I have a set of independent variables and I am calculating the correlation matrix between them using the Pearson Correlation Coefficient in Python. A part of the matrix looks like this:
From this matrix, suppose I want to find out the strongly correlated components between the variable NoOfDoors and the rest(Symboling...Compression Ratio). The process I have adopted is that I have taken the mean of that column(which is calculated as 0.039604) and based on that, I have only considered those values greater than 0.039604.
Based on that, the following variables have been selected as strongly correlated:
(Make, Aspiration, Wheel Base, Length, Width, Height, Curb Weight, Engine Type, Bore, Compression Ratio)
I want to ask, is this selection correct? If yes then is there an efficient way to do this? And if no, what is the correct way? Since I am new to this field, a well explained article would be appreciated. Thanks!