This presentation serves as the second part of the the final assignment in the Coursera Developing Data Products Course. This is the pitch for the application that I created using the shiny package in R. The goal of the final project was:

1. Write code for a Shiny application and deploy it to Rstudio’s shiny server.
2. See app at https://h-town1906.shinyapps.io/Diamond_Price_Finder/.
3. Share server.R and ui.R code on github.
4. Code can be viewed at https://github.com/murrayha26/Developing-Data-Products.
5. Create a 5-page html presentation to pitch my Shiny app and post it to github or Rpubs. Code for presentation is in the link

We can all agree that buying a diamond is an important decision. Diamonds are rare, expensive, and they make a statement.

The diamond you purchase says as much about YOU, the buyer, as it does the person or occasion it was purchased for.

Whether it’s proposing to that special someone, upgrading a fine piece of jewelry, or making an investment, you’re about to make a big decision about something most of us have very little experience in. YIKES!

When making a big money decision wouldn’t it be great to be able to rely on someone other than a salesperson working for a commission? Wouldn’t it be great if you had access to reliable information to help you make an informed decision?

Thanks to the DIAMOND PRICE FINDER now you do!

It’s an easy to use app that allows the user to quickly find the estimated price of a diamond. How you ask? It’s as easy as knowing your 4 C’s. The user simply:

1. Uses our simple slider widget to enter the Carat weight.
2. Selects the Cut via the drop-down box. (Cut goes from Fair to Ideal.)
3. Selects the Color. (Range from D (Colorless) to J (Slight tint)
4. Selects the Clarity (Clarity varies from SI2 - Slightly Included 2 to VVS1 - Very Very Slightly Included 1)

With these inputs the Diamond Price Finder instantly gives you a reliable estimated price.

Well we can’t give you all of our secrets can we? We can tell you that the prices generated are based on data science! Our experts have researched the prices of over fifty thousand diamonds of various sizes and qualities and applied a unique statistical model to derive a reliable estimated price.

## tibble [53,940 × 10] (S3: tbl_df/tbl/data.frame)
##  $ carat  : num [1:53940] 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 ...
##  $ depth  : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...