The rapid expansion of e-commerce platforms in Indonesia has made it increasingly difficult for consumers to compare product information across multiple websites. This research introduces a web application designed to help users make more informed purchasing decisions by efficiently aggregating and analysing product data. The application uses APIs and web scraping techniques to collect relevant data from various Indonesia e-commerce websites, which is then exported into a DataFrame format for further processing. Machine learning algorithms are applied to this data to generate product recommendations. These recommendations are ranked based on criteria such as price and sales performance. By streamlining the comparison process and presenting a ranked list of top products, the system enhances the user's e-commerce experience and reduces decision fatigue.