Adidas US Sales Analysis (EDA & Visual Analytics)
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Project Overview
This project focuses on performing comprehensive Exploratory Data Analysis (EDA) on the Adidas US Sales dataset to identify high-growth regions, top-performing retailers, and the most profitable sales methods.
Technical Workflow:
- Data Wrangling: Cleaned and structured an extensive Excel dataset (~9,600 rows) using Pandas, including handling missing values, re-indexing, and data type verification.
- Exploratory Data Analysis (EDA): Leveraged NumPy and Pandas to segment sales data by retailer, product category, and geographic region.
- Statistical Visualization: Created a suite of visual analytics using Matplotlib to communicate complex trends, such as the 30% revenue dominance of online sales channels.
- Business Intelligence: Identified Foot Locker as the leading retailer and determined that the West and Northeast regions serve as the primary drivers of total revenue.
Key Insights:
- Channel Performance: Online sales consistently outperform in-store and outlet methods in terms of total revenue contribution.
- Product Demand: Footwear emerged as the highest revenue-generating category across all US regions.
- Optimization: Discovered significant variance in profit margins across product lines, suggesting areas for inventory optimization.
Tech Stack:
- Languages: Python
- Libraries: Pandas, NumPy, Matplotlib
- Tools: Jupyter Notebook / Google Colab