This is a spam messages filter is based on naive Bayes Classifier.

SpamFilter.py

spamMessage = {'million': 156, 'dollars': 29, 'adclick': 51, 'conferences': 2}
hamMessage = {'million': 98, 'dollars': 119, 'adclick': 1, 'conferences': 12}

spamWordCount = 95791
hamWordCount = 306438

probabilitySpam = 0.00
probabilityHam = 0.00

def likelihood(word):

    probabilitySpam = spamMessage[word] / spamWordCount

    probabilityHam = hamMessage[word] / hamWordCount

    likelihoodRatio = probabilitySpam / probabilityHam

    return likelihoodRatio


baseRatio = 1

def checkSpam(message):

    message = message.replace('.', '')

    for word in message.split():

        if word in spamMessage:

            global baseRatio
            baseRatio = likelihood(word) * baseRatio

    if baseRatio < 1: #Condition for a ham message

        print('The given message is not spam') 

    elif baseRatio >= 1: #Condition for a spam message

        print('The given message is spam')

    # Since we are calculating probabilities and likelihood with respect to 'spam' messages we 
    # consider a fraction yielding greater than one as a spam message



checkSpam('million dollars question for you.')
print(baseRatio)