This project is used to find the best ML Algorithms for a raw dataset given as input. First raw dataset are been fed into the project and then ETL (Extract-Transform-Load) operation are done on the dataset. And then the tranformed data are fed into different regression, classification and ensemble models to find best suitable model for the dataset. Also Continuous-Integration and Continuous-Delivery Pipeline has also been implemented for easy updation, review and analysis of the project. Logger is also used and logs are created at each step of the project.
Below are the ML Algorithms used in this project: "Random Forest", "Decision Tree", "Gradient Boosting", "Linear Regression", "K-Neighbours Classifier", "XGBRegressor", "CatBoosting Regressor" and "AdaBoost Regressor".