This project builds a complete machine learning pipeline for predicting credit card default risk.
It includes full EDA, cleaning, feature engineering, clustering, model comparisons, and exporting the best model.
Visual Overview
Below are some of the key visuals generated throughout the analysis.
Default Distribution
default_distribution
Credit Limit Distribution
limit_bal_hist
Default vs Non-Default Counts by Education Level (Stacked)
K-Means Clusters (PCA 2D Visualization)
clusters
Random Forest Feature Importance (Top 15)
Top 15 features
ROC Curves — Before vs After Feature Engineering
ROC Curves
Dataset
Source: UCI Default of Credit Card Clients dataset Rows: 30,000 originally → 28,070 after cleaning Target:DEFAULT
Data Cleaning
Key cleaning operations:
Removed negative bill amounts & negative payments (~1,930 rows)
Checked & imputed missing values after feature engineering
Cleaned invalid categorical codes
Verified no duplicates
Exploratory Data Analysis (EDA)
A detailed exploration was performed including:
Distributions (histograms, boxplots)
Default behavior across demographics
Correlation heatmap
Delay behavior (PAY_0…PAY_6)
Bill/payment trends
PCA & clustering insights
Feature Engineering
Created multiple new predictive features:
Aggregation Features
TOTAL_BILL, TOTAL_PAY, BILL_PAY_RATIO
Delay Features
AVG_DELAY, MAX_DELAY
Trend
BILL_TREND
Clustering (K-Means)
CLUSTER_ID
Distances to centroids (DIST_K0…DIST_K4)
DIST_TO_CENTROID
These features significantly improved model performance.
Regression → Classification (Part 7)
Used linear regression predictions to create a continuous target, then applied a median split, resulting in a perfectly balanced classification target:
