Data Scraping and Analysis for E-commerce Site

This project involved the utilization of various tools and skills to extract, process, and analyze data from multiple e-commerce websites. The primary objective was to gain valuable insights into product pricing, inventory levels, and competitor offerings. The project was successfully completed using a combination of Python, SSMS (SQL Server Management Studio), Google Cloud Platform, Google Cloud Functions, and React.

To begin with, advanced web scraping techniques were employed to extract relevant data from the targeted e-commerce sites. This involved using Python libraries and frameworks to scrape data from web pages, ensuring accurate and efficient data retrieval. The acquired data was then processed and transformed using ETL (Extract, Transform, Load) methodologies.

Next, the extracted data was stored and managed in an SQL database using SSMS, enabling efficient querying and analysis. This step ensured the availability of structured data for further processing and analysis.

The data analysis phase involved utilizing various statistical and analytical techniques to derive meaningful insights. The pricing data helped identify pricing trends, competitive pricing strategies, and opportunities for pricing optimization. Analyzing inventory levels enabled the identification of popular products, stock management strategies, and demand forecasting. The competitor offerings analysis provided valuable information on product assortments, marketing strategies, and customer preferences.

Throughout the project, full-stack engineering expertise was employed to develop a user-friendly front-end interface using React. This allowed stakeholders to interact with the analyzed data, visualize key metrics, and generate custom reports. The front-end development aspect ensured seamless usability and accessibility for decision-makers.

In conclusion, this project successfully employed data scraping and analysis techniques to extract and analyze data from various e-commerce sites. The insights gained through this process provided valuable information on product pricing, inventory levels, and competitor offerings. The utilization of tools such as Python, SSMS, Google Cloud Platform, Google Cloud Functions, and React showcased a comprehensive skill set encompassing ETL, web scraping, full-stack engineering, and front-end development.