Developing Data Products - Week 4 Course Project

Have you ever wondered how diamonds are priced beyond the number of carats(or weight)? Price also depends on the diamond's cut, color, and clarity.

• Cut: quality of the diamond from best to worst (Ideal, Premium, Very Good, Good, Fair)
• Color: best(D) to worst(J)
• Clarity: measures how clear the diamond is from best to worst (IF, VVS2, VVS1, VS2, VS1, SI2, SI1, I1)

This shiny app will predict diamond price based on your input of these factors

Here is a summary of the “diamonds” dataset found in the ggplot2 R package. As you can see, the factors were measured in over 50,000 diamonds

Classes 'tbl_df', 'tbl' and 'data.frame':   53940 obs. of  5 variables:
 $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
 $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
 $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
 $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
 $ price  : int  326 326 327 334 335 336 336 337 337 338 ...

• shiny app: https://majusus-datascience.shinyapps.io/Coursera_Shiny_App/
• source code: https://github.com/majusus/datasciencecoursera/tree/master/Course%209/Coursera_Shiny_App