This recipe assumes you've run the previous three recipes, and have three trained models.

To begin, we import the pickle library. Pickle is a part of the standard Python libraries.

import pickle

Next, we create a variable to hold the full path to the pickle file of the logistic regression model, and specify the name of the file as the end of the string. Note that the file extension is .dat. We use the pickle.dump() method to create the file. The arguments we pass to it are as follows:

The second line of code allows us to open the previously specified file for binary writing (the "wb" argument) in one line, and then dump the contents of the logClassifier into it.

Next, we do the same for the random forest model:

hearts_classifier_file = "/Users/robertdempsey/Dropbox/private/Python Business Intelligence Cookbook/Models/hearts_rf_classifier_09.27.15.dat"
pickle.dump(rfClassifier, open(hearts_classifier_file, "wb"))

After that, we pickle the SVM model:

hearts_classifier_file = "/Users/robertdempsey/Dropbox/private/Python Business Intelligence Cookbook/Models/hearts_svm_classifier_09.27.15.dat"
pickle.dump(svmClassifier, open(hearts_classifier_file, "wb"))

Did it work? Reconstitute the logistic regression model as a test, and print out the contents of the variable:

model_file = "/Users/robertdempsey/Dropbox/private/Python Business Intelligence Cookbook/Models/hearts_lr_classifier_09.27.15.dat"
logClassifier2 = pickle.load(open(model_file, "rb"))
print(logClassifier2)

The model looks the same as the model we previously trained. Success!!