lokeshkharkwal7/Algerian_forest_fire_data_2023

The project is a ML pipeline combining regression and classification to predict/prevent Algeria's forest fires. It follows CI/CD/CT principles, ensuring an up-to-date, effective model. It features a front-end app for data visualization, results, and insights. # Algerian_forest_fire_data_2023 The project is a ML pipeline combining regression and classification to predict/prevent Algeria's forest fires. It follows CI/CD/CT principles,ensuring an up-to-date, effective model and contains all the components including Data Ingestion , Data validation, Data Transformation , Model Building and Model Selection . It features a front-end app for data visualization, results, and insights. Project Structure: Visual and Screenshots of each individual component: 1. One click Execution of the Machine learning Pipeline (Home Screen of the project) 2. Log report of the backend execution of each and every component in the pipeline 4. Data Inputs for generating predictions from the Classification and Regression Pipeline Regression: Classification: 4. Model Output from Regression Pipeline 6. Model Output from Classification Pipeline 8. Artifact Directory File Explorer: Housing Outputs generated from Each Component of the Pipeline throughout the project. 9. Data Analysis Report of the Raw Data 4. Data Drift Analysis Report Generated by the Data Validation Pipeline 5. Status of Number of Principal Components from Principal Component Analysis generated by Data Transformation Component 6. Status of the Best Regression Model generated by Model Pusher Pipeline. 7.Status of the Best Classification Model generated by the Model Pusher Pipeline. 8. Comprehensive List of Utilized Regression Models and Their Corresponding Parameters from the Model Selection Pipeline. 9. Comprehensive List of Utilized Classification Models and Their Corresponding Parameters from the Model Selection Pipeline. 10. Information about Configuration for each Components 11. Information about Our Defined Dataset Schema for the data validation.