Walmart Black Friday Purchase Behavior Analysis

About Walmart

Walmart, an American multinational retail corporation, operates a vast network of supercenters, discount departmental stores, and grocery stores worldwide. With over 100 million customers globally, Walmart is a leader in the retail industry.

This project analyzes customer purchase behavior during Black Friday, a significant shopping event generating high transaction volumes.

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Objective

The primary objective of this project was to:

• Explore customer purchase behavior during Black Friday.
• Visualize spending patterns and central tendencies using box plots and distribution analysis.
• Assess whether purchase amounts follow a normal distribution.

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Dataset

The dataset used for this analysis contains transactional data of customers who purchased products at Walmart during Black Friday.

Dataset Features:

• User_ID: Unique customer identifier.
• Product_ID: Unique product identifier.
• Gender: Customer’s gender (Male/Female).
• Age: Customer’s age group (in bins).
• Occupation: Encoded occupation identifier.
• City_Category: City category (A, B, or C).
• StayInCurrentCityYears: Number of years the customer has lived in their current city.
• Marital_Status: Marital status (0 = Single, 1 = Married).
• ProductCategory: Encoded product category.
• Purchase: Amount spent by the customer on a product.

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Key Insights

1. Gender-Based Spending:

   • Box plots revealed distinct spending patterns between male and female customers.

2. Age and Purchase Behavior:

   • Central tendency measures showed variations in spending across age groups.

3. City Category Trends:

   • Customers from category "A" cities demonstrated higher purchase amounts compared to "B" and "C" cities.

4. Normal Distribution Check:

   • Assessed purchase amounts for normality using visual distribution analysis.

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Process Overview

1. Data Cleaning:

• Addressed missing values and ensured dataset consistency.

2. Exploratory Data Analysis (EDA):

• Visualized purchase patterns using:
  • Box plots to understand distribution and detect outliers.
  • Histograms to examine overall data spread and normality.
• Analyzed central tendencies (mean, median, mode) for different demographic groups.

3. Insights:

• Focused on visual and descriptive statistics to identify patterns in the dataset.

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Tools and Libraries

This project was implemented using:

• Python:
  • Numpy & Pandas for data manipulation.
  • Matplotlib and Seaborn for visualization.
• Jupyter Notebook for interactive analysis and documentation.

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Repository Structure

• data/: Contains the dataset used for analysis.
• notebooks/: Jupyter Notebooks documenting the analysis process.
• visualizations/: Saved plots and charts used in the project.
• README.md: Overview of the project (this file).

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Acknowledgments

• Dataset Source: Provided by Scaler for this project.
• Libraries Used: Thanks to the Python data science community for open-source tools.

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License

This project is for educational and non-commercial use only. Please credit the repository if using its resources.

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Next Steps

Future extensions of this project could include:

1. Applying feature engineering to enhance insights.
2. Conducting statistical testing to validate observed patterns.
3. Developing predictive models to forecast purchase behavior.