eduardoalvarz / Webscraping-Stock-Data-Extraction-and-Visualization

A Jupyter Notebook demonstrating the extraction and visualization of stock data for Tesla and GameStop, crafted for the Python Project for Data Science IBM Certification.

Webscraping Stock Data Extraction and Visualization

Description

Hi, I'm Eduardo. Welcome to my project repository for the Python Project for Data Science IBM Certification. This project is centered around the crucial aspect of data science that is extracting essential data from datasets and visually representing this data to enable informed decision-making. In this particular case, we focus on extracting and visualizing stock data for two significant market players: Tesla and GameStop.

The project leverages Python for both webscraping to extract revenue data and utilizing the yfinance library to fetch historical stock data. The visualization part is adeptly handled by the plotly library, enabling interactive and insightful charts that detail the financial journey of these companies over time.

Project Objectives

• Extract Stock Data: Use yfinance to fetch historical data for Tesla and GameStop stocks.
• Webscraping for Revenue Data: Apply BeautifulSoup to scrape revenue data from the web.
• Visualize Data: Employ plotly to create interactive graphs that represent stock prices and revenue trends comprehensively.

Technologies

This project is implemented using:

• Python 3
• Pandas for data manipulation
• yfinance for stock data extraction
• BeautifulSoup for webscraping
• Plotly for interactive data visualization

Acknowledgements

• Joseph Santarcangelo and Azim Hirjani for their initial work and guidance on this project.
• The yfinance and plotly libraries for providing the tools necessary to extract and visualize stock data.

Happy Investing!