AkshatKumarSingh001/algerian-fires-fwi-streamlit

Streamlit app that predicts Forest Fire Weather Index (FWI) using a trained Ridge Regression model. # Forest Fire Weather Index (FWI) Prediction A Streamlit web app that predicts the Forest Fire Weather Index (FWI) using a trained Ridge Regression model. Users enter environmental conditions and FWI components, and the app returns the predicted FWI value. **Live App:** algerian-fires-fwi-app-h7dv… --- ## Features - Clean Streamlit UI with a simple form - 9 input features used for prediction - Pretrained Ridge model + StandardScaler - Works locally and on Streamlit Cloud --- ## Input Features (in order) The model expects 9 inputs in this exact order: 1. Temperature (C) 2. Relative Humidity (RH) 3. Wind Speed (Ws) 4. Rain (mm) 5. FFMC 6. DMC 7. ISI 8. Classes (0 = Not Fire, 1 = Fire) 9. Region (0 = Bejaia, 1 = Sidi Bel Abbes) --- ## Project Structure ``` . ├── streamlit_app.py ├── requirements.txt ├── models/ │ ├── ridge.pkl │ └── scaler.pkl └── README.md ``` --- ## Architecture - Streamlit UI collects 9 input features from the user. - Inputs are scaled with the saved StandardScaler. - The Ridge model predicts the FWI value. - The app displays the prediction in the UI. --- ## Workflow 1. User enters the 9 required features in the Streamlit form. 2. App loads model artifacts (`ridge.pkl`, `scaler.pkl`). 3. Inputs are scaled using the same scaler used during training. 4. The Ridge model generates the FWI prediction. 5. Result is shown in the app. --- ## Dataset `Algerian_forest_fires_dataset_UPDATE.csv' This file is used for training and reproducibility, but it is not required for Streamlit deployment. The dataset was cleaned using exploratory data analysis (EDA) and feature engineering before training. --- ## Run Locally ### 1) Create and activate a virtual environment ```bash python -m venv .venv # Windows .venv\Scripts\activate # macOS/Linux source .venv/bin/activate ``` ### 2) Install dependencies ```bash pip install -r requirements.txt ``` ### 3) Start the app ```bash streamlit run streamlit_app.py `` …