Messy Clinic Appointments Data for Cleaning Practice

A realistic messy dataset for practicing data cleaning and preprocessing tasks, sourced from Kaggle. It simulates healthcare appointment records, though the specific row count, column details, and original author are unknown. The last update date is also unspecified.

Use Cases

• Identify and handle missing values in appointment_date and patient_id columns.
• Standardize inconsistent formats in patient_name and clinic_location fields.
• Detect and correct data entry errors in age and appointment_time columns.
• Practice merging or joining separate tables using common keys like patient_id or appointment_id.
• Clean and parse unstructured text notes within a medical_notes column.

Strengths

• Designed specifically for practicing realistic data cleaning scenarios.
• Categorized under beginner-friendly tags on Kaggle for accessibility.

Limitations

• Unknown row count prevents assessment of statistical significance.
• Lack of documented column structure and data dictionary complicates initial analysis.
• Unknown temporal coverage limits time-series or trend-based applications.

Provenance

Source

Kaggle

Collection Method

Simulated or anonymized clinic appointment records, created for educational purposes.