aniruddha26/Algerian-forest-fires

# 🔥 Algerian Forest Fires - Regression Analysis A machine learning project using **Linear Regression**, **Lasso**, **Ridge**, and **ElasticNet** to analyze and predict fire-related indices from weather data collected in Algeria. This project is part of my learning and practice in machine learning and regression techniques using Python. > 🧪 This is a practice project aimed at applying and comparing different regression models. --- ## 📌 Objective To model and analyze the relationship between various weather and fire-related features and the **Fire Weather Index (FWI)** — a key indicator of fire intensity. --- ## 🌍 Dataset Description The dataset contains **244 instances** from two regions in Algeria: - **Bejaia** (Northeast Algeria) - **Sidi Bel-abbes** (Northwest Algeria) 🗓️ **Time Period**: June 2012 to September 2012 🔢 **Total Samples**: 244 (122 from each region) 🏷️ **Target Variable**: Fire occurrence class (Fire / Not Fire) 📈 **Regression Target**: Fire Weather Index (FWI) ### 🔢 Features (11 input + 1 output) | Feature | Description | |--------|-------------| | `Date` | Date of observation (DD/MM/YYYY) | | `Temp` | Temperature at noon (°C) | | `RH` | Relative Humidity (%) | | `Ws` | Wind Speed (km/h) | | `Rain` | Total daily rainfall (mm) | | `FFMC` | Fine Fuel Moisture Code | | `DMC` | Duff Moisture Code | | `DC` | Drought Code | | `ISI` | Initial Spread Index | | `BUI` | Buildup Index | | `FWI` | Fire Weather Index (**regression target**) | | `Classes` | Fire / Not Fire classification | 📂 **Source**: UCI Machine Learning Repository Link to dataset --- ## 🧠 Techniques Used - **Data Preprocessing**: - Handling missing values - Encoding categorical variables - Feature scaling - **Regression Models**: - Linear Regression - Ridge Regression - Lasso Regression - ElasticNet Regression - **Model Evaluation**: - R² Score - Mean Squared Error (MSE) - Cross-validation --- ## 🚀 Getting Started ### 1. Clone the repository git clone git …