Question

Apply PCA ( Principal Component Analysis ) in python to this data set below  that is a csv file

Then plot it with different colors. Thank you I will UPVOTE!

target	A	B	C	D	E	F	G
surprise	2	3	1	1	19	12	0
sad	2	0	0	2	12	1	15
angry	95	2	1	0	1	0	1
sad	4	56	2	0	0	3	1
neutral	1	2	2	0	39	0	11
happy	0	0	0	34	1	0	0
neutral	5	55	0	0	0	2	1
sad	0	33	3	0	0	12	1
happy	0	5	2	0	18	15	2
angry	0	0	0	19	37	0	0
happy	0	1	0	68	17	2	0

Answer 1

Find the answer below

NOTE:- I have done the computation for 3 Principal components.

#Importing modules
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

# Importing data
data = pd.read_csv('data.csv',sep='\t')
data.head()

#converting data into array
data_arr = data.to_numpy()

#removing the targets
final_data = np.delete(data_arr,0,1)

#Computing the 3 principal components
pca = PCA(n_components=3)
principalComponents = pca.fit_transform(final_data)

#setting up 3 components on the dataframe
principalDf = pd.DataFrame(data=(principalComponents), columns=['PC1', 'PC2','PC3'])

#setting up for plotting with targets
rarr = principalDf['PC1']
carr = principalDf['PC2']
tarr = principalDf['PC3']
target = data_arr[:,0]

#Plotting the principal components
plt.scatter(target,carr)
plt.scatter(target,rarr)
plt.scatter(target,tarr)
plt.legend("123")

Outputs:

Code Snippet attached below

NOTE- If you want to compute a different number of components do change in the In [5] with n_components = '<you want>'

Thanks