ML how to..

How to approach an ML problem.. here is what I think


1. Take a look at the data and decide what do you want to do like, what to predict - Explore the data

2. Decide what type of ML problem is it - unsupervised / supervised (classification / regression)

3. Clean up the data .. remove unwanted data, noise, decide on features and create features, normalize feature values - data cleanup and feature engineering

4. Create data sets - training/validation/test data sets

5. Train the model - supply train and validation sets, choose the model parameters, loss function, number of iterations etc., - Create and train a model

6. evaluate the error on training set and validation set. Tune the parameters and re-train until the error is close on both training and validation sets - model validation and tuning

7. Check the model prediction on the test data set which is the data set that the model was not aware of the output label value in the above phases - final check

8. all good ? release to live data. If not, repeat above by revisiting input features and model and model parameters to get a better fit. - going live