Riddhi256/forest-fire-prediction

A machine learning web application that predicts the Fire Weather Index (FWI) using Ridge Regression, trained on the Algerian Forest Fires Dataset from Kaggle. The project includes exploratory data analysis (EDA), data preprocessing, feature engineering, model training, and a Flask-based web interface for real-time predictions. # 🔥 Algerian Forest Fire Prediction using Ridge Regression An end-to-end Machine Learning project that predicts the **Fire Weather Index (FWI)** using the **Algerian Forest Fires Dataset**. The project includes data preprocessing, exploratory data analysis (EDA), feature engineering, model training with **Ridge Regression**, and deployment using **Flask**. --- ## 📌 Project Overview The Algerian Forest Fires Dataset contains meteorological and fire weather observations collected from two regions in Algeria. This project aims to predict the **Fire Weather Index (FWI)**, an important indicator used to estimate fire intensity and risk. The workflow includes: - Data Cleaning - Exploratory Data Analysis (EDA) - Feature Engineering - Feature Scaling - Model Training - Model Evaluation - Web Application Deployment using Flask --- ## 📂 Project Structure ``` . ├── models/ │ ├── ridge.pkl │ └── scaler.pkl ├── notebooks/ │ ├── Algerian_forest_fire_EDA.ipynb │ ├── code.ipynb │ └── modelTraining.ipynb ├── templates/ │ ├── home.html │ └── index.html ├── application.py ├── requirements.txt └── README.md ``` --- ## 🛠️ Technologies Used - Python - Pandas - NumPy - Matplotlib - Seaborn - Scikit-learn - Flask - HTML5 - CSS3 --- ## 📊 Dataset **Dataset:** Algerian Forest Fires Dataset The dataset contains weather-related features such as: - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - FFMC - DMC - DC - ISI - BUI Target Variable: - **FWI (Fire Weather Index)** --- ## 🤖 Machine Learning Model The project uses **Ridge Regression**, a regularized linear regression algorithm that helps reduce overfitting by applying L2 regularization. --- ## 🚀 Installation Clone the repository ```bash git clone github.com ``` Navigate to the project folder ```bash cd algerian-forest-fire-prediction ``` Install dependencies ```bash pip install -r requirements.txt ``` Run the Flask application ` …