End-to-End Sales Dashboard + SQL Analysis is a high-performance analytics platform that transforms raw retail logistics data into actionable executive insights. Built with a full-stack data science workflow: from SQL-relational modeling to a high-end interactive Streamlit dashboard.
Automated ELT Pipeline: Seamless extraction from raw CSV into a queryable SQLite high-performance warehouse.
Advanced SQL Analytics: Deep-dive intelligence using window functions (RANK,PARTITION BY,SUM OVER) to isolate market triggers.
High-End Visualization: A premium UI experience with interactive Plotly shards and glassmorphism styling in Streamlit.
Operational Intelligence: Identification of high-discount "Burn Zones" and region-specific profit optimization markers.
| Step | description |
|---|---|
 SQL Analysis |
Running business-critical queries to detect seasonality, loss-makers, and growth vectors. |
 Visual EDA |
Creating executive-level charts with Plotly and Seaborn to tell the data story. |
 Dashboard |
Deploying a live, interactive Streamlit application with a modern, glass-themed interface. |
Explore the insights in seconds!
- Python 3.13+
- VS Code / Jupyter Lab
- Superstore Dataset: Already included in the
data/folder.
-
Clone & Target:
git clone https://github.com/SubashSK777/CargoTrack.git cd CargoTrack -
Sync Dependencies:
pip install -r requirements.txt
-
Boot Interactive App:
streamlit run app.py
| Market Factor | Best Performer | Core Warning |
|---|---|---|
| Category | Technology (17.4% Margin) | Tables (Negative Profit) |
| Region | West (Highest Revenue) | Central (Weak Profitability) |
| Seasonality | Q4 (Holiday Surge) | Mid-Year Lull (July-August) |
Key finding: Capping discounts at 20% would potentially increase overall profit by 15.2% based on current burn.