PYZON: Python's Amazon Scrapper and Price Predictor

This project marked a comprehensive exploration of web scraping techniques applied to Amazon's website, focusing on extracting valuable product information. Leveraging Python's versatile libraries, the scraping process successfully collected a diverse dataset encompassing product features, customer reviews, and pricing details. The subsequent phase involved transforming the raw scraped data into meaningful insights. Power BI emerged as a robust tool for crafting interactive and visually compelling data visualizations. The dashboard created provided stakeholders with a user-friendly interface to explore trends, correlations, and patterns within the Amazon product data. Furthermore, the integration of a Linear Regression model for price prediction showcased the practical application of machine learning within the realm of e-commerce. By training the model on relevant features extracted through web scraping, it demonstrated the potential to forecast product prices based on historical data. This predictive capability not only enriches decision-making for sellers and buyers but also serves as a testament to the synergy between web scraping, data visualization, and machine learning in driving actionable insights.