WilsonMoses-Data/Loan-Prediction-Advanced-Data-Exploration-Statistical-Analysis-Feature-Engineering

Advanced exploration and feature engineering of a loan prediction dataset completed during Week 3 of the AnalystLab Africa Data Science Internship. The project covers statistical hypothesis testing, approval-pattern analysis, feature creation, responsible feature selection, and leakage-safe preparation of machine-learning datasets. # Loan Approval Prediction: Advanced Data Exploration and Feature Engineering > **AnalystLab Africa Data Science Internship | Week 3 Project** > **Author:** Wilson Moses > **Focus:** Advanced exploratory analysis, statistical inference, feature engineering, responsible feature selection, and machine-learning preparation. ## Project Overview This project investigates historical loan applications to understand the factors associated with loan approval and prepare a reliable dataset for predictive modelling. Building on the cleaning and preprocessing completed in Week 2, the Week 3 analysis moves beyond basic summaries to examine data quality, applicant characteristics, approval patterns, statistical relationships, engineered financial indicators, and leakage-safe machine-learning preparation. The result is a complete analytical workflow that transforms a cleaned loan application dataset into documented, interpretable, and model-ready project deliverables. ## Business Problem Lending institutions must assess loan applications consistently while balancing access to credit, operational efficiency, and financial risk. The central business question is: > **Which applicant, household, financial, and credit-related characteristics are associated with historical loan approval decisions, and how can those characteristics be prepared responsibly for predictive modelling?** The target variable is `loan_status`: - `Y`: loan application approved. - `N`: loan application not approved. For numerical analysis and modelling, the outcome is also represented as `approved`: - `1`: approved. - `0`: not approved. ## Project Objectives 1. Validate the quality, consistency, and business plausibility of the cleaned dataset. 2. Investigate distributions, outliers, approval rates, and relationships between applicant characteristics. 3. Test whether observed patterns are statistically significant. 4. Create interpretable features describing household composition, credit status, in …