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So I'm a newbie into Data Science field. I've created a model using Gradient Boosting to predict a house price. To evaluate my model, I've splitted my dataset into train and test (0.8 Train / 0.2 Test) and got 0.94 R^2 for train and 0.89 for test. If I want to deploy this model, for business purpose, do I have to make a new model with train and test data together? If I do that, couldn't my model have a completly different result, like overfitting? Or should I use the model that I've trained from train dataset and create some kind of 'model.joblib', import to google cloud and create an API from train based model?

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Below are the steps followed.

  1. Split data to train and OOT.
  2. Model on train data.
  3. Evaluate on Test set.
  4. Pickle the model
  5. Create an API.

Use the model that you have trained from train dataset and pickle,and create an API

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