Results:

This project received a score of 16.5, delivering robust and interpretable models.

Prediction of Diamond Price

Dataset Description: Diamonds

This classic dataset contains prices and other attributes for nearly 54,000 diamonds. It includes 10 features, including the target variable: price.

Feature Description

Price:

Price in US dollars. This target column contains the labels corresponding to the diamond’s characteristics.

The 4Cs of Diamonds:

Carat:

Carat is the physical weight of the diamond measured in metric carats. One carat equals 1/5 of a gram and is subdivided into 100 points. Carat weight is the most objective criterion among the 4Cs.

Cut:

The quality of the cut reflects the skill of the diamond cutter in shaping the stone. The more precise the cut, the more captivating the diamond appears to the eye.

Color:

Rated from J (worst) to D (best). Gem-quality diamond colors range from colorless to light yellow or light brown. Colorless diamonds are the rarest. Other natural colors (blue, red, pink, etc.) are called “fancy” and have a different classification system than colorless white diamonds.

Clarity:

Diamonds may contain internal features called inclusions or external features called blemishes. Diamonds without inclusions or blemishes are rare, but most features can only be observed with a magnifying loupe.

Dimensions:

x: Length in millimeters

y: Width in millimeters

z: Depth in millimeters

Additional Features:

Depth:

The depth of the diamond corresponds to its height (in millimeters), measured from the culet (the bottom tip) to the table (the flat top surface).

Table:

The “table” refers to the flat facet visible when viewing the diamond from above. It refracts incoming light rays and allows internally reflected light rays to reach the observer’s eye. An ideal table gives the diamond dazzling brilliance and sparkle.

This dataset provides an excellent overview of the features influencing diamond prices and can be used to build predictive models based on machine learning.