Automation in data analytics using robotic process automation and artificial intelligence
This project provides an integrated solution for beginner traders who do not have prior knowledge in analysing the market trends and have no idea where to start their trading journey. Hence, this project focuses on developing a web application by leveraging Artificial Intelligence (AI) and Robotic P...
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| Format: | Final Year Project / Dissertation / Thesis |
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2024
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| Online Access: | http://eprints.utar.edu.my/6953/ http://eprints.utar.edu.my/6953/1/fyp_CS_2024_GYN.pdf |
| _version_ | 1848886811001094144 |
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| author | Gan, Yu Nyuk |
| author_facet | Gan, Yu Nyuk |
| author_sort | Gan, Yu Nyuk |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | This project provides an integrated solution for beginner traders who do not have prior knowledge in analysing the market trends and have no idea where to start their trading journey. Hence, this project focuses on developing a web application by leveraging Artificial Intelligence (AI) and Robotic Process Automation (RPA), to automate data analytics and market predictions, providing valuable insights to users for their investment decisions. This project utilises React, an open-source JavaScript library, for building the frontend interface of the application, where it offers users a user-friendly platform to access real-time stock using RPA. Another tool being used in this project is a lightweight web framework written in Python, called Flask, where it contains features that make building web applications in Python easier. Flask is used for building the backend of the application. Moreover, the integrated development environment (IDE) chosen to carry out the project is Visual Studio Code.
This project currently comprises two functionalities, which are, viewing market information and predicting market trends. The first feature allows users to access real-time market data fetched from Yahoo Finance using Robotic Process Automation (RPA) implemented with UiPath. This is to provide comprehensive data analytics and visualization, where users able to get insights about the market trends and historical price movements. Furthermore, this project incorporates Long Short-Term Memory (LSTM) model for predictive analytics, where it can provide action recommendations (buy, hold, or sell) to users regarding the upcoming market trend. In a nutshell, the project aims to provide an automated trading analytics by leveraging an LSTM model for stock prediction and a sentiment analysis to analyse market news |
| first_indexed | 2025-11-15T19:44:25Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6953 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:44:25Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-69532025-02-27T07:03:22Z Automation in data analytics using robotic process automation and artificial intelligence Gan, Yu Nyuk T Technology (General) This project provides an integrated solution for beginner traders who do not have prior knowledge in analysing the market trends and have no idea where to start their trading journey. Hence, this project focuses on developing a web application by leveraging Artificial Intelligence (AI) and Robotic Process Automation (RPA), to automate data analytics and market predictions, providing valuable insights to users for their investment decisions. This project utilises React, an open-source JavaScript library, for building the frontend interface of the application, where it offers users a user-friendly platform to access real-time stock using RPA. Another tool being used in this project is a lightweight web framework written in Python, called Flask, where it contains features that make building web applications in Python easier. Flask is used for building the backend of the application. Moreover, the integrated development environment (IDE) chosen to carry out the project is Visual Studio Code. This project currently comprises two functionalities, which are, viewing market information and predicting market trends. The first feature allows users to access real-time market data fetched from Yahoo Finance using Robotic Process Automation (RPA) implemented with UiPath. This is to provide comprehensive data analytics and visualization, where users able to get insights about the market trends and historical price movements. Furthermore, this project incorporates Long Short-Term Memory (LSTM) model for predictive analytics, where it can provide action recommendations (buy, hold, or sell) to users regarding the upcoming market trend. In a nutshell, the project aims to provide an automated trading analytics by leveraging an LSTM model for stock prediction and a sentiment analysis to analyse market news 2024-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6953/1/fyp_CS_2024_GYN.pdf Gan, Yu Nyuk (2024) Automation in data analytics using robotic process automation and artificial intelligence. Final Year Project, UTAR. http://eprints.utar.edu.my/6953/ |
| spellingShingle | T Technology (General) Gan, Yu Nyuk Automation in data analytics using robotic process automation and artificial intelligence |
| title | Automation in data analytics using robotic process automation and artificial intelligence |
| title_full | Automation in data analytics using robotic process automation and artificial intelligence |
| title_fullStr | Automation in data analytics using robotic process automation and artificial intelligence |
| title_full_unstemmed | Automation in data analytics using robotic process automation and artificial intelligence |
| title_short | Automation in data analytics using robotic process automation and artificial intelligence |
| title_sort | automation in data analytics using robotic process automation and artificial intelligence |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/6953/ http://eprints.utar.edu.my/6953/1/fyp_CS_2024_GYN.pdf |