Machine Learning-powered Forest Fire Prediction System using Ridge Regression,streamlit to estimate Fire Weather Index (FWI) from environmental parameters.
# 🔥 Algerian Forest Fire Predictor
An end-to-end **Machine Learning application** that predicts the **Fire Weather Index (FWI)** using meteorological and environmental data from the Algerian Forest Fires dataset.
The project covers the complete Machine Learning workflow, including:
- Data preprocessing
- Exploratory Data Analysis
- Feature engineering
- Feature scaling
- Model training
- Model evaluation
- Model serialization
- Interactive web application
- Cloud deployment using Streamlit
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## 🌐 Live Demo
🚀 **Try the application here:**
👉
algerianforestfirepredictor…
The application allows users to enter weather and environmental parameters and receive a predicted **Fire Weather Index (FWI)** in real time.
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## 🔥 About the Project
Forest fires can cause significant environmental, economic, and ecological damage.
The **Fire Weather Index (FWI)** is an important indicator used to estimate fire danger based on weather and environmental conditions.
This project uses Machine Learning to predict the FWI from various meteorological parameters.
The trained model is integrated into a **Streamlit web application**, allowing users to interact with the model directly through a browser.
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## 🎯 Problem Statement
The objective of this project is to build a Machine Learning model capable of predicting the **Fire Weather Index (FWI)** from weather conditions.
Given environmental inputs such as:
- Temperature
- Relative Humidity
- Wind Speed
- Rainfall
- FFMC
- DMC
- ISI
- Region
the model predicts the corresponding **FWI value**.
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## 🚀 Project Highlights
- 🔥 Fire Weather Index prediction
- 🧹 Data cleaning and preprocessing
- 📊 Exploratory Data Analysis
- ⚙️ Feature engineering
- 📏 Feature scaling using `StandardScaler`
- 🤖 Ridge Regression model
- 💾 Model serialization using Pickle
- 🌐 Interactive Streamlit application
- ☁️ Cloud deployment using Streamlit
- ⚡ Real-time prediction
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## 🛠️ Tech Stack
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