Amelioration of Automation Techniques in AutoML

Abstract

AutoML is used in this project to address the limitations of traditional machine learning (ML), which often requires expert knowledge for model selection, tuning, and validation. By automating these processes, AutoML makes data analysis more efficient and accessible to a broader audience, including non-experts. This research proposes an automatic learning machine (AutoML) system that enables users to access detailed data analysis, predictive modeling, and visualization by exporting data into Excel format. Based on the features of the input data, the platform automatically chooses the best machine learning algorithm and facilitates group learning to increase prediction accuracy. A larger audience can utilize the system due to its easy-to-use interface, which allows non-experts to perform advanced data analysis effortlessly.