Predictive Blackout E-Commerce Pricing

Inspired by the societal chaos depicted in 'Nightfall' and the impending disaster in 'Interstellar', this project addresses the potential economic disruptions caused by large-scale disasters, especially those that impact infrastructure and supply chains. The core concept is to build a system that scrapes pricing and product availability data from e-commerce sites selling essential goods (food, water, batteries, generators, etc.). This data is then combined with publicly available data on potential 'blackout' events (solar flare forecasts, cybersecurity threat advisories, infrastructure vulnerability reports) to train a time-series forecasting model. The model predicts the surge in demand and competitor pricing changes in the days or hours leading up to and during such events. The system then recommends optimal pricing strategies to e-commerce businesses, enabling them to maximize profits while also (potentially) preventing extreme price gouging. Implementation involves: 1) Web scraping (using libraries like Beautiful Soup or Scrapy) of targeted e-commerce sites. 2) Data cleaning and preprocessing. 3) Time-series analysis and forecasting (using libraries like Prophet, ARIMA, or LSTM networks). 4) Development of a simple API or dashboard to provide pricing recommendations to users. This project is niche because it focuses on a specific scenario (blackout events), low-cost because it utilizes readily available open-source tools and publicly available data, and has high earning potential because businesses are willing to pay for insights that can help them navigate uncertain times and optimize pricing in volatile markets.