Supermarket Sales Analysis

A comprehensive exploratory data analysis (EDA) of 1,000 supermarket transactions, uncovering customer behavior, payment trends, revenue drivers, and branch-level performance.

---

Dataset Overview

The dataset includes 1,000 transaction entries with the following attributes:

• Invoice ID
• Branch (A, B, C)
• City (Yangon, Mandalay, Naypyitaw)
• Customer Type (Member / Normal)
• Gender
• Product Line
• Unit Price
• Quantity
• Tax (5%)
• Total
• Date & Time
• Payment Method (Cash, Ewallet, Credit Card)
• COGS
• Gross Income
• Rating

---

Data Preparation

• Loaded CSV using pandas
• Verified shape: 1000 × 17
• Confirmed no missing values
• Converted Date column to datetime
• Produced descriptive statistics using .describe()

---

Analysis & Visualizations

1. Gender Distribution

Countplot comparing Male vs Female customers.

2. Customer Ratings Distribution

Histogram showing rating distribution.

3. Transactions per Branch

Transaction counts for branches A, B, and C.

4. Popular Payment Methods

• Ewallet: 345
• Cash: 344
• Credit Card: 311

Includes a stacked bar chart by branch.

5. Customer Ratings by Branch

Boxplot comparing customer satisfaction across branches.

6. Gross Income Analysis

• Gross income by branch
• Gross income by gender
• Daily gross income trend
• Branch C showed the highest total gross income

---

Key Insights

• Customers are evenly split: 501 Female, 499 Male
• Branch C generated the highest gross income
• Ewallet was slightly more preferred than Cash and Credit Card
• Ratings were consistently positive across branches
• Fashion Accessories, Electronics, and Health & Beauty were top revenue-generating product lines
• Female customers contributed slightly higher average gross income

---

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

---

How to Run

Clone the repository:

bash git clone https://github.com/yourusername/SuperMarketAnalysis.git

Install dependencies:

bash Copy code pip install pandas numpy matplotlib seaborn

Open the notebook:

bash Copy code jupyter notebook SuperMarket.ipynb

---

Future Improvements

Add predictive modeling (sales forecasting)

Customer segmentation via clustering

Build a dashboard using PowerBI, Streamlit, or Plotly

Product-line based revenue forecasting

---

Contributing

Pull requests are welcome. Feel free to submit improvements, visualizations, or new analyses.

---

Support

If you found this project useful, consider giving the repository a star.