Abstract: Generally, predicting how the stock market will perform is one of the most difficult things to do. It can be described as one of the most critical process to predict that. This is a very complex task and has uncertainties. To prevent this problem in One of the most interesting (or perhaps most profitable) time series data using machine learning techniques. Hence, stock price prediction has become an important research area. The aim is to predict machine learning based techniques for stock price prediction results in best accuracy. The analysis of dataset by supervised machine learning technique(SMLT) to capture several information's like, variable identification, uni-variant.....
Index Terms— Stock Price Forecasting, Machine Learning, Supervised Machine Learning Technique, Industrialistic Future Prediction
[1]. Lobna Nassar, " Integrated Long-term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market", DOI 10.1109/ACCESS.2020.2969293, IEEE Access.
[2]. DeepuRajan, "A Deep Hybrid Fuzzy Neural Hammerstein-Wiener Network for Stock Price Prediction ", Xie Chen, DeepuRajan, Chai Quek School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue, Singapore.
[3]. Rubi Gupta, "Deep Learning Based Approach for Fresh Produce Market Price Prediction", Electrical and Computer Engineering Department University of Waterloo Ontario, Canada.
[4]. JiannanChen, "Prediction of Stock Prices using Machine Learning (Regression Classification) Algorithms ", 2020 International Conference for Emerging Technology (INCET) Belgaum, India. Jun 5-7, 2020
[5]. Rahma Firsty Fitriyana, "CUDA parallel computing framework for stock market prediction using K-means clustering", Proceedings of the International Conference on Smart Electronics and Communication (ICOSEC 2020) IEEE Xplore Part Number: CFP20V90-ART; ISBN: 978-1-7281-5461-9.