Data
diamonds

diamonds

active ARFF public domain Visibility: public Uploaded 18-06-2022 by Leo Grin
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  • Computer Systems Machine Learning
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on categorical and numerical features" benchmark. Original description: This classic dataset contains the prices and other attributes of almost 54,000 diamonds. It's a great dataset for beginners learning to work with data analysis and visualization. Content price price in US dollars (\$326--\$18,823) carat weight of the diamond (0.2--5.01) cut quality of the cut (Fair, Good, Very Good, Premium, Ideal) color diamond colour, from J (worst) to D (best) clarity a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best)) x length in mm (0--10.74) y width in mm (0--58.9) z depth in mm (0--31.8) depth total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79) table width of top of diamond relative to widest point (43--95)

10 features

price (target)numeric11602 unique values
0 missing
caratnumeric273 unique values
0 missing
cutnominal5 unique values
0 missing
colornominal7 unique values
0 missing
claritynominal8 unique values
0 missing
depthnumeric184 unique values
0 missing
tablenumeric127 unique values
0 missing
xnumeric554 unique values
0 missing
ynumeric552 unique values
0 missing
znumeric375 unique values
0 missing

19 properties

53940
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
7
Number of numeric attributes.
3
Number of nominal attributes.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.99
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
70
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
30
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.

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