Data-Analysis-using-LLM

This repository contains a project aimed at analyzing textual survey responses from students to extract meaningful information and provide personalized restaurant recommendations. The project leverages a large language model (LLM) for data extraction and analysis. Below is an overview of the key components of the project:

Key Components

1. Data Source

   • Textual survey responses from students.

2. Extraction

   • Using a large language model (LLM) to extract relevant information such as:
     • Student names
     • Favorite foods
     • Favorite restaurants
     • Monthly spending on eating out

3. Restaurant Recommendations

   • Suggesting restaurants based on student preferences with the assistance of the LLM model.

4. Data Storage

   • Storing the extracted data in a Pandas DataFrame for easy analysis and manipulation.

This project demonstrates the use of advanced NLP techniques to process and analyze survey data, providing insights and personalized recommendations based on student preferences.

Getting Started

Prerequisites

• Python 3.x
• Required Python packages (listed in requirements.txt)