I have a dataset of 21 features where each numeric feature has various scale something like the following:
budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
237000000 2009 936 7.9 1.78 33000
300000000 2007 5000 7.1 2.35 0
245000000 2015 393 6.8 2.35 85000
250000000 2012 23000 8.5 2.35 164000
263700000 2012 632 6.6 2.35 24000
258000000 2007 11000 6.2 2.35 0
260000000 2010 553 7.8 1.85 29000
250000000 2015 21000 7.5 2.35 118000
250000000 2009 11000 7.5 2.35 10000
250000000 2016 4000 6.9 2.35 197000
209000000 2006 10000 6.1 2.35 0
200000000 2008 412 6.7 2.35 0
I applied several classifiers such as SVM, KNN, Logistic Regression, and Random Forests. However, I am getting accuracy around 60s even after adjusting the parameters of each classifier. Thus, I decided to work more with the dataset and make further preprocessing (specifically, normalization and feature selection). Kindly, I have two questions:
- Which one is the correct way and why: Performing feature selection after or before normalization?
- I understand PCA is a dimensionality reduction but I don't understand what is the difference between PCA and feature selection as both will eventually rank the features for you.
Thank you very much.