Jitesh117 / Credit_card_Score_Classifier

Credit Score Classifier leveraging diverse machine learning models including KNN, Random Forests, XGBoost, LightGBM, SVC, and ensemble techniques, achieving 79.24% accuracy with hyperparameter tuning for enhanced predictive performance

Table of Contents

• Table of Contents
• Introduction
• Data Cleaning and handling Missing values
• EDA
  • BoxPlot of Numeric Columns
  • PairPlot of Payment_Behavior against other important features
• Outlier Detection and Removal
• How I dealt with un-balanced Distribution
• Model Selection
• Results

Introduction

• Developed a Credit Score Classifier with 79.24% accuracy using various Machine Learning Models such as :
• Employed various Classification models such as KNN, Random Forests, XGBoost, LightGBM, SVC, etc.
• Did Hyperparameter tuning on the KNN model to achieve even better results
• Employed Ensemble Modeling to improve the predictive performance of the individual models.

Data Cleaning and handling Missing values

Feature	Count	Unique	Top	Freq
Customer_ID	100,000	12,500	CUS_0xd40	8
Month	100,000	8	January	12,500
Age	100,000	1,788	38	2,833
Occupation	100,000	16	_______	7,062
Annual_Income	100,000	18,940	36,585.12	16
Num_of_Loan	100,000	434	3	14,386
Type_of_Loan	88,592	6,260	Not Specified	1,408
Num_of_Delayed_Payment	92,998	749	19	5,327
Changed_Credit_Limit	100,000	4,384	_	2,091
Credit_Mix	100,000	4	Standard	36,479
Outstanding_Debt	100,000	13,178	1,360.45	24
Credit_History_Age	90,970	404	15 Years and 11 Months	446
Payment_of_Min_Amount	100,000	3	Yes	52,326
Amount_invested_monthly	95,521	91,049	10,000	4,305
Payment_Behaviour	100,000	7	Low_spent_Small_value_payments	25,513
Monthly_Balance	98,800	98,792	-333333333333333333333333333	9
Credit_Score	100,000	3	Standard	5,317

• As can be seen from the above table, there were too many inconsistencies and errors in the dataset which had to be cleaned
• To tackle data cleaning, I created custom functions to efficiently clean both the numerical and categorical columns.

EDA

The insights gleaned from the pivot tables highlight key findings from this comprehensive analysis.

BoxPlot of Numeric Columns

PairPlot of Payment_Behavior against other important features

Outlier Detection and Removal

Before	After

How I dealt with un-balanced Distribution

• The data set showed unbalance distribution. This may cause a biased estimate.
• So we will use SMOTE, an oversampling process that allows synthetic data to be generated.

sm = SMOTE(random_state=2)
smote_train_X, smote_train_Y = sm.fit_resample(X_train, y_train)

Model Selection

After training various models on the Dataset, I came to the following conclusion:

Model	Success
KNN	78.105
RF	77.650
BC	75.010
XGB	72.745
LightGBM	69.750
SVC	69.435

Results

	Precision	Recall	F1-Score	Support
0	0.77	0.86	0.81	5,874
1	0.85	0.75	0.80	10,599
2	0.69	0.82	0.75	3,527
Accuracy			0.79	20,000
Macro Avg	0.77	0.81	0.79	20,000
Weighted Avg	0.80	0.79	0.79	20,000