Skip to content

Latest commit

 

History

History
97 lines (71 loc) · 3.51 KB

README.md

File metadata and controls

97 lines (71 loc) · 3.51 KB

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.

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.

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.

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.

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.

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.

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).

Acknowledgments

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

License

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

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.