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I am at the feature selection phase of my project but I have my vectorised data. Is there a way to find highly correlated features and then remove them? After this I would then like to remove features that are not greatly important.

Im using tweets for my project and have done various pre-processing techniques. I lemmatised my data and then vectorised and store the vectorised data under 'X'. I now have to find the highly correlated features within X and then remove them.

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    $\begingroup$ Provide an example … $\endgroup$
    – n1tk
    Aug 12 at 19:20
  • $\begingroup$ I think I literally just answered this question in a different topic datascience.stackexchange.com/a/100079/74387. Does it help? The point is that some features could be correlated with several others, and then pairwise correlation could be low despite all the features being strongly correlated. $\endgroup$
    – Cryo
    Aug 14 at 23:03
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Make a dataframe of your feature where each row is a sample and each columns is a variable. Then you can use the following line of code to plot the correlation heatmap. It is $2*2$ matrix where entry $(i,j)$ indicates the correlation value of $i$ and $j$ feature.

import seaborn as sns
import pandas as pd

corr = df.corr()  #df is your feature vector converted to dataframe
sns.heatmap(corr)
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