Synthetic Streaming Subscription Renewal Prediction Data

A synthetic dataset for predicting streaming subscription renewal behavior. It was sourced from Kaggle, but the author, organization, and specific creation date are unknown. The dataset's size, number of rows, and column-level details are not provided in the available metadata.

Use Cases

• Train a binary classification model to predict subscription renewal based on synthetic customer behavior data.
• Benchmark churn prediction algorithms using a controlled, synthetic dataset free of real-world privacy concerns.
• Analyze feature importance for renewal prediction using the dataset's simulated behavioral and demographic variables.

Strengths

• The dataset is explicitly designed for a specific machine learning task: predicting subscription renewal.
• Being synthetic, it likely avoids privacy and data licensing issues associated with real customer data.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale model training.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Kaggle

Collection Method

Synthetically generated.