supermarket sales cleaning & visualization

Data Cleaning Steps

Check for Missing Values

•Identified null or missing values (there are no nulls or missing values).

Check for Duplicates

•Detected duplicate rows (there are no duplicates).

Date Format Correction

•Standardized date formats for consistency across the dataset (Replaced / with - in the Date column).

Renamed Columns for Clarity

• Renamed Total to Total Sales

• Renamed Payment to Payment Method

visualization for these objectives

1.Sales per month

2.Sales by gender

3.which customer type buys more?

4.which city or branch has the most sales?

5.Sales by products

6.Top products by quantity

7.Top products by profit

8.Top rated products

9.how customers prefer to pay (payment method)