This end-to-end data analytics project centres on the well-known Superstore dataset — a four-year (2014–2017) transactional record of a fictional US retail chain spanning 9,994 order lines, 793 unique customers, 1,862 products, and $2.3 M in revenue across four regional markets (East, West, Central, South). The project follows a complete analytical workflow: raw data ingestion, SQL-based data cleaning and exploratory analysis, Python-powered visual exploration, and an interactive Streamlit dashboard — all designed to surface actionable insights around profitability, customer segmentation, product performance, discount governance, and geographic distribution.
- Clean and standardise raw transactional data using both SQL and Python pipelines
- Conduct comprehensive exploratory data analysis (EDA) across time, geography, and product dimensions
- Answer 30+ structured business questions with SQL to drive decision-making
- Build an interactive multi-chart dashboard with Streamlit and Plotly for real-time filtering
- Identify loss-making products, discount governance gaps, and regional profit opportunities
- Deliver customer segmentation insights to support targeted retention and acquisition strategies
The project employs a professional-grade analytics stack spanning SQL, Python, and interactive visualisation libraries to build a fully reproducible, end-to-end pipeline:
- Data cleaning, type standardisation, and outlier detection (SQL & Python)
- Feature engineering — profit margin, shipping days, discount bands, CLV, customer value segments
- SQL Exploratory Data Analysis (EDA) — trends, distributions, correlations
- 30+ structured business queries covering revenue, profitability, geography, and discounting
- Interactive Streamlit dashboard with dynamic filters and downloadable outputs
- Statistical visualisation and business storytelling with Plotly and Seaborn
- Environmental reporting and communication
📁 project-root/ ├── 📂 Dataset/ │ ├── Raw_Data.csv (9,994 rows · 21 columns) │ └── Cleaned_Data.csv (9,994 rows · 38 columns) ├── 📂 Python/ │ ├── Data_Cleaning.ipynb │ ├── EDA.ipynb │ ├── Data_Visualization.ipynb │ ├── Dashboard.py (Streamlit app) │ └── Requirements.txt ├── 📂 SQL/ │ ├── Data_Cleaning.sql │ ├── EDA.sql │ └── Business_Queries.sql (30+ queries) └── 📄 README.md
A visual walkthrough of the analysis outputs — from the Streamlit KPI dashboard to exploratory charts and SQL-driven business insight visualisations built in Python and Plotly.
Across 9,994 order lines and $2.3 M in total revenue, the analysis uncovered a 12.5% overall profit margin with significant variation by category — Technology led in profitability while Furniture lagged, and several sub-categories (notably Tables and Bookcases) operated at a net loss. The West region produced the highest profit, while specific states and cities exhibited structurally negative returns driven by excessive discounting. Orders carrying discounts above 30% were found to be the primary source of avoidable losses, pointing to a clear need for discount governance controls. On the customer side, the Consumer segment generated the most orders, but Corporate customers delivered higher average order values. CLV segmentation identified a small cohort of high-value repeat buyers who merit dedicated retention investment.
This project demonstrates how a rigorous, multi-tool analytics workflow — combining SQL for data engineering and business querying, Python notebooks for EDA and statistical visualisation, and Streamlit for interactive dashboarding — can translate raw transactional data into clear, executive-ready insights. The Superstore dataset, spanning four years and four US regions, served as a realistic sandbox for practicing the full spectrum of a data analyst's craft: from messy raw ingestion through to polished, filterable dashboards. The 30+ business queries and engineered features (profit margin, shipping days, discount bands, CLV, customer segments) collectively form a blueprint that is directly transferable to real-world retail, e-commerce, or supply-chain analytics environments.