sisi-y/kenya-crop-yield-predictor

🌽 Kenya Crop Yield Predictor A machine learning project that predicts maize crop yield for Kenyan farms using rainfall, fertiliser usage, and farm size. This project demonstrates a complete machine learning workflow: data preparation → model training → evaluation → visualisation → prediction. 📌 Project Overview The model uses Multiple Linear Regression to estimate crop yield (in kilograms) based on: Rainfall (mm) Fertiliser used (kg) Farm size (acres) The dataset contains 300 synthetic Kenyan farm records. 📊 Model Performance Algorithm: Linear Regression R² Score: ~97% Evaluation Metrics: R² Score Mean Absolute Error (MAE) The model explains approximately 97% of the variation in crop yield within the dataset. 📈 Features Used Feature Description Rainfall_mm Annual rainfall received Fertiliser_kg Fertiliser applied per season Farm_Size_acres Total cultivated land area 🛠 Tech Stack Python 3 Pandas NumPy Scikit-learn Matplotlib Google Colab ▶️ How to Run Clone this repository Install dependencies: pip install pandas numpy scikit-learn matplotlib Run the notebook or Python script. 🔍 What This Project Demonstrates Train/test data splitting Model training using Linear Regression Model evaluation using R² and MAE Feature importance analysis Custom prediction function for new farms Data visualisation 🚀 Future Improvements Use real agricultural datasets Add more features (soil quality, temperature, seed variety) Deploy as a web application for farmers 👩‍💻 Author Wangari Njoroge ML Engineer in Training BSc Actuarial Science