Credit scoring is a critical process used by financial institutions to evaluate a customer's creditworthiness and lending risk. This project develops an end-to-end Machine Learning pipeline to classify customer credit scores using financial and behavioral data.
The project demonstrates the complete data science workflow—from data preprocessing and exploratory data analysis (EDA) to feature engineering, model training, hyperparameter optimization, and model evaluation.
- Clean and preprocess raw credit data.
- Explore patterns and relationships through Exploratory Data Analysis (EDA).
- Engineer features to improve model performance.
- Train and compare multiple machine learning classification models.
- Optimize model performance using RandomizedSearchCV.
- Evaluate models using standard classification metrics to identify the best-performing algorithm.
The dataset contains customer financial and credit-related information, including:
- Annual Income
- Monthly In-hand Salary
- Occupation
- Number of Bank Accounts
- Number of Credit Cards
- Outstanding Debt
- Credit Utilization Ratio
- Payment Behaviour
- Credit Mix
- Credit History Age
- Monthly Balance
- Loan Information
Target Variable
- Credit Score
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- XGBoost
- Jupyter Notebook
Credit-Score-Classification/
│
├── 01_credit_score_data_processing.ipynb
├── 02_credit_score_modeling.ipynb
├── README.md
├── requirements.txt
└── .gitignore
- Data loading and inspection
- Handling missing values
- Removing duplicates
- Data cleaning
- Encoding categorical variables
- Feature preparation
- Distribution analysis
- Correlation analysis
- Class distribution
- Outlier detection
- Feature relationship analysis
- Categorical encoding
- Feature selection
- Data transformation
- Train-test split
Multiple classification algorithms were trained and compared to determine the best-performing model.
Models Implemented
- 🌳 Decision Tree Classifier
- 🌲 Random Forest Classifier
- 🌿 Extra Trees Classifier
- 🚀 XGBoost Classifier
To improve predictive performance, RandomizedSearchCV was used to efficiently search the hyperparameter space and identify optimal model configurations.
Each model was evaluated using:
- Accuracy
The best-performing model was selected based on its overall classification performance after hyperparameter tuning.
- End-to-end Machine Learning pipeline
- Comprehensive Data Cleaning & Preprocessing
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Comparison of Multiple Tree-Based Models
- Hyperparameter Optimization using RandomizedSearchCV
- Model Performance Evaluation
- Data Cleaning
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Machine Learning
- Classification Algorithms
- Ensemble Learning
- Hyperparameter Tuning
- Model Evaluation
- Python Programming
- Data Visualization
- Deploy the trained model using Streamlit or Flask
- Save the best-performing model with Joblib
- Add SHAP for model interpretability
- Build an interactive dashboard for real-time credit score prediction
Your Name
- GitHub: https://github.com/Saurav21
- LinkedIn: https://linkedin.com/in/your-profile
This project demonstrates a complete machine learning workflow for credit score classification using multiple tree-based algorithms. By combining robust data preprocessing, exploratory analysis, feature engineering, model comparison, and RandomizedSearchCV for hyperparameter optimization, the project showcases practical skills in developing and evaluating predictive models for real-world financial applications.