Stock Price Prediction using Data Analysis and Machine Learning
DOI:
https://doi.org/10.36676/irt.v11.i2.1578Keywords:
Stock Price Prediction, Data Analysis, Machine Learning, Financial Data, Visualization, Investment, StrategiesAbstract
The problem of stock price prediction is very important to investors for making proper decisions. The traditional methods of stock price prediction are intuition-based and lack adequate mathematical backup; hence, they are less accurate and reliable [2]. This paper, therefore, determines if data analytics and machine learning techniques can enhance the accuracy in stock price prediction. This work was based on a dataset that encompassed all the large companies’ stock prices over the years. Data visualization techniques were used in order to understand the trends and behaviours of stock prices [1]. This study will emphasize the possible application of data analysis together with advanced visualization methods for the prediction of accurate stock prices that could actually aid in formulating better investment strategies.
References
Brownlee, J. (2018). Machine Learning Mastery With Python: Understand Your Data, Create Accurate Models, and Work Projects End-To-End. Machine Learning Mastery.
Chen, T., Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in
Statistics.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830
Zhang, G., Patuwo, B. E., Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Innovative Research Thoughts

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.