aishwaryakarki001/Algerian-Forest-Fire-Analysis

**Algerian Forest Fire Dataset Analysis** This project involves analyzing the Algerian forest fire dataset, focusing on the Fire Weather Index (FWI) to predict fire risks using multiple machine learning models. The project includes data preprocessing, visualization, and the development of regression models to predict FWI. **Project Structure** 1. Dataset: The dataset used in this project includes features like temperature, humidity, wind speed, and FWI. 2. Models: The following models were implemented: a. Multiple Linear Regression b. Polynomial Regression c. Regularization Models (Lasso, Ridge) 3. Techniques: a. Cross-validation b. Hyperparameter tuning **Key Tasks** 1. Data Cleaning and Preprocessing: Handled missing values, engineered new features (like season), and prepared data for modeling. 2. Data Visualization: Created visualizations such as box plots and line plots to understand seasonal patterns in FWI. 3. Model Development: Implemented multiple regression models and applied regularization techniques to prevent overfitting. 4. Model Evaluation: Evaluated models using R-squared and Mean Squared Error (MSE) metrics. Tested on unseen data. 5. Pickle File Generation: The final models were saved as pickle files for later use. **Results** 1. The Ridge regression model with optimal hyperparameters achieved the best performance with minimal overfitting. 2. FWI was observed to be higher during the summer months, indicating higher fire risks during this period. **Conclusion** This project demonstrates how machine learning models can be applied to predict fire risks based on weather conditions, aiding in effective fire prevention strategies.