State of the art of machine learning: An overview of the past, current, and the future research trends in the era of quantum computing

This paper describes data science history and behavioral trends on the largest platform for learning and competition in analyzing and modeling data; Kaggle. We analyze the history of methods commonly used in linear predictor to predict, classify, cluster, and explore data sets. In addition, we also examine the use of the most widely used tools and frameworks to help make data modeling easier. The analysis was carried out on the forum discussion data for the last ten years based on the data available on meta-Kaggle. To see the future trend of data science and linear predictor models, we analyzed the abstracts on the articles available on the Elsevier search page. We extracted information from them using a machine learning method.