This project analyzes a supermarket dataset to gain insights into customer behavior and help market owners optimize their sales and profits. The dataset contains 2,019,501 rows and 12 columns, providing information about orders, users, products, and departments.
The dataset used in this project is the Hunter's E-grocery dataset which consists of over 2 million purchase records at a renowned Hunter's supermarket, contains 2,019,501 rows and 12 columns that provide valuable insights into consumer behavior.
To run this project, you need to have Python 3.x. and the required libraries installed, then you can run the code cells in a Jupyter Notebook or Python script to see the results. To install the required libraries use the following command:
pip install -r requirements.txt
The project uses the following libraries:
| Library | Version |
|---|---|
| Pandas | 1.5.3 |
| NumPy | 1.25.0 |
| Seaborn | 0.12.2 |
| Matplotlib | 3.7.1 |
| Scikit-learn | 1.2.2 |
The analysis of the users' behavior revealed the following insights:
- The market sells the most on Monday, Tuesday, and Wednesday
- People are most likely to order from 8 AM to 10 PM, with the rush hour at 10 AM
- The 'produce' department (fresh and packaged fruits and vegetables) is the most popular
- Dairy, eggs, beverages, and snacks are the next top-selling categories
- People often order an average of 8 items each time, with almost 10 days between each order