Analyzed Target's orders (2016-2018) with Python and MySQL. Used Pandas, Matplotlib, and Seaborn for visualization. Executed SQL queries to uncover insights on customer behavior and sales trends, creating visuals to support data-driven business decisions.
This project focuses on analyzing Target's e-Commerce operations in Brazil from 2016 to 2018, using a dataset of 100,000 orders. The aim is to extract valuable business insights related to sales performance, customer behavior, product categories, and more, leveraging Python and MySQL.
Utilized Pandas for data manipulation, and Matplotlib and Seaborn for creating visualizations to showcase sales trends, customer retention, and revenue distribution.
Connected the dataset to MySQL using mysql.connector and executed SQL queries to retrieve and analyze data across various dimensions.
Addressed business questions at multiple levels: Basic: Customer location analysis, order counts by year. Intermediate: Monthly order patterns, product performance, revenue distribution by category. Advanced: Customer retention rates, year-over-year sales growth, seller revenue rankings.
The analysis provided insights into sales performance, customer behavior, product category contributions, and seller effectiveness, supporting data-driven decision-making for Target Brazil.
Python: Pandas, Matplotlib, Seaborn
SQL: MySQL with mysql.connector
Jupyter Notebook: For code execution and analysis