Machine Learning with R

Machine Learning project by Wiricardo for credit score prediction using R and Quarto. Compares Linear Regression vs Random Forest models with customer data.

Overview

This project predicts customer credit scores based on:

• Annual Income
• Estimated Savings
• Preferred Category (product preference)

Dataset: Mall Customers Enhanced

Models Implemented

Linear Regression (3 variants) - Model 1: Annual Income only - Model 2: Annual Income + Estimated Savings - Model 3: All features including Preferred Category

Random Forest - 500 trees, optimized hyperparameters - Better performance handling non-linear relationships

Results

Model	RMSE	R²
Linear Regression (Model 3)	~80	0.75
Random Forest	~40	0.94

Random Forest significantly outperforms linear regression due to multicollinearity between income and savings (correlation: 0.81).

Project Structure

ML_R/
├── Final_R.qmd              # Main analysis notebook
├── Mall_Customers_Enhanced.csv
├── m1_plot.rds              # Linear regression plots
├── m2_plot.rds
├── m3_plot.rds
├── rf_plot.rds              # Random forest plot
└── *_metrics.rds            # Model performance metrics

Key Findings

• High multicollinearity between Annual Income and Estimated Savings (0.81)
• Linear relationships exist but with curvature at extreme values
• Random Forest handles non-linearity better than linear regression
• Feature engineering with dummy variables improves model performance

Contact

For suggestions, bug reports, or collaboration:
GitHub profile → wiricardo

License

This project is licensed under the MIT License.
See the LICENSE file for more details.