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Analysis of J&J Stock Data with Python

Using Python, the stock data of Johnson and Johnson, a well known pharmaceuticals company was analyzed, before and after the Covid-19 outbreak.

Data was collected via J&J reports and Yahoo! Finance, and was aggregated with Python and plotted as a time-series graph. Summary statistics were ran and correlation matricies were derived, with multiple features being considered. These features included stock volume, revenue, COGs, R&D, net income, and adjusted close values

Linear regression analysis was implemented using the features mentioned above to predict net income for the last two quarters. The data other than the last two quarters were used as the training sets.

After linear regression, it was concluded that revenue, cost of goods sold (COGs), and research and development (R&D) were good predictors of net income in this dataset.

This project was a good introduction of Python programming and machine learning methods such as Linear Regression.

Skills: Python and Excel



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