Dear Evan Gertis ,

Principal Component Analysis (PCA) is a linear dimensionality reduction technique that can be utilized for extracting information from a high-dimensional space by projecting it into a lower-dimensional sub-space. It tries to preserve the essential parts that have more variation of the data and remove the non-essential parts with fewer variation.

https://www.datacamp.com/community/tutorials/principal-component-analysis-in-python

Regards,

Shafagat

With the PCA library of sklearn.decomposition you can easily perform PCA. Then you need to create a data frame with the output. From the data frame you can easily export the dataset to excel or pdf.

Md Hasan Al Banna Thank you for getting back to me. I've implemented the decomposition method. It returns an array. How do you create the dataframe from this array?

I have implemented a method for determining the feature vector from a set of 3 attributes:

import numpy as np

from numpy import linalg as LA

A = [45,37,42,35,39]

B = [38,31,26,28,33]

C = [10,15,17,21,12]

A_norm = A/np.linalg.norm(A)

B_norm = B/np.linalg.norm(B)

C_norm = C/np.linalg.norm(C)

data = np.array([A_norm,B_norm,C_norm])

# determine covariance

covMatrix = np.cov(data,bias=True)

print (covMatrix)

# compute eigen vactors and eigenvalues

w, v = LA.eig(covMatrix)

print("eigen vectors")

print(v)

print("eigen values")

print(w)

varianceV = np.empty(3)

# calculate variances

varianceV[0] = w[0]/(w[0]+w[1]+w[2])

varianceV[1] = w[1]/(w[0]+w[1]+w[2])

varianceV[2] = w[2]/(w[0]+w[1]+w[2])

print(f' variance of v1 : {varianceV[0]}')

print(f' variance of v2 : {varianceV[1]}')

print(f' variance of v3 : {varianceV[2]}')

# calculate feature vector

v_initial = 0

featureVector = np.empty(3)

for i in range(0,3):

if varianceV[i] > v_initial:

featureVector = v[i]

print(f'feature vector: {featureVector}')

The below link has explained properly PCA with PYTHON. You can see.

1. https://www.datacamp.com/community/tutorials/principal-component-analysis-in-python

2. https://www.datacamp.com/community/tutorials/principal-component-analysis-in-python

I agree with Md Babul Islam