US TV E-Commerce Transaction Dataset

A comprehensive synthetic dataset simulating customer transactions on an e-commerce platform, covering data ranging from August to October 2024 with more than 400K records . This dataset includes key fields such as purchase_id, product_name, price, status, payment_method, city, location, and more. It empowers you to gain insights into:

1. TV Sales Analysis: Understand the volume of TV sales by city, identifying trends and popular regions.
2. Transaction Failures: Pinpoint the primary reasons for failed transactions, enabling you to address issues and improve customer satisfaction.
3. Customer Preferences: Discover the most preferred payment methods among customers, allowing for better optimization of payment options.
4. Demand Forecasting: Use the data to predict future demand and make informed inventory and marketing decisions.

Why do you need this data?

1. Develop and refine recommendation systems: Use this dataset to train and improve recommendation algorithms for personalized product suggestions.
2. Prototype data-driven applications: Experiment with data analysis and visualization techniques like EDA, and value insights.
3. Conduct market analysis: Identify emerging trends, customer preferences, and competitive landscape.
4. Train and test machine learning models: Use this dataset to train and evaluate models for tasks like fail transactions prediction, and demand forecasting.
5. Build data pipelines and infrastructure: Develop and test data processing pipelines using this realistic dataset.

Target Audience:

• BI Engineers
• Data scientists
• Machine learning engineers
• Data analysts
• Market researchers
• E-commerce businesses
• Academics