🧩 Credit Risk Score Prediction Project

🔍 Overview

This project aims to predict default risk scores based on various financial and demographic features. The goal is to build a robust regression model that accurately estimates the creditworthiness of individuals, helping lenders make informed decisions.

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The workflow includes:

• Data loading and cleaning
• Multicollinearity detection using VIF
• Model training and hyperparameter tuning
• Model evaluation using MSE, RMSE, and R² score

Multiple models were evaluated — Ridge Regression, Random Forest Regressor, and Gradient Boosting Regressor — using GridSearchCV for optimal performance.

🗒️📌 Key Features Used

Feature Name	Description
income	Annual income of the applicant
loan_amount	Total amount of the requested loan
loan_term	Duration of the loan in months
interest_rate	Interest rate applied to the loan
debt_to_income_ratio	Ratio of total debt to annual income
employment_years	Years employed at current job
savings_balance	Current savings balance
age	Age of the applicant

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🧩⚙️ The Approach

1. Data Loading and Cleaning

• The dataset was loaded using pandas.
• Null values were checked using .isnull().sum() and handled appropriately (e.g., imputation or removal).
• Data types were verified and converted if necessary (e.g., converting categorical variables to numeric).
• Data parsing for modeling(Regression analysis)

2. Multicollinearity Handling: Successive VIF Testing

• A Variance Inflation Factor (VIF) test was performed to detect multicollinearity.
• Features with a VIF value greater than 10 were removed iteratively.
• After dropping the feature with high VIF (credit_score), a second VIF test confirmed all remaining features were within acceptable limits.

Final VIF Results:

Feature	VIF Value
income	5.49
loan_amount	4.94
loan_term	5.01
interest_rate	5.62
debt_to_income_ratio	6.47
employment_years	3.40
savings_balance	3.52
age	7.53

All features passed the VIF threshold (<10), indicating low multicollinearity.

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3. Model Training & Selection

Three regression models were trained and tuned:

✅ Ridge Regression

• Grid search over alpha: [0.1, 1, 10, 100]
• Optimized for minimum Mean Squared Error (MSE)

🌲 Random Forest Regressor

• Hyperparameters: n_estimators, max_depth
• Searched over combinations: [100, 200], [None, 10, 20]

🚀 Gradient Boosting Regressor

• Parameters: n_estimators, learning_rate, max_depth
• Searched over: [100, 200], [0.01, 0.1], [3, 5]

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4. Evaluation Metrics

Each model was evaluated using:

• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R-squared (R²) Score

The best performing model was selected based on these metrics.

• ✅ Ridge Regression

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📁 File Structure

Default-risk-score-prediction/ │ ├── data/ # Dataset files │ └── data.csv # Main dataset │ ├── notebooks/ # Jupyter Notebooks for EDA and modeling │ └── exploratory_analysis.ipynb │ ├── src/ # Source scripts │ └── train_model.py # Script for training and evaluating models │ ├── README.md # This file └── requirements.txt # Python dependencies