https://github.com/prashhhant213/customer-behavior-analysis-for-walmart-black-friday-sales
This project is a Walmart case study analyzing customer purchase behavior by gender and demographics to inform business decisions, especially around spending habits during events like Black Friday.
https://github.com/prashhhant213/customer-behavior-analysis-for-walmart-black-friday-sales
matplotlib numpy pandas python scipy seaborn
Last synced: about 1 month ago
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This project is a Walmart case study analyzing customer purchase behavior by gender and demographics to inform business decisions, especially around spending habits during events like Black Friday.
- Host: GitHub
- URL: https://github.com/prashhhant213/customer-behavior-analysis-for-walmart-black-friday-sales
- Owner: prashhhant213
- Created: 2024-08-26T06:13:00.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-08-26T06:57:05.000Z (8 months ago)
- Last Synced: 2025-02-01T17:44:26.743Z (3 months ago)
- Topics: matplotlib, numpy, pandas, python, scipy, seaborn
- Homepage:
- Size: 526 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Customer-Behavior-Analysis-for-Walmart-Black-Friday-Sales
## 🎯 Objective
The objective of the project is to analyze customer purchase behavior at Walmart, specifically focusing on the impact of gender and other demographic factors on spending habits. The goal is to determine whether spending patterns differ between male and female customers, particularly during Black Friday, to help the business make better decisions.
## 📚 About Data
The dataset used in this project is based on Walmart customer purchase behavior, with a focus on analyzing the relationship between customer demographics (gender, age, occupation, etc.) and purchase amounts. The dataset contains 100,175 rows and 10 columns with the following features:
- User_ID: Unique identifier for each customer.
- Product_ID: Unique identifier for each product.
- Gender: Gender of the customer (Male or Female).
- Age: Age group of the customer (e.g., 26-35).
- Occupation: Customer's occupation category.
- City_Category: Category of the city (A, B, C) where the customer resides.
- Stay_In_Current_City_Years: Number of years the customer has lived in the current city.
- Marital_Status: Marital status of the customer (0 = Single, 1 = Married).
- Product_Category: Category of the product.
- Purchase: Amount of money spent by the customer on a particular purchase.
- 75% of users are male and 25% are female.
- The most active age group is 26-35 years, accounting for 40% of the total customers.
- The dataset is clean with no missing values, making it suitable for analysis.
## Performed following Tasks
1. Data Loading and Preprocessing:
- Imported and cleaned the dataset.
- Checked for missing values and ensured data integrity.
- Converted numerical data types to categorical data types for better analysis.
2. Exploratory Data Analysis (EDA):
- Conducted basic descriptive analysis to understand the distribution of variables.
- Analyzed gender distribution, age groups, marital status, and city category distributions.
- Visualized the data using histograms, count plots, and pie charts to extract insights.
3. Univariate and Bivariate Analysis:
- Performed univariate analysis to identify trends within individual variables.
- Conducted bivariate analysis to explore relationships between purchase amounts and other variables like gender, age, marital status, and city category.
- Identified patterns in purchasing behavior across different demographic segments.
4. Outlier Detection and Analysis:
- Used box plots to detect outliers in purchase amounts, occupation, and product categories.
- Provided insights into the presence of outliers and their impact on the data.
5. Visualization of Findings:
- Created visualizations such as histograms, count plots, and pie charts to present the findings.
- Utilized box plots to highlight relationships between demographic variables and purchase behavior.
6. Insight Extraction:
- Summarized key insights from the analysis, such as the higher purchasing power of males, the age group with the highest purchases, and the impact of city category on purchasing behavior.