In this case study, we will explore the diamonds dataset, then build linear and non-linear regression models to predict the price of diamonds.
The diamonds dataset contains the prices in 2008 USD terms, and other attributes of almost 54,000 diamonds.
price in 2008 USD
weight of a diamond (1 carat = 0.2 gms)
quality of the cut (Fair, Good, Very Good, Premium, Ideal)
diamond color from D (best) to J (worst)
a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))
length in mm
width in mm
depth in mm
total depth percentage = z/mean(x, y)
width of the top of diamond relative to widest point
A preliminary visual summary of the whole dataset shows all the features and their types.