Sales Prediction and Customer Support Bot with NLP and RAG

Overview

This repository contains two main components: a Sales Prediction Model and a Voice-Based Customer Support Bot, both leveraging NLP and Retrieval-Augmented Generation (RAG) for enhanced performance. The project aims to provide scalable solutions for sales forecasting and customer support through interactive notebooks.

Technologies Used

• LangChain
• Groq
• SentenceTransformers
• Whisper
• Cohere
• LLMs
• NetworkX
• Plotly
• Scikit-learn

Usage Instructions

To run the project, execute the provided Jupyter notebooks:

• Chatbot_code.ipynb: Handles customer support bot functionalities.
• Sales_Prediction.ipynb: Manages sales forecasting model operations.

Running the Notebooks

1. Local Execution:

   • Ensure Jupyter Notebook is installed on your system.
   • Clone the repository and navigate to the directory.
   • Run the notebooks using jupyter notebook in your terminal.

2. Google Colab:

   • Upload the notebooks to Google Colab.
   • Make sure to mount any necessary Google Drive data if required.

Installation

While the notebooks are designed to be run directly, some libraries might need installation. You can install the required packages using:

pip install -r requirements.txt

Data Requirements

• Sales Dataset: Ensure you have the sales data required for the Sales_Prediction.ipynb notebook.
• Customer Support Data: Prepare any necessary data for the Chatbot_code.ipynb notebook.

Contributing

Contributions are welcome! Please ensure that any new features or changes are well-documented and include necessary tests. Fork the repository and submit a pull request with your changes.

Additional Information

For more details on the project's architecture and methodology, refer to the respective notebooks. If you encounter any issues or have questions, please open an issue in the GitHub repository.

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