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I have a data set that looks like the following:

 Time        V1     V2    V3     ...    V40
13:00        0.44   0     0.33          0.55
13:01        0.55   0     0.34          0.52
13:02        0.58   1     0.20          0.58
.
.
.
15:01        0.57   0     0.24          0.70

Where V2 is the binary equivalent of on/off switches. Currently, I am still pre-processing my data and normalized the data-set from (0,1) using sklearn.preprocessing. I am wondering if applying dimensionality reduction/PCA to my dataset will affect the outcome of my model and whether if it is advisable to use it to process my data.

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If you have multi dimensional data, it's very hard to visualize. PCA helps us in reducing the dimension of the data set by keeping maximum co-variance in top K Features. So PCA will help us in presenting the data in most of the cases.

So if you perform PCA, you will definitely lose some data and creating a model with less data, will definitely affect the model outcome.

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