Manoj Kumar Saini
Anjali Bari
Aryan Pareek
Aman Gupta
- Assistant Professor, Department of Computer science, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer science and Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer science and Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer science and Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
Abstract
Stock market prediction using data science has become a popular area of research and application in recent years. This is because the stock market is a complex system with many variables and factors that affect its behavior, making it difficult to predict with certainty. The stock market has always been the aggression of buyers and sellers of stocks, therefore in the global finance market, stock trading is one of the most important activities. A stock market prediction is an act of trying to determine the future value of the company stock or other financial instruments which is done through different techniques. The stock market is renowned for its turbulence, changeability, and nonlinearity. Accurate stock price prediction is extremely challenging because of multiple (macro and micro) factors such as political, global, and economic conditions, unexpected events, the company’s financial performance, and so on. Due to its great learning capability for solving this nonlinear time, this study describes stock forecast using data science. Technical, fundamental, and time series analysis is used by most stockbrokers while making stock predictions. One of the key challenges in stock market prediction is dealing with the inherent uncertainty and volatility of the market. It is important to use robust statistical methods that can account for this uncertainty and provide accurate predictions and confidence intervals. Another important consideration in stock market prediction is the choice of features or variables used in the analysis. These features may include technical indicators such as moving averages and relative strength index, as well as fundamental data such as earnings reports and macroeconomic indicators. Overall, stock market prediction using data science is a complex and challenging task, but one that has the potential to provide valuable insights for investors and traders. By leveraging the power of data science techniques and tools, we can gain a deeper understanding of the market and make more informed investment decisions. The programming language used to predict the stock market using Data science is Python.
Keywords: LSTM, stock exchange Predictions, RNN, stock price prediction, data science, Long Short-Term Memory
[This article belongs to E-Commerce for Future & Trends(ecft)]
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E-Commerce for Future & Trends
| Volume | 11 |
| Issue | 01 |
| Received | February 21, 2023 |
| Accepted | December 15, 2023 |
| Published | January 17, 2024 |