Wanjiru-1/crop-yield-prediction

Machine Learning model for predicting maize yields in Kenya using satellite data # Kenya Maize Yield Predictor (Streamlit App) Interactive demo wrapping the Gradient Boosting model from Kenya Maize Yield Prediction in a usable interface. Enter rainfall, NDVI, and terrain values, get a maize yield prediction for Uasin Gishu County, Kenya. ## Files - `app.py` — the Streamlit app - `train_model.py` — reconstructs the exact training dataset from the notebook's output and retrains the model (reproduces R² = 0.6875, RMSE = 0.224, matching the original notebook) - `maize_yield_model.pkl` — trained Gradient Boosting model - `model_metadata.pkl` — feature names, ranges, and performance metrics - `requirements.txt` — dependencies ## Run locally ```bash pip install -r requirements.txt streamlit run app.py ``` Then open the local URL Streamlit prints (usually localhost). ## Deploy for free (so you can link it live on your portfolio) **Streamlit Community Cloud** (easiest, free): 1. Push this folder to a GitHub repo (or a subfolder of your existing `crop-yield-prediction` repo). 2. Go to share.streamlit.io, sign in with GitHub. 3. Click "New app," point it at your repo, branch, and `app.py` path. 4. Deploy. You'll get a public URL like `your-app-name.streamlit.app` you can link from your portfolio. ## Retraining If you regenerate the dataset from Earth Engine (more years, different county), edit `train_model.py` with the new data and rerun it to produce a fresh `maize_yield_model.pkl`.