import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier

#import xgboost as xgb

import warnings
warnings.filterwarnings('ignore')

#import os
#os.listdir('../input')



scaler = StandardScaler()
scaled_df = scaler.fit_transform(brainwave_df)
pca = PCA(n_components = 20)
pca_vectors = pca.fit_transform(scaled_df)
for index, var in enumerate(pca.explained_variance_ratio_):
    print("Explained Variance ratio by Principal Component ", (index+1), " : ", var)
