Forest Fire Analysis & Prediction This project is a Machine Learning-based application that predicts the likelihood of forest fires based on environmental and meteorological data. It includes data preprocessing, model training.

Features Data preprocessing with Pandas, NumPy, and Scikit-learn. Feature scaling using StandardScaler. Machine Learning model using Ridge Regression for prediction. Web interface built using Flask. Model and scaler serialized using Pickle for deployment.

Tech Stack Python 3.x Flask (for web app deployment) Scikit-learn (for ML model) Pandas, NumPy, Matplotlib, Seaborn (for data analysis and visualization) Pickle (for model persistence)

Project Structure

Forest_fire_analysis/ │── model_training.ipynb # Jupyter notebook for model building │── forest_fire.ipynb # Data exploration and preprocessing │── model/ │ ├── ridge.pkl # Saved Ridge Regression model │ ├── scaler.pkl # Saved StandardScaler │── README.md # Project documentation