Black Friday Sales Analysis

This project provides an in-depth analysis of Black Friday sales data. The analysis delves into key customer demographics, purchasing behaviors, and product trends to uncover insights that can guide strategic decision-making. By exploring multiple dimensions of the data, this project aims to enhance understanding of consumer preferences during Black Friday sales.

Table of Contents

• Overview
• Dataset
• Analysis Scope
• Analysis Highlights
• Technologies Used
• How to Run
• Results and Insights
• Contributing
• License

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Overview

Black Friday sales represent a major retail event characterized by high-volume consumer activity. This analysis focuses on understanding the interplay of various demographic and behavioral factors that drive sales. By using data visualization and statistical methods, we aim to identify patterns and trends to answer key questions about customer purchasing behavior.

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Dataset

The dataset used in this project contains transactional data from a Black Friday sales event.
Key attributes include:

• Demographics: Age, Gender, Marital Status
• Behavioral: Product ID, Purchase Amount
• Occupational Data: Occupation and City Tier

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Analysis Scope

The analysis is divided into the following sections:

1. Combining Age & Marital Status

• Examines how age groups correlate with marital status in determining purchasing power.
• Identifies trends in spending behavior across single and married individuals.

2. Occupation and Products Analysis

• Analyzes purchasing patterns based on customers’ occupations.
• Highlights which product categories are preferred by specific occupational groups.

3. Analyzing Age & Marital Status

• Provides deeper insights into how marital status affects spending within different age groups.
• Identifies demographic segments with the highest contribution to sales.

4. Analyzing Gender

• Compares purchasing trends between male and female customers.
• Evaluates the influence of gender on product preference and spending behavior.

5. Multi-Column Analysis

• Combines multiple dimensions, including age, occupation, and city tier, to gain holistic insights.
• Visualizes relationships between demographic features and total sales.

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Technologies Used

• Programming Language: Python
• Libraries:
  • Pandas (Data manipulation)
  • Matplotlib & Seaborn (Data visualization)
  • NumPy (Numerical computations)
• Jupyter Notebook: For interactive data analysis.

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How to Run

1. Clone this repository:

   git clone https://github.com/venkat-0706/Black-Friday.git

2. Navigate to the project directory:

   cd Black-Friday

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Jupyter Notebook:

   jupyter notebook BlackFridayAnalys.ipynb

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Results and Insights

• Age & Marital Status: Married individuals in the 26-35 age group contribute the most to total sales.
• Occupation Trends: Certain occupations show a strong preference for high-value products.
• Gender Analysis: Males tend to spend more, but females show more diverse product preferences.
• Multi-Dimensional Insights: Customers from Tier 1 cities in the 26-45 age range dominate high-value purchases.

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Contributing

Contributions are welcome! If you'd like to enhance this project or add new analysis dimensions, please feel free to:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -m "Add your feature").
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

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License

This project is licensed under the MIT License.

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Contact

For any queries, feel free to reach out:

• Email: chanduabbireddy247@gmail.com
• GitHub: venkat-0706
• Linkedin: chandu0706.

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