aenodehi/RidgeLassoRegression

Algerian forest fires # Algerian Forest Fires ## Overview This project focuses on predicting the Fire Weather Index (FWI) using various machine learning models. The dataset contains weather observations and FWI components, which are used to train and evaluate the models. The goal is to accurately predict the FWI, which is crucial for fire risk assessment and management. ## Dataset ### Attribute Information: - **Date**: (DD/MM/YYYY) Day, month ('june' to 'september'), year (2012) - **Weather data observations**: - **Temp**: Temperature noon (temperature max) in Celsius degrees: 22 to 42 - **RH**: Relative Humidity in %: 21 to 90 - **Ws**: Wind speed in km/h: 6 to 29 - **Rain**: Total day in mm: 0 to 16.8 - **FWI Components**: - **FFMC**: Fine Fuel Moisture Code index from the FWI system: 28.6 to 92.5 - **DMC**: Duff Moisture Code index from the FWI system: 1.1 to 65.9 - **DC**: Drought Code index from the FWI system: 7 to 220.4 - **ISI**: Initial Spread Index index from the FWI system: 0 to 18.5 - **BUI**: Buildup Index index from the FWI system: 1.1 to 68 - **FWI**: Fire Weather Index Index: 0 to 31.1 - **Classes**: Two classes, namely “Fire” and “not Fire” ## Machine Learning Models The following machine learning models are implemented and evaluated in this project: 1. **Linear Regression Model** 2. **Lasso Regression** 3. **Cross Validation Lasso (LassoCV)** 4. **Ridge Regression Model** 5. **RidgeCV** 6. **Elasticnet Regression** 7. **ElasticNetCV** ## Repository Structure - **Algerian_forest_fires_dataset**: Contains the dataset used for training and testing the models. - **Algerian_forest_fires.ipynb**: Jupyter notebook for exploratory data analysis (EDA) and initial model training. - **Dockerfile**: Dockerfile for containerizing the project. - **Model_training.ipynb**: Jupyter notebook for detailed model training, evaluation, and comparison. - **compose.yml**: Docker Compose file for orchestrating the project's containers. ## Installation To run this project, you need t …