dipeshvbante31/Algerian-Forest-Fires---Machine-Learning-Project

This project predicts the Fire Weather Index (FWI) using the Algerian Forest Fires dataset. It covers end-to-end machine learning, including EDA, data cleaning, feature engineering, feature selection, Linear Regression, Ridge, Lasso, Elastic Net, hyperparameter tuning, cross-validation, and model evaluation. # 🔥 Algerian Forest Fires - Machine Learning Project ## 📌 Project Overview This project focuses on predicting the Fire Weather Index (FWI) using the Algerian Forest Fires Dataset. The project covers the complete Machine Learning workflow starting from data preprocessing to model evaluation using various regularization techniques. The notebook demonstrates industry-standard practices including Exploratory Data Analysis (EDA), Feature Engineering, Feature Selection, Model Training, Hyperparameter Tuning, and Cross Validation. --- ## 📂 Project Structure ``` ├── Algerian_forest_fires_dataset_UPDATE.csv # Raw Dataset ├── Algerian_forest_fires_cleaned_dataset.csv # Cleaned Dataset ├── algerian_cleaned_dt.csv # Final Processed Dataset ├── algerian_forest_fires_dataset.ipynb # EDA & Data Preprocessing ├── ModelTraining.ipynb # Model Building └── README.md ``` --- ## 📊 Dataset Information The dataset contains weather observations collected from two regions of Algeria. ### Features - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - Fine Fuel Moisture Code (FFMC) - Duff Moisture Code (DMC) - Drought Code (DC) - Initial Spread Index (ISI) - Buildup Index (BUI) - Fire Weather Index (FWI) - Region - Month - Day - Year - Classes (Fire / Not Fire) ### Target Variable - **FWI (Fire Weather Index)** --- # 🚀 Project Workflow ## 1. Exploratory Data Analysis (EDA) Performed detailed exploratory data analysis including: - Understanding dataset structure - Handling missing values - Duplicate value detection - Statistical summary - Distribution analysis - Correlation analysis - Feature relationships - Count plots - Histograms - Box plots - Heatmaps --- ## 2. Data Cleaning - Removed null values - Corrected data types - Removed unnecessary columns - Fixed inconsistent class labels - Encoded categorical variables --- ## 3. Feature Engineering Feature engineering steps include: - Label En …