Chemical Composition of Ceramic Samples
Donated on 1/28/2019
Classify ceramic samples based on their chemical composition from energy dispersive X-ray fluorescence
Dataset Characteristics
Multivariate
Subject Area
Physics and Chemistry
Associated Tasks
Classification, Clustering
Feature Type
Real
# Instances
88
# Features
19
Dataset Information
Additional Information
The energy dispersive X-ray fluorescence (EDXRF) was used to determine the chemical composition of celadon body and glaze in Longquan kiln (at Dayao County) and Jingdezhen kiln. Forty typical shards in four cultural eras were selected to investigate the raw materials and firing technology. We hope to identify chemical elements that are strongest explanatory variables to classify samples into different cultural eras and kilns.
Has Missing Values?
No
Variable Information
Ceramic.Name: name of ceramic types from Longquan and Jindgezhen Part: a binary categorical variable ('Body' or 'Glaze') Na2O: percentage of Na2O (wt%) MgO: percentage of MgO (wt%) Al2O3: percentage of AI2O3 (wt%) SiO2: percentage of SiO2 (wt%) K2O: percentage of K2O (wt%) CaO: percentage of CaO (wt%) TiO2: percentage of TiO2 (wt%) Fe2O3: percentage of Fe2O3 (wt%) MnO: percentage of MnO (ppm) CuO: percentage of CuO (ppm) ZnO: percentage of ZnO (ppm) PbO2: percentage of PbO2 (ppm) Rb2O: percentage of Rb2O (ppm) SrO: percentage of SrO (ppm) Y2O3: percentage of Y2O3 (ppm) ZrO2: percentage of ZrO2 (ppm) P2O5: percentage of P2O5 (ppm)
Dataset Files
| File | Size |
|---|---|
| Chemical Composion of Ceramic.csv | 8.5 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset chemical_composition_of_ceramic_samples = fetch_ucirepo(id=583) # data (as pandas dataframes) X = chemical_composition_of_ceramic_samples.data.features y = chemical_composition_of_ceramic_samples.data.targets # metadata print(chemical_composition_of_ceramic_samples.metadata) # variable information print(chemical_composition_of_ceramic_samples.variables)
Chemical Composition of Ceramic Samples [Dataset]. (2019). UCI Machine Learning Repository. https://doi.org/10.24432/C54P5X.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.