Keanaust/Predictive-Maintenance-for-Water-Pumps-Kaggle-

Predicts water pump failures using machine learning models to optimize maintenance and improve clean water access in Tanzania. Kaggle competition project. # Pump It Up: Data Mining the Water Table This repository contains my analysis and predictive modeling project, focusing on water pump functionality in Tanzania. The goal is to build a model that predicts the operational status of water pumps, helping identify issues and improve water access in rural areas. ## Project Overview This project analyzes water point data to determine the functionality of water pumps across Tanzania. Using advanced data analytics and machine learning, I developed a model to predict whether a pump is functional, functional but needs repair, or non-functional. ## Key Highlights ### Data - **Training Set Values**: `Training set values.csv` - **Training Set Labels**: `Training set labels.csv` - **Test Set Values**: `Test set values.csv` - Features include geographic location, pump type, construction year, water quality, and other metadata. ### Methodology 1. **Data Preprocessing**: - Cleaned and imputed missing values. - Encoded categorical features. - Scaled numerical data for modeling. 2. **Exploratory Data Analysis (EDA)**: - Identified trends and relationships between features and pump functionality. - Visualized key insights through charts and graphs. 3. **Model Development**: - Built classification models including Random Forest, XGBoost, and Logistic Regression. - Evaluated performance using accuracy, precision, recall, and F1-score metrics. ### Results - The best-performing model achieved high accuracy in predicting pump functionality. - Key features influencing functionality include installation year, water quality, and region. ## 🛠 Next Steps 1. Deploy the model as a web app to provide real-time predictions. 2. Conduct further feature engineering for improved accuracy. 3. Expand analysis to include additional regions or datasets. ## Files Included - `Pump It Up - Notebook.ipynb`: The complete analysis and modeling process. - `Training set values.csv`: Training dataset containing feature values. - `Training set labels.csv`: L …