Hyundai Cars Maintenance Dataset

Published: 19 February 2024| Version 1 | DOI: 10.17632/zm45zhp8z5.1
ankita gupta


he "Hyundai Cars Maintenance Dataset" is a comprehensive dataset containing information related to maintenance aspects of Hyundai cars. It consists of 1100 entries, each representing a Hyundai car, with details on various parameters relevant to maintenance. Below is a detailed description of each variable included in the dataset: Engine Temperature (°C): This variable represents the temperature of the engine of each car, measured in degrees Celsius. Engine temperature is a critical parameter to monitor as it can indicate potential issues with the cooling system or engine performance. Brake Pad Thickness (mm): This variable indicates the thickness of the brake pads of each car, measured in millimeters. Brake pad thickness is essential for ensuring effective braking performance and safety on the road. Tire Pressure (PSI): This variable represents the pressure of the tires of each car, measured in pounds per square inch (PSI). Proper tire pressure is crucial for optimal fuel efficiency, tire longevity, and vehicle handling. Maintenance Type: This categorical variable indicates the type of maintenance performed on each car. It includes three categories: Routine Maintenance: Regular maintenance tasks such as oil changes, fluid checks, and filter replacements. Component Replacement: Replacement of worn-out or damaged components such as brake pads, tires, or belts. Repair: Repairs undertaken to fix specific issues or faults identified during inspections or diagnostics. Anomaly Indication: This binary variable indicates the presence or absence of anomalies detected during maintenance checks or sensor readings. Anomaly indication can flag potential issues or abnormalities in the car's systems, prompting further investigation or corrective action. The dataset is designed to facilitate analysis and modeling for predictive maintenance purposes. It provides a rich source of information for studying the relationship between maintenance parameters and the overall health and performance of Hyundai cars. Researchers and analysts can use this dataset to develop predictive maintenance models, identify maintenance patterns, optimize maintenance schedules, and improve the reliability and efficiency of Hyundai vehicles.



Punjabi University


Machine Learning