Stock Price Prediction Using Python in Machine Learning

G. Bala Krishna; E. Raghunath Reddy; K. Sai Prakash; G. Johnson; Dr. Pattan Hussian Basha; V. GopiKrishna

Abstract

The process of anticipating the stock market is one that is both difficult and time-consuming. On the other hand, advancements in stock market projection have begun to incorporate these methods of evaluating stock market data since the introduction of Machine Learning and its various algorithms. This has occurred since the beginning of the 21st century. We found that the Long-Short Term Memory (LSTM) technique was the most effective when predicting stock values by using historical data. This was determined by analyzing the performance of the various algorithms in this endeavor. Because the algorithm has been taught using a massive accumulation of historical data and has been selected after being tested on a sample of data, it is going to be an excellent instrument for dealers and purchasers to utilize when they are investing in the stock market. According to the findings of this research, the machine learning model is superior to other machine learning models in terms of its ability to effectively predict market price.

Keywords

Stock Price Prediction, Python, Machine Learning, Machine Learning Algorithm

References

Pritam Ahire, Hani kumar Lad, Smit Parekh, Saurabh Kabrawala, “LSTM Based Stock Prediction,” International Journal of Creative Research Thoughts(IJCRT), vol. 9, pp. 5118-5122, Feb. 2021.
Ya Gao, Rong Wang, and EnminZou, “Stock Prediction Based on Optimized LSTM and GRU Models,” Hindawi, vol. 2021, pp. 1-8, Sept. 2021.
Adil Moghar and Mhamed Hamiche, “Stock Market Prediction Using LSTM Recurrent Neural Network,” Sciencedirect, vol. 170, pp.1168-1173, Apr. 2020.
S. E. C. Gelper, R. Fried, and C. Croux, “Robust forecasting with exponential and holt-winters smoothing,” Journal of Forecasting, vol. 29, no. 3, pp. 285–300, 2010.
X. Wang, “The short-term passenger flow forecasting of urban rail transit based on holt-winters’ seasonal method,” in 2019 4th International Conference on Electromechanical Control Technology and Transportation (ICECTT), pp. 265–268, Guilin, China, 26-28 April 2019
A. Sherstinsky, “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,” Physica D: Nonlinear Phenomena, vol. 404, p. 132306, 2020.
A. Singh, S. Ahmad, and M. I. Haque, “Big data science and EXASOL as big data analytics tool,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9S, pp. 933–937, 2019.
PrasaduPeddi (2019), Data Pull out and facts unearthing in biological Databases, International Journal of Techno-Engineering, Vol. 11, issue 1, pp: 25-32.
C. C. Tan and N. C. Beaulieu, “First-order Markov modeling for the Rayleigh fading channel,” IEEE GLOBECOM 1998 (Cat. NO. 98CH36250), vol. 6, pp. 3669–3674, 1998.q

Cites this article as

G. B. Krishna, E. R. Reddy, K. S. Prakash, G. Johnson, D. P. H. Basha, V. GopiKrishna, "Stock Price Prediction Using Python in Machine Learning", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-10, no.3, pp.412- 416, 2022. Available from: https://doi.org/10.55524/ijircst.2022.10.3.66

Corresponding Author

G. Bala Krishna

Department of Computer Science and Engineering, PACE Institute of Technology & Sciences, Vallur, Ongole, Andhra Pradesh, India

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