The goal of the project is to predict the price of diamonds by establishing a linear regression model. For the model establishing and testing, the dataset "diamonds" which has been built in R will be utilized.
The data frame with 53940 rows and 10 variables:
- 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
- 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)

"carat" is selected as the only variable to run the regression
Then the model is tested by test dataset.