Stock Price Prediction Using Data Science Techniques
Keywords:
Stock prediction, data science, market trends, forecasting, support vector machineAbstract
This study centers around persuasively presenting the potential to forecast the stability of future market stocks. Previous research has delved into predicting the trajectory of future market trends, leading to fluctuations in stock data, which opens avenues for refinement. The proposed model employs data science methodologies to predict the stock price index's value. This is achieved by contrasting supervised classification data science learning algorithms that predict either stock price increases or stable states. Furthermore, the research aims to differentiate and meticulously analyze the effectiveness of diverse data science algorithms, utilizing data from the transportation traffic department. To gauge the proposed data science approach's effectiveness, the investigation utilized techniques such as constructing a confusion matrix, assigning significance to data classification, and benchmarking the outcomes against optimal precision, recall, and F1 scores.
References
Vijh M, Chandola D, Tikkiwal VA, Kumar A. Stock closing price prediction using machine learning techniques. Procedia Comput Sci. 2020 Jan 1; 167: 599–606.
Sarvesh S, Sidharth RV, Vaishnav V, Thangakumar J, Sathyalakshmi S. A hybrid model for stock price prediction using machine learning techniques with CNN. In2021 5th International Conference on Information Systems and Computer Networks (ISCON) 2021 Oct 22 (pp. 1–6). IEEE.
Lakshmi PS, Deepika N, Lavanya V, Mary LJ, Thilak DR, Sylvia AA. Prediction of Stock Price Using Machine Learning. In2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) 2022 Dec 8 (Vol. 1, pp. 1–4). IEEE.
Shen J, Shafiq MO. Short-term stock market price trend prediction using a comprehensive deep learning system. J Big Data. 2020 Dec; 7(1): 1–33.
Khairi TW, Zaki RM, Mahmood WA. Stock price prediction using technical, fundamental and news based approach. In2019 2Nd scientific conference of computer sciences (SCCS) 2019 Mar 27 (pp. 177–181). IEEE.
Nishitha SN, Bano S, Reddy GG, Arja P, Niharika GL. Stock price prognosticator using machine learning techniques. In2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) 2020 Nov 5 (pp. 1–7). IEEE.
Naresh E, Ananda BJ, Keerthi KS, Tejonidhi MR. Predicting the Stock Price Using Natural Language Processing and Random Forest Regressor. In2022 IEEE International Conference on Data Science and Information System (ICDSIS) 2022 Jul 29 (pp. 1–5). IEEE.
Ebenesh C, Anitha K. A Novel Approach to Minimize the Mean Square Error in Predicting Stock Price Index using Linear Regression in Comparison with LSTM Model. In2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) 2022 Apr 7 (pp. 1365–1370). IEEE.
Roobini MS, Babu K, Joseph J, Ieshwarya G. Predicting Stock Price using Data science technique. In2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) 2022 Feb 23 (pp. 1013–1020). IEEE.
Malik A, Gupta P, Vijh S. Towards a Stock Price Prediction on Time Series Data using Long-Short Term Memory Method. In2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) 2022 Jan 27 (pp. 525–529). IEEE.
Kumari J, Sharma V, Chauhan S. Prediction of stock price using machine learning techniques: a survey. In2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) 2021 Dec 17 (pp. 281–284). IEEE.
Kumar A, Chaudhry M. Review and Analysis of Stock Market Data Prediction Using Data mining Techniques. In2021 5th International Conference on Information Systems and Computer Networks (ISCON) 2021 Oct 22 (pp. 1–10). IEEE.
Bhattacharjee I, Bhattacharja P. Stock price prediction: a comparative study between traditional statistical approach and machine learning approach. In2019 4th international conference on electrical information and communication technology (EICT) 2019 Dec 20 (pp. 1–6). IEEE.
