A diamond distributor has recently decided to exit the market and has put up a set of 3,000 diamonds up for auction. Seeing this as a great opportunity to expand its inventory, a jewelry company has shown interest in making a bid. To decide how much to bid, you will use a large database of diamond prices to build a model to predict the price of a diamond based on its attributes. Then you will use the results of that model to make a recommendation for how much the company should bid.
The diamond price that the model predicts represents the final retail price the consumer will pay. The company generally purchases diamonds from distributors at 70% of the that price, so your recommended bid price should represent that.
We will be predicting prices for the gemstone.csv dataset.
Carat represents the weight of the diamond, and is a numerical variable. Cut represents the quality of the cut of the diamond, and falls into 5 categories: fair, good, very good, ideal, and premium. In project zero, these categories were represented by an ordinal variable, 1-5. You can decide to use the ordinal or categorical variable. Clarity represents the internal purity of the diamond, and falls into 8 categories: I1, SI2, SI1, VS2, VS1, VVS2, VVS1, and IF (in order from least to most pure). In project zero, these categories were represented by an ordinal variable, 1-8. You can decide to use the ordinal or categorical variable. Color represents the color of the diamond, and is rated D through J, with D being the most colorless (and valuable) and J being the most yellow.
