Question

I only need the best and effective advice on choosing the right data mining method for this problem. I am not sure which data model is best.  

Choose a data mining method to create the automatization. Use your model to make predictions on the testing dataset. Evaluate the precision of your model using Root Mean Square Error (rmse) and Correlation (cor) as metrics.
You are hired as a data analyst for Diamonds Inc., a company who appraises diamonds. The company would like to automate appraisals for future shipments and you are responsible for developing the method of automatization. For reference, you are given a dataset containing 48547 diamonds with the following characteristics (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 color, 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)

Answer 1