Supermarket Sales Analysis using Python

Explore the captivating world of retail data analysis! In this series, we dive into Supermarket Sales Analysis using Python to uncover fascinating insights and trends hidden within the data.

Objectives

1. Identify the branch with the highest sales.
2. Determine the best-selling product line.
3. Calculate the Gross Income per month.
4. Analyze daily sales trends across different months.

Contents

• Data Analysis: Clean and manipulate the dataset to prepare it for analysis.
• Data Visualization and Analysis: Utilize visualizations to extract insights from the data.
• Conclusion: Summarize key findings and insights from the analysis.

Tools and Libraries Used

• Python
• Pandas
• Matplotlib
• Seaborn

How to Use

1. Ensure you have Python and the required libraries installed.
2. Download the dataset (supermarket_sales - Sheet1.csv) included in the repository.
3. Run the provided Python scripts to perform data analysis and visualization.

Blog Post

For a detailed explanation and discussion of the analysis, check out my blog post on Medium. Read the blog post here!

Author

[Menchie Beronilla]

Feel free to explore the code and adapt it to your own projects. Don't forget to share your feedback and thoughts on the analysis!