This project analyzes a grocery sales dataset to uncover patterns in product categories, sales quantities and revenue. Using Python libraries like Pandas, Seaborn and Plotly, we explore the data structure, perform statistical analysis, visualize trends and draw insights to support retail decision-making. A comprehensive exploratory data analysis (EDA) project on a multi-branch grocery store dataset. This project explores sales trends, branch performance, customer behavior, payment preferences, and product profitability using Python data science tools.
This project performs an in-depth analysis of transactional data from a supermarket operating across multiple branches. The dataset includes information on sales, customer types, product categories, payment methods, ratings, and more.
The goal is to uncover valuable insights to help the business make data-driven decisions related to:
- Sales performance by branch and product line
- Peak hours and high-traffic days
- Popular payment methods and customer demographics
- Gross income patterns
- Customer feedback trends
zomato-data-analysis/
├── data/
├── images/
├── notebooks/
├── src/
├── README.md
├── requirements.txt
└── LICENSE
- 🏪 Branch C has the highest revenue among all branches
- 🕒 Evening hours and weekends show the most sales activity
- 💳 E-wallet and credit card are the most used payment methods
- 👩 Female customers slightly outnumber male customers
- 🧴 Health & Lifestyle is the most profitable product line
- ⭐ Gross income is positively correlated with customer ratings
- Python 3.x
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- ✅ Branch C generates the highest revenue
- ✅ Evening hours and weekends have peak sales
- ✅ E-wallet and credit card are the most used payment methods
- ✅ Female customers slightly outnumber male shoppers
- ✅ Health & Lifestyle is the most profitable product line
🧠 Conclusion
This project demonstrates the value of EDA in uncovering business insights for retail strategy and customer segmentation. The patterns observed can inform inventory planning, sales optimization, and marketing decisions in grocery or retail environments.