8Sharon/African-Credit-Scoring-Challenge

This project aims to boost financial inclusion in Africa by using machine learning to predict loan default risk. Models are built on anonymized credit data to help lenders make better decisions and extend credit to underserved communities. # African Credit Scoring Challenge - Predicting Loan Default Probability The African Credit Scoring Challenge focuses on predicting the probability of loan default based on customer and loan-related features. The goal is to develop a machine learning model that can predict whether a loan will default or not, using historical data of borrowers and their loan details. This challenge involves preprocessing a dataset containing both numerical and categorical features such as loan amount, loan type, customer status, and repayment history. The model is evaluated on its ability to correctly predict loan defaults, which is crucial for lenders to assess the risk of granting loans to potential customers. By addressing the imbalance in the target variable (default vs. non-default), the project aims to create a robust model for predicting loan defaults in African markets, where credit scoring data is often scarce or incomplete. # ⚠️ Disclaimer All datasets, information, and reports within this repository are fictional and created solely for illustrative purposes to showcase advanced predictive machine learning techniques. They do not include any real proprietary, confidential, or sensitive information related to any company, organization, or individual. ## Table of Contents 1. Objective 2. Problem Statement 3. Dependencies 4. Data Preprocessing 5. Modeling 6. Evaluation 7. Results 8. How to Run the Code 9. Contact Information ## Objective The objective of this project is to predict the probability of a customer defaulting on a loan based on their historical data. By training a machine learning model, this project aims to classify whether a customer will default on their loan or not, helping lenders make informed decisions on granting loans. The model will be built using various techniques for preprocessing, model training, and evaluation. ## Problem Statement In many regions, particularly in Africa, loan defaults can have significant financial implications for both the …