Super Market Sales Analysis

Hello Everyone,

Here is My EDA Project based on Super Market Sales Analysis where I analyzed the Data by using Matplotlib and Seaborn.

Dataset

I used Super Market Sales Dataset from Kaggle recorded for 3 Different Cities for 3 Months.

Link to the Dataset : Super Market Sales Dataset

Problem Statement

• To analyze sales data and gain insights into customer purchasing behavior, product performance and overall trends of the supermarket business.

Streamlit Web App

• For my Super Market Sales Analysis Project, I have created a Streamlit Web App for analyzing the Data in more interactive and user friendly way.

• This Web App allows you to dig deep into the sales data, helping you answer critical questions in just few clicks.

Link to the Web App : Super Market Sales App

Table of Contents

• Setting up the Enviroment
• Libraries required for the Project
• Getting started with Repository
• Steps involved in the Project
• Conclusion
• Link to the Notebook

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

Libraries required for the Project

NumPy

• Go to Terminal and run this code - pip install numpy

• Go to Jupyter Notebook and run this code from a cell - !pip install numpy

Pandas

• Go to Terminal and run this code - pip install pandas

• Go to Jupyter Notebook and run this code from a cell - !pip install pandas

Matplotlib

• Go to Terminal and run this code - pip install matplotlib

• Go to Jupyter Notebook and run this code from a cell - !pip install matplotlib

Seaborn

• Go to Terminal and run this code - pip install seaborn

• Go to Jupyter Notebook and run this code from a cell - !pip install seaborn

Sklearn

• Go to Terminal and run this code - pip install sklearn

• Go to Jupyter Notebook and run this code from a cell - !pip install sklearn

Getting Started

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

git clone https://github.com/TheMrityunjayPathak/SuperMarketSalesAnalysis.git

Steps involved in the Project

Reading the Data

• First I installed all the necessary libraries required for this Project.

• Then I imported the Data by reading csv file using read.csv() Method.

• Then I dropped the Invoice ID Column because we don't need it in analysis.

• After that I listed down all the columns in the Dataset by df.columns Method.

• Then I used df.shape Method to look for the rows and columns in the Data.

• Then I look for the Info of the Dataset by using df.info() Method.

Cleaning the Data

• First I start by describing the Data by using df.describe() Method.

• Then I converted Date Column to Pandas Date and Time DataType.

• And After that I extracted Year, Month, Day from the Date.

• Then I listed down all the unique values of categorical columns.

• And Finally I verified the null values in the Dataset by using df.isna().sum()

Visualizing the Data

• Subplots of Distribution of Unit Price, Ratings and Gross Income

• Per Unit Price of Each Product Lines

• Count of Different Types of Customers from Different Cities

• Count of Different Types of Products in Super Market

• Count of Different Gender Visitors at Different Branches

• Count of Different Types of Payment Methods used by Different Genders

• Count of Different Gender Visitors from Different Cities

• Quantity of Products Sold from Each Product Line

• Different Payment Methods Used by Different Cities

• Total Amount Spend on Different Product Lines by Different Genders

• Rating of Different Product Lines by Different Genders

• Gross Income from Different Product Lines in Different Cities

• Total Sale on Each Day for All Months

• Taxes on Different Product Lines

• Number of Products bought by Different Genders from Different Product Lines

• Total Gross Income from Different Branches by Different Genders

Conclusion

• In conclusion, Super Market Sales Project revealed valuable insights into customer purchasing behavior and product performance.

• It provide opportunities for data-driven strategies to enhance profitability and customer satisfaction.

Link to the Notebook

Super Market Sales Analysis

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