{"id":25535557,"url":"https://github.com/jordanconallluthaiswright/purchase-behaviour-data-analysis","last_synced_at":"2026-04-18T12:02:58.549Z","repository":{"id":278191274,"uuid":"934812857","full_name":"JordanConallLuthaisWright/Purchase-Behaviour-Data-Analysis","owner":"JordanConallLuthaisWright","description":"This project analyzes Black Friday purchase behavior for Company XYZ, uncovering trends by gender, age, and location. 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The objective is to uncover key insights about **spending trends based on gender, age, and other factors** to drive data-driven decision-making.\n\nThe analysis is performed using structured datasets and statistical techniques to identify **consumer purchasing patterns** and **recommendations for business optimization**.\n\n## Files in This Repository\n- **`XYZ_data.csv`** – The raw dataset containing transaction records.\n- **`README.md`** – This document explaining the project and its methodology.\n- **`Black Friday Purchase Behavior Analysis.zip`** – The complete project package. Download and unzip to run the analysis.\n- **`Black Friday Purchase Behavior Analysis (without any cells running).ipynb`** – A static preview of the Jupyter Notebook for quick reference.\n\n## Business Scenario\nCompany XYZ aims to understand **consumer spending behavior during Black Friday**, focusing on the following key questions:\n\n1. **Do women spend more than men?**\n2. **How do demographics such as age, marital status, and location influence purchases?**\n3. **Which product categories generate the highest sales?**\n4. **What insights can be derived to improve future marketing strategies?**\n\n## Methodology \u0026 Skills Demonstrated\n\n### 1. Data Cleaning \u0026 Preprocessing\n- Loaded and structured data using **Pandas**.\n- Removed duplicates, handled missing values, and formatted data for analysis.\n- Converted categorical variables into appropriate formats.\n\n### 2. Exploratory Data Analysis (EDA)\n- Analyzed **purchase trends by gender, age, and location**.\n- Created **visualizations using Matplotlib \u0026 Seaborn** to identify spending patterns.\n- Used statistical summaries to examine purchase behaviors.\n\n### 3. Statistical \u0026 Hypothesis Testing\n- **Confidence Interval Analysis**: Assessed average spending differences between male and female customers.\n- **Central Limit Theorem (CLT) \u0026 Bootstrapping**: Simulated population spending behaviors.\n- **Comparative Analysis**: Studied spending habits across different age groups and marital statuses.\n\n### 4. Insights \u0026 Recommendations\n- Identified **high-spending demographics** to optimize marketing campaigns.\n- Recommended **targeted promotions for different customer segments**.\n- Suggested **enhancements in product category distribution** based on purchasing trends.\n\n## Key Findings \u0026 Conclusion\n- **Men, on average, spend more than women**, but product categories influence this trend.\n- **Customers aged 26-35 make up the largest purchasing group**, driving nearly 40% of total sales.\n- **City B customers contribute the most to total revenue**, showing strong demand in Tier-2 locations.\n- **Unmarried customers tend to spend more than married customers**.\n- **Product categories 1, 5, 8, and 11 are the most purchased items**, indicating strong consumer interest.\n\n## Technologies Used\n- **Python** (Pandas, NumPy, Matplotlib, Seaborn)\n- **Jupyter Notebook** for data exploration and visualization\n- **Statistical Analysis** for hypothesis testing\n- **Data Preprocessing \u0026 Cleaning**\n\n## How to Use This Repository\n1. **Clone the repository:**\n   ```bash\n   git clone https://github.com/your-username/black-friday-analysis.git\n\n2. **Navigate to the project directory:**\n   ```bash\n   cd black-friday-analysis\n\n3. **Download \u0026 extract the dataset:**\n   ```bash\n   Unzip \"Black Friday Purchase Behavior Analysis.zip\"\n\n4. **Open the Jupyter Notebook:**\n   ```bash\n   jupyter notebook \"Black Friday Purchase Behavior Analysis.ipynb\"\n\n5. **Run the analysis:**\n   - Execute the notebook cells sequentially to process and analyze the dataset.\n   - Review the outputs and visualizations for insights.\n\n6. **Preview the analysis (without running cells):**\n   ```bash\n   Open \"Black Friday Purchase Behavior Analysis (without any cells running).ipynb\" to view the notebook contents without execution.\n\n## **Contact \u0026 Contributions**\nFeel free to explore and contribute! If you have any suggestions, reach out or submit a pull request.\n- **Email**: [jordan.c.l.wright@gmail.com](mailto:jordan.c.l.wright@gmail.com)\n\n---\n\n### **Author:** Jordan\n[GitHub Profile](https://github.com/JordanConallLuthaisWright)\n```\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjordanconallluthaiswright%2Fpurchase-behaviour-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjordanconallluthaiswright%2Fpurchase-behaviour-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjordanconallluthaiswright%2Fpurchase-behaviour-data-analysis/lists"}