Data-driven accurate stock market models can lead to timely, better decision making by the investors for a more profitable transaction. Such models can increase the chances of selecting more profitable stocks and reduce risk by avoiding risky investment. Last few decades of advances in soft-computing techniques in machine learning (ML), deep learning (DL), text mining (TM), and ensemble methods have positively reflected in the forecasting of stock market as well. In our work, we have reviewed some recent machine learning models for stock market forecasting. We have considered the works that cover various types for data sources, forecasting techniques, and efficient evaluation metrics. With our paper, we aim to provide a brief idea on the latest progress in stock market forecasting. We also summarize our analysis to highlight future research scopes in stock market movement forecasting.

Data science and machine learning, over the years have proven very well-organized and significant in many sectors including education. Machine learning is an aspect of artificial intelligence in which a computing system can able to learn from data and make conclusions. The recent development in education sector provides assessment tools to predict the student performance by exploring education data using machine learning and data mining techniques. Student performance assessment is an important measurement metrics in education which affects the university accreditation. Student performance improvement plan must be implemented in those universities, by counselling the low performer students. It helps both students and teachers to overcome the problems experienced by the student during studies and teaching techniques of teachers. In this review paper, different student performance prediction literature related to find out low performer student. The survey results indicated that different machine learning techniques are used to overcome the problems related to predicting student at risk and assessment of student performance. Machine learning techniques plays an important role in progress and prediction of student performance, thus improving student performance prediction system.