NamSingh/Pump-It-Up

Leverage machine learning and classification algorithms to predict the operational status of water pumps in Tanzania, categorizing them as functional, non-functional, or in need of repair. # Pump It Up: Data Mining the Water Table - DrivenData Competition This repository contains my submission for the DrivenData "Pump It Up: Data Mining the Water Table" competition. The challenge involves predicting the operational status of water pumps in Tanzania using machine learning. By leveraging historical data, this project builds a model to classify pumps as functional, non-functional, or needing repair, providing actionable insights for better water management and infrastructure planning. The final results of running this ML Ensemble Model (using Random Forest, XGBoost, and CatBoost) will be an Excel sheet that can be submitted into the competition. To download the data required for this model, please see the DrivenData Competition Site. ## 📑 **Table of Contents** 1. Business Value 2. Technologies Used 3. What the Code Does 4. Key Highlights --- ## 🌍 **Business Value** Access to clean water is a fundamental necessity, and the operational status of water pumps directly impacts the livelihoods of millions of people. By accurately predicting the status of water pumps, this project aims to: - **Improve resource allocation**: Help NGOs and local governments identify areas where repairs or maintenance are needed, ensuring a more efficient use of resources. - **Enhance water access**: By identifying non-functional pumps early, it enables timely repairs, reducing water scarcity and improving health outcomes. - **Scale impact**: A reliable prediction model can be deployed across various regions, assisting in the global effort to provide clean water to underserved communities. This project showcases the application of machine learning to real-world problems that have a significant social and economic impact, contributing to the achievement of sustainable development goals. --- ## 🛠️ **Technologies Used** - **Python** - **Pandas & NumPy** - **Scikit-learn** - **XGBoost, CatBoost, RandomForest** - **Optuna, Grid Search, Random Search** - **Matplotlib & Seabor …