Tableau's Sample-Superstore!

Predicting Profits by Product Category

Overview

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!

Deliverables:

This assignment contains 3 primary areas:

1. 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.
2. Code: Area to perform testing of dataset, functions, and implement models before final project output.
3. 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