Supermarket Sales Analysis

• Grocery Stores are a vital part of everyday life, providing us with foods and essentials as we need.
• People often uses grocery delivery applications to order products, making it easy to shop from home.
• Each transaction made through these applications are recorded in detail, creating a valuable dataset.
• This project looks at a same data of a supermarket transactions to understand how well it's performing.

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Dataset

• The dataset is sourced from Kaggle which simulates grocery sales in Tamil Nadu state of India.
• The dataset includes columns that provides detailed information about each transaction at the Supermarket.
• Link to the Dataset : Supermarket Sales Dataset

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Problem Statement

• To analyze Supermarket Sales data, identifying key factors for improving profitability and operational efficiency.

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Setting up the Enviroment

Jupyter Notebook is required for this project and you can install and set it up in the terminal.

• Install the Notebook

pip install notebook

• Run the Notebook

jupyter notebook

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Libraries required for the Project

NumPy

• Go to the terminal and run this code

pip install numpy

Pandas

• Go to the terminal and run this code

pip install pandas

Matplotlib

• Go to the terminal and run this code

pip install matplotlib

Seaborn

• Go to the terminal and run this code

pip install seaborn

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Getting Started

• Clone this repository to your local machine by using the following command :

git clone https://github.com/themrityunjaypathak/Supermarket-Sales-Analysis.git

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Steps involved in the Project

Importing Libraries

• Importing necessary libraries like numpy, pandas, matplotlib and seaborn.

Reading CSV File

• Reading CSV file by using pd.read_csv() method.

Overview of the Dataset

• Information about shape and size of the dataset.
• Types of column present in the dataset (numerical, categorical, text).
• Detailed info about the dataset using df.info() method.

Handling Null values in the Dataset

• This dataset does not contain any null values.

Unique values in aach Categorical Column

• Unique customer names in the data.
• Unique product categories in the data.
• Unique product sub-categories in the data.
• Unique cities in the data.
• Unique regions in the data.

Changing DataType of Columns

• Modifying the datatype of order_date column to pandas datetime format.

Utilizing existing information to create new Columns

• Extracting year, month and dates from order_date column.
• Extracting discount_amount from discount percent column by using mathematical formulas.

Statistical Analysis

• No. of products sold in each category.
• No. of products sold in each sub category.
• No. of products sold in each city.
• No. of products sold in each region.
• No. of products sold each year, month and date.

Data Visualization

• No. of products sold in each category.

• No. of products sold in each sub category.

• No. of products sold in each city.

• No. of products sold in each region.

• No. of products sold each year.

• No. of products sold each month.

• No. of products sold each date.

• Total sales in each category.

• Total sales in each sub category.

• Total sales in each region.

• Total sales in each city.

• Total sales in each month.

• Total sales in each year.

• Total profit in each category.

• Total profit in each sub category.

• Total profit in each region.

• Total profit in each city.

• Total profit in each month.

• Total profit in each year.

• Customers with highest amount of total sales.

• Customers with highest profit on their purchase.

• Total discount availed by customers.

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Conclusion

Analyzed purchasing patterns of 9,000+ customers of a Supermarket.

• More than 15% of the products sold were Snacks.
  • Shows that Snacks are a convenient choice and a major source of revenue.
• More than 32% of total sales came from the West region of the supermarket.
  • Suggests that West region is a strong-performing area as compared to others.
• Health and Soft drinks were the most profitable sub-categories in beverages.
  • Shows that both type of drink options perform well among customers.
• November was the most profitable month contributing about 15% of the total annual profits.
  • Makes it an ideal time for running promotions and special offers.

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