I am pretty new to machine learning and data analysis in general. I have been learning about different algorithms as part of my course. Now, I am stuck with a particular problem. I have been given a dataset which has 52 variables (columns) and 500 observations (rows). The task is to classify the data into normal, overload and faulty operating conditions but there and no labels or classes in my dataset. My question is, are the given variables same as features of my dataset or do I have to do feature extraction and how to add classes to unlabelled datasets?
I have working regarding faulty conditions as well.
In the case you don't have the conditions as a class, you need to perform a clustering to detect which shares the same conditions and then name them based on your experience of where conditions are the normal ones, etc. But I would say you have too few data to do that.
On the other hand, you can manually categorize the conditions of each observation based on some rules (f.i. if the response is temperature, temperature of normal condition must be in the range (-). And then you can perform a MLRegression with the rest of your columns to predict your response and determine the conditions based on your rules.
Let me know if you need anything else I will try to help you!
The given variables are the features, aka independent variables or covariates.
Generally, one of the variables should be the class, aka dependent variable or response variable. You might double check to make sure one of them isn't the class.
If you do actually have to create your own classes, it's a pretty difficult problem. It may be that there are clusters, so you could use a clustering algorithm (unsupervised learning) to see which classes there are and then label according to clusters and train a classifier to differentiate those clusters. This would however require that you be able to discern which cluster is which class, which might not be easy.