My data sets have 200 features and 500 rows. from that I must select the best 30 features that can be used in the model instead of all 200 features for the sales prediction model, but the feature importance can change over time. Interesting thing is data sets don't have target variable.
How to select the best features from all the features when data sets don't have target variable to identify where prediction model's feature importance can change over time?
If the feature importance can change over time, how do I select the best features?
Ps: - I tried using Pearson Correlation Matrix, But I want to select K best features for the model training. And I tried to use Chi-2 test to select the best features but ended up with errors since target variable couldn't provide.
Really Depends. If I understand you correctly you want to train a new model every x time steps where you select new features ?
This wouldn't really make sense in the long run because you will run int the problem that every trained model with different features will result in hugely different results.
Also I don't understand the part of not having a target variable. If you don't have a target to predict what exactly are you trying to achieve with the ML model.
I would advise you to take a step back and really think about what you want to achieve and how you want to achieve it