This project demonstrates a full retail analytics workflow, showing both technical skills and business impact:
- Cleaned and structured raw retail datasets into usable formats
- Built KPI dashboards (Total Sales, Orders, AOV) for performance tracking
- Created visualizations for trends, segmentation, and seasonality
- Designed an Excel planning pack for stakeholders
- Applied forecasting & inventory tracking to support business decisions
Why it matters: This reflects the day-to-day work of e-commerce planning and reporting teams, where turning raw data into clear insights drives better inventory, promotion, and revenue decisions.
- Data Cleaning — handled missing values, standardized formats, saved cleaned datasets
- KPI Reporting — Total Sales, Orders, Average Order Value (AOV)
- Sales Analysis — by date, product, category, and store
- Visualization — sales trends, top products, weekday/holiday patterns
- Forecasting & Inventory — simple demand forecasts and out-of-stock tracker
- Excel Reporting — automated dashboard exports for planning teams
retail_project/ ├─ notebooks/ # Jupyter notebooks (data cleaning, reporting, forecasting) ├─ data/ # Datasets (customers, products, sales, stores, holidays) ├─ output/ # Cleaned data + visuals ├─ output_planning/ # Excel dashboard + planning pack ├─ README.md # Project overview ├─ requirements.txt # Dependencies └─ .gitignore
- Python — pandas, numpy, matplotlib
- Excel — reporting & dashboards (via openpyxl)
- JupyterLab — interactive analysis
Insight: Sales show a clear monthly growth trend with noticeable spikes during holiday seasons, reflecting demand surges and consumer behavior patterns.
Insight: A small group of customers contributes disproportionately to revenue, highlighting the importance of client segmentation and retention strategies.
Insight: Revenue during holidays is significantly higher than non-holiday periods, showing the impact of promotions and seasonal events on sales performance.
Insight: Mid-week days outperform weekends in revenue generation, a pattern useful for inventory planning and targeted promotions.
Insight: Repeat customers drive steady revenue, while new customers add spikes, showing the balance between acquisition and retention.
Insight: Most customers fall in the low-to-mid CLV range, but a small segment of high-value customers has outsized business impact.
Clone the repo and set up dependencies:
git clone git@github.com:RahmaMohammad/Retail_Project.git
cd Retail_Project
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt