Synthetic Zip Code Classification Data

Columns

• f1 - f16: Anonymized numerical feature variables generated via simulation (containing both binary and continuous distributions) representing various zip code attributes.
• target: The classification label for the zip code ID, categorized into 10 distinct classes (values 1 through 10).

Distribution

• Format: CSV (Comma Separated Values)
• Size: 2,999 records (rows)
• Structure: 17 columns (16 features, 1 target variable)
• File Name: zipcode_test.csv

Usage

• Algorithm Benchmarking: Testing the performance of multiclass classification models (e.g., Random Forest, Gradient Boosting).
• Education: Teaching data science students how to handle synthetic data and variable feature distributions.
• Model Validation: Verifying model accuracy on controlled, simulated distributions before deploying on real-world housing data.

Coverage

• Scope: Synthetic simulation; does not correspond to specific real-world geographic coordinates or timeframes.
• Demographic: Simulated demographic and housing features.

License

Who Can Use It

• Machine Learning Engineers: For testing classification pipelines and multiclass logic.
• Data Science Educators: For creating course assignments on predictive modelling.
• Real Estate Analysts: For modelling theoretical zip code segmentation and clustering.

Dataset Name Suggestions

• Synthetic Zip Code Classification Data
• Simulated Multiclass Real Estate Features
• Zip Code Pattern Simulation Dataset
• 10-Class Synthetic Housing Data

Attributes