shreyas927/Algerian-forest-fire-prediction-model

# 🔥 Algerian Forest Fires Prediction Model A machine learning application that predicts forest fire probability in Algeria using Ridge Regression. This project includes a web interface built with Flask for easy predictions. ## 📋 Table of Contents - Features - Project Structure - Dataset - Installation - Usage - Model Details - Technologies - Contributing --- ## ✨ Features - **Machine Learning Model**: Ridge Regression for accurate fire probability prediction - **Web Interface**: User-friendly Flask web application with HTML forms - **Data Preprocessing**: StandardScaler for feature normalization - **Model Serialization**: Pre-trained models saved as pickle files for quick deployment - **RESTful API**: POST endpoint for predictions - **Responsive UI**: HTML templates for intuitive user interaction --- ## 📁 Project Structure ``` s-by-s-ml-project/ ├── application.py # Main Flask application ├── model_training.ipynb # Model training notebook ├── EDA_FE.ipynb # Exploratory Data Analysis & Feature Engineering ├── Algerian_forest_fires_cleaned_dataset.csv ├── Algerian_forest_fires_dataset_UPDATE.csv ├── ridge.pkl # Trained Ridge Regression model ├── scaler.pkl # StandardScaler for feature normalization ├── templates/ │ ├── index.html # Home page │ └── home.html # Prediction form & results page └── README.md ``` --- ## 📊 Dataset The project uses the Algerian Forest Fires dataset containing: - **Features**: Temperature, Relative Humidity (RH), Wind Speed (Ws), Rainfall, FFMC, DMC, ISI, Classes, Region - **Target**: Forest fire probability/severity - **Preprocessing**: Data cleaning, feature scaling, and outlier handling - **Files**: - `Algerian_forest_fires_cleaned_dataset.csv` - Cleaned version - `Algerian_forest_fires_dataset_UPDATE.csv` - Updated raw data --- ## 🚀 Installation …