Black Friday Sales - Exploratory Data Analysis (EDA)

📄 Project Overview

This project focuses on analyzing the Black Friday Sales Dataset to uncover meaningful insights about customer purchasing behavior. By performing Exploratory Data Analysis (EDA), the project aims to identify trends, patterns, and factors influencing sales during Black Friday.

📊 Dataset Description

The dataset contains anonymized information about customers and their purchase behavior, including:

• User Information: Age, Gender, Marital Status, etc.
• Product Information: Product ID, Product Category, etc.
• Purchase Information: Purchase amount for each transaction.

Key Features of the Dataset:

• Rows: (Number of rows in the dataset, e.g., 550,000)
• Columns: (Number of columns, e.g., 12)
  -The dataset used for this project is available in this repository. You can access it directly: Black Friday Sales Dataset.

🛠️ Tools and Libraries

• Python: For scripting and analysis
• Pandas: Data manipulation and cleaning
• NumPy: Numerical computations
• Matplotlib & Seaborn: Data visualization
• Jupyter Notebook: Interactive environment for analysis

🔍 Exploratory Data Analysis (EDA)

The project includes:

1. Data Cleaning

   • Handling missing values
   • Removing duplicate records
   • Correcting inconsistent data formats

2. Descriptive Analysis

   • Summary statistics
   • Distribution of features like Age, Gender, Product Categories, etc.

3. Visualizations

   • Sales trends across various demographics (e.g., Age, Gender, Marital Status).
   • Popular product categories during Black Friday.
   • Purchase patterns based on city tiers.
   • Correlation analysis to identify factors influencing purchase amounts.

4. Key Insights

   • Identifying high-value customers.
   • Which product categories drive the most revenue?
   • How does location (city tier) affect sales?

🎯 Key Findings

• Male customers aged 26-35 are the highest spenders.
• Product Category 1 drives the majority of revenue.
• Customers from Tier-1 cities tend to spend more on average.

🧑‍💻 How to Use

1. Clone the repository:

   git clone https://github.com/Braj-01/BlackFridaySales.git

2. Navigate to the Project Directory
   Move into the project directory:

   cd BlackFridaySales
   
   

📚 Future Scope

• Predict customer purchases using machine learning models.
• Create an interactive dashboard for sales visualization.
• Perform advanced customer segmentation for targeted marketing.

🤝 Contributing

Contributions are welcome! If you want to contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature-name)
3. Commit your changes (git commit -m "Add feature")
4. Push to the branch (git push origin feature-name)
5. Open a Pull Request

📬 Contact

For any queries or suggestions, feel free to connect:

Name: Braj Narayan Awasthi

• LinkedIn: LinkedIn Profile