MariusEtudiant/African-Credit-Scoring-Challenge

# Loan Default Prediction Challenge ## 🌟 Overview This project was part of the African Credit Scoring Challenge, aiming to predict **loan defaults** in Africa’s dynamic financial markets. The objective was to build a robust **machine learning model** and a **scalable credit scoring function** to assist financial institutions in mitigating risk and optimising lending decisions. --- ## 📊 My Contribution ### Key Achievements: - **F1-score**: Achieved a competitive score of **0.71** early in the competition. - **Imbalanced Data**: Addressed class imbalance using **SMOTEENN** for hybrid resampling. - **Feature Engineering**: Incorporated demographic and economic factors specific to African markets (economic-dataset.csv). - **Model Optimisation**: Fine-tuned for robustness and generalisability. - **Credit Scoring**: Developed a scalable function to classify probabilities into actionable risk categories. --- ## 🔑 Technical Highlights 1. **Data Challenges**: - Managed significant class imbalances with advanced resampling techniques. - Ensured generalisability across diverse customer demographics because the train dataset contain only Kenya data, but the test dataset on Zindi can contain other country data like Ghana. 2. **Machine Learning Techniques**: - Used **XGBoost** for high-performance classification. - Applied **SMOTEENN** for handling imbalanced data. 3. **Credit Scoring Function**: - Designed a scalable system to categorise risk levels based on model predictions. - Provided actionable insights for financial decision-making. --- ## 🚀 Reflection This project was both challenging and rewarding, demanding a mix of technical expertise and strategic thinking. It highlights my ability to handle real-world data challenges and propose scalable solutions. --- ## 🛠️ Tools & Libraries - **Languages**: Python - **Libraries**: Pandas, NumPy, Scikit-learn, XGBoost, Ensemble Learning, Matplotlib, Seaborn --- ## 📬 Contact Feel free to reach out if you'd like to discuss …