Walmart aims to analyze customer purchase behavior during Black Friday, specifically comparing spending habits between male and female customers. The management seeks insights to make informed decisions and potential improvements.
- Features: User_ID, Product_ID, Gender, Age, Occupation, City_Category, StayInCurrentCityYears, Marital_Status, ProductCategory, Purchase
- Target: Analyze purchase amount against gender and other factors.
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Data Analysis:
- Import and explore dataset structure and characteristics.
- Detect null values and outliers using boxplots, "describe" method, and checking for null values.
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Data Exploration:
- Track spending per transaction for 50 million female and male customers.
- Calculate average spending for each gender and infer results.
- Use sample averages to compute confidence intervals for male and female spending.
- Observe distribution of mean expenses using the Central Limit Theorem.
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Statistical Analysis:
- Compare confidence intervals of average spending for married vs. unmarried customers.
- Analyze spending behavior across different age groups.
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Insights and Recommendations:
- Provide insights on data attributes, their ranges, and relationships.
- Interpret results of gender, marital status, and age-based spending analysis.
- Offer actionable recommendations for Walmart based on the findings.
- Define problem statement and analyze basic metrics.
- Explore data shape, types, and attribute conversion.
- Perform non-graphical and visual analysis for insights.
- Detect missing values and outliers.
- Answer specific questions regarding spending behavior.
- Provide final insights and actionable recommendations.
By analyzing customer purchase behavior, Walmart can gain valuable insights into spending habits across different demographics. Understanding these patterns can help Walmart make targeted improvements to enhance customer experience and drive business growth.