Diamond Prediction Project Presentation

The Developled shiny app allows the user to enter the diamond in question properites and it then predicts the price of the diamond, the app uses a machine learning model based on glm cross-validated on the diamond dataset obtained from gglplot2

library(caret)
library(ggplot2)
data(diamonds)

model_glm <- train(price~carat+cut+color+clarity,
                  method="glm",
                  data=diamonds,
                  trControl = trainControl(method = "cv", number = 5, verboseIter = TRUE))

+ Fold1: parameter=none 
- Fold1: parameter=none 
+ Fold2: parameter=none 
- Fold2: parameter=none 
+ Fold3: parameter=none 
- Fold3: parameter=none 
+ Fold4: parameter=none 
- Fold4: parameter=none 
+ Fold5: parameter=none 
- Fold5: parameter=none 
Aggregating results
Fitting final model on full training set

Generalized Linear Model 

53940 samples
    4 predictor

No pre-processing
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 43152, 43152, 43152, 43152, 43152 
Resampling results:

  RMSE      Rsquared
  1157.351  0.915851