Summary: A dataset of steel plates faults, classified into 7 different types. The goal was to train machine learning for automatic pattern recognition.
| Parameter | Value |
|---|---|
| Name | Steel Plates Faults |
| Labeled | Yes |
| Time Series | No |
| Simulation | No |
| Missing Values | No |
| Dataset Characteristics | Multivariate |
| Feature Type | Integer, Real |
| Associated Tasks | Classification |
| Number of Instances | 1941 |
| Number of Features | 27 |
| Date Donated | 2010-10-25 |
| Source | UCI Machine Learning Repository |
The dataset consists of 1941 instances with 27 features representing characteristics of steel plates. The faults are classified into seven types: Pastry, Z_Scratch, K_Scratch, Stains, Dirtiness, Bumps, and Other_Faults.
Steel plates, Fault detection, Manufacturing, Pattern recognition, Classification tasks