Stock Price Prediction by using ML & its Application on Webpage
This project introduces an advanced machine learning framework for forecasting stock prices on the Pakistan Stock Exchange, employing both Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models. These models analyze historical PSE stock data and assimilate market news as well as economic factors to improve the accuracy of short-run stock price forecasts. One of the most important aspects of this project is the development of an easy-to-use webpage that brings financial analysis to the masses. This innovation opens sophisticated market insights to average investors, allowing them to make better investment decisions. By integrating machine learning into financial analysis, we are not only broadening the theoretical scope of such applications, but also significantly improving market efficiency and better investment practices. This approach represents a significant step forward in bringing advanced financial analysis closer to the masses, providing them with practical, easy to use tools.
Keywords: Machine Learning
Tools: CSS ,HTML,LSTM
Department: Department of Business Studies
Project Team Members
| Name |
Email |
|
Maaz Baloch
|
maaz2020@namal.edu.pk |
|
Muhammad Qasim Javed
|
javed2020@namal.edu.pk |
Project Poster
