Retail Sales Data Cleaning and Business Analysis with Python

This project cleans and analyzes a retail sales dataset of 2,000 orders (2023-2025) using Python, Pandas, and Plotly.

Data cleaning:

- Removed 50 duplicate rows.

- Standardized customer names, gender values, product categories, and payment methods.

- Split Location into City and Country.

- Handled missing values (customer age and ratings) and checked outliers.

Feature engineering:

- Date features (year, month, weekday, hour), revenue, profit, payment fees, order size, and customer age groups.

Analysis (10+ business questions with charts):

- Revenue and profit by category and product.

- Monthly revenue trend, best sales reps, sales by city and country.

- Order timing, discount impact, cancelled/returned orders, payment fees, and customer ratings.

- Correlation heatmap between numeric features.

Deliverable: a documented Jupyter Notebook with cleaning steps, interactive charts, and written conclusions.