A collection of multiple e-commerce dataset samples. Each sample contains over 1,000 records. These datasets are ideal for product trend analysis, pricing strategies, consumer sentiment insights, and more.

This project analyzes Amazon sales data to explore customer behavior, best-selling product categories, and seller performance by month and marketplace. Using Python tools like Pandas, Matplotlib, and Seaborn, we turn raw data into clear insights to support data-driven decisions.

I built two neural networks to try and perform the binary categorization of customers as either satisfied or unsatisfied with their Ecommerce shipping experience.

The library can be used by PHP applications to access the SQUIZZ.com platform's Application Programming Interface (API) to push and pull data from the API's endpoints.

powerful web scraping tool designed to extract data from HTTPS-secured websites, including social media, e-commerce platforms, and court records, for analysis, monitoring, or research purposes.