clydeochieng/Tanzania-Water-Wells

# Tanzanian Water Wells: Functionality Prediction ## Overview This machine learning project aims to address the critical challenge of non-functional or malfunctioning water wells in Tanzania. By building a predictive model, we seek to classify water wells as "functional," "non-functional," or "functional needs repair," thereby enabling more efficient resource allocation for repairs and maintenance. The model is designed to assist both NGOs and government agencies in improving water access for Tanzanian communities. ## Project Structure This project is organized into the following components, implemented as Python classes for modularity and reusability: 1. **Data Extractor:** Loads and combines raw data. 2. **Data Overview:** Provides a summary of the dataset (head, tail, descriptions, etc.). 3. **Data Cleaning:** Cleans and prepares data for analysis. 4. **Exploratory Data Analysis (EDA):** Examines data distributions, relationships, and visualizations. 5. **Modeling:** Trains and evaluates various machine learning models. 6. **Evaluation:** Assesses model performance and selects the best-performing one(s). 7. **Deployment:** Creates a Streamlit application for prediction based on user input. ## Business Understanding **Problem Statement:** Tanzania struggles to provide clean water due to numerous non-functional or malfunctioning water wells, leading to health risks and hindering development. **Objectives:** 1. Develop a predictive model to classify water well functionality. 2. Identify key factors influencing well functionality to inform future well design and maintenance. **Proposed Solution:** A machine learning classifier will be trained on historical data to predict the status of new wells. We aim for an accuracy score of at least 80%. **Metrics:** - Accuracy - Precision - Recall - F1-Score **Conclusion:** By identifying wells in need of repair, the project aims to improve water access and enhance the quality of life for Tanzanian commun …