ICS5111-Mining Large-Scale Data (Group Project)

Business and Finance

Develop a RAG system that retrieves and summarises financial or market information to support high-level analysis (e.g. explaining market movements, summarising company news, or describing macroeconomic indicators). Unstructured data may include financial news articles, analyst commentary or company reports; structured indicators (such as country-level economic measures or simple company-level metrics) can be used as additional context where appropriate.

Setup

Requirements

• Python 3.11.9
• pip (Python package manager)

Note

(Python 3.11.9 was used during development and is recommended for compatibility.)

Installation

1. Clone the repository

git clone https://github.com/DavidF-22/ICS5111-MiningLargeScaleData_Project.git
cd ICS5111-MiningLargeScaleData_Project

2. Create a Python environment

# for windows
python -m venv .venv
source .venv/Scripts/activate
pip install -r requirements.txt

# for linux based systems
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

3. Run the notebooks

After installing the dependencies, you can run the data preprocessing and notebook RagAssignment.ipynb.

Datasets Utilised

• Kaggle Financial Sentiment Analysis Dataset
• World Bank Open Data

Acknowledgments

This project was developed as part of the ICS5111 course at the University of Malta.

Contact

For any inquiries or feedback, please contact Gianluca Amato & David Farrugia.