly8rlc/NzRUaGxuF/Px8ISMjQ4iMjFTY3yuvvKL2u5STk6NUX0ZGhmBtbS08+eSTwsGDB4Xa2lqVcTY2Ngq//fab4OPjo7C/Tz75ROPxCYLi91X295Wpqanw9ttvq+xV8Oeffyr8zgMgbNq0SeM+mpqahMmTJyts4+DgIKxZs0Ztz4W0tDTho48+Enr37q22R0Z73m+bmpqE7t27i+XMzc2FtWvXqv1MSqVSISEhQVi+fLnQo0cP9sigdsNEBhERERHdkVo2muj707KRSZ9ERmxsrFIDzMWLFzXGu2bNGpVxaNIZDWqyxjRdhg4SBEF46623FLZ9+umntTYk5+bmKjSq9erVS6nhxtDk/0Ov6ScoKEi4evWqzvW23N6QiYyCggKFup966imdt5VIJEJ9fb3a9aqO/YUXXtBYZ0pKitCtWzeFbf7++2+15Vt+DvX9kc0b0FJubq7GIXgcHByEqVOnCu+++65w8ODBVjfItCaRoer7pO945rpqj/O7ZcsWhTKzZ88Wbt26pTGO8vJyYdiwYeI21tbWKhMEMromCWW+/PJLvRod1c0/oSved1unrYkMAMLDDz+s8Tg2bNig9LsXgDB16lSNw+wcPHhQYbuFCxdqjE3V0EpjxozROD9QVVWVMHLkSIVt3nrrLbXlWyYJ7OzstM5/sWnTJqV7naa5kFQdh7ZzLK+1c4fJ1NbWCoWFhTqXLywsVBiu09PTU+tcDS2Pz8TERNi1a5fGbY4cOaKwzejRozWWb5kc7dGjh3D+/HmdjqmhoUEoLS1Vua4977ct/0798MMPdYpXEJoTp4Z6IICoJfX9NYmIiIiIqF1s3rxZYfnjjz/G4MGDNW7z9ttvIzg4uD3DMpgNGzbA19dXa7nKykqF4TaCgoKwdetWjcPKAICnpye2bt0qLufl5eG3335rfcAGMHr0aPz666+Ij49H//79OzUWmezsbIXl++67T+dtTU1NtQ6VIa9///744osvNJYJCAjAJ598ovDexo0bdd6HoXh6emLv3r1qh2uqqqrC8ePHsWbNGsydO1ccpufzzz9HUVGRQWORSCQoLCzE4cOH8cADD+DJJ59UWH/XXXfhnnvuMeg+24tEIlG4vr169cKuXbtgbW2tcTsnJyf8/PPPMDExAQDU1dXh22+/VVve29tbr7heeeUVjBo1SlzetWuXXtt3FXfifVder169tB7HM888A0dHR3G5vLwctra22L59O6ysrNRud/fdd2PAgAHi8smTJ/WKzdLSEtu3b4ednZ3aMvb29ti+fTssLCzE97777js0NDSoLC8/xBMArFq1CmPHjtUYx5IlSxTuJ1VVVdi2bZsuhwCg+Rxv2bJF62fFUGxsbODu7q5zeXd3d6xfv15cvnHjBqKiovTa57PPPouFCxdqLDNjxgyMGzdOXE5ISEBNTY3KsvX19fj888/FZRMTE+zYsQOBgYE6xWNhYaHyd1V732/b8veDpaVlh31G6M7DTxYRERERUQeSSqXYu3evuOzm5oannnpK63YmJiZYunRpe4ZmEH5+fjr/h/fnn39WGFN55cqVOo9nfdddd8HPz09cPnTokH6BGlh8fDw2bNiAP//8U6/thOZe8uKPj49P+wQIiPNCtIdly5bplPh46qmn4OXlJS7/9ddfKCsra7e41Jk8eTLi4+Mxd+5crWWlUikSEhKwdOlS+Pj4YNWqVWhsbNR7n08++SRMTEwUfszNzdG9e3fMmTMHe/bsUSg/ffp07NixQ+/9dJYjR44gPT1dXF62bJnOc/oMHToUYWFh4rKhv8/y1zk+Ph4SicSg9Xe2O/W+K+/ZZ5+Fvb29xjKWlpaYMGGCwnuLFi3Saa6eKVOmiK8LCwsV5g3RZuHChToluAcNGoQFCxaIy8XFxThy5IhSuZqaGuzbt09cdnNzw0svvaRTLKtWrVJY/vnnn3XaDgCee+45o5+na+bMmQpJqTNnzui1/bJly3QqN2fOHPG1VCpVmEtF3sGDBxU+K/fdd5/Cva61Ovp+255/PxDpg5N9ExERERGh+WlTfZ7cc3Nza9V+Ll26hKqqKnH57rvvVngCU5O7774blpaWap/QNAZ33323+KSfNvITptra2uo9Ce+kSZPE/8hHR0frta2+zp49KzZ+SqVSVFRUIC0tDeHh4fj5559RVVWF6OhoREdHY9GiRfjvf/+rV4+G9tC3b1+YmpqKE6auW7dO7GFgSKamppg/f77OZRcsWIAvv/wSQPNTpWfPnsWMGTO0bjt27Fjs3LlT57h69Oihcb2fnx8OHDiA8+fPY9u2bdi3b5/SU6gt1dbW4v3330d4eDj++OMPODs76xyProYPH45XX30Vjz32mM7fJUNo6/ltOQGyfIOsLiZNmoTw8HAAzcmG+vp6jU/JtySRSFBVVYWqqiqlRIV8PTU1NcjNzUWfPn30is+Y3a73XX3ocg8Bmu+L8qZPn96q7YqKirTeY2S0PeEv78EHH8Svv/4qLp8+fVop4Xr27FlxcnIAmD9/vs5/RwwdOhSDBw9GSkoKACApKQm1tbWwtbXVuq0uid+OUltbi6qqKtTV1UEQBIV13bp1E5MHV65c0blO2UTiuhg4cKDCsrreei3vi88884zO8WjS3vfblom3FStW4O+//4aNjU0rIyYyDCYyiIiIiIgAmJubt+vT8DItn9obMWKEzttaWVkhICAA58+fN3BUhhMUFKRzWflGMF9fX+Tl5em1L/n/UGdnZ0Mikej8ZLG+evfurfTe8OHDsWDBAqxatQpPPvkkDh48CADYuXMnGhsblZ6w72jdunXD9OnTxSd6r1y5ggEDBuD//u//8MADD2DEiBEGaSjv16+fXg36o0ePVlhOSEjQqRHS2tq6Xb6jQUFB+Oqrr/DVV18hPT0d0dHRiI+PF39UJQ5PnjyJxx57DAcOHDB4sqG8vByenp4dmsQA2n5+5b/PTk5OaGxsRGZmps7byzfENjQ0IC8vT+NQSdXV1fjjjz+wb98+JCUlIT09XalBU52ysrLbKpFxu9539dGyYVkd+aGlACgMGaXPdvK9WrRpec/Tp2xCQoJSmZbvaRtSqqXg4GAxkdHU1ISkpCSFoZJUMTMzw5AhQ/TajyElJSVhx44diIqKQnJyssIDIZro0+Nv0KBBOpd1cnJSWFb3eZDvEWJqaqrUI6i12vt+O2zYMAwaNAiXL18GAJw6dQoDBgzAs88+i/vvvx8BAQFtPwiiVmAig4iIiIioA5WUlCgsyw+zo4vevXsbdSJD1/GsJRKJwnALKSkpOo3vro4gCCgvLzd4bwNduLq6Yu/evZg1axaOHz8OAPj999+xc+dOLFq0qMPjkffll18iODgYFRUVAJo/f+vWrcO6devQrVs3TJw4ERMnTkRoaChGjhzZqgZJf39/vcq3fLK5sLBQ7322Fz8/P/j5+eGxxx4D0PzU76FDh/DNN98gMjJSoeyff/6JP/74A/fff79OdX/66acKT81KpVLk5+cjPT0d33//PSIiIgAAGRkZmDVrFvbu3WtUT0Brk5ubK76uqKho0/cZAEpLS9XW8eOPP2LZsmWtnrNEn0boruBOvO+21LJhWR1zc8VmsNZup+vwcra2tjr33ACa5zqwsbHBrVu3AKi+P7b83Le8p2rT8ml7Xb5Hzs7OOvf6MKTc3Fy8/PLLCkNp6UOf77qunwUASudC3edB/vvWu3dvgw3N1d73WxMTE2zZsgXTpk0Tjy0nJwfLly/H8uXL0aNHD0ycOBGTJk1CaGgohg0b1qb9E+mKc2QQEREREXUgWYOyjL7/qW35VKix0TZGuUxZWZnOT0/rqrq62qD16cPc3BzffPONwnvyk452loEDByImJkZhsmOZsrIyHDx4EG+++SbGjh0LT09PvP7667hx44Ze+9D3M9mysai8vFyv7TuSra0tHnjgAZw6dQqbN29W6iXx9ddf61yXm5sbfHx8xB8/Pz9MmDABjz32GE6ePIldu3aJjaVNTU145JFHkJqaatDjaU+lpaUGrU/d93nFihV48skn2zTxumy4tdvFnXrfldfayYXbe1Li1vzOlr9Hqro/tnyvrfdgXXot6PoZM6TMzExMnDix1UkMQL/vent8FuQfXjHkUIQdcb+dPHkywsPDVc7vUlBQgD179uCVV15BYGAgfH19sWLFik6Z84ruLExkEBERERF1oJbzJug7aXB9fb0hw+k0rZksWRtDN9Dpa9CgQQpDb8THxxvFf+oDAgIQFxeHI0eO4NFHH1X7dPDNmzfxxRdfoF+/fvjuu+90rl/fIZA6+zq11vPPP49///vfCu/FxMSgtrbWIPUvXLgQa9euFZerqqrw9NNPG6TujmDo77Sqz0lERAQ+/PBDhfdGjx6Nzz77DBEREcjIyEBVVRUaGhogCIL4s23bNoPG1lXdjvddY9eaIeL0Pae36z34qaeeQlZWlrhsb2+P5557Drt27UJSUhKKi4tRW1sLqVSq8H031mHjDDlcYEfcbwFg4sSJSElJwZ49ezB//nx069ZNZbnMzEx8+OGH8Pf3F4fZJGoPTGQQEREREXWglv8J1LehuyOeXu+Ip5VdXFwUlu+55x6FhojW/HTEHCfa9OvXT3wtCIJeY1a3JxMTE8yYMQPbt29Hfn4+rl27hm3btuHJJ59Er169FMreunULzzzzjMKEs5q07GWkTcuhPtpjwuz28sorrygsNzY2KjS0tdXrr7+O4cOHi8unTp3Cb7/9ZrD625P8dzowMLDN3+fQ0FClfaxatUphef369YiLi8O///1vTJ48GT4+PrC3t1ca9kXX8fQ7E++7tyd974+A4j1S1f2x5XttvQera5zuTFFRUQoTWgcEBODKlSvYsmULFi5ciGHDhsHV1RU2NjZKCQJj+r7Lf+cM+WBDR9xvZczNzTF//nzs2bMHxcXFuHDhArZs2YKHHnpI6Z5SVlaG+fPnKw3FSGQoTGQQEREREXWglpNGyybc1JU+5eXnO2hqatJ5u45IllhZWSkMh3H9+vV232dHaNmAaqw9aPr164cnnngCP/zwA3JzcxEeHq40CenSpUshkUi01pWWlqbXvlsOl+Th4aHX9p3Jy8tLaT4AQzZOmZmZKfTKAID333+/SwyFJH8d09LS2mUIo1OnTonLU6dOxWuvvabTtvLj1Lc33ndJXm1trV6fv7y8PHF+DED1/bHlnCj6DkF37do1jfUZg0OHDiksb926FZ6enlq3q6urM6rhCuV7QObk5BhsKLb2vt+qY2pqiqFDh+K5557Djh07UFhYiP3792Pw4MFimcbGRixdurRD4qE7DxMZREREREQdaPTo0QrL8g1z2qSmpiI/P1/n8vINVvr8x/7SpUs6l22L4OBg8fXly5f1OjZj1XJ+ia7QSG9iYoKwsDD8888/GDp0qPh+Xl4eEhIStG5//fp1vT5fZ8+eVVgeMWKEztsag5bjqBt63ppZs2Zh7Nix4vKVK1ewa9cug+6jPch/n6urq3HmzBmD1p+VlaUwnMqsWbN03jY2Nlbnsm0d/oX3XWqp5T1Pn7Kq7o8jR45UWNb3uyb/fTA3N2+3iZrb8l2ST87Y29tj4sSJOm139uxZo0r8jhs3TnwtlUoRFRVlkHrb+36rKzMzM8ybNw8nTpxAz549xffj4uJw8+bNTomJbm9MZBARERERdSAXFxcEBgaKy9HR0To/Tfnjjz/qtS/5pyyvXLmi83ZHjhzRaz+tNW3aNPG1IAj473//2yH7bS9VVVUKjVDW1tY6PUFqLGxsbLBw4UKF93QZGksqleL333/XaR9SqVRhqCQzMzOMGTNGrzg7U0lJCYqLixXek2+8MZS3335bYXnNmjVGP669/PcZ0P9+pU3L4XN0TSDl5ubqlTC2srJSWG5oaNB5W4D3XVK2e/duncu2TFrKN4TLjB49Gubm5uLy77//rvOcCcnJyQo9O4cNGwY7Ozud49NHW75L8t93BwcHnbf75ZdfdC7bEcLCwhSW//Of/xik3va+3+rL3d0dc+bMUXjPkMMuEskwkUFERERE1MHkJ/AVBEGn4VGysrLwxRdf6LUf+YRJWlqaTsOIREdHG+yJQW0ef/xx2NjYiMuffPIJcnNzO2Tf7eHTTz9VaKiZOnWqUkOOsWvZONxycnp1PvnkE50a0n744QeFXiuzZ8/u0PHZq6urkZ6e3urtf/zxR4WEwpAhQ5SGmjKEefPmKUwcn5KSgr179xp8P4Y0b948hflWfvjhB5w/f95g9becF6Dl8DjqvP/++3oN8eTk5KSwrO+wVLzvUku7d+/W6XNw5coV7NmzR1x2dXXFzJkzlcrZ2dnhnnvuEZeLi4uxYcMGnWJZvny5wvKjjz6q03at0Zbvkvz3vbCwUKfeTVevXsVPP/2k8z46wty5cxXui/v27VOY+6O12vt+2xqt/fuBSB9MZBARERERdbDFixcrPMX9559/4tVXX1U7H0FOTg5mzZqF2tpavfbT8knAlhPltpSXl4fHHntMr320Rffu3fH888+Ly2VlZbj77ruRl5enVz2RkZE6N2rq4vPPP9d7HOvdu3crzW3w3HPPad3OxMRE4ceQk4PHxsbi6NGjOpeXSqVKTwMPGDBAp22vXbumNSF3+fJlLFu2TOG9F154Qef4DKG4uBgDBw7Ec889p/e5joqKwooVKxTee/DBBw0Y3f+YmJjgrbfeUnhv9erV7bIvQ7GyssKbb74pLjc2NmLevHl69UoAgKSkJMTHxyu97+/vD1tbW3H5p59+QklJica6tmzZgm3btum1/5afeX0bHXnfpZYaGhrw6KOPoqamRm2Z6upqPPbYYwoJ4aefflptMvxf//qXwvLy5cu1DmG1efNm7N+/X1y2t7fHk08+qcshtIqNjY3CvGCnTp3Sad4lAArDHEokEnz99dcayxcVFeGBBx5QmF/EGFhaWuL1118Xl6VSKR5++GFcuHBBp+0bGhpUzsPU3vfbI0eO6DUkX21tLfbt2ycuW1hYwM/PT69YiHTBRAYRERERUQdzdHTExo0bFd776quvMGrUKGzduhUJCQm4evUqTpw4gTfffBODBw/GlStX4OLigilTpui8n0WLFik8ebt9+3b8+9//VvqPvlQqxd69exEcHIyMjAylJ5/b0+rVqxEUFCQuJyUlISgoCF999RUqKyvVbnf9+nV89tlnGDVqFCZPntymp+xbWrVqFXx9ffHqq68iNjZW49PciYmJeOyxx/Dggw8qNNDcddddmDt3rsFiao0rV65g5syZGDJkCNauXYvLly+rHZ4oPT0d999/P2JiYsT3RowYgUGDBmndj+zzsnHjRjzyyCNKT91KpVLs2bMHoaGhCg0yc+fOxezZs1txZG3T2NiIb7/9Fn5+fpg+fTq2b9+u8Yn0rKwsvPXWW5g6dapCQ6Snp6fOk023xqJFixQags6fP4+DBw+22/4M4aWXXlK4pjk5ORg1ahRWrVqFoqIitdvl5ORg06ZNCA0NRVBQkMqGNSsrK9x9993iclFREaZPn46LFy8qlb158yaWLFmCJUuWAADc3Nx0PgY/Pz94eXmJyz/99BPeeOMNREZGIjU1FZmZmeJPy2HGAN53SZGdnR0sLCwQFxeHkJAQnDt3TqlMYmIiQkJCFD73np6eePfdd9XWO3nyZIUEWE1NDaZPn47vv/9eqXdcZWUlli1bhpdeeknh/fXr17f7527y5Mni67S0NCxYsAB//fUXrl69qvBdankPvv/++xXm2Pjggw/wySefoL6+XqGcVCrFgQMHEBwcjOTkZFhbW8Pe3r5dj0lfr776KkJCQsTlgoICTJw4ER999JHanibp6en46KOP4O/vj4iICJVl2vN+e/r0aYwbNw7BwcH48ssvkZGRoba+CxcuYObMmQpl5s6da/D5o4gAAAIRERER0R0oIyNDACD+9OnTx6D1rVixQus2q1evVthG04+pqamwb98+YfHixQrva7NmzRqluhwdHYVZs2YJDz/8sDB79mzB3d1dXOfm5ib88MMPCuW3bdumtv5t27YplD1x4oTuJ+3/y8zMFPr27asUp5mZmTBixAjhnnvuER599FHhnnvuESZMmCA4OzsrlT18+LDe+1XHyclJoW5ra2th5MiRwl133SU88sgjwgMPPCBMnTpV4bzJ/4wZM0aoqKjQaV8tt83IyDDYcbS8NgAEJycnYeLEicJ9990nPProo8K9994rBAQEKJWztLQU4uLidIr78ccfF6ZNm6Zw3caPHy8sWrRIuPvuu4VevXop1e/t7S3cuHFDY/wnTpxQ2CYkJKTN56Tl91T+p3fv3kJoaKiwYMEC4eGHHxbuuusuwd/fXzAxMVF5HjWdH0EQlL6rmr5H6mzevFmhjtGjR7fyyJW1x/kVBEEoKysTxowZo3TOTExMhMGDBwtz584VHn30UeG+++4TQkJCVH6PNm/erLLulJQUwdraWql8UFCQ8OCDDwoPPPCAMGbMGMHU1FRc17dvX+HLL7/U6z718ccf63RfXrx4scrted/Vn76/k/X9XSizYsWKVt1z9TnnISEhCsfx4YcfKmw7ZMgQYf78+cL8+fOFwYMHK51XKysr4dixY1pjqqioEIYNG6a0vaurqzB79mzhoYceEsLCwlR+Zx555BGt9bc8jtaIiYlReQ9t+aOq/scee0ypXLdu3YTZs2cLjzzyiDBnzhyhe/fuCus3bNgg9OnTR+f7mi7fZ1Va3j+13d/z8vKEQYMGKR2Pubm5MGbMGOH+++8XHnroIWHmzJmCp6enQpk//vhDbb3tdb9t+T2R3adCQ0OF+fPnC48++qgwd+5cwd/fX6mcs7OzkJmZqfO5JNIHExlEREREdEcyhkSGIAjC1q1bBUdHR43/wXd1dRWOHj0qCIIgPPjgg+L79vb2WutvbGwUFi5cqFOjXI8ePYTExES9/oNuiAY1QWj+z/i8efN0ilNVQ0BkZGSr9qtKy0SGrj8mJibCkiVLhKqqKp331bKO9k5k6PLj4uKitRGtZeNPaWmpysYUVT8+Pj5Camqq1vjbo6H95s2bgpeXV6vOi+xnxIgRwvnz57XuyxCJjLq6OqFnz54K9fz999+tOHJl7ZXIEITmuP/v//5PpwZMVd+jHTt2qK179+7dgqWlpU519evXT0hLS9P7PtXU1CQ88MADWutX1/DJ+67+budEhlQqFZ599lmdzqutra1w4MABnY+nrKxMCAsL0+vavfzyy4JEItFatyESGYIgCJ9//rlgZmamMSZV9dfU1AgTJkzQ+bg+/PBDQRAEo0xkCELztZo1a5be3zVNiQxBaJ/7rapEhi4/3t7eOv1+JGotDi1FRERERNSJnn32WVy9ehUfffQRgoOD4eHhAUtLS/Tu3RshISH45ptvkJqaiunTpwOAwjAELSfSVMXc3By//vorvvzyS7i7u6ssY29vj2effRZJSUkYPny4QY5LX87Ozti/fz9OnTqFu+++W2FoFlUsLS0RGhqKTz/9FDk5OZg4caLBYvn999/xr3/9C4MHD4apqfb/Mrm5ueGFF17AuXPnsGnTJqMZ1mL+/PnYsWMHHn74YYVJQdXp2bMn3njjDVy/fh1Tp07Va1/dunVDREQEVq5cqTD/izwHBwe8+uqrSE5Ohr+/v171G4qHhweys7MRGxuLd955BxMnTtRpQnZzc3PMmDEDO3bsQFxcnMKEzu3JyspKafgqbXMuGAMrKyv85z//wfnz5/HQQw9pHWLEzMwMY8eOxcqVK5GWloaHHnpIbdkHHngAUVFRmDRpktoyPXv2xFtvvYWEhIRWjdNuZmaG3bt3459//sFTTz2FwMBAdOvWDRYWFjptz/suyTMxMcHWrVvx888/Y9iwYSrLmJub45577sHFixf1GpbQ2dkZx48fx44dO9TWDTR/pqdOnYqYmBh8/fXXOv1uM5TXX38dFy5cwBtvvIHx48fD3d1dp/uura0tTpw4gffff1/t3zumpqaYOnUqjh8/rjSRubFxdnbG4cOH8ddff2Hy5MkwMzNTW9bMzAwTJkzA1q1bMWPGDI31tsf99qWXXsJ3332H++67T6eh+fz8/LB69Wpcvny5w34/0p3JRBDUDJJKRERERERGx8fHB1lZWQCA4cOHIzExUedtm5qaEBsbi5SUFJSWlsLZ2Rne3t4ICQkxmsZ3mfr6esTGxorj0NfV1cHe3h7u7u4YOHAgBg0apLXRzRAqKytx8eJFZGRkoLCwEDU1NbC0tISjoyPc3d0RFBTUaY3y+srOzsaVK1eQmZmJ8vJyNDQ0wN7eHt27d8fQoUMREBCgc+OW/Njlixcvxo8//iguSyQSREVFISMjAwUFBXB0dISvry/CwsJgbW1t6MNqs4aGBly/fh3Xrl1DXl4eqqqqIJVK4eDggG7dumHgwIEYMmSIUcbeVTQ1NSE+Ph7Xr19HSUkJampqYGdnBxcXFwwYMAABAQFwcHDQu9709HRER0eL87L07NkTvr6+GDduXIc21GrC++6dJzQ0VJzXoE+fPsjMzFRYf+HCBVy8eBE3btyAubk5vLy8EBoaqjbppQ9ZovbmzZuoqqqCi4sLevXqhUmTJqFbt25trr+z3Lp1CzExMbh8+TIqKirg4uKCnj17YuzYsWqT58auvLwcUVFRyMvLQ0lJCczMzODs7Ix+/fph+PDhrZ6/pD3ut6mpqbh69Sqys7NRUVEBiUQCBwcH9OrVC4GBgejXr1+rYiXSFxMZRERERERdRG5uLnr37i0uP/XUU/j+++87MSK6U2lKZBAR3cm0JTKIiKh1jOMRBSIiIiIi0qpl0mLMmDGdFAkREREREVHHYSKDiIiIiKgLSE1NxaeffiouW1paYv78+Z0YERERERERUcdgIoOIiIiIqJM89dRTOHbsmNZy58+fx9SpU1FTUyO+9+CDD+o0ASMREREREVFXZ97ZARARERER3ani4uKwbds2DBgwAAsWLMDYsWPh7e0NOzs7lJeX49KlS/jzzz/x+++/QyqVitu5ubnhs88+68TIiYiIiIiIOg4TGUREREREnezq1atYs2aNTmWdnJywZ88eeHh4tHNURERERERExoFDSxERERERdRJXV1e9yoeEhCAmJgYhISHtFBEREREREZHxYY8MIiIiIqJOcvLkSZw+fRrHjh3DmTNnkJaWhoKCAtTU1MDCwgIuLi7o3bs3Jk+ejHnz5mHChAmdHTIREREREVGHMxEEQejsIIiIiIiIiIiIiIiIiFTh0FJERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLSYyiIiIiIiIiIiIiIjIaDGRQURERERERERERERERouJDCIiIiIiIiIiIiIiMlpMZBARERERERERERERkdEy7+wAiIiIiDqaIAiQSqWdHYZKZmZmAACJRNLJkVBr8RreHngduz5ew66P17Dr4zXs+ngNuz5ew9tDV7yOpqamMDExMVh9TGQQERHRHUcqleLmzZudHYYSU1NT9OjRAwBQVFRktMkWUo/X8PbA69j18Rp2fbyGXR+vYdfHa9j18RreHrrqdezevbuYgDEEDi1FRERERERERERERERGi4kMIiIiIiIiIiIiIiIyWkxkEBERERERERERERGR0WIig4iIiIiIiIiIiIiIjBYTGUREREREREREREREZLSYyCAiIiIiIiIiIiIiIqPFRAYRERERERERERERERktJjKIiIiIiIiIiIiIiMhoMZFBRERERERERERERERGi4kMIiIiIiIiIiIiIiIyWkxkEBERERERERERERGR0WIig4iIiIiIiIiIiIiIjBYTGUREREREREREREREZLSYyCAiIiIiIiIiIiIiIqPFRAYRERERERERERERERktJjKIiIiIiIiIiIiIiMhoMZFBRERERERERERERERGi4kMIiIiIiIiIiIiIiIyWkxkEBERERERERERERGR0WIig4iIiIiIiIiIiIiIjBYTGUREREREREREREREZLSYyCAiIiIiIiIiIiIiIqPFRAYRERERERERERERERktJjKIiIiIiIiIiIiIiMhoMZFBRERERERERERERERGi4kMIiIiIiIiIiIiIiIyWkxkEBERERERERERERGR0WIig4iIiIiIiIiIiIiIjBYTGUREREREREREREREZLSYyCAiIiIiIiIiIiIiIqPFRAYRERERERERERERERkt884OgIiIiIg0kzwzr7NDIB1JAOR0dhDUZryOXR+vYdcnu4a9D8V3dihERERkBNgjg4iIiIiIiIiIiIiIjBYTGUREREREREREREREZLSYyCAiIiIiIiIiIiIiIqPFRAYRERERERERERERERktJjKIiIiIiIiIiIiIiMhoMZFBRERERERERERERERGi4kMIiIiIiIiIiIiIiIyWkxkEBERERERERERERGR0WIig4iIiIiIiIiIiIiIjBYTGUREREREREREREREZLSYyCAiIiIiIiIiIiIiIqPFRAYRERERERERERERERktJjKIiIiIiIiIiIiIiMhoMZFBRERERERERERERERGi4kMIiIiIiIiIiIiIiIyWkxkEBERERERERERERGR0TLv7ACMxe7du7Fnzx7xdWutXLkSly5dQkBAAFauXGmg6BQZKlbqWgoLC/HSSy8BAF544QWEhoYqrD958iQ2bdoEANiwYQM8PDxU1nPlyhUcPHgQ165dQ1VVFaRSKQBg27ZtsLOzw4svvoiioiKEhITgxRdfbL8D6gALFy4EACxYsEB8TUTGjd9bIiIiIiIiImqJiQyiO0h8fDw+++wzMXlBt5fCwkKcPn0aFy9eRF5eHqqqqtDU1ARbW1u4u7vD19cXQUFBCAoKgqWlpU51NjU14cyZMzh37hzS0tJQUVGBW7duwdbWFm5ubujbty/Gjh2LIUOGwNRUfSe/S5cuISIiAteuXUNpaSkaGhrg6OgIR0dHdO/eHYMGDUJAQAD69OmjVE9KSgo++OADvc7FnDlz8MQTT2gss3fvXuzcuRNeXl5Yv369yjK1tbU4deoUzp07h9zcXFRUVMDMzAzOzs7w9/fHqFGjEBwcrPHYdbVx40ZEREQA0JyMJCIiIiIiIiK60zCRQZ1CW+8Cah/bt2+HVCpFt27d8Mgjj8DLywvm5s23ARsbm06OTje3U48RQ6mtrcXPP/+MEydOQCKRKK2vrKxEZWUl0tLScOzYMdjZ2WH27Nm45557YGVlpbbes2fP4qeffsLNmzeV1lVVVaGqqgoZGRn4559/0LNnTyxevBgjRoxQKFdfX4+NGzciNjZWqY6ysjKUlZUhKysLcXFxAIB33nkHQUFBep6B1klISAAAjBw5UuX648ePY8eOHaiqqlJad+vWLeTn5yMqKgpeXl549tlnMXDgwHaNl4iIiIiIiIjoTsVEhoG113BSRNqEhoZqTAgVFxcjPz8fAHDfffdh8uTJKstt3LixPcLrFHfC0GuFhYX4+OOPkZubCwCws7PDuHHjEBAQAFdXV1hZWaGqqgoFBQW4cOECzp8/j5qaGuzZswdBQUHo37+/ynr/+OMP7Ny5E4IgAACGDh2K0aNHw8vLC7a2tqiurkZeXh4SEhJw4cIF5Ofn49dff1VKZKxfvx7nzp0DAPTo0QNTp06Fv78/7O3tUVdXh4KCAly5cgUJCQmoqKjQerwzZszAjBkztJZzdHTUuL6iogKpqakAVCcytm/fjoMHDwIAzMzMMH78eIwaNQru7u5oampCXl4eoqOjkZycjNzcXKxatQovv/wygoODtcZGRERERERERET6YSKD6A5RWloqvu7Vq1cnRkKGUl9fj3Xr1olJjJCQEDzxxBOws7NTKhsYGIiZM2eivLwc+/fvx+HDh9XWe+rUKfz6668AmhMCr776KoYMGaJUbtiwYZg1axays7Px448/KvVcOHfunJjECAwMxLJly2BhYaFQZuDAgQgNDYVUKkVcXBxcXV01HrOjoyO8vb01ltFFYmIiBEGAg4ODUjLn77//FpMYrq6uePPNN+Hj46MU95QpUxATE4MNGzagsbERX3/9NXr06KFUloiIiIiIiIiI2oaJDKI7RGNjo/jazMysEyMhQ9mxYwdycnIAAFOmTMHzzz+vdRtnZ2csXrwYwcHBKnstlJaW4j//+Q8AwMrKCitXroSXl5fGOr29vfHee+8hKipK4f2zZ8+Krx9//HGlJIY8U1PTDu3NIBtWavjw4QrzWxQVFWH79u0AAGtra6xYsQI9evRQW8/48eMhCAK++uorNDU14ZtvvsFnn30GExOT9j0AIiIiIiIiIqI7CBMZajQ0NODvv/9GdHS0OByPp6cnQkJCMH36dLUNwStXrsSlS5cQEBCgdpgpQRAQERGBEydOIDs7G01NTfDw8MC4ceMwZ84c2NraYuHChQCABQsWiK8NHauMRCJBREQEzpw5g8zMTFRVVcHGxgZeXl4YM2YMpk+frnFi4PT0dBw5cgRXrlxBaWkppFKpOIlvv379EBQUhJEjR4oNey2PZ9OmTdi0aZPCe7oct66qq6uxf/9+nD17FkVFRbCxsUGfPn0wbdo0jBs3DidPnhT3r2qCXV2vhbZrX1ZWhri4OFy8eBFZWVkoKyuDRCKBg4MD/P39MXHixDZNGqzuOOQnEJZpOXGy/Dwlus5BUVhYiKNHjyI5ORlFRUWor6+Hs7MzPDw8MGLECIwbNw5ubm4K29TV1SExMREXLlxAWloaCgsL0dDQAFtbW3h5eWHkyJGYMWMGrK2tlfYnO78yERERSsfV8tzrcu2kUimioqIQHR2N9PR0VFdXw9bWFt7e3ggODsbUqVPFeURa2r17N/bs2SO+but3UR8VFRU4fvw4AKBbt25aJ7ZuacCAASrfP3ToEOrr6wEADzzwgNYkhoypqanScGVFRUXia03JgI7W1NSECxcuAFAeVurQoUNi0m/BggU6xT1hwgRERkYiMTEROTk5SEhIwKhRowwfuA5acz+/dOmS+L157rnnMHXqVI372L9/P3755RcAwGeffaayh0xJSQmOHDmCpKQk8Xvu6OiI/v37Y/r06Sp7+BARERERERERqcNEhgrl5eVYs2YNsrKyFN5PS0tDWloakpKS8MYbb7SqwbmpqQmfffYZEhMTFd7PyclBTk4OIiMj8d5773VYrAUFBfjkk0/EoWlkqqqqcPnyZVy+fBlHjx7FW2+9hZ49eypt/+eff2L79u3iOPoyJSUlKCkpQUZGBo4ePYqffvpJZeN0e5ONXV9WVia+19jYiOTkZCQnJ+P8+fMYNGhQu8chlUrx/PPPK50noDnBER8fj/j4eAwbNgxLly7tlHOljwMHDuDXX39Vmli6qKgIRUVFSElJQUJCglJC5+OPP1ZIRsi0/Ly9/fbb8PT0bM9DANCc5Fq3bh2uXr2qFE9KSgpSUlLw999/45133oG7u7vGutrzvqFKTEwMGhoaAABTp041yGdGlmQFmntjTJs2rU31ySeAbty4YTRDLl28eBF1dXUwMzNDYGCg+L4gCDh16hQAwNLSUq/jnz17tnhfP3nyZKckMlp7Px80aBDc3NxQXFyMqKgorYkMWc+b3r17q0xihIeH44cffhA/nzIlJSU4ffo0Tp8+jSlTpuCZZ55h7zAiIiIiIiIi0gkTGSp8/vnnuHHjBmbPno2RI0fC3t4eeXl5+P3333Hjxg0kJCTg+PHjmD59ut51//DDD2Jjl5eXF+bOnQtvb2/cunULcXFxOHr0KL788ssOibWsrAzLly9HRUUFbGxsMHXqVAwbNgxOTk6ora1FUlISDh8+jPz8fKxduxbr1q2Dra2tuH1WVpaYxPDw8MCsWbPg4+MjTuKbn5+PlJQUxMXFKez3s88+Q1lZGdasWQMAWLRokVKjn5OTk87nQJ3a2lqsWbNGTGKMHz8eISEhcHJyQl5eHg4dOiT2imlvsgTGkCFDEBQUBG9vbzg6OqKurg43b97E8ePHce3aNVy4cAHfffcdXnrpJYPt+6GHHsLcuXORlpaGzZs3AwCWLFkCf39/sYy2eQnk7dmzR5xE287ODjNmzMDgwYPh4OCAmpoaZGRkIC4uTuXQOhKJBN7e3hg1ahT8/Pzg4uICQRBQVFSEuLg4nD59GoWFhfj000/xySefKDw5/sILL6Curk68pqNGjcKiRYsU6tenMV8qleLjjz/GtWvXADT35pg1axY8PDxQWlqKEydO4OzZs7hx4wY+/PBDfPrppxrrb8/7hiryCaGWE2y3Vm5uLiorKwE0N27Lf99bw9fXVxzC6fvvv8cbb7yhdRLujiCLKSAgQOEYc3JyUF1dDUD/4x86dCisrKxQX1+PK1euGDZgHbTlfm5iYoIJEyZg//79uHTpEkpKStTeE3Jzc8Vk3aRJk5TWh4eHY8uWLQCaEx3Tp0+Hj48PrKysUFhYiPDwcJw7dw7h4eGwtbXF448/btDzUFJSorWMs7OzmEAxVGLRkORjkn8tUVWYiIjoDmGMv7NJO3V/11DXwWvY9fEa3h54HZsxkaFCamoq3nvvPQwePFh8z8/PD0FBQXjttddQUVGBo0eP6t0gmZ6eLg4F07dvX6xYsQJWVlbi+iFDhiAgIADr16/vkFi3bt2KiooKuLq6YuXKlejevbvC+sGDB2PcuHF4//33cfPmTRw4cECh4Tg2NhaCIMDKygqrV6+Gs7OzwvYDBw5EWFgYamtrFRqkvb29FRqEXVxcDDJ5b0t79uwRG7Ueeugh3HfffeI6Pz8/BAcHY926dUhKSjL4vlsyNTXFV199pXKYmoCAAISFhYnDFEVGRmL+/Pkqe8C0houLC1xcXBQmYvbw8GjVOU9PT8dvv/0GAOjZsyfef/99pQbPIUOGYO7cuSobFF944QWVx9WvXz+MHz8eU6ZMwZo1a5CXl4eoqChMmTJFIWbgf0/529nZtelzc/ToUTGJMXnyZLz44oti8sXPzw+jRo3Cr7/+ij/++AM3b97Enj178Oijj6qtr73uG+rIEnAmJibo06ePQeqU703i6+vb5vqmTJmCgwcPor6+HlevXsULL7yAoKAgBAQEoG/fvvD19dU4b4YqlZWVOiUfe/XqpXZIMFkyueWwUm05flNTU/Tp0wfXrl1DZWUlSktL4eLiolcdbdHW+/mkSZOwf/9+CIKA6OhozJs3T+V+IiMjATR/7iZOnKiwrri4GNu2bQPQPPH8888/r9DjwtfXF2PHjhW/V4cOHcK0adPQq1cvg5wDoDlJq83mzZvh6uoKMzMzoxryTBX5oQ5zOjEOIiKiztZy+F/qengNuz5ew66P1/D2cCdfxzs3haPB7NmzFRojZezt7REWFgagucGrtrZWr3qPHTsmPpn/3HPPKSQxZIKDgzFmzJh2jzU7O1tszHv66aeVGr1kfH19MXPmTADAiRMnFNaVl5cDaG4wbJnEkGdra9vh2cLGxkYx3j59+uCee+5RKmNubq7U0NZeTExMtDaYLViwAA4ODhAEAfHx8e0eU2scOHAAgiDAxMQEr776qsaeHKrWaUvODBs2TOyd07Inj6EdOXIEAODg4ICnn35aZQ+SBx98UBzi6vjx4woTprfUXvcNdWQ9J2xtbTUmA2QN/6p+CgsLVdYJGKZXlJubG1599VUxcdnQ0IC4uDj8+OOPeO+99/DEE09gxYoV+PPPP8WeENocPXoUS5cu1fpTWlqqcvvs7Gxx7o6WiQz5ZJ+me5o68udM1+MxBEPcz729vcXEYMtJ2+VFR0cDaE5Ut5wD56+//kJ9fT26deumcdiohQsXir2xZEN5ERERERERERFpwh4ZKrR8ylSen5+f+LqwsFCvMd8vXrwIAPDx8dH4BPXkyZN1bsRtbayyhnIrKyutw9IEBATgwIEDKCsrQ3Fxsdh41a1bNwDNQ42kpqaib9++OsXcEdLT01FTUwOg+clgdYkUV1dXBAYGKs1Z0t6kUinKy8tRV1eHpqYmhXiqqqqU5lkwBlKpVOy9EhAQYJAn9isrK1FTU6OQIJANPdSe56C0tBQ3btwAAIwbNw42NjYqy5mamiI0NBS//PKLOGxW//79VZZtr/uGOnV1dQC0D6f1zz//YNeuXSrXtZwcXVanLvXqauTIkVi/fj3279+P6OhohQb+xsZGce6G33//HU888QRCQkIMsl91ZPc+Ly8vpQb/W7duia9bc/zyyWlDJax0YYj7OdDcK+OXX35BZmYmcnNzlSZ6v3r1qpj8UjWslCyOkSNHKk0oLs/MzAz9+/dHbGys2CvKUGTD52kiS1JJJBKFCemNhampqfiETWFhIaRSaSdHRERE1Pn4O7Fr4t81XR+vYdfHa3h76KrX0d3d3aAPkDORoYKmSYbt7e3F1/KNXto0NDSgoKAAgGKjpirycxdo09pY09LSAAD19fVK8wxoUl5eLjZ8TZgwAX/88QcaGxuxfPlyBAYGYsSIERg0aBC8vLxUPuHeUeSHntF2Pvv27dshiQxBEBAZGYkTJ07g+vXrShPhypN/MtxYFBYWismhtkyQfuXKFRw+fBjJyckan1pvz3OQk/O/QVr69eunsaz8+uzsbLWJjPa4b2hibW2NmpoaheSDIeqUMWS9bm5uePrpp/HEE08gIyMD169fR0ZGBq5evYr8/HwAQE1NDTZu3AipVCr2YFFlwYIFWLhwYatjkc2P0bI3BgCFhFZrjl9+m7bOL6IPQ9zPgeZ7+o4dOyAIAqKiopTqkg0rZW5ujuDgYIV1tbW14u+4Y8eO4dixYzrHYEj6zPcDwOj/+JNKpUYfIxERUUfg78Suj9ew6+M17Pp4DW8Pd/J1ZCJDBVVDPsnIN87r86GRfzpX22S3+kyG29pY5YeQ0Yd847unpydeeeUVbN26FTU1NUhMTBQTAg4ODggKCsK0adPa1OjdWrIGd0D7EDmGGEJHm4aGBnz++ec4d+6czuWNTVuH3QEgzgOii/Y8B/IJFG3XX/5YNSVe2uO+oYlscvXa2lo0NTWpnQ9i/vz5mD9/vsJ76hIB8veeiooKg8Qpz8zMDH379lXovZWeno4ff/xRnCD7p59+QnBwsNpeMm1RWVmJ1NRUAKoTGfIJp9Y0sMufM/m62psh7udAc8Jp0KBBuHTpklIiQyKR4PTp0wCAoKAgpeNr7eelvr6+VdsRERERERER0Z2FiQwj1BE9GWSNqR4eHli2bJnO27WcUCY4OBjDhg1DTEwMzp8/jytXrqCyshJVVVWIjIxEZGQkQkJCsGTJkg6dJ0M2F4mhy7bW3r17xSRGQEAAZs6cCV9fXzg7O8PS0lI8NytWrMDly5c7JKa2aM1nNDk5WUxidO/eHXPnzhXH2be2thbPwa5du/D7778bNF5NtB2LsV6LPn36oKCgAIIgIDMz0yBDu8kPeZeRkdHm+nTh5+eHd955B8uWLUNBQQFqamqQnJys11xBukpMTIQgCHBwcFDZs0Z+yC99j18qlYo9wRwdHTt0om9D3c+B5iHSLl26hMLCQly7dk08T0lJSWIyU9WwUvIJujlz5mDKlCk6xaAuAUdEREREREREJI8tCB1EfpgRbU/PtseT0C3JnqatqKiAp6dnm8Yrs7W1xbRp0zBt2jQAzXNmxMfH4/DhwygrK0NERAR8fX0xZ84cg8SuC/mnhSsqKtCrVy+1ZbVdDxMTEwiCoLVBW92TxYIgIDw8HEDzBLnvv/++2qROR04QrC8HBwfxdVlZmd7bHz9+HABgZ2eH1atXq+0JId+bpr3o8+R9Zz1lr01AQADOnDkDADh//rxBEhleXl5wcHBAVVUVLl++jNra2g4ZIsna2hoTJkwQE1iyIYoMTTaHw/Dhw1V+B3v37g17e3tUV1frffwXLlwQ7wEDBw40XNA6MOT9fNy4cdi2bRsaGxsRGRkpJjJkE4Db2Nio7M0if39oaGgQJw4nIiIiIiIiIjKEjntE/g5naWkpTiwrG89cHW3rDUE2UXN9fT2uXr1q0Lq9vLxw7733Ys2aNeJwO7IhSWTau9eJfCOatvMpG2pGHdm8AZoa2KVSqTjWf0vV1dViY/m4cePUJjHq6uqQl5enMZbO5OHhATs7OwDA5cuX9d5eNi/F4MGDNQ7n1BGf/969e4uvr1+/rrGs/OfDmBpnx48fDwsLCwDNSSJDDMVlYmKC0NBQAM33BlkCriN069ZNIQ5Da2pqwoULFwCoHlZKtt/JkycDaG6M13WeBwD4+++/xdeyc9hRDHk/t7Ozw/DhwwE037clEgnq6+tx9uxZAMDYsWNVTuQt3wslOTnZaHsyEREREREREVHXxERGBxo6dCgAICsrC1lZWWrLnTp1qt1jGTVqlPh6//797bIPNzc3sSdEy14PsgZYAGhsbDT4vv38/MRG91OnTqltVCstLRUbN9WRDb+iqYH93LlzCvOgyJNIJOJrTePBh4eHK5Q1NqampmID56VLl1o19A6gee6LzMxMrYkFWSNqWz43Li4u4uTcsbGxaifglkqlOHnyJIDmBl5Zg7ExcHJywtSpUwEAJSUl+OmnnwxS71133SUmIHft2oUbN27otJ1UKlW6d+nTmJ2eni6+VjXkUVtdvHgRdXV1MDMzQ2BgoNpyc+bMEe9Pe/bs0al3SHR0tDg/kJeXF0aMGGGYoHVk6Pv5xIkTATTfty9cuICzZ8+K9y7ZOk1x3Lx5E7GxsW2Og4iIiIiIiIhIhomMDjRt2jTxSeOtW7eqbNSOjY1FXFxcu8fSt29fsTHv3Llz2L17t8byhYWF4tAiMnFxcRp7KRQXF4uNoC0bJh0cHMSx0W/evKl3/NpYWFggLCwMQHPj+IEDB5TKSCQSbN26FU1NTRrrCggIAND8ZL5sQmJ5ZWVl2LZtm9rtHR0dxaRKTEyMyv2lpqZi586dGuMwBvPmzROH2vryyy9RUlKitmzLdT169AAAXLlyReU1r6ysxDfffKM1Btnk22393MycOVPc7w8//KCy0f23335Dbm4uAGDq1KkKCThj8PDDD8PLywsAcPToUWzdulVtQk1G2/BlLi4ueOqppwA0J95WrlyJS5cuadwmNzcXa9aswcGDBxXe/89//oO9e/dq3eeFCxcQEREBoHnSdFnS15ASEhIANH+fNQ0X5eHhgUceeQRAcy+pDz/8EJmZmWrLx8TEYOPGjQCa53t46aWXOnQ+IMAw93N5I0eOFO9ZsrmOgObv3pAhQ9RuN2/ePPE78p///Edr76rExESNSX0iIiIiIiIiIhnOkdGB/Pz8MHXqVBw7dgypqal4++23MW/ePHh7e6O2thZxcXE4evQo+vbtKw5n055DMC1ZsgRvv/02ysrKsGfPHiQlJSEsLAze3t6wsLBAVVUVsrOzcf78eVy8eBGjR49WeBr3r7/+wjfffIPhw4djyJAh8PLygq2tLaqrq5Geno7Dhw+LT9/PmDFDYd9mZmbw9/fH1atXceLECfj4+MDHx0dMbtjb27d5PoIFCxbg9OnTKCkpwS+//ILMzEyEhITA0dER+fn5+PPPP5GWlgZ/f3+NDW7Tpk3D0aNHIZFIsG7dOixYsAADBw5EU1MTrl69ioMHD0IqlaJnz54qh5cyNTXFxIkTceTIEWRmZuL999/HXXfdhR49eqC2thbnzp3DkSNHYG1tjW7duqkdosoY+Pj4YOHChdi1axfy8/OxdOlSzJw5E4MHD4aDgwNqamqQmZmJuLg4mJqaYsWKFeK2ISEhSEhIQF1dHVauXIl77rkHfn5+EAQBV69exaFDh1BeXo7+/fvj2rVramMYMGAAUlJSkJaWhn379iEoKEgc/svS0lLnSZZnzJiBqKgoXLt2DRERESguLsbMmTPh4eGB8vJyhIeHi0nF7t27Y8GCBW04c+3D2toab775Jj7++GPcuHEDx48fx5kzZzB+/HgEBATAxcUF1tbWqKurQ2FhIS5duqQwzJus50VLYWFhKCkpwe7du1FRUYGVK1ciMDAQo0aNgqenJ+zs7FBdXY38/HwkJibi/PnzkEqlCpOFA0BVVRWOHTuGPXv2YPjw4Rg8eLA4D4dEIkFBQQHi4+Nx+vRpMZG0aNEijYmGyspKcVJtTSwtLcXkGQCxx4S6YaXkzZkzB4WFhfjrr79QXFyMt99+GxMmTMCoUaPg5uYGiUSCGzduIDo6GsnJyQD+l8Tw8/PTWr8uYmNj4ejoqLWcbBirtt7P5VlYWGDs2LEIDw/H2bNnxeTrhAkTNCZpPDw88Mwzz2DTpk2orq7G8uXLMXnyZIwYMQJubm6QSqUoKSlBamoqYmNjcfPmTbz55ptKnxsiIiIiIiIiopaYyOhgTz31FEpLS5GYmIjc3Fxs2rRJYb2Hhwf+9a9/4V//+hcAtOsT4C4uLli9ejXWr1+PtLQ0XL9+XeOwPjY2Nkrv1dfXIzY2Vu0wIqampnjwwQcxevRopXX33Xcf1q1bh6qqKnz99dcK6xYsWICFCxfqeUSKbG1t8c4772DVqlUoLy9HdHQ0oqOjFcqEhoYiICBA6TrI6927Nx555BH89NNPqKmpwX//+1+F9XZ2dnjjjTewe/dutUmIhx56CFevXkVmZiZSU1Px1VdfKay3t7fHv//9b411GIv58+fD1NQUu3fvRk1NDfbu3Yu9e/cqlZP1ZJEJDg5GaGgoTp48iZKSEvzwww8K601NTbF48WJUV1drTGTMmDEDR48eRXV1NXbs2IEdO3Yo7HPlypU6HYepqSneeustrFu3DlevXkVKSgpSUlKUynl6euKdd94RkyXGpnv37lizZg1++uknREREoLq6GkePHsXRo0fVbmNra4tZs2bh3nvvVVtmwYIF6N27N3766ScUFRUhKSkJSUlJasv37t0bjz76qMJ7sqRSU1MTzp49K86zoIqFhQUWLlyIu+66S20ZAFqPTaZPnz749NNPAQDZ2dkoKioCoFsiAwCeeOIJ9OrVCzt37kR1dTVOnTqldtg/T09PPPPMM0qf+bb4+eefdSonS2QY4n4ub+LEiQgPD1foOThp0iSd4rG0tMTWrVtx69YthIeHq51rxcTERG0yjYiIiIiIiIhIHhMZHczc3BxvvvkmIiIiEB4ejuzsbEgkEri5uWHMmDGYO3euQi8MTU8mG4K7uzvWrl2Ls2fPIiYmBqmpqaioqEBTUxPs7OzQs2dP9OvXD6NGjcKgQYMUtn3ttdeQnJyMCxcuIDMzE+Xl5aiqqoKFhQXc3d0xaNAgzJgxQ+0EySNGjMDy5cvx119/IS0tDZWVlQafI6J3795Yv3499u3bh7Nnz6K4uBjW1tbw9vbG1KlTMXHiRHEOBE3uvvtueHl54dChQ0hNTUVDQwO6deuG4cOH45577oGbm5vG7W1tbbFq1Sr8+eefOH36NPLz82FmZgZXV1eMGDECc+bMgaurq4GOuv3dd999CA4OxpEjR5CcnIzi4mI0NTXBxcUFHh4eGDlyJMaPH6+03QsvvIAhQ4bg+PHjyMzMRFNTE5ydnTFo0CDMmjULffv21TosjouLC9auXYt9+/bh0qVLKCkpafV8Gfb29vjggw8QFRWFqKgoZGRkoLq6Gra2tvD29sbYsWMxbdo0saeQsbK1tcXzzz+P+++/HzExMbh48SLy8/PF75StrS1cXFzg5+eHIUOGYMyYMSonbG5p7NixGDlyJGJjY3Hu3DmkpaWhoqICdXV1sLGxgbu7O/r164fg4GAMHjxYqQfZk08+iblz5+L8+fO4fPkycnJyUFRUJM5VYWdnBy8vLwwePBiTJ0/W+j1qrfj4eADN81d0795d5+1mzJiBCRMmIDIyEomJicjJyUFlZSXMzMzg5OQEf39/jB49GsHBwTAzM2uX2PXRlvt5SwEBAXB1dRWHiOvZs6fOvU3Gjx+PYcOG4dixY0hKSkJubi6qq6thbm4OZ2dn8ZoHBwe32zUnIiIiIiIiotuLiaDPbKzUIa5cuYL3338fALB8+fJ2GS+e/ufkyZNij4wNGza0y0TDRNR53n33XVy/fh3z5s1T6jVCdy6JRNIuczS1lampqTgsWkFBAaRSKQBA8sy8zgyLiIio0/Q+FK/wO5G6DnV/11DXwWvY9fEa3h666nXs3r27QR/85GTfRkg2CauZmZnBxlsnIroTVVZWinMOjRo1qpOjISIiIiIiIiKi1jDu8VJuQ7JhSezs7FSuP3/+PI4dOwagudFNXTkiItKuurpanNelf//+nR0OERERERERERG1AhMZHSwnJweffPIJxo0bh6FDh6J79+4wNTVFUVER4uPjERkZCalUCktLSzz00EOdHS4RUZfWq1cvLFy4sLPDICIiIiIiIiKiNmAioxPcunUL4eHhCA8PV7nexsYGr7/+Onr16tXBkRmPpqYm5OXltWpbDw8PWFtbGzgiIsMqLS1FdXW13ttZW1tzHhciIiIiIiIiIrqjMJHRwfz9/fHCCy/g/PnzyMrKQmVlJWpra2FjY4MePXogKCgIs2bNgqOjY2eH2qlKS0uxdOnSVm27YsUKDB482MARERnWr7/+ioiICL23CwgIwMqVKw0fEBERERERERERkZFiIqODWVtbIzQ0FKGhoZ0dCv1/vB5ERERERERERERExouJDDJKHh4e2L17d2eHQdRuXnzxRbz44oudHQYREREREREREZHRM+3sAIiIiIiIiIiIiIiIiNRhIoOIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLSYyiIiIiIiIiIiIiIjIaDGRQURERERERERERERERouJDCIiIiIiIiIiIiIiMlrmnR0AEREREWlm9p8DnR0C6cjU1BQ9evQAABQUFEAqlXZyRNQavI5dH69h1yd/DYmIiIjYI4OIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLSYyiIiIiIiIiIiIiIjIaDGRQURERERERERERERERouJDCIiIiIiIiIiIiIiMlpMZBARERERERERERERkdFiIoOIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLfPODoCIiIiINJM8M6+zQyAdSQDkdHYQ1Ga8jl0fr2HXJ7uGvQ/Fd3YoREREZATYI4OIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLSYyiIiIiIiIiIiIiIjIaDGRQURERERERERERERERouJDCIiIiIiIiIiIiIiMlpMZBARERERERERERERkdFiIoOIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLSYyiIiIiIiIiIiIiIjIaDGRQURERERERERERERERouJDCIiIiIiIiIiIiIiMlpMZBARERERERERERERkdFiIoOIiNrNypUrsXDhQqxcubJN9SxcuBALFy7E7t27W13HyZMnxXoKCwuV1hsqVmobXgciIiIiIiIiasm8swMgIqLOs3HjRkRERIjLYWFhWLJkidbtIiMj8c0334jL7u7u2LhxY7vESMCpU6ewYcMGAICJiQk2bNgAd3f3To6KiIiIiIiIiKhjsEcGERGJYmNj0dDQoLXcqVOnOiAakpE/34Ig8PwTERERERER0R2FPTKIiAgAYGFhgVu3biE+Ph7jx49XW668vBzJycniNo2NjWrLcnigtistLRXPt7W1Nerq6nDq1CnMnz+/kyMjIiIiIiIiIuoY7JFBREQAgFGjRgHQ3tsiKioKUqkU3bp1Q9++fTsitDtaZGQkBEGAmZkZHn/8cQBAfn4+rl271smRERERERERERF1DCYyiIgIABASEgIASEpKQkVFhdpyskTHxIkTYWJi0iGx3clk53vYsGEIDQ2FnZ2dwvtERERERERERLc7Di1FRHe07OxsnD17FleuXEFOTg4qKythZmaGbt26oX///pgxYwb69++vsY7Kykrs378f8fHxKC4uhq2tLby9vTFz5kyMGTMGJ0+exKZNmwAAGzZsgIeHh8p6JBIJIiIicObMGWRmZqKqqgo2Njbw8vLCmDFjMH36dFhaWhr8HMgEBgbCyckJFRUViI6Oxpw5c5TKZGdnIzMzEwAwefJkpKWlaaxz5cqVuHTpEgICAjQOMxUZGYnjx48jKysLTU1N8PDwwNixY3HXXXeJDffaVFdXY//+/Th79iyKiopgY2ODPn36YNq0aRg3bpxOdei6nyNHjiAxMREFBQW4desWHBwc4O/vj5CQEIwdO9Zg+0pPT0dOTg4AYNKkSTA3N0dwcDCOHz+OmJgYPPHEEzA31/6rvKioCH/88QeSkpJQXl4Oe3t7+Pv7Y86cORgyZAh2796NPXv2AAB2796ttp76+nocO3YM8fHxyM3NRXV1Nezs7ODj44MJEyYgJCQEpqaan5G4du0aDh48iCtXrqC2thbOzs4YNmwY5s6di169eulxdoiIiIiIiIjoTsFEBhHdsVJSUvDBBx8ovd/U1ISCggIUFBTg1KlTuPfee/Hwww+rrCMzMxOrV69GZWWl+F5FRQWSk5ORnJyMadOmaU2EAEBBQQE++eQT5ObmKrxfVVWFy5cv4/Llyzh69Cjeeust9OzZU88j1Y2ZmRnGjx+Pw4cPIzIyUmUiQ9YLoE+fPujTp0+b9ymRSPDll1/izJkzCu/n5OQgJycHkZGRWL58udZ6cnNzsWrVKpSVlYnvNTY2itfh/PnzGDRoUJvjTUxMxDfffIOamhqF98vKyhAfH4/4+HiMGDECr776Kqytrdu8v4iICADNc2OMHj0aQHNC4/jx46iurkZCQoLWxMmFCxfw6aefor6+XinehIQEPPjggzrFkpqais8++wylpaUK71dWVuLChQu4cOEC/vnnHyxbtgzOzs4q6zh48CB+/vlnCIIgvldUVITjx48jKioKr7/+uk6xEBEREREREdGdhYkMIrpjSSQSWFlZYcSIERgyZAg8PT1hY2ODiooK5Obm4vDhwygqKsK+ffvQs2dPhIWFKWxfXV2NtWvXikmMiRMnYvLkyXB0dERBQQH++usvHDt2DFlZWRrjKCsrw/Lly1FRUQEbGxtMnToVw4YNg5OTE2pra5GUlITDhw8jPz8fa9euxbp162Bra9su52Ty5Mk4fPgw0tLScOPGDXh6eorrpFIpoqKiADQ3phvCf//7XzGJ0atXL8ybNw99+vRBbW0tTp8+jePHj+OLL77QWEdtbS3WrFkjJjHGjx+PkJAQODk5IS8vD4cOHcKJEyeQnZ3dplgvXLiATz75BFKpFO7u7pgxYwb69esHGxsblJaWIiYmBpGRkUhMTMSGDRuwdOnSNu1PIpEgOjoaADBmzBhYWVkBAAYNGgR3d3cUFRUhIiJCYyKjoKBATGKYmppi+vTpGDt2LGxsbJCTk4MDBw5g586dWuc6yc7OxgcffID6+no4OTlh+vTpGDRoEBwcHFBRUYH4+HgcO3YMqamp+PTTT/HBBx8o9RSJjY3F9u3bAQC2tra45557MHjwYADAxYsXsX//fnz99ddwdHRs9TkjIiIiIiIiotsTExlEdMfy8fHBli1bVA5dFBQUhFmzZuHjjz/GhQsXsGfPHqVhc3777TeUl5cDAB577DHMnTtXXOfn54fg4GB89tlniI+P1xjH1q1bUVFRAVdXV6xcuRLdu3dXWD948GCMGzcO77//Pm7evIkDBw5g0aJFbThy9fz9/eHp6YkbN27g1KlTeOihh8R1Fy9eRGlpKUxMTDBx4sQ27ysrKwtHjhwBAPj6+uKDDz5Q6MUwdOhQDBgwABs3btRYz549e1BSUgIAeOihh3DfffeJ62TXYd26dUhKSmp1rHV1dfjmm28glUoRGBiIpUuXiokFWfwjR47EoEGD8O233yIuLg7JyckYOnRoq/d57tw5MUk2efJk8X0TExNMmDAB+/btw7lz51BVVQUHBweVdfz0009iT4xXX30VwcHB4jp/f3+MGzcOH3zwAVJTU9XGIQgCvvnmG9TX16NPnz5Yvny5UrIhMDAQI0eOxEcffYTr16/j1KlTmDJliri+qakJ27ZtA9CcxFi9ejW8vLzE9f3798eoUaOwfPly5Ofn63qKlMg+B5o4OzvDzMwMALQOg9UZ5GOSfy3pjGCIiIiMhDH+zibt1P1dQ10Hr2HXx2t4e+B1bMZEBhHdsbQ9+W1ubo5HH30Uy5YtQ1FRETIzM+Hn5wcAaGhoEIf98fX1xd133620vampKZ599lkkJSWhsbFR5T6ys7ORmJgIAHj66aeVkhgyvr6+mDlzJg4cOIATJ060WyIDaG40//XXXxEVFYVFixaJE3rLhpUaOnQoXFxc2ryff/75Rxxi6LnnnlM5FFNISAhiYmJw7tw5lXU0NjbixIkTAJqHu7rnnnuUypibm+P555/HSy+9BImkdc3BJ0+eREVFBSwsLPDSSy8pJDHkTZs2DeHh4UhNTcWJEyfalMiQfb66deuGIUOGKKybPHky9u3bJ/bamDVrltL2paWlSEhIANDco0M+iSFjZWWFZ599FsuWLVMbR2Jiotir6KWXXlL7vQkKCkJwcDBOnz6NEydOKCQy4uLixB4z8+fPV0hiyHh7e+P+++/HL7/8ojYWbZYsWaK1zObNm+Hq6gozMzP06NGj1fvqCPLz6eR0YhxERESdTd0cc9R18Bp2fbyGXR+v4e3hTr6Od24Kh4iohcbGRhQXFyM3NxfZ2dlKQxHJJrkGmidhrq2tBdDc2C5r7G/J2dkZgYGBavcp660hG+JKk4CAAADNQ1EVFxdrPZ7WmjRpEkxMTFBUVITLly8DaJ7kOS4uDoBi74C2SE5OBtDcgC1LEKnSckgveenp6eJ8FZommnZ1ddV4HbQ5e/YsgOZr4OTkpLGsbC6O69evt3p/1dXVYoJrwoQJSsfl5eUFX19fAP9LMLWUkpIiJopCQkLU7svHx0fjfCeyY+/Vq5fWeVFkx56WlgapVCq+L7vWJiYmGmMJCwtT+10iIiIiIiIiojsXe2QQ0R2trq4Ohw8fRkxMDHJychQaX1uqqqoSX+fk/O/5aE2N8EDzED7qhpdKS0sD0Jwo0KeXRXl5Odzc3HQurw83NzcEBAQgJSUFp06dQkBAAM6cOYO6ujpYWVlhzJgxbd5HQ0MDCgoKADSfH000zd8gn2zSpR5ZckBf6enpAICkpCQsXLhQp21kw44BzUMr5eXlqS3bq1cvhTklYmJixF486uYjmTRpEjIyMpCamoq8vDz06tVLYb2+n1F1c7nIjj0vL0/nY29qakJ1dbXYe0N2nTw8PDT2hHJ0dIS7uzsKCwt12k9Lmzdv1lpGNhG5RCJBUVFRq/bTnkxNTcUnbAoLCzXek4iIiO4U/J3YNfHvmq6P17Dr4zW8PXTV6+ju7i4O7WwITGQQ0R2rsLAQH374oc6Npg0NDeJrWS8AQPsQVZrWy+ZA0Jd8LO1h8uTJSElJQWxsLJ566ilERkYCaB6iSNUQUPqqra0Vewto6+Ggab38dWhLPZo0NTUp7EdXsrkpgOZhnjRN/r1hwwaF7qGyYaXke160NGHCBPz888+QSqWIiIhQmM8EaO7VIaPt2DV9RisqKjRuq4788cvOny4TeTs5ObU6keHq6qpXeWP/408qlRp9jERERB2BvxO7Pl7Dro/XsOvjNbw93MnXkYkMIrpjbdiwAYWFhTAxMUFoaCgmTJgAT09PODo6wsLCAkDzLwhZTwlZw7shyX75eHh4aJynoKX2HhMxODgY33//PWpra3Hs2DFxaCBDDStlqHOpTz2t3af8Hwjjxo3D/PnzW1WPrvLz88VhqXJzc3XqBREZGakwn4khyY5/wIABeOaZZ3Terlu3buJr2bnnsFFERERERERE1BpMZBDRHenGjRu4cuUKAODee+9VeppdRt2T+HZ2duLryspKpWF95GnqdWFvbw+g+al3T09Pg3a5awsbGxuMHj0a0dHR+OWXXyCVStGtW7c2TV4tT/78aXviX9N62fmTlWvtddDE0tISVlZWqK+vR21tLby9vfWuw8PDA7t379aprKw3hj6Ki4uRkpKiMCl4y3OjqbeCpnPj4OCAiooKVFZWturY5WPRpXdHa3uAEBEREREREdHti4kMIrojyc8fMGHCBLXlZHNYtNS7d2+FMgMHDtS7DgDw9fXFuXPnUF9fj6tXr4oTehuDyZMnIzo6WpyrQdWk061laWmJnj17Ij8/X+P5AYDU1FS16+Qb1tPS0sTJpvWtRxsfHx9cvXoVV65cQX19PaysrFpdlyaCIIjDePXp0wf33nuv1m22bNmC+vp6nDp1SiGR4eXlJb5OS0vTmMjQdA18fHyQm5uL/Px8FBUVwd3dXYcjUeTt7Y3r16+jsLAQVVVVcHBwUFmusrLSKOetICIiIiIiIqLOZZgWKSKiLkZ+uCD5sfxb+ueff1S+7+fnB1tbWwDAqVOn1A5bVF5ejqSkJLX1jxo1Sny9f/9+jTF3tMDAQLi6usLCwgIWFhYGG1ZKRta7Izs7GxkZGWrLnThxQu06Pz8/sXeHputQWlqKCxcutDpW2XWqr6/HkSNHWl2PNpcuXRIb8idPnowJEyZo/QkKCgIAnDlzRuGzPGTIEHEop1OnTqndZ2ZmptqJvgHDfEZl11oQBI09Tk6ePNkuQ7gRERERERERUdfGRAYR3ZF69OghvlbXsHr06FGcPXtW5TpLS0uxYT8jIwN//vmnUhmpVIpvv/1W7NGgSt++fREYGAgAOHfunNbhhwoLCxEVFaWxjKGYmppi8+bN+OWXX/DLL7/Ax8fHoPVPmzZNbGj/9ttvUVdXp1QmMjIS586dU1uHhYUFwsLCADQ3yB84cECpjEQiwdatW9HU1NTqWKdPny72Iti1a5fGmADgypUruHTpkt77kf8sjh07VqdtgoODAQC3bt1CXFyc+L6LiwtGjBgBAIiLi0NsbKzStg0NDfj222+11u/p6QmgObEXHh6usXx2djbi4+MV3hszZow4Z8bvv/+OvLw8pe1yc3Oxd+9ejXUTERERERER0Z2JQ0sR0R3J19cXvXv3Rk5ODo4ePYqamhpMmjQJzs7OKCkpQWRkJGJjYzFgwABcvXpVZR0LFy5EbGwsysvLsX37dmRkZGDy5MlwdHREQUEBDh8+jKtXr6Jv377isEaqJjtesmQJ3n77bZSVlWHPnj1ISkpCWFgYvL29YWFhgaqqKmRnZ+P8+fO4ePEiRo8ejYkTJ7br+ekIPj4+mDlzJv7++2+kpaXh7bffxj333ANvb2/U1tbi9OnTOHbsGPz9/TUOfbRgwQKcPn0aJSUl+OWXX5CZmYmQkBA4OjoiPz8ff/75J9LS0rTWo4mtrS1eeeUVfPTRR2hsbMTHH3+MsWPHIjg4GN27dwcAlJWVIT09HWfPnkVWVhaeeuopvYYKq6+vx5kzZwA09zTRdUL3ESNGwMLCAo2NjTh16hQmTZokrlu8eDEuXryI+vp6fPnll5gxYwbGjBkDW1tbZGdn48CBA8jNzdV4bkxNTfHaa6/hvffeQ11dHbZs2YLTp09j4sSJ6NWrF8zMzFBZWYmMjAwkJibi6tWruPvuuxV6cpibm+PJJ5/E+vXrUVNTg3fffRf33HOPeH5SUlLE3h6yIceIiIiIiIiIiGSYyCCiO5KJiQlefvllfPDBB6ipqUF0dDSio6MVynh7e+P111/Hc889p7IOe3t7vPPOO1i9ejUqKysRFRWl1FsiNDQUAwcOFBMZFhYWSvW4uLhg9erVWL9+PdLS0nD9+nVcv35dbew2Njb6Hq7Revzxx1FaWoq4uDjcuHEDmzZtUljv4eGB1157DS+99JLaOmxtbfHOO+9g1apVKC8vV3ktQ0NDERAQoFS/PoYNG4Z3330XX3/9NcrLyxEbG6uyl4OMvtcpLi4Ot27dAvC/Xha6sLGxQWBgIOLj45GcnIzS0lK4uLgAaO559MYbb+DTTz9FfX09/v77b/z9998K2y9YsACCICAtLU3l5xNo/i6sWrUK69evR35+PpKSkjQOmSYbdk1ecHAwHn30Ufzyyy+oqanBjh07FNZbWVnhtddew4EDB5jIICIiIiIiIiIFTGQQ0R3Lx8cHn376Kf744w+cP38epaWlsLGxQY8ePTBu3DjMnDkTlpaWWutYv3499u3bh4SEBBQXF8Pa2hre3t6YOnUqJk6ciEOHDonlVTXwAoC7uzvWrl2Ls2fPIiYmBqmpqaioqEBTUxPs7OzQs2dP9OvXD6NGjdI4oXVXY25ujqVLl+LUqVM4fvw4srKyIJFI4ObmhjFjxmDu3Lmwt7fXWk/v3r3F63D27FmV1+HkyZNtjnfIkCH45ptvcOLECSQmJiIrKwtVVVUwMTGBo6MjvLy8MGjQIAQHB6NXr1561S0/j4Wuw0rJl4+Pj4dUKkVUVBTmzZsnrhs2bBg+//xz7Nu3D0lJSSgrK4OdnR38/f0xa9YsBAUF4ccffwSg/vMJNE8+vn79ekRFRSEuLg7p6emorKyEIAhwcHBAr169MGDAAIwZMwZ+fn4q65g3bx4GDBiAgwcP4urVq6itrYWzszOGDBmCuXPnwsvLS+XwYERERERERER0ZzMROKsmEVG72rJlC8LDw+Hq6orNmzd3djhESlatWoXk5GQMHDgQH374YWeH0yEkEglu3rzZ2WEoMTU1FefwKSgogFQqBQBInpmnaTMiIqLbVu9D8Qq/E6nrUPd3DXUdvIZdH6/h7aGrXsfu3bvDzMzMYPVxsm8ionbU0NAgThjer1+/To6GSFlpaak4MTk/o0RERERERERkjJjIICJqg4KCAqjr2CaVSvGf//wHVVVVAICQkJCODI0IQPNnVJ2GhgZs2rQJEokEAD+jRERERERERGScOEcGEVEb/P7770hNTcWECRPQt29fODk5oaGhAVlZWTh+/DgyMjIAAEOHDsWIESM6OVq6E23ZsgX19fUYN24c/Pz8YG9vj1u3biEtLQ1Hjx4VEx1TpkyBt7d3J0dLRERERERERKSMiQwioja6ceMGdu/erXb9gAED8Oqrr8LExMSg+62oqEBFRYXe25mbm+s9ETV1bWlpaUhLS1O7fsyYMXjqqac6MCIiIiIiIiIiIt0xkUFE1Ab33XcfevbsieTkZBQVFaGyshISiQT29vbw9/fH+PHjMX78eJiaGn4kvyNHjmDPnj16b+fu7o6NGzcaPB4yTo8//jji4uKQkpKCkpISVFZWQhAEODk5oV+/fggJCWFvISIiIiIiIiIyakxkEBG1Qa9evXD//ffj/vvv7+xQiFTy8/ODn59fZ4dBRERERERERNRqTGQQEXVRCxcuxMKFCzs7DCIiIiIiIiIionZl+LFOiIiIiIiIiIiIiIiIDISJDCIiIiIiIiIiIiIiMlpMZBARERERERERERERkdFiIoOIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLSYyiIiIiIiIiIiIiIjIaJl3dgBEREREpJnZfw50dgikI1NTU/To0QMAUFBQAKlU2skRUWvwOnZ9vIZdn/w1JCIiImKPDCIiIiIiIiIiIiIiMlpMZBARERERERERERERkdFiIoOIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLSYyiIiIiIiIiIiIiIjIaDGRQURERERERERERERERouJDCIiIiIiIiIiIiIiMlpMZBARERERERERERERkdFiIoOIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjZd7ZARARERGRZpJn5nV2CKQjCYCczg6C2ozXsevjNez6ZNew96H4zg6FiIiIjAB7ZBARERERERERERERkdFiIoOIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLSYyiIiIiIiIiIiIiIjIaDGRQURERERERERERERERouJDCIiIiIiIiIiIiIiMlpMZBARERERERERERERkdFiIoOIiIiIiIiIiIiIiIwWExlERERERERERERERGS0mMggIiIiIiIiIiIiIiKjxUQGEREREREREREREREZLSYyiIiIiIiIiIiIiIjIaDGRQURERERERERERERERouJDCIiIiIiIiIiIiIiMlpMZBCRQa1cuRILFy7EypUrOzsUFBYWYuHChVi4cCFOnjzZ2eFQF/Liiy9i4cKF2LhxY2eHckfZvXu3+J0lIiIiIiIiIpIx7+wAiIiIWquwsBAvvfSS0vsmJiawsbGBra0tunXrBl9fX/Tv3x9jxoyBtbV1J0RKREREREREREStxUQGEXUo2ZPWCxYsuCOfuu7I45dv5H/hhRcQGhrarvszJoIgoLa2FrW1tSguLsb169dx9OhR2NjYYMqUKXjwwQeZ0CAiIiIiIiIi6iKYyCAigzKGIaXozjRq1CgsWrRIXG5oaEBNTQ1yc3Nx6dIlJCYm4tatWzh06BASExPx1ltvoWfPnirr4pBSRERERERERETGg4kMIiK6LdjZ2cHb21vp/cDAQNx1110oLi7Gli1bcOHCBeTn5+Pjjz/G2rVrYWdn1wnREhERERERERGRrjjZNxER3RHc3NzwzjvvYMSIEQCA/Px8/Pbbb50cFRERERERERERacMeGUSdYPfu3dizZ4/4ura2Fn/++SfOnDmDwsJCWFtbw8/PD/fffz8GDBggbldRUYHDhw8jLi4ORUVFsLCwwMCBA/HAAw/A19dX5b5u3ryJuLg4pKSkICcnB+Xl5QAAJycn9OvXD2FhYQgKClIb68mTJ7Fp0yYAwIYNG9CtWzccPXoUMTExKCgoQFVVlcJ8DytXrsSlS5cQEBCgMMzUiy++iKKiInF5z5494jmQCQkJwYsvvigul5WVIS4uDhcvXkRWVhbKysogkUjg4OAAf39/TJw4EcHBwTA17ficbF5eHv7++2+kpKSgsLAQTU1NcHR0hKOjI3x9fREUFITRo0fDwsICQMcff8v5NzZt2iReRxn569byM6lOSkoKPvjgAwDAihUrMHjwYKUy6enpOHLkCK5cuYLS0lJIpVLx3PTr1w9BQUEYOXIkTExM1O6nvZiamuKFF17Aiy++iPr6ehw/fhz3338/HB0dFcrJrlfLa/LBBx8gJSUFrq6u2LRpk8ZjaGpqwjPPPIOamhqMGTMGS5cuVSpTWVmJv/76C4mJiSgsLERjYyOcnZ0xcOBATJ8+HQMHDlRbf8sY09PTcfjwYVy6dAllZWVoampSupa3bt3CsWPHkJiYiNzcXNTU1MDBwQGurq4ICAjA+PHj4efnp3J/giDgzJkziIqKQlpaGiorK2FpaYkePXpg5MiRmD17ttbeLSUlJfjjjz9w/vx5lJWVwd7eHn5+fpg9ezaGDRumcVsiIiIiIiIiunMxkUHUyYqLi7Fq1Srk5+eL79XX1+PcuXNISkrCK6+8gnHjxiErKwsfffQRSktLFcrFx8cjKSkJb7/9NoYMGaJQd2FhIV5++WW1+y0uLsbp06cxadIkvPDCCzAzM9MYa1VVFT777DNkZma2/oB1JJVK8fzzz0MQBKV1ZWVliI+PR3x8PIYNG4alS5d26MTNp0+fxjfffIOmpialuMrKypCVlYWTJ0/is88+UznUkS6M+fg1+fPPP7F9+3aluEtKSlBSUoKMjAwcPXoUP/30U6fF7OjoiEmTJuHYsWOor6/HhQsXMHHiRJ22nTRpElJSUlBSUoLLly8jICBAbdnExETU1NSI27WUlJSE9evX49atWwrvFxUVoaioCJGRkZg5cyaefPJJrcm6o0ePYtu2bZBIJGrLXLhwAV999RWqqqoU3pd9blNTU3HgwAGViazKykp8+umnuHr1qsL7jY2NSEtLQ1paGo4cOYJly5ahX79+Kvd/6dIlrFu3TuF4y8rKkJCQgISEBKXkGxERERERERGRDBMZRJ3siy++QGlpKe69914EBQXBysoKV65cwe7du3Hr1i1s2bIF/v7++Pjjj9HQ0IBFixYhICAA5ubmOH/+PPbu3YvGxkZs2rQJX3/9NczN//e1lkqlMDc3R2BgIIYNGwYvLy/Y29ujuroa+fn5OHLkCHJychAZGYnu3btrbUjcsmULsrOzMXnyZIwfPx7Ozs4oLi4Wex1o8u6776KpqUl8Kn3GjBmYMWOGQhl7e3vxtawhfMiQIQgKCoK3tzccHR1RV1eHmzdv4vjx47h27RouXLiA7777Di+99JLO57wtysvLsWnTJjQ1NcHJyQkzZ85E//794eDggMbGRty8eROXL1/GmTNnFLbr6OP/7LPPUFZWhjVr1gAAFi1ahFGjRimUcXJyMsxJ+f+ysrLEJIaHhwdmzZoFHx8f2Nvbo66uDvn5+UhJSUFcXJxB99saQ4cOxbFjxwAAly9f1jmRERwcjO+//x6NjY2IjIzUmMiIiooCANja2orDWclkZmZi3bp1aGpqgpmZGWbOnInRo0fDysoKmZmZ2LdvHwoLC3HkyBFYWVnh0UcfVbuftLQ0REZGwtXVFXPnzoWfnx+kUimuXLkilrl48SI++ugjSCQSmJqaYvLkyRg1ahTc3NzQ2NiI3NxcnDt3DgkJCUr119XVYcWKFbhx4wbMzc0RGhqKESNGwNXVFfX19bh06RIOHTqEiooKrF27Fp988gnc3d0V6igsLBSTGCYmJpg2bRqCg4Nha2uLrKws7N+/H7t374a/v79O10GdkpISrWWcnZ3FpG1n9ObSRj4m+dfqU1RERES3P2P8nU3aqfu7hroOXsOuj9fw9sDr2IyJDKJOlpmZiZUrVyo8xezv74+ePXvi448/xq1bt/DOO+9AEASsXbsWPXr0EMv17dsXDg4O+P7771FcXIzExESMGTNGXO/s7IyNGzeiW7duSvsdOnQopk+fjs2bN+PkyZM4ePAg7r77btja2qqNNSsrC88//zymTJkivqduGJqWevXqpbDs6OiosbeCqakpvvrqK4XjlQkICEBYWJg4HFJkZCTmz5+Pnj176hRLWyQmJqK+vh4AsHz5cqVj6N+/PyZNmoQnnnhC4f2OPn5vb2+FHg8uLi6t7h2iq9jYWAiCACsrK6xevRrOzs4K6wcOHIiwsDDU1tbC0tKyXWPRRn4oNvneUNrY2tpi+PDhiIuLQ2xsLJ5++mmF5KFMbW2tmBQIDg5WSvZt3boVTU1NMDU1xVtvvYXAwEBxXd++fREcHIz3338fubm5OHjwIEJCQtC7d2+VMeXm5sLb2xsffPCBwtBOsmGpGhoa8M0330AikcDKygpvvfWW0pBgAwYMwNSpU1FcXKxU/44dO3Djxg3Y2tpi+fLlSsmGgQMHYtKkSXjvvfdQVlaGnTt3KvUE++mnn8SeGC+//LJC4sjf3x/jxo3D+++/j7S0NJXHqKslS5ZoLbN582a4urrCzMxM5ffLmHh4eIivczoxDiIios4m/zuRuiZew66P17Dr4zW8PdzJ1/HOTeEQGYk5c+aoHIplxIgR4lPNlZWVWLRokcpGt7CwMLGR9PLlywrrrK2tVSYxZExMTPD444/D1NRUHGJHkyFDhigkMdqTiYmJ1kbGBQsWwMHBAYIgID4+vkPiks0xYmdnpzExYGlp2abGemM9fk1k56ZXr15KSQx5tra2nf4EgYODg/haNvyTrmTDRNXU1ODcuXMqy8TFxaGxsREAlHp7pKamig32U6ZMUUhiyNjb2+PZZ58F0Nw758iRIxpjevrpp9XOTxEREYGysjIAzT1zVM1rIuPm5qawXFlZiePHjwNonndFXY8Jd3d3zJ8/HwAQExMjJvuA/w2FBjTf11T1frGxscFzzz2nNi4iIiIiIiIiurOxRwZRJ5swYYLadd7e3igqKoKJiQnGjRunsoylpSV69uyJ7Oxs3Lx5U+O+mpqaUFFRgVu3bkEqlYrvOzg4oKKiAllZWQgODla7va7D77QHqVSK8vJy1NXVKcxN4erqiqqqKmRlZXVIHLLEUE1NDc6ePYvRo0d3yH6N5fg1kZ2b3NxcpKamom/fvp0ckXryvVVazlGhzYgRI2BnZ4eamhpERUWp/AzIhpVycXFRGn5KPmGoKTE4cOBAeHp64saNG0hOTlZbztXVFYMGDVK7PjExEQBgZWWFadOmqS2nSlJSkpiQGT9+vMayshgkEgnS09PF5ZSUFPF+ExYWpnb7vn37onfv3sjJaX3fg82bN2stI0uySSQSFBUVtXpf7cXU1FR8wqawsFDhXk1ERHSn4u/Erol/13R9vIZdH6/h7aGrXkd3d3et8/Hqg4kMok6maTgk2RPWDg4OCvMnqCtXV1entK6pqQnHjh1DZGQkMjIylCaoltdyEuCW+vTpo3G9oQmCgMjISJw4cQLXr19HQ0OD2rLaYjeUUaNGiY3Yn332GQICAjBy5EgEBATAx8fHoD0NjPH4NZkwYQL++OMPNDY2Yvny5QgMDMSIESMwaNAgeHl5wcTEpLNDFMknL2xsbPTa1sLCAmPHjkV4eDgSEhJw69YthTrKy8tx8eJFAM3npOVnQtZQb25urjDElSr9+vXDjRs3UFBQgKamJpXDWGn7XmZmZgJoHgbOyspK6/HJkx/qSdZDRBey3jkAkJ2dLb7WNgeGv79/mxIZrq6uepU39j/+pFKp0cdIRETUEfg7sevjNez6eA27Pl7D28OdfB2ZyCDqZJoaFmUNv9oaH2XlWt7IqqursXr1aqSnp+sUi6aGcgBqh65pDw0NDfj888/VDt2jqnxHcHBwwLJly/DVV1+htLQUKSkpSElJAdDcID506FCEhYVh5MiRbdqPsR6/Jp6ennjllVewdetW1NTUIDExUewN4ODggKCgIEybNk1j74GOIp/40ZQkVGfSpEkIDw9HQ0MDzpw5g9DQUHFddHS0+F2UDUMlr7q6WtyvticTZL0HBEFAdXW1yiG7tH0vKysrAUDjMHPattWX/NBSsuMFtE8wr2lIMiIiIiIiIiK6czGRQXQb27Ztm5jEGD16NMLCwtCnTx84OTnBwsJ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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 459,
"width": 793
}
},
"output_type": "display_data"
}
],
"source": [
"#Visualize features\n",
"plot_imp(best_features_spfsr, feature_importances_spfsr, 'Figure 3: spFSR')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "2da7c093",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.999"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#CV mean results for spFSR\n",
"cv_results_spfsr = cross_val_score(estimator=clf,\n",
" X=Data[:, fs_indices_spfsr],\n",
" y=target, \n",
" cv=cv_method, \n",
" scoring=scoring_metric)\n",
"cv_results_spfsr.mean().round(3)"
]
},
{
"cell_type": "markdown",
"id": "2bb893dd",
"metadata": {},
"source": [
"```Figure 3``` above shows the spFSR feature importances and we can observe that with previous feature selection methods, amt_weekends and amt_weekdays shows the most important against all other features."
]
},
{
"cell_type": "markdown",
"id": "26da91f2",
"metadata": {},
"source": [
"### 3.1.5 Performance Comparison using Paired T-Tests <a class=\"anchor\" id=\"3.1.5\"></a>\n",
"\n",
"For comparing the performance of the feature selection methods, we will use statistical tests to determine if there are any performance differences on whether the feature selection methods are statistically significant. As the feature selection methods harbored different data observations due to the random states, we will test it on the same data partitions to compare the full set of features, random forest importance, and spFSR feature selection methods."
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "fec4432a",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Full Set of Features: 0.999\n",
"F-Score: 0.999\n",
"RFI: 0.999\n",
"spFSR: 0.999\n"
]
}
],
"source": [
"#CV mean results comparison\n",
"print('Full Set of Features:', cv_results_full.mean().round(3))\n",
"print('F-Score:', cv_results_fscore.mean().round(3))\n",
"print('RFI:', cv_results_rfi.mean().round(3))\n",
"print('spFSR:', cv_results_spfsr.mean().round(3)) "
]
},
{
"cell_type": "markdown",
"id": "9471db18",
"metadata": {},
"source": [
"The above results indicated that all the feature selection methods has similar performance. Hence, we shall perform statistical tests to diagnose our selection methods on any differences."
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "e4093a94",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"spFSR v. F Score\n",
"nan\n",
"\n",
" spFSR v. RFI\n",
"nan\n",
"\n",
" spFSR v. Full\n",
"nan\n",
"\n",
" F Score v. RFI\n",
"nan\n",
"\n",
" F Score v. Full\n",
"nan\n",
"\n",
" RFI v. Full\n",
"nan\n"
]
}
],
"source": [
"#Paired T-test for feature selection\n",
"from scipy import stats\n",
"\n",
"print('spFSR v. F Score')\n",
"print(stats.ttest_rel(cv_results_spfsr, cv_results_fscore).pvalue.round(3))\n",
"\n",
"print('\\n','spFSR v. RFI')\n",
"print(stats.ttest_rel(cv_results_spfsr, cv_results_rfi).pvalue.round(3))\n",
"\n",
"print('\\n','spFSR v. Full')\n",
"print(stats.ttest_rel(cv_results_spfsr, cv_results_full).pvalue.round(3))\n",
"\n",
"print('\\n','F Score v. RFI')\n",
"print(stats.ttest_rel(cv_results_fscore, cv_results_rfi).pvalue.round(3))\n",
"\n",
"print('\\n','F Score v. Full')\n",
"print(stats.ttest_rel(cv_results_fscore, cv_results_full).pvalue.round(3))\n",
"\n",
"print('\\n','RFI v. Full')\n",
"print(stats.ttest_rel(cv_results_rfi, cv_results_full).pvalue.round(3))\n"
]
},
{
"cell_type": "markdown",
"id": "abebeb36",
"metadata": {},
"source": [
"From the above output, due to the similar cross validation values of 0.999 we received a *nan* error. Thus, we can use either of the four feature selection methods above. Since there are no differences, we shall use the full feature selection method for further analysis."
]
},
{
"cell_type": "markdown",
"id": "b7f4aea0",
"metadata": {},
"source": [
"## 3.2 Model Fitting and Tuning <a class=\"anchor\" id=\"3.2\"></a>\n",
"\n",
"### 3.2.1 Data Sampling & Train-Test Splitting <a class=\"anchor\" id=\"3.2.1\"></a>\n",
"\n",
"Here we will investigate and acquire our optimal parameters to be used for the algorithms. Since we have a low sample of 1561 observations within the dataset, we shall use the 70:30 split for training data and test data, respectively. Furthermore, we will also use a 5-fold stratified cross-validation evaluation method for hyperparameter tuning for all algorithms."
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "178d5c51",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape: (1561, 42)\n",
"Target shape: (1561,)\n"
]
}
],
"source": [
"#Data and target shape\n",
"print('Data shape:',Data.shape)\n",
"print('Target shape:',target.shape)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "65cbd47d",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data train shape: (1092, 42)\n",
"Data test shape: (469, 42)\n",
"Target train shape: (1092,)\n",
"Target test shape: (469,)\n"
]
}
],
"source": [
"#Train test splitting\n",
"D_train, D_test, t_train, t_test = train_test_split(Data, \n",
" target, \n",
" test_size = 0.3, \n",
" random_state=8)\n",
"\n",
"print('Data train shape:',D_train.shape)\n",
"print('Data test shape:',D_test.shape)\n",
"print('Target train shape:',t_train.shape)\n",
"print('Target test shape:',t_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "66ee236b",
"metadata": {},
"outputs": [],
"source": [
"#CV method\n",
"cv_method = StratifiedKFold(n_splits=5, shuffle=True, random_state=999)"
]
},
{
"cell_type": "markdown",
"id": "da21126b",
"metadata": {},
"source": [
"### 3.2.2 K-Nearest Neighbors (KNN) <a class=\"anchor\" id=\"3.2.2\"></a>\n",
"\n",
"Here we will use the K-Nearest Neighbors (KNN) algorithm for predictive analysis. However, before we start with the algorithm we will tune the algorithm by defining the number of neighbors (1-7) and p value of 1 (Manhattan), 2 (Euclidean), and 5 (Minkowski). Furthermore, we shall utilize the cross validation method to aid in finding the optimal parameters for KNN."
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "d90b3eb1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 21 candidates, totalling 105 fits\n"
]
}
],
"source": [
"#Fit KNN algorithm\n",
"model_KNN = KNeighborsClassifier()\n",
"params_KNN = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7], \n",
" 'p': [1, 2, 5]}\n",
"\n",
"gs_KNN = GridSearchCV(estimator=model_KNN, \n",
" param_grid=params_KNN, \n",
" cv=cv_method,\n",
" verbose=1, \n",
" scoring='accuracy',\n",
" return_train_score=True)\n",
"\n",
"gs_KNN.fit(D_train, t_train);"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "8a0f192a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal Parameters for KNN: {'n_neighbors': 7, 'p': 1}\n",
"Score of Best Paramaters for KNN: 0.8324284696912573\n"
]
}
],
"source": [
"#Parameter and score for KNN\n",
"print(f\"Optimal Parameters for KNN: {gs_KNN.best_params_}\")\n",
"print(f\"Score of Best Paramaters for KNN: {gs_KNN.best_score_}\")"
]
},
{
"cell_type": "markdown",
"id": "1911142e",
"metadata": {},
"source": [
"From the above output, we can observe that the optimal paramaters for KNN are an optimal number of neighbors of 7, and optimal distance is 1 (Manhattan Distance). We will now look at the other KNN parameters to observe if the difference is significant or not."
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "a44934b4",
"metadata": {},
"outputs": [],
"source": [
"#Function for table\n",
"def get_search_results(gs):\n",
"\n",
" def model_result(scores, params):\n",
" scores = {'mean_score': np.mean(scores),\n",
" 'std_score': np.std(scores),\n",
" 'min_score': np.min(scores),\n",
" 'max_score': np.max(scores)}\n",
" return pd.Series({**params,**scores})\n",
"\n",
" models = []\n",
" scores = []\n",
"\n",
" for i in range(gs.n_splits_):\n",
" key = f\"split{i}_test_score\"\n",
" r = gs.cv_results_[key] \n",
" scores.append(r.reshape(-1,1))\n",
"\n",
" all_scores = np.hstack(scores)\n",
" for p, s in zip(gs.cv_results_['params'], all_scores):\n",
" models.append((model_result(s, p)))\n",
"\n",
" pipe_results = pd.concat(models, axis=1).T.sort_values(['mean_score'], ascending=False)\n",
"\n",
" columns_first = ['mean_score', 'std_score', 'max_score', 'min_score']\n",
" columns = columns_first + [c for c in pipe_results.columns if c not in columns_first]\n",
"\n",
" return pipe_results[columns]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "1eede93a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean_score</th>\n",
" <th>std_score</th>\n",
" <th>max_score</th>\n",
" <th>min_score</th>\n",
" <th>n_neighbors</th>\n",
" <th>p</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.832428</td>\n",
" <td>0.028838</td>\n",
" <td>0.876147</td>\n",
" <td>0.799087</td>\n",
" <td>7.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.831515</td>\n",
" <td>0.032102</td>\n",
" <td>0.880734</td>\n",
" <td>0.794521</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.829680</td>\n",
" <td>0.031592</td>\n",
" <td>0.876147</td>\n",
" <td>0.794521</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.814109</td>\n",
" <td>0.028129</td>\n",
" <td>0.857798</td>\n",
" <td>0.785388</td>\n",
" <td>6.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.814105</td>\n",
" <td>0.025826</td>\n",
" <td>0.853211</td>\n",
" <td>0.788991</td>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean_score std_score max_score min_score n_neighbors p\n",
"18 0.832428 0.028838 0.876147 0.799087 7.0 1.0\n",
"19 0.831515 0.032102 0.880734 0.794521 7.0 2.0\n",
"20 0.829680 0.031592 0.876147 0.794521 7.0 5.0\n",
"16 0.814109 0.028129 0.857798 0.785388 6.0 2.0\n",
"15 0.814105 0.025826 0.853211 0.788991 6.0 1.0"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Parameter table\n",
"results_KNN = get_search_results(gs_KNN)\n",
"results_KNN.head()"
]
},
{
"cell_type": "markdown",
"id": "00ab4633",
"metadata": {},
"source": [
"From the table output above, we can observe that the differences between the parameter combinations has no significant differences between all parameters."
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "de8c2d42",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0.5, 1.0, 'Figure 4: KNN Performance Comparison')]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 459,
"width": 578
}
},
"output_type": "display_data"
}
],
"source": [
"#Visualize parameters\n",
"results_KNN_1 = pd.DataFrame(gs_KNN.cv_results_['params'])\n",
"results_KNN_1['mean_score']=gs_KNN.cv_results_['mean_test_score']\n",
"results_KNN_1['distance']=results_KNN['p'].replace([1,2,5],[\"Manhattan\",\"Euclidean\",\"Minkowski\"])\n",
"\n",
"sns.lineplot(x=results_KNN_1['n_neighbors'],\n",
" y=results_KNN_1['mean_score'],\n",
" hue=results_KNN_1['distance']).set(title=\"Figure 4: KNN Performance Comparison\")"
]
},
{
"cell_type": "markdown",
"id": "2c7ca1c9",
"metadata": {},
"source": [
"```Figure 4``` above shows the line plot for KNN Performance Comparison where we can observe that as the number of neighbors increases, mean score of the performance increases. Furthermore, we can confirm that Manhattan distance is best performing, which is consistent with our results before. \n",
"\n",
"### 3.2.3 Decision Tree (DT) <a class=\"anchor\" id=\"3.2.3\"></a>\n",
"\n",
"In this section we will use the Decision Tree (DT) algorithm by assessing the criterion gini and entropy to find the best parameters from a max depth of 1 to 8, and a minimum sample split of 2 and 3."
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "3e25ed4b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 32 candidates, totalling 160 fits\n"
]
}
],
"source": [
"#DT algorithm\n",
"df_classifier = DecisionTreeClassifier(random_state=999)\n",
"\n",
"params_DT = {'criterion': ['gini', 'entropy'],\n",
" 'max_depth': [1, 2, 3, 4, 5, 6, 7, 8],\n",
" 'min_samples_split': [2, 3]}\n",
"\n",
"gs_DT = GridSearchCV(estimator=df_classifier, \n",
" param_grid=params_DT, \n",
" cv=cv_method,\n",
" verbose=1, \n",
" scoring='accuracy')\n",
"\n",
"gs_DT.fit(Data, target);"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "0131bdda",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal Parameters for DT: {'criterion': 'gini', 'max_depth': 2, 'min_samples_split': 2}\n",
"Score of Best Paramaters for DT: 0.9993589743589745\n"
]
}
],
"source": [
"#Optimal and score of parameters for DT\n",
"print(f\"Optimal Parameters for DT: {gs_DT.best_params_}\")\n",
"print(f\"Score of Best Paramaters for DT: {gs_DT.best_score_}\")"
]
},
{
"cell_type": "markdown",
"id": "738f6a79",
"metadata": {},
"source": [
"From the output above, we can observe that the optimal parameters for DT are with a criterion of gini, max depth of 2, and a minimum sample split of 2. Additionally, the score for the optimal parameters are 0.9993. We will now look at the other parameters to see whether if there is any significant differences between other parameters."
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "08f477ba",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['criterion', 'max_depth', 'min_samples_split', 'test_score'], dtype='object')"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Output table for parameters for DT\n",
"results_DT = pd.DataFrame(gs_DT.cv_results_['params'])\n",
"results_DT['test_score'] = gs_DT.cv_results_['mean_test_score']\n",
"results_DT.columns"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "bcd6a39f",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>criterion</th>\n",
" <th>max_depth</th>\n",
" <th>min_samples_split</th>\n",
" <th>test_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>gini</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0.996795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>gini</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0.996795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>gini</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>gini</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>gini</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>gini</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>gini</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>gini</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>gini</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>gini</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>gini</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>gini</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>gini</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>gini</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>entropy</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0.996795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>entropy</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0.996795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>entropy</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>entropy</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>entropy</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>entropy</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>entropy</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>entropy</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>entropy</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>entropy</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>entropy</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>entropy</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>entropy</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>entropy</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>0.999359</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" criterion max_depth min_samples_split test_score\n",
"0 gini 1 2 0.996795\n",
"1 gini 1 3 0.996795\n",
"2 gini 2 2 0.999359\n",
"3 gini 2 3 0.999359\n",
"4 gini 3 2 0.999359\n",
"5 gini 3 3 0.999359\n",
"6 gini 4 2 0.999359\n",
"7 gini 4 3 0.999359\n",
"8 gini 5 2 0.999359\n",
"9 gini 5 3 0.999359\n",
"10 gini 6 2 0.999359\n",
"11 gini 6 3 0.999359\n",
"12 gini 7 2 0.999359\n",
"13 gini 7 3 0.999359\n",
"14 gini 8 2 0.999359\n",
"15 gini 8 3 0.999359\n",
"16 entropy 1 2 0.996795\n",
"17 entropy 1 3 0.996795\n",
"18 entropy 2 2 0.999359\n",
"19 entropy 2 3 0.999359\n",
"20 entropy 3 2 0.999359\n",
"21 entropy 3 3 0.999359\n",
"22 entropy 4 2 0.999359\n",
"23 entropy 4 3 0.999359\n",
"24 entropy 5 2 0.999359\n",
"25 entropy 5 3 0.999359\n",
"26 entropy 6 2 0.999359\n",
"27 entropy 6 3 0.999359\n",
"28 entropy 7 2 0.999359\n",
"29 entropy 7 3 0.999359\n",
"30 entropy 8 2 0.999359\n",
"31 entropy 8 3 0.999359"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Output table for parameters for DT cont.\n",
"results_DT"
]
},
{
"cell_type": "markdown",
"id": "ad26d9ea",
"metadata": {},
"source": [
"From the table output above, we can observe that the differences between the parameter combinations has no significant differences between all parameters. Interestingly, many of the combinations yielded similar test score results."
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "0dbd7a1e",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 459,
"width": 596
}
},
"output_type": "display_data"
}
],
"source": [
"#Visualising parameters\n",
"for i in ['gini', 'entropy']:\n",
" temp = results_DT[results_DT['criterion'] == i]\n",
" temp_average = temp.groupby('max_depth').agg({'test_score': 'mean'})\n",
" plt.plot(temp_average, marker = '.', label = i)\n",
" \n",
" \n",
"plt.legend()\n",
"plt.xlabel('Max Depth')\n",
"plt.ylabel(\"Mean CV Score\")\n",
"plt.title(\"Figure 5: DT Performance Comparison\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f5e3de9f",
"metadata": {},
"source": [
"```Figure 5``` above shows the performance comparison for DT with gini and entropy criterion. Interestingly, due to similar values both criterion follows the same line, and as max depth increases, so does mean cv score. In this case, we will use the recommended DT parameters of criterion gini, max depth of 2, and minimum sample split of 2.\n",
"\n",
"### 3.2.4 Gaussian Naive Bayes (NB) <a class=\"anchor\" id=\"3.2.4\"></a>\n",
"\n",
"In this section we will use the Gaussian Naive Bayes (NB) algorithm by optimizing the variance of Laplace smoothing by first performing a power transformation on the data before fitting the model. "
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "a0746673",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
]
}
],
"source": [
"#NB algorithm\n",
"np.random.seed(999)\n",
"\n",
"nb_classifier = GaussianNB()\n",
"\n",
"params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}\n",
"\n",
"gs_NB = GridSearchCV(estimator=nb_classifier, \n",
" param_grid=params_NB, \n",
" cv=cv_method,\n",
" verbose=1, \n",
" scoring='accuracy')\n",
"\n",
"Data_transformed = PowerTransformer().fit_transform(Data)\n",
"\n",
"gs_NB.fit(Data_transformed, target);"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "b3d6299e",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal Parameters for NB: {'var_smoothing': 3.5111917342151273e-09}\n",
"Score of Best Paramaters for NB: 0.9961599901695749\n"
]
}
],
"source": [
"#Optimal and score of best parameter for NB\n",
"print(f\"Optimal Parameters for NB: {gs_NB.best_params_}\")\n",
"print(f\"Score of Best Paramaters for NB: {gs_NB.best_score_}\")"
]
},
{
"cell_type": "markdown",
"id": "5943c129",
"metadata": {},
"source": [
"From the output above, we can observe that the optimal parameters for NB are with variance of Laplace smoothing value of 3.5111917342151273e-09, which is a very low number. Additionally, the score for the optimal parameters are 0.99616. We will now look at the other parameters to see whether if there is any significant differences between other parameters."
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "cc8907ce",
"metadata": {},
"outputs": [],
"source": [
"#Table for parameters for NB\n",
"results_NB = pd.DataFrame(gs_NB.cv_results_['params'])\n",
"results_NB['test_score'] = gs_NB.cv_results_['mean_test_score']"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "75bc7ed0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean_score</th>\n",
" <th>std_score</th>\n",
" <th>max_score</th>\n",
" <th>min_score</th>\n",
" <th>var_smoothing</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>0.99616</td>\n",
" <td>0.003728</td>\n",
" <td>1.0</td>\n",
" <td>0.990415</td>\n",
" <td>1.000000e-09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>0.99616</td>\n",
" <td>0.003728</td>\n",
" <td>1.0</td>\n",
" <td>0.990415</td>\n",
" <td>1.232847e-09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>0.99616</td>\n",
" <td>0.003728</td>\n",
" <td>1.0</td>\n",
" <td>0.990415</td>\n",
" <td>1.519911e-09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>0.99616</td>\n",
" <td>0.003728</td>\n",
" <td>1.0</td>\n",
" <td>0.990415</td>\n",
" <td>1.873817e-09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>0.99616</td>\n",
" <td>0.003728</td>\n",
" <td>1.0</td>\n",
" <td>0.990415</td>\n",
" <td>2.310130e-09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean_score std_score max_score min_score var_smoothing\n",
"99 0.99616 0.003728 1.0 0.990415 1.000000e-09\n",
"98 0.99616 0.003728 1.0 0.990415 1.232847e-09\n",
"97 0.99616 0.003728 1.0 0.990415 1.519911e-09\n",
"96 0.99616 0.003728 1.0 0.990415 1.873817e-09\n",
"95 0.99616 0.003728 1.0 0.990415 2.310130e-09"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Table for parameters for NB\n",
"results_NB_1 = get_search_results(gs_NB)\n",
"results_NB_1.head()"
]
},
{
"cell_type": "markdown",
"id": "a5c21d8f",
"metadata": {},
"source": [
"From the table output above, we can observe that the differences between the parameter combinations has no significant differences between all parameters. Interestingly, many of the combinations yielded similar mean score results."
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "17909e02",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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l1zmWJCbwBgAAAAAELRJLgC+5zrEkMc8SAAAAACBokVgCfCksXDIMyy7zZKYcBQUy807JdDgKf0p4LMltGwAAAACAqiw00AEAQeXAnsLEkmk6d5nPPVD4r1R4zDAkh0Oy/S+v63AUJqRiGkjpx6WC01J4hIzeg2SMulxGfGu/3wYAAAAAAJ6gxxLgI451y+V45t7CRFFpTPOP4w7HH49P50vHUgqTSpKUnydzzVI5nrlXjnXLKzdwAAAAAAAqiMQS4APm/t0yZ06X7HbfVmy3y5w5Xeb+3b6tFwAAAAAAHyCxBPiAuWiO75NKRex2mYvmVk7dAAAAAACcARJLwBkyHQ6ZG1dX7jU2JjChNwAAAACgyiGxBJyp0/lSfl7lXiM/r/A6AAAAAABUISSWgDMVFi6FR1TuNcIjCq8DAAAAAEAVQmIJOEOGzSaj96DKvUbvwTJsvFwBAAAAAFULn1QBHzBGXS6FhFRO5SEhMkZdVjl1AwAAAABwBkgsAT5gxLeWceMU3yeXQkJk3DhFRnxr39YLAAAAAIAPhAY6ACBY2PoPk9mspcxFc2VuWFXyZNuGUfjjcEhFQ9scjj/mTyp+Tsu2st0wmaQSAAAAAKDKIrEE+JAR31rGTVNk3jBZOp0vMyRUhr3A+a8lgeTy2PzgdZlrlvxRV/vOJJUAAAAAAFUaiSWgEhg2mxQRKUOSQkOd/zpFRLo9NmPqWyvJSKvkKAEAAAAAODPMsQRUFfUaWDbNTBJLAAAAAICqjcQSUEUY9Vx6LGWmByYQAAAAAAA8RGIJqCpirD2WlJkm0zQDEwsAAAAAAB4gsQRUFS5D4ZSfL+XmBCYWAAAAAAA8QGIJqCpch8JJDIcDAAAAAFRpJJaAKsIIj5Ciall3ZhwPTDAAAAAAAHiAxBJQlcS4rgxHjyUAAAAAQNVFYgmoSlgZDgAAAABQjZBYAqoQwy2xlBaYQAAAAAAA8ACJJaAqcV0ZLoPEEgAAAACg6iKxBFQlzLEEAAAAAKhGSCwBVQlzLAEAAAAAqhESS0AVYrgOhWOOJQAAAABAFUZiCahKXHssncqVeSo3MLEAAAAAAFAOEktAVRJT330fw+EAAAAAAFUUiSWgCjEio6WIKOtOhsMBAAAAAKooEktAVeMyHI6V4QAAAAAAVRWJJaCqcR0Ol0GPJQAAAABA1URiCahiWBkOAAAAAFBdkFgCqhq3xBJD4QAAAAAAVROJJaCqiWGOJQAAAABA9UBiCahq6jHHEgAAAACgeiCxBFQxzLEEAAAAAKguSCwBVU2MS2IpJ1tmfl5gYgEAAAAAoAwkloCqxnUonMQE3gAAAACAKonEElDVRNWSwsKt+0gsAQAAAACqIBJLQBVjGIb7cDjmWQIAAAAAVEEkloCqyGU4nJlBjyUAAAAAQNVDYgmoilznWaLHEgAAAACgCiKxBFRBRkxD6w7mWAIAAAAAVEEkloCqyG0oHD2WAAAAAABVD4kloCpiKBwAAAAAoBogsQRUQUY911XhGAoHAAAAAKh6SCwBVVGMS2Lp5AmZBacDEwsAAAAAAKUgsQRURa5D4SQpM8PvYQAAAAAAUBYSS0BVVKuOFBpq2WVmpAYoGAAAAAAASkZiCaiCDMOQoutY9pnTHpbj/Vdl7t8doKgAAAAAALAisQRUQY51y6UTLhN2F5yWuWapHM/cW3gcAAAAAIAAI7EEVDHm/t0yZ04vvYDdLnPmdHouAQAAAAACjsQSUMWYi+ZIdnvZhex2mYvm+iUeAAAAAABKQ2IJqEJMh0PmxtWeld2YINPhqOSIAAAAAAAoHYkloCo5nS/l53lWNj+vsDwAAAAAAAESWn6Rmik1NVULFy7U5s2blZqaqtDQUMXFxWngwIG64IILFBERccbX2Lt3r7777jslJiYqLS1NNptNDRs2VK9evTR69Gg1atSo3DocDocSEhK0atUq7dq1SydPnlTt2rXVokULDR48WCNGjFBISIhH8ezYsUPff/+9tm3bpoyMDNWqVUutWrXS8OHDNXjw4DO9XXgiLFwKj/AsuRQeUVgeAAAAAIAAIbFUgk2bNun1119XTk6Oc19eXp6Sk5OVnJysJUuWaOrUqWrSpEmFr/H555/ryy+/lGmalv0HDhzQgQMHtGjRIt15553q169fqXWkp6dr2rRpSkpKsuzPzMxUZmamtm7dqsWLF+vBBx9UTExMmfHMnj1bX3zxhSWejIwMZWRk6Oeff9aqVat0zz33KDycREZlMmw2Gb0HyVyztPyyvQfLsNHpEAAAAAAQOCSWXOzZs0evvvqq8vLyFBkZqcsvv1xdu3ZVfn6+EhIStHjxYh06dEjPP/+8nnvuOUVGRnp9jTlz5mj27NmSpPr16+uSSy5Rx44dJUnbt2/XvHnzlJGRoddee02PPvqo81hx+fn5ev7557V7d+HKYN26ddOoUaMUGxurkydPau3atVq8eLGSk5P1wgsv6Mknn1RYWFiJ8SxevFiff/65JKlJkya64oor1LJlS6Wnp2vhwoXaunWrNm7cqLfffluTJ0/2+n7hHWPU5TJ/WlH2BN4hITJGXea/oAAAAAAAKAGJJRcffvih8vLyFBISoocfflgdOnRwHuvatauaNm2qjz/+WAcPHtT8+fM1btw4r+pPS0vTF198IakwqfTcc8+pQYMGzuPt27fXoEGDNHXqVKWnp+vdd9/VCy+8IJtLz5TvvvvOmVQaPny4Jk2aJMMwnMe7d++u9u3b66233lJycrK+//57jRkzxi2e7Oxsffzxx5KkRo0a6ZlnnlHdunWdx3v37q2XXnpJGzdu1KpVq/SnP/1JnTt39uqe4R0jvrWMG6fInDm95OSSYZNx4xQZ8a39HhsAAAAAAMUxjqaYnTt3auvWrZKkESNGWJJKRcaMGaPmzZtLkhYsWKCCggKvrpGQkKDTp09LksaPH29JKhVp0KCBxo8fL6lwHqYtW7a4lVm+fLkkKSIiQjfccIMlqVRkxIgRzt5Oc+fOlaOEFcQWL16s7OxsSdKECRMsSSVJstlsuuWWW5yJrXnz5nl6qzgDtv7DZHvoFRkDR0quw92at5St/7DABAYAAAAAQDEklopZv3698/GIESNKLGOz2TRsWOGH+uzsbCUmJnp1jeTkZOfjnj17llrunHPOcT5eu3at5Vh+fr72798vSerQoYOio6PLrSczM1Pbtm1zO150z1FRUerfv3+JdTRs2FDdunWTJP366686depUqdeD7xjxrWW7aYqMO/5hPXBgj8yjhwMTFAAAAAAAxZBYKqYo8RIREaE2bdqUWq74ULCSkjVlOXnypPNxvXr1Si1X/Jhr8srTOsqrp6CgQDt37pRUmKAKDS19ZGTRPZ8+fdp5DvzD6NZbqm3tSWauLX9ybwAAAAAAKhtzLBVz4MABSVJcXJxCQkJKLdesWTO3czwVERHhfJyTk6M6deqUWK74inTHjh1TXl6e89ziE4YXL1dePa6xHj58WPb/zeFTNLyvNMWPHzx4UF27di2zfHHHjx8vt0xMTIzzOXedT6oqKh5jpccbHiH1O1eOJfOdu8x1y2VcNqHEIZCAt/zangE/oE0jmNCeEUxozwg2tOlCJJb+Jz8/X1lZWZIKh36VpXbt2oqIiFBeXp5HSZPimjdv7hx+lpiYWOrws99//9352DRNHT9+3JnQio6OVv369ZWenq6kpCQVFBSU2tuoeD2pqamWY8VjL2mup+KKPyfe3vOkSZPKLfPWW2+pYcOGCgkJUVxcnFf1B1psbGylXyNvzDgdLZZY0tHDapB+VBGde1T6tVGz+KM9A/5Em0YwoT0jmNCeEWxqcpuuuSk1F8XnDSreI6g0RWW8nW+ob9++zsezZ89Wfn6+W5n8/HzNnj271PgkqU+fPpKkrKwsffPNNyVea9u2bdq0aZNzOzc313K8+HZ591y8pxVzLPlfeIcuCm3RyrIve8mCAEUDAAAAAEAheiz9T/EET1lzDbmWKSkxVJb27durT58+2rBhg/bu3avHH39c1157rXMFuh07dmjWrFnau3evQkNDnavOuV7n8ssv16pVq5Sbm6v//ve/yszM1AUXXKDGjRvr5MmTWrt2rWbNmiVJCgkJkd1ud6ujaHU6T+45LCzM+djbe37rrbfKLRMTEyNJstvtOnbsmFf1B4LNZnNmpI8ePVriinu+5uh7rnTgI+d29rLvdWr0VTKia8mowd0uceYC0Z6BykSbRjChPSOY0J4RbKprm27cuHGZ0/94i8TS/4SHhzsfFyVzylJUpvh5nrrzzjv17LPPKikpSTt37tRTTz3lVqZt27aKj4/XsmXLJLn3KGrcuLHuvfdevfLKK8rNzdXChQu1cOFCt3omTJigL7/8Una7XVFRUZZjxZNF5d1z8SSUt/dc3tBCV9XlxVjE4XD4J+Z+50pf/5FYUm627H+7VgqPkNF7kIxRl8uIb135cSCo+a09A35Cm0YwoT0jmNCeEWxqcpumm8P/FE/ceDLUq6iMJ8PmXNWqVUtPPPGEJk6cqBYtWliOxcTEaOzYsXryySctQ9Vq167tVk+PHj304osvauTIkW6TgLdv315Tp07VBRdc4Iy1Vq1aljLFE03l3XNeXp7zcUXuGWfOaNREimvhfiA/T+aapXI8c68c65b7PzAAAAAAQI1Fj6X/CQ8PV506dZSVlVXu5NQnT550Jlq87Y1TJDQ0VBdffLEuvvhi5eTkKDMzU5GRkYqJiXGu9LVv3z5JhQmg0ibXbtKkie644w7dfvvtysjIUG5urmJiYhQdHS1J2r59u7OsaxKreJ1paWllxlv8OanoPePMmPt3S0cOlV7Abpc5c7rMZi3puQQAAAAA8At6LBVTlHhJSUmR3W4vtdyhQ398uHdN1lREdHS0mjZtqvr16zuTShkZGUpJSZFUOCyuvKULDcNQ/fr11axZM2dSSSqcwLtI+/btLec0a9bMWe/BgwfLrL/48ebNm3twV/A1c9EcySyna6XdLnPRXL/EAwAAAAAAiaViOnbsKKlw2NeuXbtKLZeYmOh2jq8lJCTINE1J0sCBAytcz6pVqyQV9sjq3bu35VhoaKjatWsnqXDS8LLmWSq657CwMLVt27bC8aBiTIdD5sbVnpXdmCCzho7tBQAAAAD4F4mlYvr16+d8vHTp0hLLOBwOLV9eOI9NrVq11KVLF5/HkZOTo7lz5zqvMWTIkArVs2bNGu3du1eSNGTIELc5liSpb9++kqTc3FytW7euxHqOHz+uX3/9VZLUtWtXt0nA4Qen86X8vPLLSYXlTnu3ch8AAAAAABVBYqmYdu3aqVOnTpIKE0s7duxwKzN//nznsLALL7xQoaHWaaqWLVum8ePHa/z48fr8889LvE56enqpvYNyc3P18ssvKyMjQ5J0/fXXl5rISU1NLfVekpKS9Pbbb0sqnPj72muvLbHceeed5xw69+mnnyorK8ty3OFw6N1333XObn/JJZeUek1UorBwKTzCs7LhEYXlAQAAAACoZEze7eKGG27QI488ovz8fD399NO64oor1KVLF+Xn52v16tX68ccfJUlNmzatcJJl5cqV+uabbzRs2DB17txZ9evXV25urnbs2KEffvjBmTAaPny4Ro4cWWo9zz33nKKiojRkyBC1atVKERERSktL04YNG7R8+XLZ7XaFhYVp8uTJqlevXol11K5dWxMmTNC///1vHTt2TP/85z915ZVXqmXLlkpPT9eCBQu0detWSdLgwYPVtWvXCt0zzoxhs8noPUjmmpJ70lnK9h4so5w5uQAAAAAA8AUSSy5at26tKVOmaMaMGcrNzdWsWbPcyjRt2lRTp049oyFhmZmZmjdvnubNm+d2LCQkRGPGjCm1l1FxO3bsKLFnlVS4ettf//pXdevWrcw6Ro0apfT0dH355Zc6cuSI3nrrLbcyPXv21KRJk8qNB5XHGHW5zJ9WSGVMLK+QEBmjLvNfUAAAAACAGo3EUgn69OmjadOmaeHChdq0aZPS0tIUGhqquLg4DRgwQKNHj1ZEhIfDkkrQv39/nT59Wr/99puOHDmizMxMhYWFqWHDhurevbtGjhyp+Pj4cuu5/vrrtWHDBm3fvl3p6enKzs5WnTp11Lx5c/Xt21cjR45UZGSkRzGNHz9ePXr00Pfff6/ff/9dmZmZqlWrllq1aqXhw4dXeJ4n+I4R31rGjVNkzpxeanLJuOZWGfGt/RsYAAAAAKDGMsyipceAKsBut+vIkSOBDqNcNptNcXFxkqSUlBTnHFT+YO7fLXPRXJkbV0n51km6jYvGy3bFn/0WC4JDINszUBlo0wgmtGcEE9ozgk11bdNNmjRRSEiIz+pjIhagmjHiW8t20xTZZnwuDbLOwWUu/1Zm3qkARQYAAAAAqGlILAHVlGGzyTZ6rHVndpbM1UsCExAAAAAAoMYhsQRUY0bTeKlbH8s+88e5Mh1lTPANAAAAAICPkFgCqjmb6ypwRw9LP68PTDAAAAAAgBqFxBJQ3Z3dXXJZCc7x/Vcy807JrCaTxwEAAAAAqicSS0A1ZxiGjPOvsO5M3ibHXePluPtqOd5/Veb+3YEJDgAAAAAQ1EgsAUHA6DNEqlXH/UB+nsw1S+V45l451i33f2AAAAAAgKBGYgkIBof3SzknSz9ut8ucOZ2eSwAAAAAAnyKxBAQBc9EcyTTLLmS3y1w01y/xAAAAAABqBhJLQDVnOhwyN672rOzGBCb0BgAAAAD4DIkloLo7nS/l53lWNj+vsDwAAAAAAD5AYgmo7sLCpfAIz8qGRxSWBwAAAADAB0gsAdWcYbPJ6D3Is7K9B8uw8bIHAAAAAPgGnzCBIGCMulwKCSm7kM0mY9RlfokHAAAAAFAzkFgCgoAR31rGjVPKTi41aCw1b+W3mAAAAAAAwY/EEhAkbP2HyfbQKzIGjix5zqXUIzJ/WuH/wAAAAAAAQYvEEhBEjPjWst00RbYZn8l4bZbUpLnluPn1RzI9XUEOAAAAAIBykFgCgpBhs8kWXUu2q26yHkg7JnPx/MAEBQAAAAAIOiSWgGDWvY/UsZtll/ntFzKzMgMUEAAAAAAgmJBYAoKYYRjuvZZyc2R+89/ABAQAAAAACCokloAgZ7RqK2PAcMs+c8V3MlMOBCYgAAAAAEDQILEE1ADG5ddLoWF/7LDb5Zj9ocy8UzIdjsAFBgAAAACo1kgsATWA0bCxjFGXWnf+vE6Ou8bLcffVcrz/qsz9uwMTHAAAAACg2iKxBNQQxuhxUkSU+4H8PJlrlsrxzL1yrFvu/8AAAAAAANUWiSWgpjh+VMrPK/243S5z5nR6LgEAAAAAPEZiCaghzEVzJLOc+ZTsdpmL5volHgAAAABA9UdiCagBTIdD5sbVnpXdmMCE3gAAAAAAj5BYAmqC0/llD4MrLj+vsDwAAAAAAOUgsQTUBGHhUniEZ2XDIwrLAwAAAABQDhJLQA1g2Gwyeg/yrGzvwTJsvDUAAAAAAMrHp0eghjBGXS6FhJRdKCRExqjL/BIPAAAAAKD6I7EE1BBGfGsZN04pM7lk/PmvMuJb+y8oAAAAAEC1FhroAAD4j63/MJnNWspcNFfmxgT3Cb1DeUsAAAAAAHiOHktADWPEt5btpimyzfhM6tLTcsxcvypAUQEAAAAAqiMSS0ANZdhsMvoPt+7cullm9smAxAMAAAAAqH5ILAE1mHFOfyk07I8d9gKZm9cELiAAAAAAQLVCYgmowYyoaKlbb8s+c/3KAEUDAAAAAKhuSCwBNZzR91zrjt9/kXkiIyCxAAAAAACqFxJLQA1ndO8jhUf8scN0yNy0OnABAQAAAACqDRJLQA1nRETK6NHPso/hcAAAAAAAT5BYAiCj71DrjqREmenHAxMMAAAAAKDaILEEQOraS4qK/mPbNGVuXBW4eAAAAAAA1QKJJQAywsJlnDPAss9cT2IJAAAAAFC20EBdOC0tTWvWrNG2bdt0/PhxZWdny+FwaMaMGZZyeXl5OnbsmCSpdu3aiomJCUC0QPAz+g6VuWbJHzt2bZd5LEVG47jABQUAAAAAqNL8nljKz8/XRx99pCVLlqigoKDc8qZp6tFHH1V2drZatmypl156yQ9RAjVQpx5SrTpSdpZzl7khQcaFYwMYFAAAAACgKvPrULicnBw98sgj+uGHHzxKKklSZGSk/vSnP0mS9u3bp71791ZmiECNZYSGyug10LLP3MDqcAAAAACA0vk1sTRjxgzt2bNHklS3bl1de+21euaZZzR06NAyzxsyZIjz8ebNmyszRKBGc1sdbt8umSkHAxMMAAAAAKDK89tQuN9++02bNm2SJLVo0UKPPPKIc76k6OjoMs6UWrZsqbp16+rEiRPasWNHZYcK1Fwdu0p1Y6QTGc5d5oaVMsZcE7CQAAAAAABVl996LK1cWTikxjAMTZ482etJuM866yxJ0sGD9J4AKothC5HRe7Bln/nTSpmmGaCIAAAAAABVmd8SS9u2bZMktW3bVq1atfL6/KJEVGZmpi/DAuDC6OcyHO7wfukgc5sBAAAAANz5LbGUkZEhqXAYXEWEh4dLkvLy8nwVEoCStDlbqt/IsstcvypAwQAAAAAAqjK/JZYcDkfhBW0Vu2R2drak8udjAnBmDJtNRt8hln3m+hUMhwMAAAAAuPFbYqlu3bqSpOPHj1fo/N27d0uS13MzAfCe2+pwx1KkfcmBCQYAAAAAUGX5LbHUsmVLSdKOHTuUn5/v1bnJyclKSUmRJLVv397nsQFw0aqd1DjOssuxeqnM//U8BAAAAABA8mNiqWfPnpKk3NxcLVy40OPz7Ha7Zs6c6dzu1auXz2MDYGUYhnuvpSXfyHH31XK8/6rM/bsDExgAAAAAoErxW2Jp2LBhzmFsX3zxhRISEso958SJE3r++eeVlJQkSWratKn69etXmWECKBIZ5b4vP0/mmqVyPHOvHOuW+z8mAAAAAECVEuqvC0VEROiGG27Q9OnTVVBQoNdff13Lli3TwIEDlZaW5iy3b98+HT58WD///LNWrVrlXAXOZrPptttu81e4QI1m7t8tc84npRew22XOnC6zWUsZ8a39FxgAAAAAoErxW2JJkgYOHKjjx4/r448/lmma+uWXX/TLL79Yytx///1u59lsNt1yyy3q3Lmzv0IFajRz0RzJYS+7kN0uc9FcGTdN8UdIAAAAAIAqyG9D4YqMGTNGDz30kOLi4sovLCkuLk7//Oc/dd5551VyZAAkyXQ4ZG5c7VnZjQlM6A0AAAAANZhfeywV6datm6ZPn66NGzdq06ZNSkpKUlpamnJychQZGal69eqpXbt26t27t/r37y+bze/5L6DmOp0v5ed5VjY/r7B8RGTlxgQAAAAAqJICkliSCled6tOnj/r06ROoEACUJCxcCo/wLLkUHlFYHgAAAABQI/ktsfTggw9KksLDw/XYY48pNDRgOS0AZTBsNhm9B8lcs7T8wuf0l0GPQgAAAACosfz2iXDPnj3as2ePatWqRVIJqOKMUZdLISHlF0w/LrO8Sb4BAAAAAEHLb4mlOnXqSJIaNGjgr0sCqCAjvrWMG6eUn1xK2ipz9gf+CAkAAAAAUAX5LbHUsGFDSVJOTo6/LgngDNj6D5PtoVdkDBxZOJeSVDifkkuPQ3PRXDlWfB+ACAEAAAAAgea3MWk9e/bUnj17tH37dn9dEsAZMuJby7hpiswbJheu/hYWLu1MlOOVRyV7gbOc+enbMhvHyejUI4DRAgAAAAD8zW89lkaMGKGwsDClpaVpyZIl/rosAB8wbDYZEZGF/3boKuP6O60F7HY53n5eZsrBwAQIAAAAAAgIvyWWmjRpohtuuEGS9N577ykhIcFflwbgY7bB58kYPda6MydbjhlPyczOCkxQAAAAAAC/89tQuNTUVJ1zzjmaMGGCZs2apddff10LFy7U4MGD1aZNG9WtW1fh4eEe1dWoUaNKjhZAeYwrri/sobRl7R87jx6S463nZZvyuIzQsMAFBwAAAADwC78llu688063fTt37tTOnTu9qscwDP33v//1VVgAKsiw2WS75V45XvyHtG/XHwe2/yrz03ek6++UYRiBCxAAAAAAUOn8NhTOV0zTDHQIAP7HiIiU7a5HpHoNLPvNlT/IXDRXpsMhM++UTIejwtfwRR0AAAAAgMrhtx5LDF8DgpNRv6Fsdz0kx0tTpfx8537zi/dlfv2RVHBaCo+Q0XuQjFGXy4hv7VG95v7dMhfNkblxtZSfV6E6AAAAAACVy2+JpTfffNNflwLgZ8ZZ7WW76V453n7eeqDgdOG/+Xky1yyV+dMKGTdOka3/sDLrc6xbLnPmdMlu/2Onl3UAAAAAACqf3xJLAIKb0XuQNOJiaemC0gvZ7TLff1WOrEwZjZqUWMRMPSLzi/el0oa+2e0yZ06X2awlPZcAAAAAIMBILAHwnVM55ZdxOGR+9q7OaLY0u13morkybppyJrUAAAAAAM5QtZu8G0DVZDoc0sbV/rvexgQm9AYAAACAAKsSPZYyMjJ04sQJnTp1SpGRkapbt65iYmICHRYAb5zOL5xk21/y8wqvGRHpv2sCAAAAACwClljatm2bfvjhB23dulUZGRlux2NiYtSlSxedf/75Ovvss/0fIADvhIVL4REeJpcMqVGsZBjW3aYppR6VPBkoFx5ReE0AAAAAQMD4PbGUlZWlt956Sxs3biyzXEZGhhISEpSQkKA+ffrojjvuUJ06dfwUJQBvGTabjN6DZK5ZWn7ZgSNkK2V+JMf7r3pWR+/BMmyM5gUAAACAQPLrp7ITJ07o0UcfLTGpFBYWptq1ayssLMzt2IYNG/Too48qKyvLH2ECqCBj1OVSSEjZhUJCZIy67MzqsJVdBwAAAADAP/zaY2nGjBk6dOiQc/ucc87Reeedp44dO6pevXrO/SdOnNC2bdu0dOlSbdq0SZJ06NAhvf7663rooYf8GTIALxjxrWXcOEXmzOmS3e5eICRExo1TZMS3rngdktRzQJl1AAAAAAD8w2+JpV9++UW//PKLJCk8PFx33323+vXrV2LZunXrql+/furXr582bdqk6dOnKy8vz1lH9+7d/RU2AC/Z+g+T2aylzEVzZW5MKJxzKTyicOjaqMs8SgiVWEdxyb/LLCiQEVol1h8AAAAAgBrLb0PhVq/+Yxny22+/vdSkkqtevXrpjjvucG4nJCT4PDYAvmXEt5btpimyzfhMtjc+L/z3prJ7KpVVh/HAc9aDGWkyN6/xcdQAAAAAAG/5LbG0bds2SVJcXJyGDBni1bmDBg1SXFycpR4AVZ9hs8mIiDyjSbYNm0229l2kttbVIc0l8880PAAAAADAGfJbYik9PV2S1L59+wqdX3ReRkaGr0ICUI0YI8dYd+z8Xea+5MAEAwAAAACQ5MfEkv1/k/CGVnBOlKLz7KVN5gsgqBm9Bkn1Glj20WsJAAAAAALLb4mlolXf9u/fX6HzDxw4YKkHQM1ihIbKGD7ass9ct0JmVmZgAgIAAAAA+C+xdNZZZ0mSdu7cqd27d3t17p49e5SUlCRJatWqla9DA1BNGOdeIBXv9VhwWubKHwIXEAAAAADUcH5LLPXp08f5ePr06UpLS/PovPT0dL322mvObU9XkwMQfIy69WX0sU7+by7/ViZDZAEAAAAgIPyWWDr33HMVGxsrSUpJSdF9992nhQsX6uTJkyWWz87O1nfffaf7779fhw4dkiQ1adJEQ4cO9VfIAKogY+Ql1h1pqdKWdYEJBgAAAABquIrNpF0BISEhmjx5sp588knl5+crOztbH374oT766CM1a9ZMjRs3VkREhPLy8pSamqqDBw/K4XA4z4+IiNDkyZMVEhLir5ABVEFG6/ZS6w7S7h3OfY4l3yik96AARgUAAAAANZPfeixJUvv27TV16lQ1aPDHyk4Oh0MHDhzQ5s2btXbtWm3evFn79++3JJUaNGigqVOnql27dv4MF0AVZYwcY92xY6vMA97N3QYAAAAAOHN+TSxJUufOnTVt2jSNHTtWMTExZZaNiYnR2LFjNW3aNHXq1Mk/AQKo8ow+g6W6MZZ95pIFgQkGAAAAAGowvw2FK65WrVoaP368xo8frwMHDmjPnj06ceKETp06pcjISNWtW1dnnXWWWrRoEYjwAFRxRmiYjGGjZX7zX+c+c90ymVf+RUbtugGMDAAAAABqloAklopr0aIFCSQAXjPOHS1z4RdS0Ypw+fkyVy2SMXpsYAMDAAAAgBrE70PhAMAXjJgGMnoPtuwzl30r02EPUEQAAAAAUPOQWAJQbblN4n38qPTz+sAEAwAAAAA1kF+Hwn366ac6ffq06tevr0svvdTj8+bNm6f09HRFRkbq6quvrsQIAVQrbTpKrdpJe3c6dzmWzFdIzwEBDAoAAAAAag6/9Vj67bffNHfuXC1cuFA2m3eXNQxDCxcu1FdffaVt27ZVUoQAqhvDMNx7LW37RebBvYEJCAAAAABqGL8lljZs2FB4QZtNQ4cO9ercoUOHyjAMSdJPP/3k89gAVF9G36FSnXqWfeaSBQGKBgAAAABqFr8llpKSkiQVrgJXr169ckpbxcTEKD4+3lIPAEiSERYmY+gFln3m2qUys08GKCIAAAAAqDn8llhKSUmRJGeCyFstWrSw1AMARYzhF0rFh9jm58lM+DFg8QAAAABATeG3xFJOTo4kKTo6ukLnF51XVA8AFDHqN5TRa5Bln7l0gUyHPUARAQAAAEDN4LfEUmRkpKSKJ4aKzgsN9etCdgCqCbdJvFOPSL9uDEwwAAAAAFBD+C2xVDSv0u7duyt0/p49eyz1AIBFu05SfGvLLseS+QEKBgAAAABqBr8lltq1aydJOnTokJKTk706d+fOnTp06JAkqU2bNj6PDUD1ZxiGjPMuse5M3CLz8P7ABAQAAAAANYDfxpX17t1bK1eulCS9++67evzxxxUREVHueadOndK///1vSz3+kJqaqoULF2rz5s1KTU1VaGio4uLiNHDgQF1wwQUexV6evXv36rvvvlNiYqLS0tJks9nUsGFD9erVS6NHj1ajRo3KrcM0Ta1du1YrV67U7t27deLECRmGoZiYGLVr107Dhw/XOeecU+r5W7du1RNPPOFRvOPGjdP48eM9vT3A74y+Q2XOnimdzHLuM5cskDHhjgBGBQAAAADBy2+Jpf79+ysuLk4pKSnatWuXnnzySd15551q1qxZqeccOnRIb7zxhnMYXGxsrAYPHlzpsW7atEmvv/66ZT6ovLw8JScnKzk5WUuWLNHUqVPVpEmTCl/j888/15dffinTNC37Dxw4oAMHDmjRokW688471a9fv1LryMnJ0UsvvaStW7e6HTt69KiOHj2q1atXa9CgQbrrrruYnwpBzwiPkDH0fJnffuncZ65ZIvOK62VE1wpgZAAAAAAQnPyWabDZbLrjjjv01FNPyW63a+fOnbr33nvVrVs3denSRbGxsYqMjNSpU6d09OhRbd26Vb/++qsz8RISEqI77rhDNlvljt7bs2ePXn31VeXl5SkyMlKXX365unbtqvz8fCUkJGjx4sU6dOiQnn/+eT333HPOScm9MWfOHM2ePVuSVL9+fV1yySXq2LGjJGn79u2aN2+eMjIy9Nprr+nRRx91HnP12muvOZNKsbGxuvTSS9WyZUsVFBRo165dmjt3rrKysrR69WrVrl1bt9xyS5lxTZo0SW3bti31OPNboTowhl0k87uvJdNRuCPvlMzVi2X86dLABgYAAAAAQcivXVg6deqkO++8U2+99ZZOnz4t0zT1yy+/6JdffinzvNDQUE2aNEldunSp9Bg//PBD5eXlKSQkRA8//LA6dOjgPNa1a1c1bdpUH3/8sQ4ePKj58+dr3LhxXtWflpamL774QlJhUum5555TgwYNnMfbt2+vQYMGaerUqUpPT9e7776rF154wS2htmvXLm3evFmS1KRJE7344ouKioqyxDpo0CDdf//9ys7O1qJFizR+/HjVrVu31NhiY2PVsmVLr+4HqGqMho2lngOkTaud+8ylC2SOHCOjkhPTAAAAAFDT+P1T1uDBg/XUU09ZEjZl6dixo55++mkNGTKkkiMrnCS8qAfQiBEjSoxxzJgxat68uSRpwYIFKigo8OoaCQkJOn36tCRp/PjxlqRSkQYNGjjnMtq7d6+2bNniVmbbtm3OxxdddJElqVSkUaNGGj58uKTCuZiSkpK8ihWormwjx1h3HD0sbd0UmGAAAAAAIIgFZNKd1q1b66mnntLOnTu1ZcsWJSUlKSMjQ6dOnVJkZKRiYmLUvn17nXPOOc7V5Pxh/fr1zscjRowosYzNZtOwYcP06aefKjs7W4mJierevbvH1yi+Il7Pnj1LLVd8wu21a9eqV69eluPFE1plzfVU/FhRQgsIeh26SM1bSQf3Onc5lsxXSLc+AQwKAAAAAIJPQGdzbteunV8TR+Up6gUUERGhNm3alFquc+fOlnO8SSydPHnS+bisOYuKH0tMTHQ7XnzS8yNHjpRaT/FjZU2UDgQTwzBkjBwj86M3/9j52yaZKQdlxDUPXGAAAAAAEGRYJqyYAwcOSJLi4uIUEhJSarniCZqiczwVERHhfJyTk6M6deqUWK74inTHjh1TXl6e5dwePXqocePGOnbsmBYuXKgRI0a4TSR+/PhxLVu2TJLUoUOHcudPmjVrllJTU5WZmanIyEg1btxYnTt31vnnn39GSanjx4+XWyYmJsb5nFf2BO2+UDzG6hBvTWQMHKGCLz+Ucv5I5mrZQtmuuz1wQVVRtGcEG9o0ggntGcGE9oxgQ5suRGLpf/Lz85WVlSVJatiwYZlla9eurYiICOXl5XmUNCmuefPmziF3iYmJ6t+/f4nlfv/9d+dj0zR1/PhxS3InLCxMkydP1gsvvKAjR47o/vvv16WXXqr4+HjZ7Xbt2rVL8+bNU3Z2tho3bqy//vWv5ca2Y8cO5+Ps7GxlZ2drz549+vbbbzV27FhdddVVMgzDq/uVClebK89bb72lhg0bKiQkRHFxcV5fI5BiY2MDHQJKkXHhFcr68iPntrl6iWLvuE+26FoBjKpqoz0j2NCmEUxozwgmtGcEm5rcpqtUYun06dNKTk5Wenq6wsLC1KhRI5111ll+ufapU6ecj117/pQkMjJSeXl5lvM80bdvX82ZM0eSNHv2bPXs2VPh4eGWMvn5+Zo9e3ap8RXp2LGjXnzxRS1cuFDffvut/v3vf7vFOH78eJ1//vllrgZXv3599evXT2effbaaNGkim82m1NRUbdy4UStWrJDdbtfs2bNVUFCg6667zqv7BQKp9sVXKevrTySHQ5Jk5mYre/F81bnk6gBHBgAAAADBoUoklk6dOqX//ve/Wrx4sfLz8y3HYmJidNlll2n06NGV2rWs+HVDQ8t/WorKuMZbnvbt26tPnz7asGGD9u7dq8cff1zXXnutcwW6HTt2aNasWdq7d69CQ0Odk3SXdB3TNLV27VqtW7dOdrvd7fipU6eUkJBgWR3OVdu2bfXmm2+63XObNm3Ur18/jRo1Sk8//bRycnI0d+5cDRo0yOtk31tvvVVumZiYGEmS3W7XsWPHvKo/EGw2mzMjffToUTn+l7hAVWOT0b2fzC1rnXsy5szSyd5DZdTgrqquaM8INrRpBBPaM4IJ7RnBprq26caNG5c5/Y+3fJ5YMk1Td999t7Kzs2Wz2fTcc8+V2SUsOztbjz32mPbv31/i8YyMDH344Yfatm2bpkyZUmnJpeK9hoqvuFaaojKuvY08ceedd+rZZ59VUlKSdu7cqaeeesqtTNu2bRUfH++cI8m1F5XD4dD06dO1dm3hB+aRI0fqggsuUPPmzeVwOLRnzx7NmzdPGzZs0L/+9S/t27dPf/nLX9yuU17vrHbt2unmm2/WjBkzZJqmvvvuO91xxx1e3W95QwtdVZcXYxGHw1HtYq5JjJEXWxJLSjkgx9bNMrqUvipjTUZ7RrChTSOY0J4RTGjPCDY1uU37PEuTnJysY8eOKScnR61bty53nOGbb75ZalKpuHXr1jmHkFWG4gkWT4a3FZXxZNicq1q1aumJJ57QxIkT1aJFC8uxmJgYjR07Vk8++aRyc3Od+2vXrm0p98MPPziTSldddZXuuOMOtW7dWuHh4YqMjNTZZ5+tBx54QOeee64kaf78+dq0aZPXsUrSoEGDFB0dLck69xNQLZzdXWpmnbjesfibAAUDAAAAAMHF5z2Wiicehg4dWmbZxMREbdy40bkdExOj66+/Xj179lRYWJj27Nmjzz77TL/99psk6euvv9bo0aOdSQ5fCg8PV506dZSVlVXuhNwnT55UXl6eJO974xQJDQ3VxRdfrIsvvlg5OTnOldhiYmKcE2Tv27dPkhQVFaUGDRpYzl+yZInz2OWXX17qda699lqtWLFCkrR48WL16tXL61hDQkLUtGlTJScnKy0tzevzgUAyDEPGiItlflJsSOavG+Q4sEdGs5aVOiTOdDik0/lSWDhD7wAAAAAEJZ9/0tm1a1dhxTab+vTpU2bZH3/80fk4PDxcjz32mIYMGaJatWopPDxcHTp00EMPPaSuXbtKKpxnqKiXTmUo6j2UkpJS4pxFRQ4dOuR2zpmIjo5W06ZNVb9+fWdSKSMjQykpKZIKh8W5DgE8ePCg8/phYWGl1t2wYUPVq1fPLW6gJjEGDJeirCvBmU9MluPuq+V4/1WZ+3f79Hrm/t1yvP9qYf13ja+06wAAAABAoPk8sXT48GFJUtOmTVWrVulLejscDktvpfPOO0/NmjVzD9Bm08SJE53bRb2XKkPHjh0lSXl5ec4EWUkSExPdzvG1hIQEmaYpSRo4cKDb8aJEU1kJsCJFZSo6P5XdbncmperXr1+hOoBAMiKjpLZnux/Iz5O5Zqkcz9wrx7rlPrmWY91yOZ65V+aapVJ+XqVdBwAAAACqAp8nloqGkZXXk2fXrl2WuYyK5gIqScuWLZ1Jp7179/ogypL169fP+Xjp0qUllnE4HFq+vPCDYa1atdSlSxefx1G0AlvRNYYMGeJWpmjuqv379ys7O7vUuvbt26eTJ09azvFWQkKCc76nzp07V6gOIJDM/bulxM2lF7DbZb4/XY6tm2WmH6/wj2PrZpnvT5dKS/ja7TJnTqfnEgAAAICg4fM5looSEK6TTbtKSkpyPo6OjlabNm3KLN+yZUsdOnRIGRkZZxxjadq1a6dOnTrp999/19KlSzV8+HB16NDBUmb+/PnOYWgXXnihQkOtT+GyZcv0r3/9S5I0btw4jR8/3u066enpqlOnjtu5UuHz9/LLLzvv8/rrr1dUVJRbud69e2v//v06ffq0PvzwQ02aNMk5jK5Ifn6+Zs6caTmnuJMnT2rv3r1lJsd27tzprMMwDI0aNarUskBVZS6aI5W3QoPDLnP6YzIrOxi7XeaiuTJumlLZVwIAAACASufzxFLR8nrlLbOXnJzsfFxeUkmSc1idJyu2nYkbbrhBjzzyiPLz8/X000/riiuuUJcuXZSfn6/Vq1c754Vq2rSpLrnkkgpdY+XKlfrmm280bNgwde7cWfXr11dubq527NihH374QampqZKk4cOHa+TIkSXWMWbMGC1dulSZmZlatmyZUlJSNGrUKDVv3lwOh0O7d+/Wt99+qwMHDkiSmjdvruHDh1vqyMnJ0RNPPKFWrVqpb9++atOmjWJiYmSz2ZSamqqNGzdqxYoVzqF0l1xyidq2bVuhewYCxXQ4ZG5cHegwLMyNCTJvmMyE3gAAAACqPZ8nlqKjo5WVlVVuz6LiiaXWrVuXW68ncwn5QuvWrTVlyhTNmDFDubm5mjVrlluZpk2baurUqSX2JPJUZmam5s2bp3nz5rkdCwkJ0ZgxY3TttdeWen7dunX10EMPadq0aTp69Ki2bdumbdu2lVj2rLPO0v33319iDympcHhhWUMMbTabxo4dq3HjxpVzV0AVdDr/j7mOqor8vMK4IiIDHQkAAAAAnBGfJ5YaN26srKwsJScnyzRNt+FZUuGKZ8VXKHMdblaSrKwsSYWJq8rWp08fTZs2TQsXLtSmTZuUlpam0NBQxcXFacCAARo9erQiIiIqXH///v11+vRp/fbbbzpy5IgyMzMVFhamhg0bqnv37ho5cqTi4+PLreess87StGnTtHz5cm3YsEF79+7VyZMnZRiG6tatq9atW2vAgAEaNGhQiUmlBg0a6N5779WOHTu0c+dOpaWlKSsrS/n5+YqOjlazZs3UpUsXjRw5ssLzMwEBFxYuhUdUreRSeERhXAAAAABQzfk8sdS2bVvt2rVLJ06c0Pr16y0TYhdJSEhwPg4JCfFoQuh9+/ZJKkxc+UPjxo01ceJEy4p0nhg+fLjbkDNXTZo00dixYzV27NgziLBQZGSkLrjgAl1wwQVenxsaGqoBAwZowIABZxwHUFUZNpuM3oMKV2krT79zZZtwR4Wv5fj4LWn9yvILNm3BMDgAAAAAQcHnn2wGDx7sfPzee+9ZeiZJ0qFDhzRnzhzndrdu3cqd6DsjI0PHjh2TJOfqcADgKWPU5VJISNmFQkJkGz1WRnTtCv/YLhxX/nUkaW+yHD+6D4MFAAAAgOrG5z2WOnXqpA4dOmjHjh3KyMjQgw8+qN69eys2NlZHjx7Vpk2blJf3x5CUMWPGlFvn2rVrnY89GTYHAMUZ8a1l3DhF5szpUknztYWEyLhxioz48ud7O6PrFGN+9q4coaGyDb/ojK4JAAAAAIHk88SSJE2aNEmPPPKITp48qfz8fK1Zs6bEcgMHDlS3bt3KrW/ZsmXOx54MmwMAV7b+w2Q2aylz0VyZGxMK51wKj5DRe7CMUZedcVKpvOuoWUtpT5KlrPnJ23KEhMo29HyfXBsAAAAA/K1SEkvNmjXTo48+qunTp7sNhSvSt29f/fWvfy23rl9//VW7d++WJMXGxqpFixY+jRVAzWHEt5Zx0xSZN0wuXJUtLLxS5joq7TqOb7+U+dWHlrLmR28WJpcGjfR5HAAAAABQ2SolsSRJrVq10rRp07R+/Xr98ssvOn78uAzDUFxcnPr16+dxz6O9e/dq4MCBkuRR7yYAKI9hs0kRkX6/ju3CsXIUnJY579M/CpmmzA9eLxwW1+/cSo8JAAAAAHyp0hJLUuGKb2e66pgnczABQHVhjLlaKiiQufDzP3aaDpnvvSIzJFRG70GBCw4AAAAAvMR61wDgR4ZhyLh8gozzr7AecDjk+PdLMresC0xgAAAAAFABJJYAwM8Mw5Ax7gYZ511iPWC3y/HOCzJ/3RiYwAAAAADASySWACAADMOQcfUtMoaNth4oKJDjX8/KTNwSkLgAAAAAwBsklgAgQAzDkHHdHTIG/8l6oOC0HG8+LXP7b4EJDAAAAAA8RGIJAALIsNlk/OVOGQNGWA/k58sx40mZOxMDExgAAAAAeIDEEgAEmGELkXHDZBl9h1oP5J2S47UnZO7eEZjAAAAAAKAcJJYAoAowQkJk3HSP1Gug9cCpXDlefUzm3uTABAYAAAAAZSCxBABVhBEaKtut90k9+lkP5GbL8eqjMg/sDkxgAAAAAFAKEksAUIUYoWGy3f6g1LWX9UB2lhwvPyLz0L7ABAYAAAAAJSCxBABVjBEWJtukqVKnHtYDJ0/I8fLDMlMOBCYwAAAAAHBBYgkAqiAjPEK2Ox+SOnSxHjiRUZhcOno4MIEBAAAAQDE+TywVFBT4ukoAqJGMiEjZ7n5Eanu29UBGWmFyKfVIYAIDAAAAgP/xeWLp1ltv1bvvvqsdO1geGwDOlBEZLdvkx6Sz2lsPpB0rTC6lHQtMYAAAAACgSkgs5eTkaNGiRXrkkUf0t7/9TV9//bVSU1N9fRkAqDGM6FqyTXlCatnGeiD1SOGE3hnHAxMYAAAAgBqvUudYSklJ0X//+1/dddddeuqpp7RixQrl5eVV5iUBICgZtWrLds+TUvNW1gNHDxUml06kByYwAAAAADWazxNLf/7zn9WyZUvLPtM09dtvv+nNN9/Ubbfdpn/961/67bfffH1pAAhqRu26st37lNQ03nog5YAcrzwqM+tEYAIDAAAAUGMZpmmalVHxvn37tGzZMiUkJCgjI6PEMo0aNdK5556rc889V02bNq2MMFDN2O12HTlS9SckttlsiouLk1TYM8/hcAQ4ItQkZkaaHC/9Uzp6yHogvrVsf39aRq06XtVHe0awoU0jmNCeEUxozwg21bVNN2nSRCEhIT6rr9ISS0UcDod++eUXLV++XBs2bFB+fn6J5Tp06KBhw4Zp0KBBio6OrsyQUIWRWAI8Y6alyjHtn9KxFOuBVu1ku/cpGdG1PK6L9oxgQ5tGMKE9I5jQnhFsqmubrnaJpeJyc3O1Zs0arVy5UomJiSWWCQsLU+/evTVs2DCdc845stkqdRooVDEklgDPmcePyvHiVMl1Zbg2HWW75wkpPFI6nS+Fhcso472U9oxgQ5tGMKE9I5jQnhFsqmubrtaJpeJSU1O1fPlyrVy5UocPHy6xTL169TR06FANGzbMbd4mBCcSS4B3zGMphckl15Xh6tST8k5J+XlSeISM3oNkjLpcRnxrtzpozwg2tGkEE9ozggntGcGmurbpoEksFZeUlKTly5drzZo1OnnyZIllzjrrLA0bNkxDhgxR3bp1/Rwh/IXEEuA988ghOV6aKmWWszJcSIiMG6fI1n+YZTftGcGGNo1gQntGMKE9I9hU1zYdlImlIgUFBdq0aZNWrFihzZs3q6CgwK1MaGioPvnkkwBEB38gsQRUjHl4vxzPPyjllJycdwoJke2hVyw9l2jPCDa0aQQT2jOCCe0Zwaa6tmlfJ5ZCfVaTD4SGhqpfv37q16+fTp48qYSEBK1YsUI7d+50likp2QQANZ3RNF5q31n6+aeyC9rtMhfNlXHTFL/EBQAAACC4VdmZse12u06fPq3Tp08HOhQAqPJMh0P6/WfPym5MKCwPAAAAAGeoSvVYys/P1/r167VixQr98ssvJXYjCw8PD0BkAFDFnc4vnKjbE/l5heUjIis3JgAAAABBr0oklrZu3aoVK1Zo3bp1ys3NLbFMhw4dNGzYMA0aNMjP0QFANRAWLoVHeJZcCgktLA8AAAAAZyhgiaVDhw5p+fLlWrVqlVJTU0ss06hRI5177rkaNmyYc0IsAIA7w2aT0XuQzDVLyy9sL5D53/+Trr5Vhg8n7QMAAABQ8/w/e/cdXlWV9XH8t08qvUPo0iGA9CIiCShWFAsyrzqOOjozYkXHhgoWsGPBho46jo7iqDgDiCggAqEI0kQgtIB06QQIgZR79vvHJdeEm5BCctK+n+eZZy7Z6+yzTmbPTe7KPut4Wlg6evSoFixYoLlz52rz5s3ZxkRGRqpXr16KiYlR+/btvUwPAEo1M/BK2Z/iJJ8v11g7e5rs7p1y/vawVKWqB9kBAAAAKIuKvLCUnp6upUuXKi4uTj///LN82XzgMcaoffv2iomJUa9evRQREVHUaQFAmWMaN5O5Zbjsh6/lqbiktSvlPvegnHtGSewKBQAAAFAARVZYWrduneLi4vTjjz8qOTk525gGDRqoX79+6tevn2rVqlVUqQBAueH0ipFt0ER25mTZZQv8PZfCI2S6nStbr6E09T9Seqanbe7ZqfRn7teJx15SZOeexZc4AAAAgFKp0AtLX3zxhebNm6e9e/dmO16pUiX16dNHMTExatWqVWGfHgDKPdO4mcyfh8vefI//6W9h4TKOI0my7c6W+9Yz0pHE3w9IPqZ9I+9WjdsflLr1LZ6kAQAAAJRKhV5Y+uqrr4K+5jiOOnfurJiYGHXv3l2hoSXiYXQAUKYZx5EiIrN+rXkbOY+9LPfNMdL2X38fcH069PbzcgZcJg29jabeAAAAAPKkSCs8TZs2VUxMjM477zxVrUpzWAAoCUzNOnIefkHuB69IKxZlGXN/+EbavVPOXx+SqVS5mDIEAAAAUFoUemGpWrVq6tu3r2JiYtS0adPCnh4AUAhMRKSc2x+RnTxBdtoXWQfjf/Y39b57pEy9BsWTIAAAAIBSodALS++8846ck708AAAll3Ecmav+KDVsLN+/3vD3Y8qwZ6fcZx+Qc/vDMu06FV+SAAAAAEq0Qq8AUVQCgNLF6d1fdZ9/V071U57OmZwk97Un5M6ZVjyJAQAAACjxiqTHUlJSkp544gmlpKTIGKO//OUvOvvss/M9zy+//KL33ntP1lpVqFBBo0ePVmRkZO4HAgDyJaJtR9V79SP9Nuoeafvm3wdcV/bTd+Tu2ibzh7/Q1BsAAABAFkWyvei///2vduzYoX379qlLly4FKipJ0tlnn63OnTtr37592rZtm6ZMmVLImQIAMoTWjVLoiBelLr2DxuzsaXJff0r2WFIxZAYAAACgpCr0wlJqaqpmzZolyd/I+4Ybbjij+W644YbAE+WmT5+u9PT0M84RAJC9jKbe5tKhwYMnm3rbPbu8TwwAAABAiVTohaWffvpJJ06ckCQNHjxYERERZzRfZGSkBg8eLMl/i92yZcvOOEcAQM6M48i56o8yt94vhYZlHTzZ1NuuXVk8yQEAAAAoUQq9sLRmzRpJkjFG5557bqHMmXmeVatWFcqcAIDTc3rHynngGalq9awDgabe3xZLXgAAAABKjkIvLG3e7G/62qRJE1WvXr1Q5qxRo4aaNm0qSdq0aVOhzAkAyJ1p0VbOoy9LjZplHXBd2U/Hy53wrqzPVzzJAQAAACh2hV5YOnjwoCSpbt26hTpvxnwHDhwo1HkBAKdnatWR8/DzUufsmnp/Q1NvAAAAoBwr9MJScnKyJAUabheWjPmOHTtWqPMCAHJnIivIGfaIzKXXBg/S1BsAAAAotwq9sBQaGipJgQbehSUlJSXL/AAAb/mbet9IU28AAAAAAYVeWMrYWXT48OFCnTcxMTHL/ACA4kFTbwAAAAAZCr2wVLNmTUnSxo0blZ6eXihzpqena+PGjVnmBwAUH5p6AwAAAJCKoLAUHR0tyX/r2ooVKwplzhUrVgRuhWvXrl2hzAkAODO5N/V+WjaZpt4AAABAWVbohaVOnToFXv/nP/+R67pnNJ/ruvr8888D/+7cufMZzQcAKDynb+q9gqbeAAAAQBlX6IWltm3bqlkz/60RO3bs0DvvvHNG87377rvavn27JKlZs2Zq27btGecIACg8vzf1vi+4qfdumnoDAAAAZVmhF5Yk6f/+7/8Cr+fOnasXXngh0Hw7r44cOaIXX3xRc+bMyXZeAEDJ4vTu72/qXaVa1gGaegMAAABlVpEUljp37qzLLrss8O/ly5fr3nvv1T/+8Q/Fx8cH+iWdKiUlRfHx8frHP/6hu+++W8uWLQuMXXLJJdwGBwAlnGnRVs5jr+Ta1Nu6rmzKCdkzvF26OJWFawAAAADOVGhRTXzjjTfq4MGD+vHHHyVJJ06c0KxZszRr1iw5jqPatWurYsWKioyM1IkTJ5ScnKz9+/dn25Opd+/euummm4oqVQBAIcpo6u1+8Kr086IsY3b2N7LLf5SOH5NSU6TwCJlufWQGXinTuFkOM5YsdvuvsjMnyS5bWGqvAQAAACgsRVZYMsZo+PDhatmypSZMmCBfpsdOu66rvXv35jqH4zi67rrrdMUVVxRVmgCAIpDR1NtO/lR22pdZBw8f/P11aorsj7Nlf4qTuWW4nF4x3iaaT+7iubIfviZl+plW2q4BAAAAKExFVljKMGjQIPXs2VNff/215syZo9TU1FyPCQ8PV2xsrC6//HLVrVu3qFMEABQB4zgyV90ot34j2X+9IfnScw72+WQ/eEW+X5bInNqjqYSwRw9LS+ZJ1mYf4PPJfviabIMm7FwCAABAuVHkhSVJqlu3rm699Vb96U9/0saNG7Vu3Trt3btXSUlJOnHihCIjI1W5cmXVrVtXbdu2VatWrRQWFpb7xACAEs/p3V++xXHS6mWnD7RW+ilOOZRtSgefT3bmZJk/Dy/uTAAAAABPeFJYyhAWFqbo6GhFR0d7eVoAQDGyrittWF3caXjGLlsge/M9Mk6RPB8DAAAAKFH4rRcAULTSUv1NrsuL1BTZ9eWnkAYAAIDyzdMdSwCAcigsXAqPyFtxyXGkPufLGFP0eeWDtVZaOEvK5sml2ca/8rh83frIufpPMnUbFHF2AAAAQPGhsAQAKFLGcWS69ZH9cXbusb1i5dx0twdZ5Z/rS8/TNQQsWyj358UyMZfIDPpDiW1KDgAAAJwJboUDABQ5M/BKKSTk9EEhITIDB3uST0Hk6RpO5fPJ/jBV7mN/kzvtS9nydEsgAAAAygUKSwCAImcaN5O5ZXjOhZmQEJlbhss0buZpXvmR6zU4IVLvWKli5eCx48my//u33MeHyV0wS9b1FWWqAAAAgGe4FQ4A4AmnV4xsgyayMyfLLlvg77kUHiHT7VyZgYNLdFEpQ16uwR5Lkv32S9lZU6X0tKwTHNov+69xst9PlnPNzVL7LiWunxQAAACQH8Zaa4s7CSCDz+fTnj17ijuNXDmOo6ioKEnS7t275eaxoS9QEhXHerau639aXFi4jFM6N8/mdg32wF7ZSZ/KLjpNX6Z2neQMuVmmSYsizLT84T0aZQnrGWUJ6xllTWld0/Xq1VNIfls8nEbp/G0eAFCqGceRiYgstUUlKfdrMLXqyrn1PjkjX5Xadcp+krUr5Y65X+4Hr8oe2FuE2QIAAABFo/T+Rg8AQClgmrSQc9/Tcu59QmrYNDjAWtlFs/39lyZ+KJuc5H2SAAAAQAHRYwkAgCJmjJE6dJMT3Vn2x9mykz6VEg9kDUpPk53+P9n538sMGioTc6lMWFjxJAwAAADkETuWAADwiHFC5Jx7gZwx78hcdaMUWSE46NhR2c8/kDvqDrk/xfl7OQEAAAAlFIUlAAA8ZiIi5Fx6rZxn/yEzYJCUXfPE/Xtk3xsr97kHZdev9j5JAAAAIA8oLAEAUExMlWpyrvurnKffkrr1yT5oy0a5Yx+V743Rsru2eZsgAAAAkAt6LAEAUMxM3QYKuf0R2U3r5E78UEpYGxz0yxK5q5bJ9L1A5orrZarX9D5RAAAA4BTFUljav3+/Fi5cqISEBO3bt0/Jycny+Xx5OtYYozfeeKOIMwQAwHumRVs5Dz0v/bxY7lcfSXt2Zg2wruy8GbKL58pceJXMRVfKRFYsnmQBAAAAeVxYSktL00cffaTvv/9e1lovTw0AQKlgjJG69JbTsbvs/BmyUz6Tjh7OGpSaIjv1P7Jx38lcfp1M34EyoWxCBgAAgPc87bE0duxYzZw5k6ISAAC5MKGhcmIvlfPsuzKD/iCFRwQHHUmU/XS83Kfull2xiJ+vAAAA8Jxnf96cN2+efv7558C/W7RoodjYWDVr1kxVqlRRSHZPxAEAoJwzkRVlBt8gG3Ox7JTPZOd/L1k3a9DunXLfflZqGS1nyM0yLdoWT7IAAAAodzwrLMXFxQVeX3755frjH//o1akBACj1TPVaMn+6S/b8K+T+9yPplyXBQQnxcp9/SOrWR87Vf5Kp28D7RAEAAFCueHYr3JYtWyRJtWvX1vXXX+/VaQEAKFNMwyYKuXuknAeekZq2zD5o2UK5o+6S+5/3ZI8e8TZBAAAAlCueFZaSk5MlSe3atZPjeNraCQCAMse06Sjn0bEyf3lAqlU3OMCXLjvra7mP/VXutC9lU1O8TxIAAABlnmcVnmrVqkmSwsLCvDolAABlmnEcOT37yRk9XmborVLFysFBx5Nl//dvuY8Pk7tglqzr8z5RAAAAlFmeFZaaNm0qSdqzZ49XpwQAoFwwYWFyBg6W8+w/ZC66SgrN5o84h/bL/muc3NH3ya5e7n2SAAAAKJM8Kyz1799fkrR+/XodOnTIq9MCAFBumEqV5Qy5Rc6Y8TK9+2cftGOL3HFPyvfqKNltm71NEAAAAGWOZ4Wlnj17qlu3bkpPT9e7774r13VzPwgAAOSbqVVXzq33yRn5qtSuU/ZB8T/LHXOf3A9elT2wz9sEAQAAUGZ42kX7rrvuUocOHbRixQqNHj068KQ4AABQ+EyTFnLue1rOvU9IDZsGB1gru2i23MdvlzvxX7LJSd4nCQAAgFIt1KsTvf3225KkGjVqKDQ0VPHx8Xr44YdVr149NWnSRBUrVszTPMYYDRs2rChTBQCgzDDGSB26yYnuLPvjbNlJn0qJB7IGpafJTv+v7PyZMoOGysRcKnPKwzas60ppqVJYuAxPdwUAAMBJnhWW5s6dm+3X9+zZk++G3hSWAADIH+OEyJx7gWz382RnTZH9dqJ04njWoGNHZT//QHbWVJmrbpTpcZ60Y4vszEmyyxZKqSlSeIRMtz4yA6+UadyseC4GAAAAJYZnhSUAAFD8TESEzKXXyp53oezUz2Xnfiv5fFmD9u+RfW+s7P8+kQ7ulTL3RUxN8e98+ilO5pbhcnrFeHsBAAAAKFE8KyyxywgAgJLDVKkmc91fZc8fJPe/H0vLFgYH7d+d8wQ+n+yHr8k2aMLOJQAAgHLMs8JSbGysV6cCAAB5ZOo2UMjtj8huWid34odSwtq8H+zzyc6cLPPn4UWWHwAAAEo2um8CAACZFm3lPPS8nDseleo2yPNxdtkCf2NvAAAAlEsUlgAAgCT/E+RMl94yj47N+0GpKbLJx4ouKQAAAJRoFJYAAEAWpkJFKTwiz/H2iTvlfvOF7NEjRZgVAAAASiIKSwAAIAvjODLd+uT9gCOJspM+kfvIn+X++23Z33YUXXIAAAAoUTxr3n2q5ORkrVu3Tlu2bNGRI0d04sQJuXno0WCM4QlzAAAUMTPwStmf4iSfL+8HpabKxn0nG/ed1LG7nIGDpbZnyxhTdIkCAACgWHleWEpKStKnn36q+fPnKzU1tUBzUFgCAKBomcbNZG4ZLvvha9kXlxxHqtdQ+m179hOsWip31VKp0VkyAwfL9OgnExZWpDkDAADAe54Wlvbt26cnnnhCBw4c8PK0AACgAJxeMbINmsjOnCy7bIGUmiKFR8h0O9dfLGrcTHbbJtmZU2SX5LC7accW2Q/Hyf73Y5nYS2ViLpGpUtXzawEAAEDR8KywZK3V2LFjA0WlJk2a6LzzztPKlSu1evVqSf6dSCdOnNC+ffu0du1abdq0SZIUGRmpIUOGqEqVKl6lCwAAdHLn0p+Hy958j5SWKoWFyzi/t2g0TVrI3Hqf7DV/kp09TXbOt1JyUvBEhw/JTv5UdtqXMuf0l7ngCpn6jT28EgAAABQFzwpLixcv1pYtWyRJnTt31kMPPaSQkBDt378/UFiKjY3NcszmzZv17rvvasuWLZo2bZoef/xxNWzY0KuUAQDAScZxpIjInMer15K56kbZS6+V/fEH2e+/lvbsDA5MS5WNmy4bN50+TAAAAGWAZ0+F++mnnyT5m2/fdtttCgkJyfWY5s2ba/To0WrdurUOHjyoV199tcB9mQAAQNEzEZFyYi+V8/Rbcu4aKbXpmHPwqqVyXxkp9+l75S6YJZuW5l2iAAAAKBSeFZYSEhIkSc2aNVOdOnXyfFx4eLjuvPNOOY6j7du3a/78+UWVIgAAKCTGcWQ69VDIA8/IGfmazDn9pZAcNkrv2CL7r3FyH7lV7tT/yB497G2yAAAAKDDPCkuHD/t/STz1VrbMW99z2o0UFRWl1q1bS5IWLlxYRBkCAICiYJo0l/Pn++Q8/57MpddKlXLomXgkUXbyBLkP3yr332/J5vTEOQAAAJQYnvVYSju5vT0yMmt/hsz/TkpKUs2aNbM9vn79+lq3bp127dpVdElmsn//fk2bNk0rVqzQ/v37FRoaqqioKJ1zzjm66KKLFBERccbn2Lp1q7777jvFx8fr4MGDchxHtWrVUteuXXXxxRerdu3auc5hrdWiRYs0b948/frrrzpy5IiMMapevbpatmyp2NhYde7cOU/5bNiwQdOnT9e6deuUmJioSpUqqWnTpoqNjdW55557hlcLACjvfu/DNPRkH6Ypufdh6tDN34epXSf6MAEAAJRAnhWWKlSooKSkpKBdSZmf9LZnz54cC0vJycmSft/5VJSWL1+u119/PXBOSUpJSdGmTZu0adMm/fDDDxoxYoTq1atX4HN88cUX+uqrr2StzfL1HTt2aMeOHZo5c6buvPNO9ezZM8c5kpOT9dJLL2nNmjVBY3v37tXevXu1cOFC9enTR3fddZdCQ3P+n3vixIn68ssvs+STmJioxMRErVy5UvPnz9d9992n8PDwAlwtAAC/MxERMrGXyPa7SFq1TO73k6V1v2QfvHqZ3NXLpIZNZQYOlukZIxMW5mm+AAAAyJlnhaWoqCglJCQoMTExy9cbNWoUeL1mzRq1a9cu6FjXdfXrr79KUqHsFDqdLVu26NVXX1VKSooiIyN15ZVXqkOHDkpNTdWCBQs0a9Ys7dq1S88//7yee+65oB1YeTFp0iRNnDhRklSjRg1dfvnlatOmjSRp/fr1mjJlihITEzVu3DiNGjUqMHaqcePGBYpKdevW1RVXXKEmTZooPT1dmzdv1uTJk3X06FEtXLhQlStX1m233ZbtPLNmzdIXX3whSapXr56uuuoqNWnSRIcOHdK0adO0Zs0aLVu2TO+8847uueeefF8vAADZMY4jdeqhkE49ZLdtlv1+suxP8yRfenDwzq2y/3pd9r8fy/S/VCbmEpkq1bxPGgAAAFl4Vlhq2rSpEhIStGPHjixfb926tUJDQ5Wenq6ZM2fqwgsvVNWqVbPETJs2TXv37pUkNW7cuEjz/Oijj5SSkqKQkBA9/vjjgd5OktShQwfVr19fn3zyiXbu3KmpU6dqyJAh+Zr/4MGD+vLLLyX5i0rPPfdcll1arVq1Up8+fTRixAgdOnRI77//vl544QU5TtZ2WJs3b9aKFSsk+YtBL774oipUqJAl1z59+ujBBx/UsWPHNHPmTA0dOjToe3vs2DF98sknkqTatWvrmWeeyRLTrVs3vfTSS1q2bJnmz5+vCy64QNHR0fm6ZgAAcmOaNJf5832yV98kO3ua7NxvpWNHgwNP9mGy0ybK9I6VueAKmQZNvE8YAAAAkjxs3t2+fXtJ0oEDB7Rnz57A1ytWrKhevXpJ8t969cgjj+ibb77RL7/8oiVLlujtt98OFD4kFWmvn4SEhMAOoP79+2cpKmUYNGhQoAH5N998o/T0bP6qehoLFiwI9JsaOnRotrf+1axZU0OHDpXk78P0888/B8WsW7cu8PrSSy/NUlTKULt2bcXGxkry92LauHFjUMysWbN07NgxSdINN9wQVHhyHEe33XZboLA1ZcqUPFwlAAAFY6rXlHPVH+W88E+ZP94hRTXMPjAtVXbeDLlP3CXfuCdl41cE3V4OAACAoufZjqUuXboEdiYtWrRIgwcPDoz98Y9/1M8//6xjx47pwIED+vjjj7Odo3nz5howYECR5bhkyZLA6/79+2cb4ziOYmJiNGHCBB07dkzx8fE6++yz83yOTZs2BV536dIlx7jMDbcXLVqkrl27ZhnPXNA6Xa+nzGMZBa3MMq65QoUKgQLfqWrVqqWOHTtq5cqVWrVqlU6cOFGgWwABAMgrExEhE3Ox7HkX+vsszTxdH6blclcv/70PU+9YT3MFAAAozzzbsVSxYkXde++9uu2227L0VZL8O3RGjhypOnXq5Hh8dHS0HnnkkdM2oD5TGbuAIiIi1Lx589PmcuoxeZWUlBR4Xa1azr0hMo/Fx8cHjTdo0CDwOvMOsFNlHst8jOQvTiUkJEj6/ZbEnGRcc1paWuAYAACKmnEcmbN7KOTvY+SMGidzzgApJIefVyf7MKU/9GcdnvCefIcPeZssAABAOeTZjiVJp33CWbNmzfTaa69p0aJFWr16tQ4dOiRjjOrWratu3bqpU6dORZ5fRv+nqKgohYSE5BiXuUBzas+o3GRuPp6cnJzlqXiZZX4i3b59+5SSkpLl2E6dOqlOnTrat2+fpk2bpv79+wftIjpw4IDmzJkjyV84atIkaw+K3377TT6fT5ICt/flJPP4zp071aFDh9PGn5pHbqpXrx74np/aT6okypxjacgXOB3WM0qNpi2k2+6XHXKz3NnfyJ3zrZR0JDjuSKKOfPqujnzxoZxz+ssZOJg+TCi1eI9GWcJ6RlnDmvbztLCUm9DQUPXt21d9+/b1/Nypqak6etTfJLRWrVqnja1cubIiIiKUkpKSp6JJZg0bNgzcfhYfH5/j7Wdr164NvLbW6sCBA1kKWmFhYbrnnnv0wgsvaM+ePXrwwQd1xRVXqHHjxvL5fNq8ebOmTJmiY8eOqU6dOrrjjjuCzpE59+x6PWWW+XuS32seNmxYrjHjx49XrVq1FBISoqioqHzNX9zq1q1b3CkAhYb1jFIhKkpqGy33lruVPPsbHZ30mdJ3bAmOS0uVGzddbtx0RXbroypXXa+Izr1kjPE8ZaAw8B6NsoT1jLKmPK/pElVYKk4nTpwIvM5L/6DIyEilpKRkOS4vevTooUmTJkmSJk6cqC5duig8PDxLTGpqqiZOnJhjfhnatGmjF198UdOmTdO3336r9957LyjHoUOHZvukPUk6fvx4ltjTybxbKr/XDABAUXAiI1X5kmtU6aKrdGLZQh393wSlrPwp29gTyxbqxLKFCmvaQpWvvF6VYi+WCY/INhYAAAB5R2HppNTU1MDrvPRxyojJfFxetGrVSt27d9fSpUu1detWPfnkk7ruuusCT6DbsGGDPvvsM23dujXQ7Dyn81hrtWjRIi1evDhwS1tmJ06c0IIFC7I8HS6zzM28c7vmsLCwwOv8XvP48eNzjalevbokyefzad++ffmavzg4jhOoSO/du1eu6xZzRkDBsZ5RJjRuKd0zSqHbN8v9forcRXOkbJ7cmrZ1kw6NG61DH74hJ/ZSOf0vlala3fN0gbziPRplCesZZU1pXdN16tQ5bfuf/CrWwlJSUpLWr1+vAwcOKCkpSa7rasiQIcWSS+ZdQ+nZ/CJ6qoyYU3cb5cWdd96pZ599Vhs3blRCQoJGjx4dFNOiRQs1btw40CPp1B1FrusGelJJ0oABA3TRRRepYcOGcl1XW7Zs0ZQpU7R06VK9/fbb2rZtm/70pz9lmSNzsSi3a85chMrvNed2a+GpSsv/GTO4rlvqcgZywnpGqdfwLIXcMlxRf3tASd98qSNTv8yxD5M7ZYLcaV/K9I6VuWCwTEP6MKFk4z0aZQnrGWVNeV7TxVJY2rhxoyZOnKiVK1fKWptl7NTCUmJiol566SW5rqvWrVvrlltuKZKcMhdu8nKrV0ZMXm6bO1WlSpX01FNPafr06Zo1a1aWBuDVq1fX+eefr6uvvlqvv/564OuVK1fOMseMGTMCRaVrr71W1157bZbxtm3bqm3btnrzzTcVFxenqVOnqkOHDuratWsgpkKFCkHXk5OUlJTA64JcMwAAXgqpWVvVbhym5JhL5Vs4S3bmFGl3Ng/cSE+TnT9Tdv5MqX0XOQOvlKI704cJAAAgjzwvLE2aNEmff/55nit51atXV40aNbRkyRJt27ZNQ4YMyfFJamciPDxcVapU0dGjR3NtTp2UlBQotOR3N06G0NBQXXbZZbrsssuUnJysw4cPKzIyUtWrVw/8Mrtt2zZJ/gLQqc21f/jhh8DYlVdemeN5rrvuOsXFxUmSZs2alaWwlHnOgwcPnjbfzN+Tgl4zAABeM+ERcvpdLNv3QmnNCrkzJ0lrV2YfvGaF3DUrpAZNZC64wr+TKSz/O5MBAADKE0+fh/fdd9/ps88+CxSVGjRooIEDB6p58+anPS6jP1B6erpWrFhRZPk1atRIkrR79+5sexZl2LVrV9AxZ6JixYqqX7++atSoESgqJSYmavfu3ZL8t8Wd+ujCnTt3Bs6f+Za2U9WqVUvVqlULylvyf/8z5s2YLyeZxxs2bJiXywIAoMQwjiPTsZtC7h8t54lxMueeL+XUX3DXNtmP35T78K1yp0yQPZLoaa4AAACliWeFpUOHDunTTz+V5O/tM2zYML366qu67bbb1KpVq9Me27lz50DxZPXq1UWWY5s2bST5b/vavHlzjnHx8fFBxxS2BQsWBG4TPOecc4LGMwpCpyuAZciIObU4FRoaqpYtW0ryNw0/XZ+ljGsOCwtTixYt8nAFAACUTKZRMzk33yvn+Q9kBv1BqpzDTuijh2W//o+/wPTRG7I7t3mbKAAAQCngWWFp5syZgaeJ3XTTTdk+pSwnoaGhatLE31Bz+/btRZGeJKlnz56B17Nnz842xnVdzZ07V5K/V1L79u0LPY/k5GRNnjw5cI6+ffsGxWR0nt++fbuOHTuW41zbtm1TUlJSlmMy69GjhyTp+PHjWrx4cbZzHDhwQKtWrZIkdejQIUtvJgAASitTrYacwTfIeeGfMjfeKdVvnH3gyT5M7pN3yffqE7Krlwf1iJQk67qyKSdky2njTgAAUD55VlhaudLfz6BmzZq64IIL8n18vXr1JKlIH0XfsmVLtWvXTpK/sLRhw4agmKlTpwZuC7vkkksUeso2+jlz5mjo0KEaOnSovvjii2zPc+jQoRx3Bx0/flwvv/yyEhMTJUk33nhjtoWcbt26SfI/re2jjz7K9hfc1NRUffjhh0HHZHb++eerYsWKkqQJEybo6NGjWcZd19X7778fuH3x8ssvzzZvAABKK38fpovkPPmGnHuekKI75xwcv0LuuCflPnGX3HkzZNNSZbf/Kvefr8q9+w9y7xrq/+9/viq7/VevLgEAAKDYeNa8e8+ePZL8t44V5EkrGcWP48ePF2pep7r55ps1cuRIpaamasyYMbrqqqvUvn17paamauHChfr+++8lSfXr1y9wkWXevHn6+uuvFRMTo+joaNWoUUPHjx/Xhg0bNGPGDO3fv1+Sv7fUgAEDsp1j0KBBmj17tg4fPqw5c+Zo9+7dGjhwoBo2bCjXdfXrr7/q22+/DTxxrmHDhtnuEqtcubJuuOEGvffee9q3b58effRRXX311WrSpIkOHTqkb775RmvWrJEknXvuuerQoUOBrhkAgJLOOI7UsZtCOnaT3bFF9vvJsovnStn9Mei37bIfvyn7xQdSygkp8x94UlNkf5wt+1OczC3D5fSK8e4iAAAAPOZZYSk5OVmS/9augsi4jS4kJKTQcspOs2bNNHz4cL3xxhs6fvy4Pvvss6CY+vXra8SIEWd0S9jhw4c1ZcoUTZkyJWgsJCREgwYN0nXXXZfj8VWrVtVjjz2msWPHau/evVq3bp3WrVuXbexZZ52lBx98MGh3VYaBAwfq0KFD+uqrr7Rnzx6NHz8+KKZLly4aNmxYHq8OAIDSzTQ6S+bme2Wv/pPs7G9l50yTko4EB544zR+8fD7ZD1+TbdBEpnGzoksWAACgGHlWWKpUqZKOHDly2n5Ap5Ox46lKlRwabBai7t27a+zYsZo2bZqWL1+ugwcPKjQ0VFFRUerdu7cuvvhiRUREFHj+Xr16KS0tTatXr9aePXt0+PBhhYWFqVatWjr77LM1YMAANW6cQ5+HTM466yyNHTtWc+fO1dKlS7V161YlJSXJGKOqVauqWbNm6t27t/r06ZNjUSnD0KFD1alTJ02fPl1r167V4cOHValSJTVt2lSxsbHZ9nkCAKCsM1VryAy+XvaSa2QXzZH9for0Wz76Pfp8sjMny/x5eJHlCAAAUJw8KyzVqVNHR44c0aZNm/J9bFJSUuC4Ro0aFXZq2apTp45uuukm3XTTTfk6LjY2NtfG5PXq1dM111yja6655gwy9IuMjNRFF12kiy666IznatOmTZE95Q4AgNLMhEfI9LtItu9Af5+lGZOktSvzdKxdMk/uH4fJCS/4H6UAAABKKs+ad3fs2FGStHfvXq1evTpfx06aNCnQ7DpjHgAAAK8Zx5Hp0E3OnY/l/aD0NNkHb5H76XjZhLXZPnADAACgtPKssNSvXz85jv907733no4cyaZPQTbmzJmjr7/+WpIUERGhmBgaYAIAgGIWFi7lZwdScpLsnG/lvvCw3Mf+JnfyBNk9u4ouPwAAAI94Vlhq2LBh4Alnu3fv1iOPPKL58+crLS0t2/gNGzbolVdeydJIetCgQZ70WAIAADgd4zgy3foU7OB9u2Wn/kfu47fL9+wDcn+YKnv0cOEmCAAA4BHPeixJ0i233KJt27Zpw4YNOnDggN544w29/fbbCgsLC8Q8/PDD2rt3b+Apchk6deqka6+91st0AQAAcmQGXin7U5zk8xV8kl83yP66QfaLD6T2XWV6x8p06ilDPyYAAFBKeLZjSZJCQ0P1+OOP67zzzgt8zefz6cSJE4F/b9myJaioFBMTo4ceekjGGM9yBQAAOB3TuJnMLcOlkJDsA0JCZG77u5xn/yFz5R+lqIY5T+bzSb8skf3HS3L//ie5/xonu3alrOsWSe4AAACFxdhi6iC5du1aTZ06VatWrVJKSkrQeGhoqDp06KDBgwcrOjq6GDJEcfD5fNqzZ09xp5Erx3EUFRUlyX9rp8sv/ijFWM8oa7xe03b7r7IzJ8suWyClpkjhETLdzpUZOFimcbPf46yVtibILprj3+mUl9vfatSW6dnPv5Op0VlFdxEosXiPRlnCekZZU1rXdL169RSS0x/GCqDYCksZXNfV1q1bdejQISUnJysiIkLVqlXTWWedpfDw8OJMDcWAwhLgPdYzypriWtPWdaW0VCksXMY5/aZwm54urV0pu2i27M+LpNTU3E/QqJm/wNSrn0z1WoWUNUo63qNRlrCeUdaU1jVd2IUlT3ssZcdxHDVr1kzNmjXLPRgAAKCEMo4jRUTmLTY0VOrYTaZjN9kTybLLF8kumi2t+0XK6W9+O36Vnfir7Ff/ktqe7S8ydT1HJrJi4V0EAABAPhV7YQkAAKA8M5EVZfoMkPoMkD10QHZJnOyPc6Qdv2Z/gLX+3U5rV8p+Ol6mc2+Z3rFSdBeZQvzrIwAAQF5QWAIAACghTI1aMhdeJV14leyOLf5+TIvnSokHsj8gNVX2pzh/z6Yq1fz9mHrFSme15KEnAADAExSWAAAASiDT6CyZITfLXn2jtGGNvx/TsoXSiePZH3D0sOysr2VnfS1FNZTpFSvTK0amTpS3iQMAgHKl0AtLEydOLOwpgwwZMqTIzwEAAFASGCfE31Op7dmy198uu3KJvx/TmuWSz5f9Qbt3yk7+VHbyp1LLaH8/pu59ZSpV9jZ5AABQ5hV6YenLL78s7CmDUFgCAADlkQmPkOnRV+rRV/boYdkl82QXzZF+3ZDzQQnxsgnxsv/5h9Sxu5zesVLHHjJhYV6lDQAAyjBuhQMAACiFTJVqMgMGSQMGye7ZdbIf0xxp3+7sD0hPl1YskrtikVSxkn8HU+/+Uou2/ifaAQAAFECRFpYaNWqkxo0bF+UpAAAAyj1Tr4HM4Otlr7hO2rze349pyXzp2NHsD0g+Jhs3XTZuulSrrr8fU+9YmfqNvE0cAACUekVaWNqxY4dCQ0MVExOjvn37qmrVqkV5OgAAgHLNGOPfgdSirewfbpNWL5O7aI60comUnpb9QQf2yk77QnbaF1LTljLn9JfpcZ5M1epepg4AAEqpQi8sXXbZZVqwYIESExMlSVu2bNGWLVv0ySefqHPnzurXr5+6d++u0FDuwgMAACgqJjRM6txbIZ17yyYnyS5b6O/HtGF1zgdtTZDdmiD7xQdSdBf/LqbOvWUiIjzLGwAAlC7GWmsLe1LXdfXLL78oLi5OS5YsUWpqapbxSpUq6ZxzzlFMTIxat25d2KdHKebz+bRnz57iTiNXjuMoKsr/+Obdu3fLdd1izggoONYzyhrW9OnZA3tlF8/1F5l+2577AREVZLr29vdjatvR/5Q6eIb1jLKE9YyyprSu6Xr16ikkpPB+nhdJYSmz48eP68cff1RcXJzWrl0bNB4VFaV+/frpvPPOU926dYsyFZQCFJYA77GeUdawpvPGWitt2+xv+v3TXOlIYu4HVa8p0zNGpnes1Ogs/613KFKsZ5QlrGeUNaV1TZe6wlJm+/bt09y5czVv3jzt3p31iSXGGLVt21YxMTHq3bu3KlSo4FVaKEEoLAHeYz2jrGFN55/1+aS1K2UXz5Fd/qOUmpL7QQ2b+m+V6xkjU7N20SdZTrGeUZawnlHWlNY1XaoLS5lt2LBBc+fO1cKFC5WcnJxlLDw8XN27d1dMTIw6derEX8PKEQpLgPdYzyhrWNNnxp44LvvzIv+tcvErJZvL988YqXUHf9Pvrn1kKlT0JM/ygvWMsoT1jLKmtK7pMlNYypCenq6lS5dqzpw5WrlyZdD/EM8995yaN29eTNnBaxSWAO+xnlHWsKYLj008KLtknuyi2dK2zbkfEBYu06mnvx9T+y4yPKzljLGeUZawnlHWlNY1XdiFpWL/aR8aGqrevXurd+/eOnLkiD755BPNnTu3uNMCAAAo90z1mjIDB0sDB8vu3Oa/VW7xXOngvuwPSEuVXTpfdul8qXJVmR7n+fsxNWvNDnQAAMqoYi8sSdLhw4c1b948xcXFaevWrcWdDgAAAE5hGjaRufpPslf+UdoY7y8yLV0gHT+W/QFJR2RnfyM7+xupbgN/P6ZeMTJ163ubOAAAKFLFVlhKS0vTkiVLNHfuXP3yyy9BW8Zq1Kihvn37BraVAQAAoPgZx5HadJBp00H2ur9KvyyRu2iOtGqZ5EvP/qC9u2SnTJCdMkFq0dZfZOreV6ZyVU9zBwAAhc/zwtLatWs1d+5cLV68ONum3T169FBMTIw6duwox3G8Tg8AAAB5ZMLCpW7nKqTbubJJR/y3wS2aI21al/NBm9bJblon+5/3pY7d5PSOlc7u4Z8LAACUOp4Ulnbv3q24uDjNmzdPe/fuDRqPjo5Wv379dM455ygyMtKLlAAAAFCITOWqMrGXSrGXyu79TXbxXH/T772/ZX+AL136ebHcnxdLFSrJdD9Xples1CravysKAACUCkVWWEpOTtaCBQsUFxenDRs2BI3Xr19f/fr1U79+/VS7du2iSgMAAAAeM3Xry1z+f7KD/iD9ukF20RzZJfOkpCPZH3D8mOy8GbLzZkg16/h7MfWOlWnQJNtw67pSWqr/KXQUoQAAKFaFXlhatmyZ5s6dq2XLlik9Pet99pUrV9Y555yjfv36qXXr1oV9agAAAJQgxhipeRuZ5m1kh94qrVnhb/r982J/YSg7B/fJfjtR9tuJUpMW/gJTz34y1WrIbv9VduYk2WULpdQUKTxCplsfmYFXyjRu5u3FAQAASUVQWHrxxRez/DskJESdO3dWTEyMunXrptDQEvEgOgAAAHjIhIZKnXrIdOohm3xMdsWPsj/OljaslqzN/qBtm2S3bZL98kOpYWNp53bJZnrgS2qK7I+zZX+Kk7lluJxeMd5cDAAACCjSKk+jRo107rnnqmrVqjp69KjmzJlTKPNecMEFhTIPAAAAvGcqVpI59wLp3AtkD+6TXRzn78e0a1v2B1hX2rE15wl9PtkPX5Nt0ISdSwAAeKxIC0s7duzQ559/XujzUlgCAAAoG0zNOjKXXCN78dXSji2yi2bLLo6TDh/M30Q+n+zMyTJ/Hl4keQIAgOxxXxoAAACKnTFGatxMpnEz2Wtuktat8heZMvop5YFdtkD25nto6A0AgIcKvbDUrl07/y8GAAAAQAEYJ0SK7iwT3VnukFtk//6nvB2YmuJvCh4RWbQJAgCAgEIvLD355JOFPSUAAADKKVO5qmx4RB53LRnZXVtlmrUp8rwAAIAf+4QBAABQYhnHkenWJ4/RVvaFEXJn/E/WdXMPBwAAZ4zCEgAAAEo0M/BKKSQkb8G+dNkvP5T7xtOyRxKLMi0AACAKSwAAACjhTONmMrcMz3txSZJWL5f79L2ya1cWWV4AAIDCEgAAAEoBp1eMnMdekTlngBQe4f9ieITMOQNkHntZ5pIh0qkPkDl8SO6ro+T+92PZ9HTvkwYAoBwo9ObdAAAAQFEwjZvJ/Hm47M33+J/+FhYu45z8O+lZrWTbdZL7wSvS4UO/H2St7LcTZdevkvOXB2Rq1yue5AEAKKPYsQQAAIBSxTiOTETk70WljK+36yRn1DipQ7fggzavl/v0cNml8z3KEgCA8oHCEgAAAMoMU7W6nLtHylx7ixRyyub848fkvvui3H+/JZuSUjwJAgBQxlBYAgAAQJliHEfOhVfJeeQFqU5U0LiNmy732b/L7txaDNkBAFC2UFgCAABAmWTOaiVn5GsyvWKCB3dtk/vM3+XO/U7WWu+TAwCgjKCwBAAAgDLLVKgoc+v9MrfcK0VEZh1MS5X95G2577wgeyypeBIEAKCUo7AEAACAMs0YI6fP+XIef0Vq3Cw4YPlCuU/fK5sQ731yAACUchSWAAAAUC6YqEZyRoyVOf/y4MGD++S+9Kjcb76QdX3eJwcAQClFYQkAAADlhgkLk/N/f5Fz1+NS5SpZB11XdtIncl8ZJZt4oHgSBACglKGwBAAAgHLHdOopZ9TrUusOwYPrV8l96l7ZX5Z4nxgAAKUMhSUAAACUS6ZGLTl/Hy1zxfWSOeXX4qQjct8YLffzD2TT0oonQQAASgEKSwAAACi3jBMi5/L/k/PAM1KN2kHj9vvJcp9/SHbPrmLIDgCAko/CEgAAAMo907q9nCfGSZ17Bw9u2yR39H1yF832PjEAAEo4CksAAACAJFOpipw7Rshcf7sUGpZ1MOW47Aevyv3nq7InjhdPggAAlEAUlgAAAICTjDFy+l8q57GxUv3GQeP2x9lyR98nu3VTMWQHAEDJQ2EJAAAAOIVp1EzOYy/L9B0YPLh3l9znH5T7/RRZa71PDgCAEoTCEgAAAJANExEp56a7Zf76oFShYtbB9HTZz9+X+8Zo2aOHiydBAABKAApLAAAAwGk4Pc6TM/I1qVnr4MFVS+U+da/sul88zwsAgJKAwhIAAACQC1MnSs5Dz8tcdHXw4OGDcl8ZKXfSJ7I+n/fJAQBQjCgsAQAAAHlgQkPlDLlZzvCnpCrVsg5aK/vNF3LHPip7YF/xJAgAQDGgsAQAAADkg2nfRc6Tr0vRXYIHE9bKffoe2eULPc8LAIDiQGEJAAAAyCdTtYace5+QueYmKSQk62DyMbnjn5f76XjZ1JTiSRAAAI9QWAIAAAAKwDiOnIuvkfPQ81LtekHjds63cp99QHbXtmLIDgAAb1BYAgAAAM6Aad5GzsjXZHqcFzy4c6vcZ+6XO2+GrLXeJwcAQBGjsAQAAACcIVOxksxfHpC56W4pPCLrYGqq7Mdvyv7jJdnkY8WTIAAARYTCEgAAAFAIjDFy+g6U8/grUqOzgsbt0vlyn75XdtM675MDAKCIUFgCAAAACpGp31jOo2Nl+l8aPHhgr9yXRsj99itZ1/U+OQAAChmFJQAAAKCQmbBwOdffLueOR6WKlbMO+nyy//1I7mtPyB4+VDwJAgBQSCgsAQAAAEXEdOkt54lxUqvo4MG1K+U+dY/s6mXeJwYAQCGhsAQAAAAUIVOzjpy/PyMz6P8kc8qv30cPyx33lNwvP5RNTyueBAEAOAMUlgAAAIAiZkJC5Ay+Xs7fx0jVawWN2xn/k/vCI7J7fyuG7AAAKDgKSwAAAIBHTJsOckaNkzr1DB7cslHu6OFyF8/1PjEAAAqIwhIAAADgIVOlqpw7H5P5v79KoaFZB08cl33/Zbn/GiebcqJ4EgQAIB8oLAEAAAAeM8bIOX+QnBFjpaiGQeN2wSy5Y+6T3ba5GLIDACDvKCwBAAAAxcQ0aS7nsVdk+pwfPLh7p9KfuV9Hv/5c1lrvkwMAIA8oLAEAAADFyERWkHPLvTK3/V2KrJB1MD1die+8pANjHpBNOlI8CQIAcBoUlgAAAIASwOkVI2fka1LTlkFjxxfNVfqT98huWO19YgAAnAaFJQAAAKCEMHXry3nkBZkLrwwePLRf7tjH5U6ZIOvzeZ4bAADZobAEAAAAlCAmNEzOtX+Wc88TUpVqWQetK/v1f+S+/JjswX3FkyAAAJlQWAIAAABKINOxm0KffEMRnXoGD26Ml/v0cNmfF3mfGAAAmVBYAgAAAEooU72m6ox5U9VuuktyTvnV/dhRuW89K3fCu7JpqcWTIACg3KOwBAAAAJRgxnFUdejNCnnkRalW3aBxO/sbuc8+KPvbjmLIDgBQ3lFYAgAAAEoBp0VbOaNek+l2bvDgjl/ljrlP7oLvZa31PjkAQLlFYQkAAAAoJUzFyjJ/e0jmxjul8PCsg6kpsv96Xfb9l2WPJxdPggCAcofCEgAAAFCKGGPk9LtIzmOvSA2bBo3bn+Lkjh4u++vGYsgOAFDeUFgCAAAASiHToImcR8fKxFwcPLhvt9wXHpI7/X+yrut9cgCAcoPCEgAAAFBKmfAIOX+8Q87tj0gVK2Ud9PlkJ34o9/WnZI8cKp4EAQBlHoUlAAAAoJQz3frIGTVOatE2eHDNCrlP3Ssbv8L7xAAAZR6FJQAAAKAMMLXqynnwOZlLh0rGZB08kij3tSflfvWRbHp68SQIACiTKCwBAAAAZYQJCZFz1R/l3Pe0VK1m1kFrZb/7Su6Lj8ju2108CQIAyhwKSwAAAEAZY9p1kvPEOKlj9+DBXzfIHT1c7pJ53icGAChzKCwBAAAAZZCpUk3O3SNl/nCrFBKadfB4suw/XpL78ZuyKSnFkyAAoEygsAQAAACUUcYYORcMljPiJalug6BxO2+G3Gful92xxfvkAABlAoUlAAAAoIwzTVvIGfmKTO/+wYO/bZf7zN/lzpkma633yQEASjUKSwAAAEA5YCIryrn1Ppk/3ydFRGYdTE+T/fQdueOfkz12tHgSBACUShSWAAAAgHLEOae/nJGvSU1aBA+uWCT36XtlN8Z7nhcAoHSisAQAAACUM6ZeAzmPvChzwRXBgwf3y33pUblTP5d1fd4nBwAoVSgsAQAAAOWQCQuT84fb5Nw9UqpcNeugdWUnfyr3lVGyhw4UT4IAgFKBwhIAAABQjpmze8h5YpzUpmPw4PpVcp++R3blEu8TAwCUChSWAAAAgHLOVK8l5/6nZa78o+Sc8hEh6ajcN0fL/c97smlpxZMgAKDEorAEAAAAQMYJkXPZUDkPPifVrBM0bmd9Lff5B2V37yyG7AAAJRWFJQAAAAABpmU7OaPGSV37BA9u2yx3zH1yF/7gfWIAgBKJwhIAAACALEylynJuf1jmhmFSWHjWwZQTsh++JveDV2RPJBdPggCAEoPCEgAAAIAgxhg5sZfIeexlqX7joHG7aI7c0ffJbk0ohuwAACUFhSUAAAAAOTINm8p57BWZfhcFD+79Te5zD8mdMUnWdb1PDgBQ7CgsAQAAADgtExEh58Y75fztIalCpayDvnTZL/8p980xskcPF0+CAIBiQ2EJAAAAQJ6Y7n3ljHpNat4meHDVUrlP3Su7dqXneQEAik9ocSdQUu3fv1/Tpk3TihUrtH//foWGhioqKkrnnHOOLrroIkVERJzxObZu3arvvvtO8fHxOnjwoBzHUa1atdS1a1ddfPHFql27do7HvvXWW5o7d26+znfHHXcoNjY2y9fWrFmjp556Kk/HDxkyREOHDs3XOQEAAFC2mNr15Dz4nOyUCbLffSVZ+/vg4YNyXx0lc8kQmSuulwkJKb5EAQCeoLCUjeXLl+v1119XcvLvT7lISUnRpk2btGnTJv3www8aMWKE6tWrV+BzfPHFF/rqq69kM/8glrRjxw7t2LFDM2fO1J133qmePXsW+BynatCgQaHNBQAAgPLLhIbKXP0n2Xad5H7winT40O+D1spO+1J2/So5f3lAplbd4ksUAFDkKCydYsuWLXr11VeVkpKiyMhIXXnllerQoYNSU1O1YMECzZo1S7t27dLzzz+v5557TpGRkfk+x6RJkzRx4kRJUo0aNXT55ZerTRv/duL169drypQpSkxM1Lhx4zRq1KjAWGbXXXedLr/88tOe59ixY3ryySdlrVX9+vXVunXr08YPGzZMLVq0yHG8WrVquV0aAAAAyhHTrpOcUePkfjhOWr0s6+CmdXKfvlfOn+6W6daneBIEABQ5Ckun+Oijj5SSkqKQkBA9/vjjWYoxHTp0UP369fXJJ59o586dmjp1qoYMGZKv+Q8ePKgvv/xSkr+o9Nxzz6lmzZqB8VatWqlPnz4aMWKEDh06pPfff18vvPCCHCdrO6yaNWtmOS47M2bMCOyI6tevX6651a1bV02aNMnX9QAAAKB8M1Wry7l7pOz3k2X/+2/Jl/77YPIxue88L9PvYpk/3CoTHuF/elxaqhQWLuPQ8hUASjveyTNJSEjQmjVrJEn9+/fPdofPoEGD1LBhQ0nSN998o/T09KCY01mwYIHS0tIkSUOHDs22OFSzZs1AL6OtW7fq559/ztc5MmT0YDLG5KmwBAAAABSEcRw5F14l55EXpDpRQeM27ju5T94t35tj5N79B7l3DfX/9z9fld3+azFkDAAoLBSWMlmyZEngdf/+/bONcRxHMTExkvy3msXHx+frHJs2bQq87tKlS45xnTt3DrxetGhRvs4hSb/99ps2btwoSYqOjladOnXyPQcAAACQH+asVnJGvibTMyZ4cN9uaeVPUmqK/9+pKbI/zpb7zP1yF+fvoTQAgJKDwlIm69atkyRFRESoefPmOcZFR0cHHZNXSUlJgden61mUeSy/xStJWZ4Yx24lAAAAeMVUqChz2/0yN98rhefhSco+n+yHr7FzCQBKKXosZbJjxw5JUlRUlEJO82jUzE9XyzgmryIifv/hmpycrCpVqmQbl/mJdPv27VNKSkqWY0/HWqt58+YFzte7d+88HffZZ59p//79Onz4sCIjI1WnTh1FR0frwgsvPKMnyh04cCDXmOrVqwe+56f2kyqJMudYGvIFTof1jLKGNY2ypFSv5/MGyrZqp/RnHpCSk04f6/PJfvOFQu4Y4U1uKBalej0D2WBN+1FYOik1NVVHjx6VJNWqVeu0sZUrV1ZERIRSUlLyVDTJrGHDhoFb7uLj49WrV69s49auXRt4ba3VgQMH8lzcWbt2rfbt2ydJ6tGjhypUqJCn4zZs2BB4fezYMR07dkxbtmzRt99+q2uuuUbXXnutjDF5miuzYcOG5Rozfvx41apVSyEhIYqKCr4vvySrW5dH6KLsYD2jrGFNoywpjevZ1q2rnb502bzELlsg38O3KrJDF0W076zw6M4Ka9KcBt9lVGlcz8DplOc1TWHppBMnTgReR0ZG5hofGRmplJSULMflRY8ePTRp0iRJ0sSJE9WlSxeFh4dniUlNTdXEiRNzzC83mW+Di42NzTW+Ro0a6tmzp9q2bat69erJcRzt379fy5YtU1xcnHw+nyZOnKj09HRdf/31ec4DAAAA5ZtNTZFNyfvvse7+PUqe852S53wnSXKqVFN4u7MV0b6zItp3UXjLdjJhYUWVLgCgACgsnZSamhp4HRqa+7clIybzcXnRqlUrde/eXUuXLtXWrVv15JNP6rrrrgs8gW7Dhg367LPPtHXrVoWGhgaeOpfX86SmpgaafdesWVMdOnQ4bXyLFi301ltvBV1z8+bN1bNnTw0cOFBjxoxRcnKyJk+erD59+uiss87K1zWPHz8+15jq1atLknw+X2C3VUnmOE6gIr137165rlvMGQEFx3pGWcOaRllS2tezdV1/n6WMht355B49rBM/zdOJn/xtHhQWLtO8tUzL9jKto2VatJOpULEQM0ZRKu3rGThVaV3TderUOW37n/yisHRS5l1DGcWc08mIOXW3UV7ceeedevbZZ7Vx40YlJCRo9OjRQTEtWrRQ48aNNWfOHEl520Ul+Z9sd/z4cUnSeeedl+t9nrnN27JlS91666164403ZK3Vd999p9tvvz1PuWTI7dbCU5WW/zNmcF231OUM5IT1jLKGNY2ypLSuZ9Otj+yPswtnsrRU2fWrZdevlr6RZByp8VkyrdrLtGwntWovU61G4ZwLRaq0rmcgJ+V5TVNYOilzgSUvt51lxOS14JNZpUqV9NRTT2n69OmaNWtWlgbg1atX1/nnn6+rr75ar7/+euDrlStXztPccXFxgdcxMdk85rUA+vTpow8++EDJyclZej8BAAAAuTEDr5T9KU7y+XIOckJk/vagdPSItHGNbMJa6cDe3Ce3rrRts+y2zbKzvvZ/rU6UTKv2UqtomZbRUr0GBeoTCgDIGwpLJ4WHh6tKlSo6evRorg25k5KSlJLi386b3904GUJDQ3XZZZfpsssuU3JycuBJbNWrVw/84Nu2bZskqUKFCqpZs2aucyYmJmrlypWS/LeyNWrUqEC5nSokJET169fXpk2bdPDgwUKZEwAAAOWDadxM5pbhsh++ln1xKSRE5pbhcrr28f875mJJkj24T3ZjvJQQ7//vXdskm4c24Pt2y+7bLS2c5W8aXqWav8iUUWhq3FymEG8BAYDyjsJSJo0aNdLatWu1e/du+Xy+HO853LVrV5ZjzlTFihVVsWLWe8MTExO1e/duSf7b4vLy6ML58+cHtt4V1m4lAAAA4Ew5vWJkGzSRnTlZdtkCf8+l8AiZbufKDBws07hZ0DGmZh2ZXjFSL//vtfZYkrRprezGeNmNa6QtCZIv9xYWOnpYWv6j7PIf/YWmiEipeRv/7XOtoqVmbWQiIgr3ggGgHKGwlEmbNm20du1apaSkaPPmzWrVqlW2cfHx8VmOKQoLFiyQPfkXmXPOOSdPx2Q8DS4kJETnnntuoeXi8/kCxbQaNbhnHQAAAPlnGjeT+fNw2ZvvkdJS/Y248/DH08DxlSpLZ/eQObuHJP8T57Rlo7/QlBAvbVonHU/OfaKUE9LalbJrV/oLTSEhUpMW/h1NraKlFtEyVaoW7CIBoByisJRJz549NWnSJEnS7Nmzsy0sua4bKOBUqlRJ7du3L/Q8Mp7AlnGOvn375nrMtm3btHXrVklSly5dVLVq4f0wXLBgQaAheHR0dKHNCwAAgPLHOI5/19CZzhMeIbXuINPa/xRk6/qkHVuz3j53OA9tHHw+6dcNsr9ukJ0xyf+1+o39RaaWJ4tNterSpwkAckBhKZOWLVuqXbt2Wrt2rWbPnq3Y2Fi1bt06S8zUqVO1c+dOSdIll1yi0NCs38I5c+bo7bffliQNGTJEQ4cODTrPoUOHVKVKlaBjJen48eN6+eWXlZiYKEm68cYbVaFChVxzz3h6nJT32+CSkpK0devW0xbHEhIS9OGHH0qSjDEaOHBgnuYGAAAAvGScEKlJc5kmzaXzB/l3/+/b7d/NlLGraffOvE3223bZ37ZLcdP9u5pq1P79qXOt2kkNmuZrtxUAlGUUlk5x8803a+TIkUpNTdWYMWN01VVXqX379kpNTdXChQv1/fffS5Lq16+vyy+/vEDnmDdvnr7++mvFxMQoOjpaNWrU0PHjx7VhwwbNmDFD+/fvlyTFxsZqwIABuc7nuq4WLFggyf/0uG7duuUpj+TkZD311FNq2rSpevTooebNm6t69epyHEf79+/XsmXLFBcXJ9/JJouXX365WrRoUaBrBgAAALxkjJHq1pepW1/qc74kyR5JlBIy9WnavlnKy+PBD+2XXTJPWjLPX2iqWElq0e732+eatpIJCyvKywGAEovC0imaNWum4cOH64033tDx48f12WefBcXUr19fI0aMyNNOopwcPnxYU6ZM0ZQpU4LGQkJCNGjQIF133XV5muuXX37RoUOHJEl9+vTJdifU6WzdujVwG112HMfRNddcoyFDhuRrXgAAAKAkMVWrS13Pkenq72FqTxyXNq+Xzbh1bvN6f2Px3CQfk1YtlV211F9oCg2TmrWSaRkt06q91KKtTMVKRXkpAFBiUFjKRvfu3TV27FhNmzZNy5cv18GDBxUaGqqoqCj17t1bF198sSLO4MkRvXr1UlpamlavXq09e/bo8OHDCgsLU61atXT22WdrwIABaty4cZ7ni4uLC7zOz9Pgatasqfvvv18bNmxQQkKCDh48qKNHjyo1NVUVK1ZUgwYN1L59ew0YMEB169bN1zUCAAAAJZ2JrCBFd5aJ7ixJsunp0vbNshvXyG5cKyXES0lHcp8oPc1/u93GeNlvJ0rGSA3P8u9mOrmryVSvVbQXAwDFxNiMR48BJYDP59OePXuKO41cOY6jqKgoSdLu3bvl5mULNVBCsZ5R1rCmUZawnouXtVbavdN/21zGrqb9BfxdtXa9TA3B20tRDctdQ3DWM8qa0rqm69Wrp5CQkEKbjx1LAAAAAJANY4xUv5FM/UZSv4skSfbQgZMNwU/uatq5RcrL3+r375Hdv0f6cbb/9rnKVQNPnTOtoqXGzWXy2dICAEoC3rkAAAAAII9MjVoyPc6TepwnSbLJSdKm9Sdvn4uXtmyQ0tNznyjpiPTzItmfF/kLTeERUvM2/kJTy2j/68iC93QFAK9QWAIAAACAAjIVK0sdu8l09D+Z2aalSlsSfm8InrBWOn4s94lSU6R1v8iu+8VfaHIcqUmLkw3Bo6WW7fzNxwGghKGwBAAAAACFxISFBxp26xLJuj5p1zZ/kelkg28lHsh9IteVtmyU3bJR9vvJ/q9FNfTvZmp1sk9T7Xrlrk8TgJKHwhIAAAAAFBHjhEiNmsk0aib1v8zfEPzA3pOFpjWyCWul37bnbbLdO2V375Tmz/TvaqpWM1ND8GipUVP/+QDAQxSWAAAAAMAjxhj/TqPa9aRz+kuS7NEj0ib/bia7MV7atkny+XKf7PBB2aXzpaXz/YWmChWlFm1/v32uWWv/DioAKEIUlgAAAACgGJkqVaXOvWU695Yk2ZQT0ub1sglrZTeukTavl1JO5D7R8WRp9XLZ1cv9habQUOmsVr8Xmlq0k6lUuUivBUD5Q2EJAAAAAEoQExEptesk066TJMn6fNL2zb83BN8YLx09nPtE6elSwlp/geq7ryRjpAZNMt0+116mZu0ivhoAZR2FJQAAAAAowUxIiH/n0VmtpAsG+/s07dnl382Usatp3+7cJ7JW2rlVdudWac63/l1Ntepm7dMU1UjGcYr6kgCUIRSWAAAAAKAUMcb4nxAX1VA670JJkk08KJ3c0WQT4qXtWyTr5j7Zgb2yB/ZKi+b4C02Vq/hvmWvVXqZlO6lpC5nQsKK8HAClHIUlAAAAACjlTPWaUve+Mt37SpJs8jF/n6aN8bIJa6TNG6T0tNwnSjoqrfxJduVP/kJTeLjUrI1Mq2iZltFSizYykRWL9FoAlC4UlgAAAACgjDEVK0kdusp06CpJsmlp0taE3/s0JayVkpNynyg1VVq/Snb9Kn+hyThSk+YyLf27mtSqnUzVGkV6LQBKNgpLAAAAAFDGmbAwqWU7/+1tF18j67rSb9v9/Zk2rvXvajq4P/eJrOsvUG1NkJ31tf9rdRvItGontWrv39VUt77/dj0A5QKFJQAAAAAoZ4zjSA2byjRsKsVeKkmyB/adbAh+clfTrm15m2zvLtm9u6QFs/y7mqrVOFnE8j95To3PknFCiuxaABQvCksAAAAAAJladWRqxUq9YyVJNumItGmd7MY1sglrpS0Jki8994kOH5KWLZRdttBfaIqsIDVvK9u6vU706qvwNh3ylI91XSktVQoL50l1QAlGYQkAAAAAEMRUrip16inTqackyaakSFs2+gtNG+OlTeuklOO5T3TiuBS/Qm78Cu2b9IkUGirTpIXU6uSOppbtZCpVCYTb7b/Kzpwku2yhlJoihUfIdOsjM/BKmcbNiupyARQQhSUAAAAAQK5MRITUpoPMyR1H1ueTdmw52RB8jbQxXjqSmPtE6emym9f7n1o3/X/+rzVoItMyWtZxpLjpkuv7PT41RfbH2bI/xcncMlxOr5jCvzgABUZhCQAAAACQbyYkRGraQqZpC+n8y2Wtlfb95t/NtPFkn6a9u/I22a5tsrn1dPL5ZD98TbZBE3YuASUIhSUAAAAAwBkzxvifEFe3gXTuBZIke+SQ/6lzG9dICWtlt2+WXLfgJ/H5ZGdOlvnz8MJJGsAZo7AEAAAAACgSpmoNqVsfmW595DiO6latotT1q3Rg8Xy5G9ZIv66XUlPzNaddtkD25nto6A2UEBSWAAAAAACecCpWUmSX3gqpf5aM68qmp0lbN8ld94s06ZO8TZKaInv8WJaG3wCKDyVeAAAAAECxMKFhMi3ayrlkiBQekefj7LMPyq5aWoSZAcgrCksAAAAAgGJlHEemW5+8H7B3l9zXn5Zv3JOyv20vusQA5IrCEgAAAACg2JmBV0ohIfk7aPVyuU/dI/fz92WPJRVJXgBOj8ISAAAAAKDYmcbNZG4ZnnNxyXGkOlHBX/f5ZL+fIvfxv8md862s6yvSPAFkRfNuAAAAAECJ4PSKkW3QRHbmZNllC6TUFCk8QqbbuTIDB0sNm8ouniv734+kxINZD046KvvpeNm538r5w20ybc8unosAyhkKSwAAAACAEsM0bibz5+GyN98jpaVKYeEyzu8325hz+st26S377VeyM/4npadlnWDHFrkvPy517SNnyM0y2e1yAlBouBUOAAAAAFDiGMeRiYjMUlQKjEVWkHPVH+U8/ZbUNYem38sXyh11p9z//Vv2xPEizhYovygsAQAAAABKJVMnSiHDHpHzwDNSo7OCA9LTZKd9KffxYXJ/nC3rup7nCJR1FJYAAAAAAKWaadNRzshXZf54h1S5SnDA4YOy/3xV7vMPyW5e732CQBlGYQkAAAAAUOoZJ0ROzMVyxrwrc8EV2T9d7tcNcp97UO4Hr8omHvA+SaAMorAEAAAAACgzTKXKcv5wm5wn3pA6dM02xi6a7b897psvZNNSPc4QKFsoLAEAAAAAyhxTv5FC7n1Szj2jpHoNgwNSTshO+kTuyDtkly2Utdb7JIEygMISAAAAAKDMMh27y3nydZlr/yxVqBgccGCv3Heel/vy47I7fvU8P6C0o7AEAAAAACjTTGiYnAuvlDPmHZl+F0nGBAetXyX36fvkfvK27NHD3icJlFIUlgAAAAAA5YKpWl3OjXfKefwVqXX74ADrys79Tu7jt8v9fopserr3SQKlDIUlAAAAAEC5Ypq0kPPAs3L+9pBUs05wQPIx2c/fl/vUPbKrl3mfIFCKUFgCAAAAAJQ7xhiZ7n3ljH5bZvANUnhEcNDuHXLHPSXf60/L7t7pfZJAKUBhCQAAAABQbpnwCDmD/iBn9HiZXjHZB61aKvfJu+R++U/Z5GPeJgiUcBSWAAAAAADlnqlZW85tf5fz8AtS05bBAT6f7IxJ/v5LcdNlXZ/3SQIlEIUlAAAAAABOMi3byXl0rMwt90rVagQHHD0s+++35I65X3bDau8TBEoYCksAAAAAAGRiHEdOn/PljBkvc8k1UmhocND2X+W+9Kjcd1+UPbDX+ySBEoLCEgAAAAAA2TCRFeVcfZOcp96SOvfONsYunS935B1yJ38qm3LC4wyB4kdhCQAAAACA0zB16yvkzkfl3Pe01KBJcEBaquzUz+U+Pkzu4rmy1nqfJFBMKCwBAAAAAJAHJrqznFHjZK6/XapUJTgg8YDs+y/LfeFh2V83ep8gUAwoLAEAAAAAkEcmJERO/0vlPPOOzIBBkpPNx+pN6+Q++3e5H46TTTzofZKAhygsAQAAAACQT6ZSFTnX/VXOqNel6M7ZxtiFs/y3x337lWxamrcJAh6hsAQAAAAAQAGZhk3kDH9Kzp2PSXXrBwekHJf970dyn7hTdsUi+i+hzKGwBAAAAADAGTDGyHTuJefJN2WG3CxFVggO2rdb7tvPyn11lOzOrV6nCBQZCksAAAAAABQCExYm56Kr5Yx5R+bcCyRjgoPWrpT71L1yJ7wjm3TE+ySBQkZhCQAAAACAQmSq1ZBz8z1yHntZatkuOMC6srOnyX3sdrmzpsqmp3ufJFBIKCwBAAAAAFAETNOWch56XuYvD0g1agcHJCfJ/ucfcp++VzZ+hfcJAoWAwhIAAAAAAEXEGCOnZz85o8fLXP5/Unh4cNBv2+W++oR8b46R3bvL+ySBM0BhCQAAAACAImYiIuRccb2cp8fL9Dgv+6CVP8kddZfcif+SPZ7sbYJAAVFYAgAAAADAI6ZWHTl/fVDOg89JTZoHB/jSZaf/V+7jt8udP1PWdb1PEsgHCksAAAAAAHjMtG4v57GXZf50l1SlWnDAkUTZj96Q++wDsgnx3icI5BGFJQAAAAAAioFxQuScd6GcZ96VuegqKSQ0OGhrgtwXHpH73ljZg/u8TxLIBYUlAAAAAACKkalQUc6QW+Q89abUqWe2MfanOLkjh8md8plsSorHGQI5o7AEAAAAAEAJYOo1UMhdj8u590mpfuPggNRU2a8/kztqmNwl82St9TxH4FQUlgAAAAAAKEFMh65yRo2T+b+/ShUrBQcc3C/7j5fkvjhCdmuC9wkCmVBYAgAAAACghDGhoXLOHyRnzLsysZdKJpuP7wnxcp/5u9yP3pA9csjzHAGJwhIAAAAAACWWqVJVzg23yxn1mtT27OAAa2Xnz5T72O1yp/9PNj3N8xxRvlFYAgAAAACghDONzpJz/2g5dzwq1a4XHHDiuOzED+U+cbfsyp/ovwTPUFgCAAAAAKAUMMbIdOkt5+m3ZK7+kxQRGRy0d5fcN8fIfe1J2V3bvE8S5Q6FJQAAAAAAShETFi7nkiFyxoyXOWdA9kHxK+Q+dY/c/7wne+yotwmiXKGwBAAAAABAKWSq15Lz5+FyHh0rNW8THOC6srO+9vdfmj1N1ufzPkmUeRSWAAAAAAAoxUyz1nIefkHm1vul6jWDA44dlZ3wjtzRw2XXrvQ+QZRpFJYAAAAAACjljOPI6R0rZ/R4mcuGSqFhwUE7t8p9ZaR8bz8ru2+390miTKKwBAAAAABAGWEiK8i58o9yRr8tdeuTfdCKRXJH3SH3vx/Jnkj2NkGUORSWAAAAAAAoY0ztegq5/RE5DzwrNWoWHJCeLvvtV3IfHyZ34SxZ1/U+SZQJFJYAAAAAACijTJsOcka+InPjHVLlqsEBhw/JfjhO7nMPym5a532CKPUoLAEAAAAAUIYZJ0ROv4vlPPOOzAWDpZCQ4KAtG+U+/5DcD16RPXTA+yRRalFYAgAAAACgHDAVK8v5w61ynnhD6tg92xi7aI7cx2+XO/Vz2dQUjzNEaURhCQAAAACAcsTUb6SQe0bJuWeUFNUwOCA1RXbyp3JH3Sm7bIGstd4niVKDwhIAAAAAAOWQ6dhdzhOvywy9VapQKTjgwF6577wgd+xjsts2e58gSgUKSwAAAAAAlFMmNEzOwMH+/kv9LpaMCQ7asFrumPvk/vst2aOHvU8SJRqFJQAAAAAAyjlTpZqcG++Q8/irUusOwQHWysZNl/vY7XJnTpZNT/M+SZRIFJYAAAAAAIAkyTRpLueBZ+Tc/rBUq25wwPFjsl98IPfJe2RXLfU+QZQ4FJYAAAAAAECAMUam27lynn5L5so/SuERwUF7dsp9/Wn5xj0l+9sO75NEiUFhCQAAAAAABDHhEXIuGypnzDsyvWOzD1q9TO5Td8v9/APZ5CRP80PJQGEJAAAAAADkyNSoJefW++U88qJ0VqvgAJ9P9vvJ/v5Lc7+TdX3eJ4liQ2EJAAAAAADkyrRoK2fESzK3DJeq1QgOSDoi+8nbckffL7t+lef5oXhQWAIAAAAAAHliHEdOnwFyxoyXuWSIFBoaHLTjV7ljH5Pvnedl9+/xPkl4isISAAAAAADIFxNZUc7Vf5Lz9NtSl97ZBy1bKHfkHXL/94nsiePeJgjPUFgCAAAAAAAFYupEKeSOR+XcP1pq2DQ4ID1NdtoXckcOk7totqzrep8kihSFJQAAAAAAcEZMu05yRr4mc8PtUuUqwQGJB2U/eFXuCw/L/rrB+wRRZCgsAQAAAACAM2ZCQuTEXipnzDsy518uOdmUHDavl/vsA3L/+aps4gHvk0Sho7AEAAAAAAAKjalURc7//UXOE69L0V2yjbE/zpb7+DC5076UTUv1OEMUJgpLAAAAAACg0JkGTeQMf1LOXSOluvWDA1JOyP7v33JH3Sm7/EdZa71PEmeMwhIAAAAAACgSxhiZTj3kPPWmzJBbpMgKwUH798gd/5zcV0bK7tjieY44MxSWAAAAAABAkTKhYXIuukrOM+/I9B0oGRMctO4XuU8Pl/vpeNmjR7xPEgVCYQkAAAAAAHjCVK0h56a75Tz2itQyOjjAurJzvpX7+N/kzvpaNj3d+ySRLxSWAAAAAACAp0zTFnIeek7mrw9KNWsHByQfk/3Pe3Kfvld29XLvE0SeUVgCAAAAAACeM8bI6XGenKfHy1x+nRQeHhz023a5456U743Rsrt3ep8kckVhCQAAAAAAFBsTESHniuvkjB4v07Nf9kG/LJH75N1yv/xQNvmYtwnitCgsAQAAAACAYmdq1pHzlwfkPPS81KRFcIAvXXbG/+Q+frvceTNkXZ/3SSIIhSUAAAAAAFBimFbRch57Weamu6Wq1YMDjh6W/fhNuc/8XXbDGs/zQ1YUlgAAAAAAQIliHEdO34Fyxrwjc9HVUkhocNC2zXJfGiH3Hy/JHtjnfZKQRGEJAAAAAACUUKZCRTlDbpbz1JtSp57Zxtgl8+SOHCZ38gTZlBMeZwgKSwAAAAAAoEQz9Roo5K7H5dz3lFS/cXBAWqrs1P/IHXmH3MVzZa31PslyisISAAAAAAAoFUx0FzlPvC5z3V+lipWDAw7tl33/ZbkvPiK7ZaP3CZZDFJYAAAAAAECpYUJC5AwYJOeZd2T6XyqZbEobCWvlPvuA3H+Nkz18yPskyxEKSwAAAAAAoNQxlavKuf52OaNek9p1Cg6wVnbBLLmP3y73u69k09I8z7E8oLAEAAAAAABKLdPoLDn3PS3nzkelOlHBASeOy371kdwn7pT9eRH9lwoZhSUAAAAAAFCqGWNkOveW89RbMlffJEVUCA7at1vuW8/KfXWU7M5t3idZRlFYAgAAAAAAZYIJC5NzyTVyxoyX6XN+9kFrV8p9+h65E96VPXbU2wTLoNDiTqCk2r9/v6ZNm6YVK1Zo//79Cg0NVVRUlM455xxddNFFioiIOONzbN26Vd99953i4+N18OBBOY6jWrVqqWvXrrr44otVu3btHI996623NHfu3Hyd74477lBsbGyO4xs2bND06dO1bt06JSYmqlKlSmratKliY2N17rnn5utcAAAAAAAUF1O9pswt98rGXir38/ekTeuyBriu7OxvZH+Kkxl8vUy/i2VCQoon2VKOwlI2li9frtdff13JycmBr6WkpGjTpk3atGmTfvjhB40YMUL16tUr8Dm++OILffXVV0H3du7YsUM7duzQzJkzdeedd6pnz54FPsepGjRokOPYxIkT9eWXX2bJJzExUYmJiVq5cqXmz5+v++67T+Hh4YWWDwAAAAAARck0ayXn4Rdkf4qTnfgvKfFA1oBjR2UnvCs751s5f7hNJrpzcaRZqhlL16ostmzZopEjRyolJUWRkZG68sor1aFDB6WmpmrBggWaNWuWJKlhw4Z67rnnFBkZme9zTJo0SRMmTJAk1ahRQ5dffrnatGkjSVq/fr2mTJmixMREhYWFadSoUYGxzA4ePKikpKTTnufYsWN68sknZa1V/fr1NW7cuGzjZs2apXfffVeSVK9ePV111VVq0qSJDh06pGnTpmnNmjWSpL59++qee+7J9/Xmh8/n0549e4r0HIXBcRxFRfmbwu3evVuu6xZzRkDBsZ5R1rCmUZawnlGWsJ5R3GzKCdnvvpKd/j8pLTX7oM695Fx7i0zdnDdmZCita7pevXoKKcTdWexYOsVHH32klJQUhYSE6PHHH1fr1q0DYx06dFD9+vX1ySefaOfOnZo6daqGDBmSr/kPHjyoL7/8UpK/qPTcc8+pZs2agfFWrVqpT58+GjFihA4dOqT3339fL7zwghwnazusmjVrZjkuOzNmzAjsQOrXr1+2MceOHdMnn3wiSapdu7aeeeYZVa1aNTDerVs3vfTSS1q2bJnmz5+vCy64QNHR0fm6ZgAAAAAAipuJiJQZfINs34GyE/8lu3R+cNDPi+WuXiZzwWCZy66ViazofaKlDM27M0lISAjszunfv3+WolKGQYMGqWHDhpKkb775Runp6fk6x4IFC5SWliZJGjp0aLbFoZo1a2ro0KGS/H2Yfv7553ydI0NGDyZjTI6FpVmzZunYsWOSpBtuuCFLUUnyV2Bvu+22QGFrypQpBcoFAAAAAICSwNSqK+dvD8l58FmpcbPggPR02e++kvvY7XIXfC9bSnYiFRcKS5ksWbIk8Lp///7ZxjiOo5iYGEn+3T7x8fH5OsemTZsCr7t06ZJjXOfOnQOvFy1alK9zSNJvv/2mjRs3SpKio6NVp06dbOMyrrlChQrq1atXtjG1atVSx44dJUmrVq3SiRMn8p0PAAAAAAAliWndQc7jr8jceKdUpVpwwJFE2X+9LvfZB2QT1nqfYClBYSmTdev8XeIjIiLUvHnzHOMy3wqWcUxeZe6LVK1aNgs3m7H8Fq8kZXliXE67ldLT05WQkCBJat26tUJDc74zMuOa09LSAscAAAAAAFCaGSdETr+L5IwZLzNwsJRd76GtCXJfeFjue2NlD+7LMmRdV+6J4+V6VxM9ljLZsWOHJCkqKuq0jawyP10t45i8ioiICLxOTk5WlSpVso3L/ES6ffv2KSUlJcuxp2Ot1bx58wLn6927d7Zxv/32m3w+nyQFbu/LSebxnTt3qkOHDnnKRZIOHDiQa0z16tUD3/NT+0mVRJlzLA35AqfDekZZw5pGWcJ6RlnCekaJVrmq9H9/kY29RL7PP5D9ZUlQiP0pTvbnxXIuGSLTvot8c77VzmULZFNOSOERMt3PVciFV8o0znmjSllEYemk1NRUHT16VJL/1q/TqVy5siIiIpSSkpKnoklmDRs2DNx+Fh8fn+PtZ2vX/r7NzlqrAwcOZClonc7atWu1b5+/itqjRw9VqFAh27jMuefWCDzz9yS/1zxs2LBcY8aPH69atWo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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 459,
"width": 587
}
},
"output_type": "display_data"
}
],
"source": [
"#Visualizing parameters for NB\n",
"plt.plot(results_NB['var_smoothing'], results_NB['test_score'], marker = '.') \n",
"plt.xlabel('Var. Smoothing')\n",
"plt.ylabel(\"Mean CV Score\")\n",
"plt.title(\"Figure 6: NB Performance Comparison\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "fefe5a28",
"metadata": {},
"source": [
"From ```Figure 6``` above which shows the performance comparison for NB, we can observe that there are many convergence around the variable smoothing of 0.0, and as the variable smoothing increases, mean cv score decreases. Hence, we will continue with our recommended hyperparameter for variance smoothing of 3.5111917342151273e-09.\n",
" \n",
"### 3.2.5 Model Comparison <a class=\"anchor\" id=\"3.2.5\"></a>\n",
"\n",
"In this section we will evaluate the performance of the algorithms using the optimal parameters we have evaluated for KNN, DT, and NB to compare the algorithms. We will also be using the full feature selection as the feature selection method chosen."
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "68b4ad6e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN Results with Best Parameters: 0.7932280942576069\n",
"DT Results with Best Parameters: 0.997872340425532\n",
"NB Results with Best Parameters: 0.9914893617021278\n"
]
}
],
"source": [
"#Model using optimal hyperparameters\n",
"\n",
"#KNN\n",
"cv_results_KNN = cross_val_score(estimator=gs_KNN.best_estimator_,\n",
" X=D_test,\n",
" y=t_test,\n",
" cv=cv_method,\n",
" n_jobs=-2,\n",
" scoring=scoring_metric)\n",
"\n",
"#DT\n",
"cv_results_DT = cross_val_score(estimator=gs_DT.best_estimator_,\n",
" X=D_test,\n",
" y=t_test,\n",
" cv=cv_method,\n",
" n_jobs=-2,\n",
" scoring=scoring_metric)\n",
"\n",
"#NB\n",
"cv_results_NB = cross_val_score(estimator=gs_NB.best_estimator_,\n",
" X=D_test,\n",
" y=t_test,\n",
" cv=cv_method,\n",
" n_jobs=-2,\n",
" scoring=scoring_metric)\n",
"\n",
"#Scores comparison\n",
"print(\"KNN Results with Best Parameters:\",cv_results_KNN.mean())\n",
"print(\"DT Results with Best Parameters:\",cv_results_DT.mean())\n",
"print(\"NB Results with Best Parameters:\",cv_results_NB.mean())"
]
},
{
"cell_type": "markdown",
"id": "cc0e9f20",
"metadata": {},
"source": [
"From the above output, we can observe that for DT and NB, there are very high scores, but DT resulted in a higher score. Interestingly, KNN resulted in a significant score difference against the other algorithms. We will perform T test comparison to further evaluate the models."
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "12a4c7b1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN v. DT: 0.0001\n",
"KNN v. NB: 0.0002\n",
"DT v. NB: 0.208\n"
]
}
],
"source": [
"#P-value comparison\n",
"print(\"KNN v. DT:\", stats.ttest_rel(cv_results_KNN, cv_results_DT).pvalue.round(4))\n",
"print(\"KNN v. NB:\", stats.ttest_rel(cv_results_KNN, cv_results_NB).pvalue.round(4))\n",
"print(\"DT v. NB:\", stats.ttest_rel(cv_results_DT, cv_results_NB).pvalue.round(4))"
]
},
{
"cell_type": "markdown",
"id": "823bda15",
"metadata": {},
"source": [
"From the output above, we can see that for the t test comparison, KNN and DT had a p value of 0.0001. Since p-value is less than the test statistic, 0.05, we can conclude that there is statistical difference between the KNN and DT. KNN and NB had a p value of 0.0002. Since p-value is less than test statistic, 0.05, we can conclude that there is statistical difference between KNN and NB. However, for DT and NB there was a p value of 0.208. Since p-value is greater than test statistic, 0.05, we can conclude that there is no statistical differences between KNN and NB. Interestingly, only DT and NB had a statistical difference, which meant that DT could be better than NB, or vice versa. Hence, we will continue to explore this with a confusion matrix to evaluate their performance."
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "6f1319ac",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN Confusion Matrix \n",
" [[344 11]\n",
" [ 69 45]] \n",
"\n",
"DT Confusion Matrix \n",
" [[355 0]\n",
" [ 0 114]] \n",
"\n",
"NB Confusion Matrix \n",
" [[ 0 355]\n",
" [ 0 114]]\n"
]
}
],
"source": [
"#Confusion Matrix\n",
"pred_KNN=gs_KNN.predict(D_test)\n",
"pred_DT=gs_DT.predict(D_test)\n",
"pred_NB=gs_NB.predict(D_test)\n",
"\n",
"print(\"KNN Confusion Matrix\",\"\\n\",metrics.confusion_matrix(t_test, pred_KNN),\"\\n\")\n",
"print(\"DT Confusion Matrix\",\"\\n\",metrics.confusion_matrix(t_test, pred_DT),\"\\n\")\n",
"print(\"NB Confusion Matrix\",\"\\n\",metrics.confusion_matrix(t_test, pred_NB))"
]
},
{
"cell_type": "markdown",
"id": "293ac1c9",
"metadata": {},
"source": [
"In the above confusion matrix outputs, KNN had the 11 false positives and 69 false negatives. DT has performed well with no false positives or false negatives. However, the worst performing would be NB as there were 0 true positives and 355 false positives. With this in mind, we will continue with the model comparison with a classification report."
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "12b9dc79",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN Classification Report \n",
" precision recall f1-score support\n",
"\n",
" False 0.83 0.97 0.90 355\n",
" True 0.80 0.39 0.53 114\n",
"\n",
" accuracy 0.83 469\n",
" macro avg 0.82 0.68 0.71 469\n",
"weighted avg 0.83 0.83 0.81 469\n",
" \n",
"\n",
"DT Classification Report \n",
" precision recall f1-score support\n",
"\n",
" False 1.00 1.00 1.00 355\n",
" True 1.00 1.00 1.00 114\n",
"\n",
" accuracy 1.00 469\n",
" macro avg 1.00 1.00 1.00 469\n",
"weighted avg 1.00 1.00 1.00 469\n",
" \n",
"\n",
"NB Classification Report \n",
" precision recall f1-score support\n",
"\n",
" False 0.00 0.00 0.00 355\n",
" True 0.24 1.00 0.39 114\n",
"\n",
" accuracy 0.24 469\n",
" macro avg 0.12 0.50 0.20 469\n",
"weighted avg 0.06 0.24 0.10 469\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/opt/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/opt/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
}
],
"source": [
"#Classification Report\n",
"print(\"KNN Classification Report \\n\",metrics.classification_report(t_test,pred_KNN),\"\\n\")\n",
"print(\"DT Classification Report \\n\",metrics.classification_report(t_test,pred_DT),\"\\n\")\n",
"print(\"NB Classification Report \\n\",metrics.classification_report(t_test,pred_NB))"
]
},
{
"cell_type": "markdown",
"id": "64564968",
"metadata": {},
"source": [
"The above outputs show the classification report for KNN, DT, and NB, respectively. From the output we can observe that only DT had a recall and f1-score of 1, which meant that it was able to predict and identify the smokers and non-smokers without any misclassification. KNN had a recall value of 0.97 and 0.39, and f1-score of 0.90 and 0.53 for smokers and non smokers. Although the values are high and close to 1 for smokers, it is still not fully accurate, and non smokers values are relatively low meaning that there are higher misclassification within the prediction data. The worst model was with NB where there were recall values of 0 and 1, and f1-score values of 0 and 0.39. This meant that there were 0 accurate non-smokers and many misclassification within the data.\n",
"\n",
"This is consistent with our previous comparisons where the best model were DT and the inaccurate models were KNN and NB. Hence, with these comparisons it is suffice to say that the best algorithm for the smoker dataset is the Decision Trees (DT) model.\n",
"\n",
"***\n",
"# 4.0 Critique and Limitations <a class=\"anchor\" id=\"4.0\"></a>\n",
"\n",
"Throughout both phases, one of the strengths of the dataset was that there was not much missing values which were difficult to replace with, and the dataset were mostly clean and tidy. However, since there were only 1561 observations within the dataset, we were unable to fully and accurately test our data for modeling. This comes as a weakness as if we had a bigger dataset with more observations, we could be able to produce a much more accurate model for selection.\n",
"\n",
"Furthermore, with a rather small dataset, we had to resort with a smaller test data during the performance comparison phase, and had to resort with using the 5 repeated 3 fold cross validation method. However, since we involved this method, there were limited effects of overfitting which is helpful for prediction analysis as overfit data often results in incorrect predictions. Therefore, future updates on this project with a larger dataset would be recommended to ensure the correctness of our results.\n",
"\n",
"***\n",
"# 5.0 Summary and Conclusions <a class=\"anchor\" id=\"5.0\"></a>\n",
"\n",
"## 5.1 Project Summary <a class=\"anchor\" id=\"5.1\"></a>\n",
"\n",
"In the Phase 1 of the project, we have successfully obtained a clean and tidy dataset, and to prepare the dataset for analysis in Phase 2. During Phase 1 after importing the data, firstly we have used the ```unique``` function to identify the unique values for each columns to identify any inconsistencies. This was useful as we found that there were missing values in features amt_weekdays, amt_weekends and type. Furthermore, we have identified inconsistencies for features nationality, ethnicity, and gross_income where there were values of \"refused\" and \"unknown\". \n",
"\n",
"To tackle these issues we first replaced the \"refused\" value to \"unknown\" value for consistencies. Then, we removed the type column as it was not relevant to our research objectives. However, to rectify the missing values for amt_weekdays and amt_weekends, we first found out that the reason these values were missing was because the respondent was not a smoker. Hence, we simply replaced all missing values with 0 values as they were not smokers and did not smoke on weekdays and weekends. Then, we discretized the numeric features of age by classifying them with values of 'Young', 'Middle-Aged', and 'Old' using the ```qcut``` function from pandas. \n",
"\n",
"Furthermore, in Phas 1 we also dealt with incorrect data types for the features, and dealt with them as necessary. Once the data cleaning and preprocessing were completed, we then used a multitude of plots to visualize the data through One-Variable, Two-Variable, and Three-Variable plots.\n",
"\n",
"With Phase 1 completed, we then moved on to Phase 2 by first exporting the data from Phase 1 as a csv file, and imported it into our Phase 2 for analysis. During Phase 2, after importing the data we first checked the dataset for any inconsistencies with our Phase 1 output. We found that there were inconsistencies within the data types, however as the encoding only allowed object type features, we decided to leave it as object features as necessary. Then, we performed encoding by first separating our target and data features. Target features were first encoded using binary values as there were only true and false values within the target feature. Then, we performed an one-hot encoding for our data features and also performed a feature scaling using min-max scaling of descriptive features as it was required for feature selection. \n",
"\n",
"Then, we moved on with feature selection by using selection methods of full feature selection, F-score, Random Forest Importance, and spFSR methods. Then, we compared the feature selection methods through paired t-test to identify our best selection method. Since there were no statistically significant differences between all selection methods, we simply selected the full feature selection method. \n",
"\n",
"Once feature selection was completed, to ensure consistencies of the test and training data, we used a 70:30 split of the data for future analysis. We also fitted our models with different algorithms to identify the best hyperparameters for the corresponding model, and to tune the model as required. Here we have used the K-Nearest Neighbors, Decision Trees, and Gaussian Naive Bayes algorithms and identified the best hyperparameters for each models. For each model, we have also tested the performance of each hyperparameters to aid in identifying the best parameters and to compare if there were any significant differences. However, we also compared our models to find the best models which worked for our dataset. For this, we have fitted each model with their best respective hyperparameters and fitted them with the same split data. We first compared the scores of each models, then compared the p-values using Paired T-test method, and also generated a confusion matrix and classification report. \n",
"Through this, we were able to identify the best model for our dataset.\n",
"\n",
"## 5.2 Summary of Findings <a class=\"anchor\" id=\"5.2\"></a>\n",
"\n",
"A comprehensive summary of your findings. That is, what exactly did you find about your particular problem?\n",
"\n",
"For both phases of the project, we have first found that missing values within the amt_weekdays, amt_weekends, and type was due to the respondent not being a smoker, and that some smokers only smoke on weekends and some on weekdays. During our data visualizations, we also found that there were more female than male smokers within the dataset, and more smokers within the younger population.\n",
"\n",
"Additionally, during the Phase 2 of the project, we have identified that for all feature selection method except for full feature selection method, amt_weekends and amt_weekdays were identified as the most important descriptive feature against our target feature, which then is followed by age, but were not as significant as amt_weekends and amt_weekdays. When comparing the feature selection methods, we identified that there were no statistical differences between all feature selection methods as they all yielded the same results. \n",
"\n",
"For the models, we have first identified the optimal hyperparameter for K Nearest Neighbours are number of neighbors of 7, and distance of 1 (Manhattan distance), with a score of 0.8324. Decision tree algorithm was identified with having the optimal hyperparameter of gini criterion, max depth of 2, and a minimum sample split of 2. For Gaussian Naive Bayes, the optimal hyperparameter were a variance of Laplace smoothing value of 3.5111917342151273e-09. \n",
"\n",
"During our model comparisons, we identified that the best algorithm was Decision Trees as it yielded the best scores, had no false positive or negatives within the confusion matrix, and had no misclassification in the classification report.\n",
"\n",
"## 5.3 Conclusion <a class=\"anchor\" id=\"5.3\"></a>\n",
"\n",
"In summary, we have cleaned the data during Phase 1 and prepared the data for analysis in Phase 2. In this Phase 2 report, the Decision Tree model selected by the full feature selection produces the highest cross-validation score, and the most accurate values on the training data. For this reason the Decision Tree model is selected as the best performing model compared to the K-Nearest Neighbors and Gaussian Naive Bayes models. In relation to our goals and objectives set out in Phase 1, we have successfully attained in goal to obtain a clean and tidy dataset in Phase 1, and here in Phase 2 we have successfully identified the best model for further prediction analysis.\n",
"***\n",
"# 6.0 References <a class=\"anchor\" id=\"6.0\"></a>\n",
"\n",
"Akmand D (2022) *SK Part 2: Feature Selection and Ranking*, GitHub website, accessed 27 May 2024. https://github.com/akmand/ml_tutorials/blob/master/SK2.ipynb\n",
"\n",
"Akmand D (2022) *spFSR*, GitHub website, accessed 27 May 2024. https://github.com/akmand/spFSR\n",
"\n",
"Chen Y and Lin C (2006) ‘Combining SVMs with Various Feature Selection Strategies’, *Studies in Fuzziness and Soft Computing*, vol 207, doi: 10.1007/978-3-540-35488-8_13.\n",
"\n",
"Evidently (n.d.) *Accuracy vs. precision vs. recall in machine learning: what's the difference?*, Evidently AI website, accessed 27 May 2024. https://www.evidentlyai.com/classification-metrics/accuracy-precision-recall#:~:text=Recall%20is%20a%20metric%20that,the%20number%20of%20positive%20instances.\n",
"\n",
"*Feature Selection* (2024) Heavy.AI website, accessed 27 May 2024. https://www.heavy.ai/technical-glossary/feature-selection\n",
"\n",
"Gleichmann N (2020) *Paired vs Unpaired T-Test: Differences, Assumptions, and Hypotheses*, Technology Networks Informatics website, accessed 27 May 2024. https://www.technologynetworks.com/informatics/articles/paired-vs-unpaired-t-test-differences-assumptions-and-hypotheses-330826#:~:text=A%20paired%20t%2Dtest%20(also,difference%20between%20the%20two%20groups.\n",
"\n",
"MacQuarrie M (2024) *UK Smoking Data* [data set], Kaggle website, accessed 10 April 2024. https://www.kaggle.com/datasets/mexwell/uk-smoking-data?resource=download\n",
"\n",
"Martins C (2023) *Gaussian Naive Bayes Explained With Scikit-Learn*, builtin website, accessed 27 May 2024. https://builtin.com/artificial-intelligence/gaussian-naive-bayes#\n",
"\n",
"Rosidi N (2023) *Advanced Feature Selection Techniques for Machine Learning Models*, KDnuggets website, accessed 27 May 2024. https://www.kdnuggets.com/2023/06/advanced-feature-selection-techniques-machine-learning-models.html#:~:text=Exhaustive%20feature%20selection%20compares%20the,ensures%20the%20best%20feature%20subset.\n",
"\n",
"Sharma D, Chatterjee M, Kaur G and Vavilala S (2022) ‘3 - Deep learning applications for disease diagnosis’, *Academic Press*, pp. 31 – 51, doi: 10.1016/B978-0-12-824145-5.00005-8.\n",
"\n",
"Sharma N (2023) *Understanding and Applying F1 Score: AI Evaluation Essentials with Hands-On Coding Example*, arize website, accessed 27 May 2024, https://arize.com/blog-course/f1-score/#:~:text=F1%20score%20is%20a%20measure,better%20understanding%20of%20model%20performance.\n",
"\n",
"*What is a decision tree?* (n.d.) IBM website, accessed 27 May 2024. https://www.ibm.com/topics/decision-trees#:~:text=A%20decision%20tree%20is%20a,internal%20nodes%20and%20leaf%20nodes.\n",
"\n",
"*What is k-nearest neighbors (KNN) algorithm?* (n.d.) IBM website, accessed 27 May 2024, https://www.ibm.com/topics/knn#:~:text=The%20k%2Dnearest%20neighbors%20(KNN,used%20in%20machine%20learning%20today.\n",
"\n",
"*What is the difference between a confusion matrix and a classification report?* (n.d.) LinkedIn website, accessed 27 May 2024, https://www.linkedin.com/advice/3/what-difference-between-confusion-matrix-classification-hsehf#:~:text=A%20classification%20report%20is%20a,these%20metrics%20across%20all%20classes.\n",
"\n",
"Yeung C, Bunker R and Fujii K (2023) ‘F-Score for the XGBoost model’, *PLOS One*, doi: https://doi.org/10.1371/journal.pone.0284318.s001"
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