Your assignment consists of finding the price of a diamond given its properties. You will use the diamonds data set in ggplot2 package (which is inside tidyverse). You need to do your exploratory analysis well and come up with a predictive model. Your performance depends on the difference between the actual price of the diamond and the predicted price by the model. Use the price column as the response variable and other columns (except diamond_id) as predictors.
You are recommended to use CART but welcome to use any advanced method you like. Add your exploratory analysis to form a basis of your model and include references (with links) if you are inspired from similar analysis. Use the following code (and random seed) to form your train and test data. Remember, you should train your model on the train data and your real performance depends on the test data.
set.seed(503)
library(tidyverse)
diamonds_test <- diamonds %>% mutate(diamond_id = row_number()) %>%
group_by(cut, color, clarity) %>% sample_frac(0.2) %>% ungroup()
diamonds_train <- anti_join(diamonds %>% mutate(diamond_id = row_number()),
diamonds_test, by = "diamond_id")
diamonds_train## # A tibble: 43,143 x 11
## 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 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 6 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 7 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 8 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 9 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## 10 0.30 Good J SI1 64.0 55 339 4.25 4.28 2.73
## # ... with 43,133 more rows, and 1 more variables: diamond_id <int>diamonds_test## # A tibble: 10,797 x 11
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 3.40 Fair D I1 66.8 52 15964 9.42 9.34 6.27
## 2 0.90 Fair D SI2 64.7 59 3205 6.09 5.99 3.91
## 3 0.95 Fair D SI2 64.4 60 3384 6.06 6.02 3.89
## 4 1.00 Fair D SI2 65.2 56 3634 6.27 6.21 4.07
## 5 0.70 Fair D SI2 58.1 60 2358 5.79 5.82 3.37
## 6 1.04 Fair D SI2 64.9 56 4398 6.39 6.34 4.13
## 7 0.70 Fair D SI2 65.6 55 2167 5.59 5.50 3.64
## 8 1.03 Fair D SI2 66.4 56 3743 6.31 6.19 4.15
## 9 1.10 Fair D SI2 64.6 54 4725 6.56 6.49 4.22
## 10 2.01 Fair D SI2 59.4 66 15627 8.20 8.17 4.86
## # ... with 10,787 more rows, and 1 more variables: diamond_id <int>