I just trained my first model in Python 3.7/scikitlearn (Linear Regression) (well I copied most of the code but its something ^^).

Now I want to actually Use the model. Specifically its about sons heights incorrelating to their fathers. So I now want to enter a new Father-height and get a predictions for its sons height.

How could something like this look like?

I read about "Pickle" to save a model and use it later, seems awsome but how would I use such a saved model?

If anybody can give me a simple answer or even just a link to atutorial would be great. Below is a piece of "my" code just for some context.

#Spliting the data into test and train data

#Doing a linear regression

#Predicting the height of Sons

1 Answer 1


You have your model saved as the variable lm. You can use the lm.predict(X_test) for any other test scenario. Note that your X_test should be similar to your X_train meaning if you have transformations made on your X_train, you need to do similar transformation on X_test too. You can use pickle in the following way

import pickle
filename = 'model.pckl'
pickle.dump(model, open(filename, 'wb'))

#To load the model from disk, use this
model = pickle.load(open(filename, 'rb'))
prediction = model.predict(X_test)
  • $\begingroup$ Awsome, Thanks. Quiet easy actually ^^. I cant abvote yet but I "accepted" your anwer. (The link is perfect, very easy to understand) I assume that Pickle is the standard next to anybody uses for storing a model? just to get started with the right tools ^^ $\endgroup$
    – G.M
    Sep 6, 2019 at 8:05
  • $\begingroup$ Yes. One other way to go is to dump the model into a json and also dump the trained weights . While loading the model, you need to then define the model architecture and also load the weights after compiling. You can find lot of links explaining it. $\endgroup$ Sep 13, 2019 at 7:27
  • $\begingroup$ machinelearningmastery.com/save-load-keras-deep-learning-models Have a look here for the json way of saving the model $\endgroup$ Sep 13, 2019 at 7:28

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