Tichaona123/Credit-Risk-modelling--Zimbabwe-Data

This project applies a full data science lifecycle to credit risk modeling using 100,000 anonymized loan records from a Zimbabwean microfinance firm. After rigorous preprocessing, feature engineering, and resampling, ensemble models with hyperparameter tuning and robust evaluation deliver optimized predictions of borrower default risk. # Credit-Risk-modelling--Zimbabwe-Data The notebook represents a comprehensive and rigorous approach to credit risk modeling, utilizing a substantial dataset from a Zimbabwe-based microfinance company. This project exemplifies the full data science lifecycle, from initial problem formulation to advanced model optimization and evaluation. At its core, the notebook addresses the critical financial challenge of predicting borrower default probabilities. It begins by contextualizing credit risk within the broader financial landscape, explaining how defaults impact lenders and the importance of regulatory frameworks like IFRS 9, which mandates the use of Expected Credit Losses (ECL) for accurate impairment provisioning. This theoretical foundation underscores the practical necessity of developing robust predictive models to assess borrower creditworthiness. The dataset comprises 100,000 loan records with 21 features, encompassing borrower demographics, employment details, loan specifics, and repayment history. Sourced from a Zimbabwe microfinance institution, the data has been anonymized to protect client privacy. The notebook meticulously describes each feature, such as loan amount, outstanding balance, interest rates, and borrower age, while identifying potential data quality issues like duplicate columns (e.g., "number_of_defaults" and "number_of_defaults.1"). Data preprocessing forms a substantial portion of the notebook, ensuring data integrity before modeling. Missing values are handled by dropping rows with categorical missing data, given their low percentage. Outlier detection employs the IQR method on key numerical features—loan amount, outstanding balance, and salary—to remove extreme values that could skew model performance. Duplicate and irrelevant columns, such as unique identifiers, are eliminated to streamline the dataset. Feature engineering enhances model interpretability and performance. Locations are aggregated into Zimbabwean provinces to reduce dimen …