Principal component analysis 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.
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.