The main objective of this project is to create a no-code machine learning platform that provides a seamless experience for users. With this platform, users can easily obtain the best-tweaked model for their data without needing to write any code. It will cover the entire process of experimentation, from exploratory data analysis to machine learning modeling. The platform is designed to be flexible, allowing users to provide any dataset along with a few arguments, and receive exploratory data analysis as well as the best scoring model. By simplifying the process of experimentation, this platform aims to make it easy for users to experiment with machine learning, regardless of their technical background.
Fig.1 Proposed implementation - Frontend Flow
Fig.2 Implementation - Backend Flow
To build this platform, I used a variety of packages, including Pandas, Scikit-learn, Pandas Profiling, Pycaret, and Flask. These packages allowed me to handle dataFrame, split datasets, test data, generate EDA reports, find the best ML model, and build a backend server. The goal of this project was to create a platform that would make it easy for users to build ML models in a few minutes. Anyone can use it irrespective of any background. It can be further developed with more customized features to align with users' requirements.