• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 48, Pages: 4620-4623

Original Article

Predicting a Small Cap Company Stock Price using Python with Best Accuracy Rate: How the Data Science Working for Predictions and Accuracy Rate

Received Date:04 November 2023, Accepted Date:13 November 2023, Published Date:30 December 2023


Objective: Predicting a smallcap company stock price with best accuracy using data science and machine learning techniques. Method: A Dataset of one year data collected from different sources like paytm money and yahoo finance. The prediction if a stock price involves like data collection, data pre processing, testing, training, fitting an algorithm and then prediction of a stock price finally find the best accuracy using machine learning techniques. Findings: This model get the accuracy of 98%. Lstm algorithm is giving the high accuracy rate than all other algorithms. We can predict the stock price using some other algorithms also. But till now the best accuracy rate given by LSTM algorithm only. Novelty: This study contributes to finding out the best algorithm to predict the stock price in real time market conditions. Sometimes we may not predict the stock price correctly due to international market conditions like war situations. In this case total index will show in negative only. This is the real challenge in future predictions of stock price. Ultimately predicting a stock price in all conditions and market situations is a great challenge till now. To make it success we have to introduce a new algorithm by making some changes in previous algorithms according to the recent trends markets.

Keywords: Numpy, Matplotlib, RNN, Machine learning, Small cap, Stock price


  1. Allaire JJ, Xie Y, Mcpherson J, Luraschi J, Ushey K, Atkins A, et al. rmarkdown: Dynamic Documents for R. 2023. Available from: https://CRAN.R-project.org/package=rmarkdown
  2. Baumer BS, Kaplan DT, Horton NJ. Modern Data Science with R (2). CRC Press. 2021.
  3. Depratti R. Jupyter Notebooks versus a Textbook in a Big Data Course. Journal of Computing Sciences in Colleges. 2020;35(8):208–220. Available from: https://dl.acm.org/doi/abs/10.5555/3417639.3417658
  4. Koenzen A, Ernst N, Storey MA. Code Duplication and Reuse in Jupyter Notebooks. 2020. Available from: https://doi.org/10.48550/arXiv.2005.13709
  5. Sonkavde G, Dharrao DS, Bongale AM, Deokate ST, Doreswamy D, Bhat SK. Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. International Journal of Financial Studies. 2023;11(3):1–22. Available from: https://doi.org/10.3390/ijfs11030094


© 2023 Shankarlingam & Reddy. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.