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Starbucks-sales-performance-analysis

End-to-end data analytics: Python scarping & automation, MySQL cleaning, Tableau visualization & business insights.

Starbucks Sales Performance Analysis

Python MySQL Tableau

Overview

Complete analysis of Starbucks sales and store performance. Built with Python for data scraping and automation, MySQL for data cleaning, and Tableau for powerful visualizations and business insights.

Technologies Used

  • Python: Scraping, automation, data processing with Pandas
  • MySQL: Data cleaning, transformation, and storage
  • Tableau: Advanced dashboards with calculated fields and storytelling

Project Workflow

  1. Collected Starbucks-related data through Python scraping and automation
  2. Cleaned and organized data in MySQL
  3. Developed interactive Tableau dashboards
  4. Interpreted results to support business decisions

Key Insights

  • Discovered top performing products and store locations

How to Run the Project

  1. Clone this repository
  2. Install dependencies: pip install -r requirements.txt
  3. Set up MySQL database and run the SQL scripts in the sql/ folder
  4. Run Python scripts in src/ folder
  5. Open the Tableau workbook (.twbx) in the tableau/ folder

Future Improvements

  • Add customer segmentation analysis
  • Include predictive sales forecasting
  • Expand to more locations and metrics