An enormous measure of computerized information is being produced over a wide assortment in the field of data mining strategies. The creation of student achievement prediction models to predict student performance in academic institutions is a key area of the development of Education Data Mining. A prediction system has been proposed by using their 10th, 12th and previous semester marks. The study is evaluated using Binomial logical regression, Decision tree, and Entropy and KNN classifier. In order to attain their higher score, this framework would assist the student to recognize their final grade and improve their academic conduct.

A serious game is an environment provided for everyone of all age groups with the aim of teaching, learning, or improving self in while enjoying the game. In simple terms, it means the games that are not intended for fun or entertainment but for learning. They are called serious games. In the field of education, the purpose of serious games is to involve learners in the process of learning with the ultimate aim to get them involved in the learning and to improvise the memory. Similarly, in the case of the healthcare sector, healthcare professionals use serious games to involve the patients in tough tasks that could be life threatening situations for them as a treatment, and to simulate training environments for learning. Each learner can complete a specific outcome by playing a serious game. This chapter explains the impact of different machine learning algorithms on decision-making with serious games with respect to teaching in the field of education sector and therapy in the field of healthcare.

Stock trading is one of the foremost activity in finance world. Stock market prediction is used to find the long run values of the stock and other financial factors influenced on a financial exchange. The technical and fundamental or the statistical analysis is employed by most of the stockbrokers while making the stock predictions. Python programming language in machine learning is used for the stock market prediction. In this paper we have proposed a Machine Learning (ML) approach which trains from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. In stock market prediction, the aim is to predict the longer term value of the financial stocks of a corporation [1]. The recent trend in market prediction technologies is that the use of machine learning approach which makes predictions supported the values of current stock market indices by training on their previous values. Machine learning itself employs different models to form prediction easier and authentic. This paper focus on Regression and Long Short Term Memory (LSTM) based Machine learning to predict stock values. The factors that are being considered include re-open, close, low, high and volume [2,3].

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