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Athletic Sales Analysis

Background

This project involves analyzing U.S. sales data to identify top cities for athletic wear sales, leading retailers in total sales and women's athletic footwear, and peak sales periods for women's athletic footwear over a two-year span.

Setup

  • Create a repository named athletic_sales_analysis.
  • Clone the repository to your local machine.
  • Push changes to GitHub or GitLab.

Files

  • Module 5 Challenge files.

Challenge Instructions

Follow the provided starter code to:

Combine and Clean the Data

  • Import CSV files for 2020 and 2021 sales.
  • Ensure DataFrame columns are consistent.
  • Combine DataFrames and reset the index.
  • Clean and convert data types as necessary.

Analyze Sales Data

  • Determine top-selling regions, states, and cities.
  • Identify retailers with the highest total sales.
  • Find leading retailers in women's athletic footwear sales.
  • Pinpoint the day and week with peak sales for women's athletic footwear.

Data Analysis Tasks

  • Combine DataFrames: Use appropriate join methods to combine sales data.
  • Clean Data: Handle null values and ensure correct data types.
  • Region Analysis: Group data to find regions with most products sold and highest sales.
  • Retailer Analysis: Identify top retailers by sales and women's athletic footwear sales.
  • Sales Period Analysis: Determine top days and weeks for women's athletic footwear sales using pivot tables and resampling.

Hints and Considerations

  • Utilize concatenation, joins, merging, and data reshaping techniques.
  • Review class activities if you need a refresher on specific functions.
  • Commit your work regularly and ensure your repository includes a detailed README.md file.

Requirements

  • Data Combination and Cleaning: Successfully combine and clean sales data.
  • Regional Sales Analysis: Accurately determine top regions for product sales and revenue.
  • Retailer Sales Analysis: Correctly identify top retailers by sales metrics.
  • Women's Athletic Footwear Analysis: Effectively analyze sales data for women's athletic footwear.
  • Daily and Weekly Sales Analysis: Determine peak sales periods for women's athletic footwear.

Grading

Assignments will be graded according to the completeness and accuracy of the analysis against the requirements.

Submission

Submit the URL of your GitHub repository for grading.

Important Notes

  • Ensure to document any code sources used within your repository's README section.
  • Utilize student support services if needed, including peer support, learning assistants, office hours, and tutoring sessions.

References

  • Sales Product Data: Kaggle Dataset