Retail Stores Business Data Analytics

This project delivers a detailed business data analysis for multiple retail stores. Using exploratory data analysis (E.D.A.) techniques, it identifies key sales patterns, uncovers operational inefficiencies, and proposes data-driven strategies to improve inventory management and revenue optimization.

🚀 Technologies Used:

• Python.
• Pandas.
• NumPy.
• Jupyter Notebook.

📊 Project Highlights:

• Data ingestion, cleaning, and preparation for analysis.
• Exploratory data analysis of seasonal, regional, and product-level sales trends.
• Visualization of business insights using charts and summary statistics.
• Recommendations for improving stock rotation, sales focus, and profitability.

📂 Files:

• retail_stores_chain_data.csv — primary dataset.
• Retail_Business_Data_Analytics.ipynb — Jupyter Notebook with complete analysis.

▶️ How to View & Run:

Click in the Retail_Business_Data_Analytics.ipynb Jupyter Notebook in this repository (recommended for non-technical people) OR Access the read-only executable version of the notebook in Google Colab:

This allows you to review the full analysis and execute the code in a controlled environment without local setup.

📄 License

MIT License

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