https://github.com/binarybardakshat/mutual-fund-performance-insights

This project was developed for the RAGathon challenge hosted by IIT Madras Shaastra.
https://github.com/binarybardakshat/mutual-fund-performance-insights

hackathon iitm openai python streamlit

Last synced: 11 days ago
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This project was developed for the RAGathon challenge hosted by IIT Madras Shaastra.

• Host: GitHub
• URL: https://github.com/binarybardakshat/mutual-fund-performance-insights
• Owner: BinaryBardAkshat
• Created: 2024-05-06T03:51:34.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-05-06T05:29:03.000Z (9 months ago)
• Last Synced: 2024-11-21T11:10:21.049Z (2 months ago)
• Topics: hackathon, iitm, openai, python, streamlit
• Language: Python
• Homepage: https://akshatshuklaproject.streamlit.app/
• Size: 5.96 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Mutual Fund Performance Insights

# Website (https://akshatshuklaproject.streamlit.app/)

## Overview

This project was developed for the RAGathon challenge hosted by IIT Madras Shaastra. The goal of the project is to provide insights into mutual fund performance by analyzing detailed month-wise reports of various mutual fund schemes across several financial years, spanning from FY’ 21-22 to FY’ 23-24. The provided datasets contain a range of fields offering comprehensive financial, operational, and management insights essential for assessing the performance, size, and operational scope of the mutual funds. These datasets serve as critical tools for performance tracking, investor reporting, and ensuring compliance with regulatory standards.

## Contributors

- **Akshat Shukla** ([GitHub: @binarybardakshat](https://github.com/binarybardakshat))

- **Email:** [email protected]

## Code Description

The provided code is a Streamlit application that facilitates interactive analysis and chat-based interaction with the dataset. It leverages various language models (LLMs) to provide insights and answer questions related to the mutual fund data. Users can upload their datasets, select different LLMs, and interact with the system through a chat interface.

## Usage

1. Install the necessary dependencies.

2. Run the Streamlit application.

3. Upload the dataset containing mutual fund performance data.

4. Choose the desired language model for analysis.

5. Interact with the system by asking questions or inputting queries related to mutual fund performance.

## Dependencies

- Python 3.x

- Streamlit

- Pandas

- Matplotlib

- OpenAI

- Langchain (LLM)

- Baidu AIStudio

- pathlib

## License

This project is licensed under the [MIT License](LICENSE).

## Acknowledgments

Special thanks to IIT Madras Shaastra for organizing the RAGathon challenge and providing the opportunity to work on this project.