In this data science project, we utilized various Python libraries, including yfinance and BeautifulSoup, to extract historical financial data, such as historical share prices and quarterly revenue reporting, for popular stocks including Tesla, Amazon, AMD, and GameStop. The data was then visualized using the Plotly library to identify patterns and trends. This project served as an introduction to utilizing these libraries for financial data extraction and visualization.
-yfinance (A Python package that allows developers to easily access and analyze financial data from the Yahoo Finance website.)
-BeautifulSoup (A Python library for pulling data out of HTML and XML files. It creates parse trees from page source code that is helpful to extract the data easily)
-plotly (A Python library that allows developers to create interactive, web-based visualizations of data. It can be used to create a wide variety of charts and plots, including line plots, scatter plots, bar charts, heatmaps, and more.)
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