This project aims to explore the Walmart Sales data to understand top performing branches and products, sales trend of of different products, customer behaviour. The aims is to study how sales strategies can be improved and optimized. The dataset was obtained from the Kaggle Walmart Sales Forecasting Competition.
The major aim of thie project is to gain insight into the sales data of Walmart to understand the different factors that affect sales of the different branches.
The dataset was obtained from the Kaggle Walmart Sales Forecasting Competition. This dataset contains sales transactions from a three different branches of Walmart, respectively located in Mandalay, Yangon and Naypyitaw. The data contains 17 columns and 1000 rows:
| Column | Description | Data Type |
|---|---|---|
| invoice_id | Invoice of the sales made | VARCHAR(30) |
| branch | Branch at which sales were made | VARCHAR(5) |
| city | The location of the branch | VARCHAR(30) |
| customer_type | The type of the customer | VARCHAR(30) |
| gender | Gender of the customer making purchase | VARCHAR(10) |
| product_line | Product line of the product solf | VARCHAR(100) |
| unit_price | The price of each product | DECIMAL(10, 2) |
| quantity | The amount of the product sold | INT |
| VAT | The amount of tax on the purchase | FLOAT(6, 4) |
| total | The total cost of the purchase | DECIMAL(10, 2) |
| date | The date on which the purchase was made | DATE |
| time | The time at which the purchase was made | TIMESTAMP |
| payment_method | The total amount paid | DECIMAL(10, 2) |
| cogs | Cost Of Goods sold | DECIMAL(10, 2) |
| gross_margin_percentage | Gross margin percentage | FLOAT(11, 9) |
| gross_income | Gross Income | DECIMAL(10, 2) |
| rating | Rating | FLOAT(2, 1) |