wangechi01-a/Financial-Inclusion-in-Africa

# Financial-Inclusion-in-Africa # Aim The objective of this project is to create a machine learning model to predict which individuals are most likely to have or use a bank account. The models and solutions developed can provide an indication of the state of financial inclusion in Kenya, Rwanda, Tanzania and Uganda, while providing insights into some of the key factors driving individuals’ financial security. The metrics are that 1 indicates that the individual does have a bank account and 0 indicates that they do not. # Importances of this Challenge Increased Security: Bank accounts enhance financial security,Predictive models help identify key factors driving financial security offering insights into how financial inclusion can be improved. Convenience: Easy and accessible banking services reduce barriers to financial participation for underserved communities, enhancing their economic engagement. Economic growth: Access to bank accounts fosters long-term economic growth by empowering households and businesses to participate in financial transactions and establish creditworthiness. # Tools Used: Python: For data preprocessing, feature engineering, and building machine learning models. Machine Learning Models: Implementing algorithms in classification to predict an individual likelihood of having or using a bank account. Data Analysis: Utilizing tools like NumPy and Pandas for data manipulation and analysis(Visualization).