I've got a data set that looks like this
physical_data1 physical_data2 switch1 switch2 state 400 500 1 0 Normal 400 500 1 1 Normal 500 650 0 0 Normal 600 700 1 0 Normal 1000 300 1 1 Anomaly!
where physical_data are data that are from 0 to 1000 and switch are switches that are binary (1 means on 0 means off).
I am still relatively new to machine-learning and so I am wondering what kind of machine-learning algorithm is best suited for these kinds of data to detect anomalies, where my data has mixed features of physical and binary quantities.
Things that i have done are, normalizing to [0,1], however i am not quite sure if applying PCA to this kind of data will result in a loss of detection rate because it requires all features to determine if a particular reading is normal or an anomaly.
One other question I have is that what if in my data set i do not have the Anomaly data, instead, only have normal data. In that case what kind of model can i use?