language classification

dt_clf = DecisionTreeClassifier() # Create Decision Tree classifer object
dt_clf = dt_clf.fit(X_train,y_train) # Fit/Train Decision Tree Classifer on training set

# Save model to file in the current working directory so that it can be imported and used.
# I use the pickle library to save the parameters of the trained model
pkl_file = "decision_tree_model.pkl"
with open(pkl_file, 'wb') as file:
    pickle.dump(dt_clf, file)

# Load previously trained model from pickle file
with open(pkl_file, 'rb') as file:
    dt_clf = pickle.load(file)

dt_clf # parameters of the Decision Tree model are shown below and can be further optimized to improve model performance

y_pred = dt_clf.predict(X_test) #Predict the response for test dataset