GiuliaGGG/Microfinance-Services-ML

Code and Paper - Microfinance Services: Using Machine Learning to Predict Feasibility in Kenya # Machine learning for Microfinance Services Microfinance Services: Using Machine Learning to Predict Feasibility in Kenya - Code and Paper ## Overview During my time at the Chinese University of Hong Kong, I had the opportunity to enhance my data science skills through various courses. This included an advanced economics model where I utilized machine learning techniques to predict the performance of microfinance services in Kenya. My paper investigates usage of informal Kenyan microfinance investment groups, known as chamas, and analyses the feasibility of implementing a web application to assist in chama organisation and management. ## Dataset and Methodology For my final paper, I utilized the Kaggle dataset "Islamic Microfinance Services Feasibility Study" by Kinuthia, R. (2018). The data from this dataset, which pertains to microfinance services in Kenya, was used to train predictive models. Retrieved from kaggle.com. ## Predictive Models I trained two logistic regression models and a neural network on the survey results to predict chama (informal microfinance investment groups) usage. The first logistic regression model utilized hyperparameter tuning for variable selection, while the second model used p-value significance. The neural network also utilized variables with p-value significance. ## Results and Analysis The performance of the models was evaluated using confusion matrices and accuracy scores. The first logistic regression model demonstrated high accuracy, suggesting its effectiveness in predicting chama usage. However, the second logistic regression model performed poorly. A neural network analysis was conducted, showing significant improvement in accuracy. The paper demonstrates the potential of using advanced analytics to improve the understanding and use of chama microfinance services in Kenya. # Results The paper focuses on an unrepresented and vastly unbanked pa …