End-to-end sales data analysis on the AdventureWorks dataset — covering data cleaning, exploratory data analysis and interactive Power BI dashboards to support business decision-making.
The business needed clarity on which products, regions and customer segments were driving revenue and profit — across 121,000+ transaction records spread over 5 relational tables.
Adventureworks-project/
├── 📂 Raw data/ → Original dataset files
├── 📂 Data_cleaning/ → Cleaned & processed data
├── 📂 EDA_analysis/ → Exploratory data analysis notebooks
├── 📂 SQL Databases/ → SQL queries for KPI extraction
├── 📂 PowerBI Dashboard/ → .pbix dashboard file
└── 📄 README.md
- ✅ Cleaned and merged 5 relational tables using Pandas & NumPy
- ✅ Performed full EDA — nulls, outliers, distributions, correlations
- ✅ Wrote SQL queries for aggregation, joins and KPI extraction
- ✅ Built 2 Power BI dashboards with 19+ visuals and dynamic filters
- ✅ Delivered business recommendations based on findings
| Metric | Value |
|---|---|
| 📦 Total Records Analyzed | 121,000+ |
| 💰 Total Revenue | $114,667 |
| 📈 Total Profit | $49,248 |
| 🛒 Total Orders | 2,630 |
| 👥 Total Customers | 17,775 |
| 🏆 Top Product | Road Bikes ($29.65K) |
| 🥇 Top Category by Profit | Components (50.94%) |
Components category drives 50.94% of total profit despite not being the top revenue product — signaling a high-margin opportunity for focused inventory investment.
Road Bikes lead in revenue at $29.65K — a key product line for promotional campaigns.
