Diamonds Dataset¶

This is the classic Diamonds dataset.

Data Description¶

This dataset contains the prices and other attributes of almost 54,000 diamonds. The variables are as follows:

Format

A data frame with 53940 rows and 12 variables:

carat: Weight of the diamond (0.2–5.01).
cut: Quality of the cut (Fair, Good, Very Good, Premium, Ideal).
color: Diamond color, from D (best) to J (worst).
clarity: A measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best)).
depth: Width of top of diamond relative to widest point (43–95).
table: Total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43–79).
price: Price in US dollars ($326–$18,823).
x: Length in mm (0–10.74).
y: Width in mm (0–58.9).
z: Depth in mm (0–31.8).

Original Diamonds Dataset¶

The original Diamonds dataset was built for R, but has since found a home in Python. Seaborn now stores the dataset as a built in to its package.

Loading the Data¶

import seaborn as sns
sns.get_dataset_names()

sns.load_dataset('diamonds')

or

diamonds = "https://raw.githubusercontent.com/kyle-w-brown/diamonds-prediction/main/data/diamonds.csv"
df_diamonds = pd.read_csv(diamonds)
df_diamonds.head()

	carat	cut	color	clarity	depth	table	price	x	y	z
0	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

Updated Data¶

Column volume, cut_rk, color_rk, and clarity_rk were created for ordinal encoding and model features.

volume: The amount of space the diamond occupies (x x y x z).
cut_rk: The cut rank was ordinal encoded of cut 1-5 from best (Ideal) to worst (Fair) diamonds.
color_rk: The color rank was ordinal encoded of color 1-7 from best (D) to worst (J) diamonds
clarity_rk: The clarity rank was ordinal encoded of clarity 1-8 from best (I1) to worst (IF) diamonds.

Loading Updated Data¶

diamonds = "https://raw.githubusercontent.com/kyle-w-brown/diamonds-prediction/main/data/diamonds-new.csv"
df_diamonds = pd.read_csv(diamonds)
df_diamonds.head()

	carat	cut	color	clarity	depth	table	price	x	y	z	volume	cut_rk	color_rk	clarity_rk
0	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43	38.202	1	2	7
1	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31	34.506	2	2	6
2	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31	38.077	4	2	4
3	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63	46.725	2	6	5
4	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75	51.917	4	7	7

Diamond Price Prediction

Diamonds Dataset