Shiny Application and Reproducible Pitch

This app was built for the course Developing Data Products as part of the Coursera Data Science Specialization. It predicts the price in $ for a diamond based on a few attributes. You can see it by clicking the first link below and view the source code by clicking the second link.

• ShinyApp
  Click to view the ShinyApp
• GitHub
  Click to view the Source Code on GitHub

# A tibble: 6 × 10
  carat       cut color clarity depth table price     x     y     z
  <dbl>     <ord> <ord>   <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1  0.23     Ideal     E     SI2  61.5    55   326  3.95  3.98  2.43
2  0.21   Premium     E     SI1  59.8    61   326  3.89  3.84  2.31
3  0.23      Good     E     VS1  56.9    65   327  4.05  4.07  2.31
4  0.29   Premium     I     VS2  62.4    58   334  4.20  4.23  2.63
5  0.31      Good     J     SI2  63.3    58   335  4.34  4.35  2.75
6  0.24 Very Good     J    VVS2  62.8    57   336  3.94  3.96  2.48

The linear regression model behind the app:
/It is not fancy but it does the job for this assignment/
lm(price ~ carat + cut + clarity + color + x + y + z, data = diamonds)
Residual standard error: 1132 on 53918 degrees of freedom
Multiple R-squared: 0.9195
Adjusted R-squared: 0.9194
F-statistic: 2.931e+04 on 21 and 53918 DF
p-value: < 2.2e-16