Store Sales Analysis Project

️ Store Sales Analysis with Python

This end-to-end data project analyzes store sales performance using real-world datasets from Kaggle. I performed data cleaning, exploration, and visualization to identify customer trends, best-selling products, and sales patterns.

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Objective

To explore customer behavior, product performance, and time-based sales trends to uncover actionable business insights.

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? Tools Used

- **Python** (Pandas, NumPy, Seaborn, Matplotlib)

- **Jupyter Notebook**

- **Power BI**

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️ Data Collection

All datasets were sourced from Kaggle:

- `sales.xls` – Transaction-level sales data

- `products.xls` – Product catalog with categories and prices

- `customers.xls` – Customer profiles and demographics

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Key Questions Explored

- Which products generate the most revenue?

- What is the monthly and yearly trend in sales?

- Who are the most valuable customers?

- Which locations or customer segments drive the highest sales?

- Are there seasonal patterns in product demand?

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## Visual Insights

The interactive Power BI dashboard showcases:

- Sales trends over time (monthly/yearly)

- Top-performing products and categories

- Revenue by customer segments

- Sales heatmaps by region or store

- Customer Lifetime Value (CLV) insights

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## ️ About Me

I'm **Adam Abozaid**, a data analyst passionate about using data to uncover stories and support decisions.

This project demonstrates my full data workflow — from **data cleaning and analysis**, to **insightful dashboard creation**.

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Contact

I'm open to feedback, collaborations, and opportunities.

adamabozaid18@gmail.com