This project presents an in-depth business data analysis on customer purchase behavior during Black Friday sales for Company XYZ. The objective is to uncover key insights about spending trends based on gender, age, and other factors to drive data-driven decision-making.
The analysis is performed using structured datasets and statistical techniques to identify consumer purchasing patterns and recommendations for business optimization.
XYZ_data.csv– The raw dataset containing transaction records.README.md– This document explaining the project and its methodology.Black Friday Purchase Behavior Analysis.zip– The complete project package. Download and unzip to run the analysis.Black Friday Purchase Behavior Analysis (without any cells running).ipynb– A static preview of the Jupyter Notebook for quick reference.
Company XYZ aims to understand consumer spending behavior during Black Friday, focusing on the following key questions:
- Do women spend more than men?
- How do demographics such as age, marital status, and location influence purchases?
- Which product categories generate the highest sales?
- What insights can be derived to improve future marketing strategies?
- Loaded and structured data using Pandas.
- Removed duplicates, handled missing values, and formatted data for analysis.
- Converted categorical variables into appropriate formats.
- Analyzed purchase trends by gender, age, and location.
- Created visualizations using Matplotlib & Seaborn to identify spending patterns.
- Used statistical summaries to examine purchase behaviors.
- Confidence Interval Analysis: Assessed average spending differences between male and female customers.
- Central Limit Theorem (CLT) & Bootstrapping: Simulated population spending behaviors.
- Comparative Analysis: Studied spending habits across different age groups and marital statuses.
- Identified high-spending demographics to optimize marketing campaigns.
- Recommended targeted promotions for different customer segments.
- Suggested enhancements in product category distribution based on purchasing trends.
- Men, on average, spend more than women, but product categories influence this trend.
- Customers aged 26-35 make up the largest purchasing group, driving nearly 40% of total sales.
- City B customers contribute the most to total revenue, showing strong demand in Tier-2 locations.
- Unmarried customers tend to spend more than married customers.
- Product categories 1, 5, 8, and 11 are the most purchased items, indicating strong consumer interest.
- Python (Pandas, NumPy, Matplotlib, Seaborn)
- Jupyter Notebook for data exploration and visualization
- Statistical Analysis for hypothesis testing
- Data Preprocessing & Cleaning
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Clone the repository:
git clone https://github.com/your-username/black-friday-analysis.git
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Navigate to the project directory:
cd black-friday-analysis -
Download & extract the dataset:
Unzip "Black Friday Purchase Behavior Analysis.zip" -
Open the Jupyter Notebook:
jupyter notebook "Black Friday Purchase Behavior Analysis.ipynb" -
Run the analysis:
- Execute the notebook cells sequentially to process and analyze the dataset.
- Review the outputs and visualizations for insights.
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Preview the analysis (without running cells):
Open "Black Friday Purchase Behavior Analysis (without any cells running).ipynb" to view the notebook contents without execution.
Feel free to explore and contribute! If you have any suggestions, reach out or submit a pull request.
- Email: jordan.c.l.wright@gmail.com