Dynamic Pricing System

This project implements a dynamic pricing system using Facebook Prophet for time series forecasting and Twitter RoBERTa for sentiment analysis. It adjusts prices dynamically based on future demand predictions and real-time customer sentiment analysis from Twitter data.

Table of Contents

• Project Overview
• Technologies Used
• Installation
• Usage
• Model Details
• Contributing
• License

Project Overview

The dynamic pricing system leverages two key components:

1. Prophet: A forecasting tool to predict future demand based on historical data.
2. Twitter RoBERTa: A pre-trained transformer model for sentiment analysis to gauge real-time market sentiment from Twitter data.

Using these models, the system adjusts product prices to maximize revenue or competitiveness depending on predicted demand and social sentiment.

Technologies Used

• Python 3.8+
• Prophet
• Hugging Face RoBERTa
• pandas, numpy, scikit-learn
• Tweepy (for Twitter API)
• Matplotlib for visualization

Installation

1. Clone this repository:

   git clone https://github.com/HeathKnowles/DynamicPricingSystem.git
   cd DynamicPricingSystem

2. Run the data fetchers

   python commerce.py
   python demand_forecasting.py
   python sentiment_analysis.py

3. Run the preprocessor

   python preprocess.py

4. Run the main python file

   python main.py

5. This outputs a dynamic Price of the product in the json folder