Udacity Machine Learning mini-project 1

Prep stuff:



In [1]:

    
from sklearn.naive_bayes import GaussianNB
import sys
from time import time
sys.path.append("../tools/")
from email_preprocess import preprocess

Training and Testing data:



In [2]:

    
features_train, features_test, labels_train, labels_test = preprocess()









    



no. of Chris training emails: 7936
no. of Sara training emails: 7884

Fitting the model



In [3]:

    
clf = GaussianNB()
clf.fit(features_train,labels_train)









    Out[3]:





GaussianNB()

Getting Model accuracy



In [4]:

    
clf.score(features_test,labels_test)









    Out[4]:





0.97383390216154719

Timing model fit and prediction



In [5]:

    
%timeit clf.fit(features_train,labels_train)









    



1 loops, best of 3: 418 ms per loop



In [6]:

    
%timeit pred = clf.predict(features_test)









    



10 loops, best of 3: 84.9 ms per loop

Not shockingly, making predictions is a lot faster.