# The difference between Feature selection and PCA to improve accuracy

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:

1. Which one is the correct way and why: Performing feature selection after or before normalization?
2. 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.