Aakarsh Sharan
November 4, 2022
This presentation contains documentation for the Diamond price prediction application. The application can be found HERE (https://sharanaki.shinyapps.io/diam/)
This application it is building linear regression model using
diamonds data set and is predicting the price of a diamond
depending of its properties.The application allows the user to
select:
Builds a plot and gives predicted price of the diamond.
The data used for this application is diamonds data set,
which is part of ggplot2 package. This data set contains
the information about 53940 diamonds with 10 variables:
## Warning: package 'ggplot2' was built under R version 4.1.3## carat cut color clarity depth
## Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065 Min. :43.00
## 1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258 1st Qu.:61.00
## Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194 Median :61.80
## Mean :0.7979 Premium :13791 G:11292 VS1 : 8171 Mean :61.75
## 3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066 3rd Qu.:62.50
## Max. :5.0100 I: 5422 VVS1 : 3655 Max. :79.00
## J: 2808 (Other): 2531
## table price x y
## Min. :43.00 Min. : 326 Min. : 0.000 Min. : 0.000
## 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710 1st Qu.: 4.720
## Median :57.00 Median : 2401 Median : 5.700 Median : 5.710
## Mean :57.46 Mean : 3933 Mean : 5.731 Mean : 5.735
## 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540 3rd Qu.: 6.540
## Max. :95.00 Max. :18823 Max. :10.740 Max. :58.900
##
## z
## Min. : 0.000
## 1st Qu.: 2.910
## Median : 3.530
## Mean : 3.539
## 3rd Qu.: 4.040
## Max. :31.800
## The application is build using Shiny package and the source code is
in 2 files: - ui.R - server.R
Both files can be found here: [ GitHub repo ] (https://github.com/SharanAki/Predicting-Diamond-Price)
The application is drawing plot of diamonds in the
diamonds data set distributed by their size (carat) and
price ($). The regression line is shown on the plot as well.
By selecting specific features of the diamond (carat, cut, clarity, color) the user is able to sub select the data set and the regression is recalculated based only on the diamonds in the data set that share the same features. If no features are selected the regression model is using all diamonds in the data set.