This project delves into the fascinating world of Adidas sales in the USA, leveraging the power of Python and data analysis libraries like pandas, NumPy, Seaborn, and Matplotlib. Our goal? To answer key questions and unlock insights that propel Adidas to even greater success.
Key Objectives:
- Regional Differences: Unearth how operating profit and sales volume vary across diverse US regions. Are there hidden trends hiding beneath the surface?
- Retailer Optimization: Should Adidas invest differently in its retail partners? This project equips you with data-driven insights to make informed decisions.
- Product Powerhouses: Identify the best-selling product class in each region, allowing Adidas to tailor its offerings for maximum impact.
- Golden City: Discover the city that generates the highest sales for Adidas, revealing invaluable market potential.
- Sales Efficiency: Compare different approaches to sales and pinpoint the most efficient strategy for maximizing revenue.
Data: Dive deep into 2 years of Adidas sales data (2020-2021) and uncover valuable patterns and trends.
Tools:
- Python: The versatile programming language empowers data manipulation and analysis.
- pandas: Streamline data wrangling and analysis with powerful data structures.
- NumPy: Perform efficient numerical computations with ease.
- Seaborn: Create stunning and informative data visualizations.
- Matplotlib: Craft custom visualizations to tailor your insights.
Join the Exploration:
- Clone this repository and embark on your analytical journey.
- Reproduce the analysis, tweak parameters, and uncover your own discoveries.
- Contribute to the discussion and share your findings with the community.
Keywords: python, data analysis, pandas, numpy, seaborn, matplotlib, statistics
Let's leverage the power of data to empower Adidas' future!