Abdulrahman-hayatu/Rainfall-prediction-model-model-Linear-regression-

This model was built using Linear regression. It is simple yet effective ML solution for predicting rainfall patterns in some selected areas of Nigeria. Welcome to the Rainfall Prediction Model project — a simple yet effective machine learning solution for forecasting rainfall patterns using Linear Regression. This project was built as part of an undergraduate research project in Computer Science, and it demonstrates how historical climate data can be transformed into meaningful predictions to support agriculture, disaster management, and environmental planning. What This Project Does • Predicts 1-month and 3-month rainfall anomalies. • Uses key climatic features like 10-day rainfall, rolling rainfall averages, and anomalies. • Built with scikit-learn, pandas, and matplotlib. • Evaluated using MAE and R² for performance tracking. Model Performance Model MAE R² Score 1-Month 16.92 0.95 3-Month 37.10 0.97 Tech Stack • Python 3.9+ • Scikit-learn • Pandas • Matplotlib • Streamlit (for interactive UI) UI Preview Insert screenshots of the UI and prediction results here for better engagement. How to Use 1. Clone the repo: git clone github.com 2. Navigate to the project folder and install dependencies: pip install -r requirements.txt 3. Run the app: streamlit run app.py 🤔 Why This Matters In countries like Nigeria and across the tropics, accurate rainfall prediction is crucial for food security and infrastructure planning. This model is a step toward making climate data accessible and actionable through lightweight machine learning tools.