🛒 Exploratory Data Analysis on Retail Sales Data

I have used a dataset from Kaggle (public domain) related to the Retail Sales Data collected from 10 different shopping malls in Istanbul, Turkey.

📁 Link to the dataset:

Kaggle - Customer Shopping Dataset

📊 Columns in the dataset:

invoice_no: Invoice number
customer_id
gender: Either male or female
age
category: Such as clothing, shoes, cosmetics, et cetera
quantity
price: This is the price per unit item
payment method: Either cash, debit card, or credit card
shopping_mall: Name of the mall

🏗️ Project Structure:

Data Loading and Cleaning
Feature Engineering
Descriptive Statistics
Data Visualization
Data Analysis
Recommendations

1️⃣ Data Loading and Cleaning

This step involved:

Loading the dataset into a Pandas dataframe
Handling missing values
Removing duplicates
Correcting data types
Formatting the dates

2️⃣ Feature Engineering

This step involved creating columns based on:

Categorization of malls (Luxury mall, mega mall, mixed-use mall, and outlet mall)
Location of mall (whether located on the European side or the Asian side of Istanbul)
Total price of items
Categorization of ages into different groups (baby, young adult, middle-aged adult, and old adult)
Total price of items purchased by each gender
Total price of items purchased by each age group
Total price of items purchased in each category
Total price of items purchased through each payment method
Total price of items purchased in each shopping mall
Total price of items purchased in each mall location
Total price of items purchased in each type of mall
Unit price of items purchased by each gender
Total quantity of items purchased by each gender
Total quantity of items purchased from each category
Total quantity of items purchased through each payment method
Total quantity of items purchased in each shopping mall
Total quantity of items purchased in each type of mall
Total quantity of items purchased in each mall location
Total quantity of items purchased by each age group

3️⃣ Descriptive Statistics

This step involved getting KPIs from the crucial columns using measures of central tendency. Here are a few important insights derived from this step:

The average age of a person shopping in the malls is 43 years.
On average, a customer buys about 3 items of any type from the shopping mall.
On average, a person spends 689.26 Turkish Liras per item in a shopping mall.
The number of purchases made by females is higher than those of males.
The payment method for most invoices is cash.
The majority of the invoices come from malls located on the European side of Istanbul.

4️⃣ Data Visualization

In this step, I utilized various types of graphs such as bar charts and pie charts using matplotlib and seaborn libraries to explore and understand the data.

A few pivotal graphs created at this stage were:

Comparison of Payment Methods
Total Amount Spent
Purchase Trends in Each Month

5️⃣ Data Analysis

From the graphs and descriptive statistics, I made the following inferences:

Customers aged 37 years have the most invoices in shopping malls.
On average, a customer buys about 3 items of any type from the shopping mall.
On average, a person spends 689.26 Turkish Liras per item in a shopping mall.
The maximum number of invoices are from the Mall of Istanbul.
More invoices are issued to females, who spend more and purchase a higher quantity of items than males.
Both male and female customers frequently visit luxury and mega malls, as well as malls located in the European part of Istanbul.
Young adults (aged 3 to 39 years) have more invoices compared to middle-aged and old adults, hence the total price and quantity of items purchased is higher.
Clothing emerges as the most popular category among all the invoices, followed by cosmetics and food/beverages.
Clothing and shoes, followed by technology items, have the highest total price.
Price per item is the highest for technology, followed by shoes and clothing.
Customers spend more in the month of October, with a sharp decrease in November and a slight increase in December. Males shop the most in September, while female customers shop more in July and October.
Most payments are made in cash, followed by credit card.
More invoices are issued by Kanyon and Mall of Istanbul than other malls.
Invoices are dominated by purchases from mega malls.
Most invoices are from malls located in the European part of Istanbul.

6️⃣ Recommendations

Here are my top recommendations based on the insights gathered:

Malls should locate themselves in the European part of Istanbul and upgrade their infrastructure to become luxury or mega malls.
Malls should focus more on their young adult (aged 3 to 39 years) customers.
Malls should also focus more on their female customers, as the number of invoices, total price, and quantity of items purchased by females is higher than that of males.
Malls should prepare adequately for the month of October, as it has the highest sales volume.
Malls should ensure that customers can easily pay using cash.
Malls should focus more on selling clothing items to customers.

AmruhaAhmed/Exploratory-Data-Analysis-on-Retail-Sales-Data