Independent Component Analysis(ICA) is an unsupervised ML algorithm which is used to find independent groups of columns in data. More information about this algorithm can be found here.
You can learn more about ICA in the below video.
The below code helps to run ICA for any data in python.
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 |
# Sample code to do ICA in Python %matplotlib inline from sklearn.decomposition import FastICA import matplotlib.pyplot as plt # Creating the employee attitude survey data for ICA rating=[43,63,71,61,81,43,58,71,72,67,64,67,69,68,77,81,74,65,65,50,50,64,53,40,63,66,78,48,85,82] complaints=[51,64,70,63,78,55,67,75,82,61,53,60,62,83,77,90,85,60,70,58,40,61,66,37,54,77,75,57,85,82] privileges=[30,51,68,45,56,49,42,50,72,45,53,47,57,83,54,50,64,65,46,68,33,52,52,42,42,66,58,44,71,39] learning=[39,54,69,47,66,44,56,55,67,47,58,39,42,45,72,72,69,75,57,54,34,62,50,58,48,63,74,45,71,59] raises=[61,63,76,54,71,54,66,70,71,62,58,59,55,59,79,60,79,55,75,64,43,66,63,50,66,88,80,51,77,64] critical=[92,73,86,84,83,49,68,66,83,80,67,74,63,77,77,54,79,80,85,78,64,80,80,57,75,76,78,83,74,78] advance=[45,47,48,35,47,34,35,41,31,41,34,41,25,35,46,36,63,60,46,52,33,41,37,49,33,72,49,38,55,39] #Joining all the vectors together to form input matrix X SurveyData=list(zip(rating,complaints,privileges,learning,raises,critical,advance)) import pandas as pd InpData=pd.DataFrame(data=SurveyData, columns=["rating","complaints","privileges","learning","raises","critical","advance"]) print(InpData.head(10)) #Creating input data numpy array X=InpData.values ###################################################################### # Creating ICA object ICA = FastICA(n_components=2) IndependentComponentValues=ICA.fit_transform(X) #Creating the dataframe print('####### Final Independent Components ######') ReducedData=pd.DataFrame(data=IndependentComponentValues, columns=['IC1','IC2']) print(ReducedData.head(10)) |
Sample Output:
Author Details
Lead Data Scientist
Farukh is an innovator in solving industry problems using Artificial intelligence. His expertise is backed with 10 years of industry experience. Being a senior data scientist he is responsible for designing the AI/ML solution to provide maximum gains for the clients. As a thought leader, his focus is on solving the key business problems of the CPG Industry. He has worked across different domains like Telecom, Insurance, and Logistics. He has worked with global tech leaders including Infosys, IBM, and Persistent systems. His passion to teach inspired him to create this website!
It’s an easy example to understand and the code looks neat.
But can you bit explain the meaning of final independent components IC1 and IC2 ?
What do they suggest ? What does each row say ?
The independent components IC1 and IC2 are representing those 7 original columns.
When you do such compression, the individual components row values don’t have a business meaning which can be explained. While the original columns, each one of them has a individual meaning.
Would be nice to project these on 2D or 3D (for n_components=3) axis, as a biplot. Is there a way? Thanks
Also, how do you obtain the scores?