This project showcases an end-to-end data analytics pipeline:
- 📥 Data Cleaning with Python
- 🔍 Exploratory Data Analysis (EDA)
- 📊 Interactive Dashboard using Tableau
- Removed missing values, duplicates
- Standardized column formats
- Performed EDA on sales, profit, customers, region
- Visualized using Seaborn and Matplotlib
📄 Notebook: Retail_Sales_Analytics_EDA.ipynb
- Total Sales, Profit, Orders, and Customers
- Sales by Category, Region, and Customer
- Monthly sales & profit trends
- Filters by Year and Region
- Key insights generated with GenAI
📊 Tableau File: Retail Sales Dashboard - 2023.twbx
📸 Preview:
- Technology segment had highest sales but low profit margin.
- West region contributed the highest overall profit.
- November and December were peak performing months.
- Python (Pandas, NumPy, Matplotlib, Seaborn)
- Tableau Public
- ChatGPT (for summarization & insight extraction)
This project demonstrates an end-to-end business data analysis pipeline — useful for dashboards, business intelligence, and stakeholder storytelling.
👤 Author: Madhu Sudhan
📫 LinkedIn