Vashishtha05/Algerian-Forest-Fire-ML-Model

# 🔥 Algerian Forest Fire — End-to-End Machine Learning Project An end-to-end Machine Learning workflow built on the Algerian Forest Fire dataset. This project demonstrates Exploratory Data Analysis, Feature Engineering, Regression Modeling, and deployment-ready model serialization using Python, Scikit-Learn and streamlit. --- ## 📌 Table of Contents * Overview * Project Goals * Dataset Information * Project Workflow * Features * Tech Stack * Folder Structure * Model Pipeline * Algorithms Used * Results & Evaluation * How to Run * Skills Demonstrated * Future Improvements * Author --- ## 🧠 Overview Wildfire prediction is a critical real-world application of Machine Learning. This repository showcases a complete ML lifecycle: * Data Cleaning & EDA * Regression Modeling (Linear → Elastic Net) * Model Evaluation * Saving trained models * Preparing a prediction application The goal is not just prediction — but demonstrating strong **ML engineering fundamentals**. --- ## 🎯 Project Goals * Perform structured Exploratory Data Analysis * Understand feature relationships * Compare multiple regression algorithms * Apply regularization techniques * Build a deployable ML pipeline --- ## 📊 Dataset Information Dataset: Algerian Forest Fires Dataset Features include: * Temperature * Relative Humidity * Wind Speed * Rain * FFMC, DMC, ISI indices * Region classification Target: * Fire Weather Index (FWI) --- ## 🔄 Project Workflow 1. Data Cleaning 2. Feature Engineering 3. Exploratory Data Analysis 4. Model Training 5. Model Comparison 6. Hyperparameter Tuning 7. Model Serialization --- ## ✨ Features * Structured ML pipeline * Multiple regression models * Elastic Net regularization * Model saving using `.pkl` * Notebook-based experimentation * Modular project layout --- ## 🧱 Tech Stack | Technology | Purpose | | ---------------- | --------------------- | | Python | Core Language | | Pandas | Data Proce …