kaifoerster / mlproject_kenyafinancial

This repository contains all the code and data used for the Machine Learning Project conducted during the ML course at Hertie School

MLProject_KenyaFinancial

This repository contains all the code and data used for the Machine Learning Project conducted during the ML course at Hertie School of Governance in Berlin.

"The introduction of mobile money services is revolutionising the financial lives of low-income households in Kenya by giving them access to formal financial services. While M-Pesa has a potential to change the financial lives of poor Kenyan households, usage of mobile money in everyday financial transactions is less understood. This study runs explainable machine learning algorithms on the Kenya Financial Diaries, a high-frequency financial transaction dataset. The best algorithm, which is the Random Forest classifier, is able to predict the transaction method for a given transaction with a 98.52% accuracy and an MCC of 93.55%. The main predictors of mobile money transactions were transactions related to: remittances, telecom providers, monthly consumption and income measures as well as mental and physical health characteristics. These findings contribute to a greater understanding of the financial lives of low-income Kenyans."