iamisaackn/Predictive-Analytics-for-Waterpoint-Operational-Status-in-Tanzania

# Predictive Analytics for Waterpoint Operational Status in Tanzania Generated by Copilot ## Overview This project aims to predict the operational status of waterpoints in Tanzania, focusing on enhancing the maintenance and management of these critical resources. By accurately classifying waterpoints as functional, needing repair, or non-functional, the project seeks to provide actionable insights for stakeholders, ultimately improving access to clean water for local communities. The analysis leverages a comprehensive dataset that includes various features related to waterpoint characteristics, user quality, and geographical factors. ## Business and Data Understanding ### Stakeholders The primary stakeholders involved in this project include: - **Tanzanian Ministry of Water**: Responsible for the maintenance and management of waterpoints, ensuring that communities have reliable access to clean water. - **Local Communities**: Depend on these waterpoints for their daily water needs, making their functionality critical for health and well-being. - **Maintenance Teams**: Tasked with repairing and maintaining the waterpoints, requiring data-driven insights to prioritize their efforts effectively. - **Non-Governmental Organizations (NGOs)**: Often involved in funding and supporting water infrastructure projects, seeking to optimize resource allocation. - **Data Scientists and Analysts**: Working on the project to develop predictive models that can inform decision-making processes. ### Dataset Choice The dataset utilized in this analysis comprises various features related to waterpoints, including user quality levels, source types, waterpoint types, and geographical information. This rich dataset allows for a comprehensive exploration of factors influencing waterpoint functionality, enabling the development of a robust predictive model. ## Modeling The modeling phase involved the application of various machine learning techniques to classify the operational sta …