1. I troductio 2. Load data3. Pri t list of members a d methods of loaded data object4. Pri t details of loaded dataset5. Fi d u ique values of some colum s6. Cha ge the omi al values of cut i to ordi al ( umeric values)7. Cha ge omi al values of color a d clarity as well i to ordi al values8. set price as target a d create a ew dataset X by droppi g this colum from origi al dataset9. Use RobustScaler to tra sform X10. Create a dataset Y with o ly target colum 11. Create trai a d test data12. Create a dataframe to store result of differe t models13. Create KNN model14. Fit the data i to model15. Update result of model i to model matrix16. Impleme t Baggi g Model17. Ra domForest Model18. Boosti g Model19. Save Models i to a file1.I troductio ▲Dow load data from https://raw.githubuserco te t.com/tidyverse/ggplot2/master/data-raw/diamo ds.csv 1.1.Code▲ 2.Load data▲ 2.1.Code▲10:# Dow load data from https://raw.githubuserco te t.com/tidyverse/ggplot2/master/data-raw/diamo ds.csv 20:import pa [...]
Training in Machine Learning to build a web application where users can look up a predicted price for their desired diamonds. Data.