To stay on top of supply and demand, businesses are looking to find ways to better understand their future profits and success in their respective industry.
For this repo, I will pretend to be a data scientist for Tableau's 'Sample-Superstore', in which I use machine learning techniques to predict future profit by product category for 'Sample-Superstore' by selecting a regression model that best fits to the data.
Check out the navigation below to see the entire story!
This assignment contains 3 primary areas:
- Summary and Report: Jupyter Notebook including a detailed abstract on problems in assignment, code relevant to project, and visualizations supporting the completion of the project.
- Code: Area to perform testing of dataset, functions, and implement models before final project output.
- Dataset Used for Assignment:. This data contains ~10k records and 17 columns.
Contributors : Lee Whieldon
Languages : Python Tools/IDE : Anaconda Libraries : pandas, matplotlib, numpy, sklearn, seaborn, io, requests
Assignment Submitted : September 2020