AkshatKumarSingh001/algerian-fires-fwi-streamlit
Domain:
environment and energyRecord type:
softwaretools
Creator:
Aks
Host:
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
`` …