Supermarket Sales Analysis

This project involves analyzing a supermarket sales dataset to uncover actionable insights through Exploratory Data Analysis (EDA). The dataset contains records of sales transactions, customer demographics, and other key features. Insights gained from this analysis aim to assist supermarket management in making data-driven decisions.

Dataset

Dataset Link: Supermarket Sales Dataset

The dataset includes features such as sales transactions, payment methods, customer demographics, and ratings.

Objectives

• Identify high-performing branches.
• Analyze customer preferences and spending behavior.
• Study payment methods and their impact on sales.
• Understand factors influencing sales and customer ratings.

Features of Analysis

1. Data Loading and Cleaning:

   • Imported dataset and inspected structure using Pandas methods (.head(), .info(), .describe(), .shape()).
   • Checked and handled missing values, formatted columns, and removed duplicates.

2. Exploratory Data Analysis (EDA):

   • Analyzed sales by:
     • Gender.
     • Product category.
     • Branch.
     • Payment method.
   • Examined relationships between key features like sales, ratings, and branches.
   • Studied customer demographics and spending patterns.

3. Data Visualization:
   Visualized the data using Matplotlib and Seaborn for better understanding:

   • Pie Chart: Sales distribution by branch.
   • Bar Chart: Payment methods vs. total sales.
   • Heatmap: Correlation between numerical features.
   • Boxplot: Sales distribution by customer gender.

4. Summary and Insights:

   • Highlighted revenue-generating branches.
   • Determined the most popular payment method.
   • Investigated gender influence on average sales amount.

Visualizations

Key visualizations include:

• Sales Distribution by Branch: (Pie chart).
• Payment Methods vs. Total Sales: (Bar chart).
• Correlation Between Features: (Heatmap).
• Sales Distribution by Gender: (Boxplot).

Tools and Technologies

• Programming Language: Python.
• Libraries: Pandas, Matplotlib, Seaborn, NumPy.

Contribution

Feel free to contribute by forking the repository and submitting a pull request.

License

This project is licensed under the MIT License.