News RSS with stock recommender - Chin, Zhi Yi (2024)
This project is about the development of window applications for News RSS with Stock Recommendations involves sentence matching techniques and provide the recommend stock. This project aims to empower new graduates and institutional investors financially while imparting knowledge and expertise on developing window applications. The goal of this project is to overcome the analysis of news data to locate relevant news and provide listed companies in Bursa Malaysia. By providing real-time news updates, automatic suggestions, and educational materials, the initiative will improve the effectiveness of the recommendation process and serve as a perfect entry point for anyone wishing to start investing. The project uses a combination of text mining approaches to extract meaningful information from massive amounts of news data. Moreover, accessibility and usability are given top priority in the application's user-centric design, guaranteeing that both inexperienced and expert investors may easily utilize its features. The project seeks to develop a robust and user-friendly platform that can give rapid and precise stock recommendations to all users through exhaustive testing and upgraded enhancements. Lastly, outlines the goals of the project, its approach, and the anticipated effects on users. It highlights how the project can free up a significant amount of users' valuable time while also enabling people to make informed investment decisions while browsing and reading the news in a constantly changing financial environment.