Created
March 31, 2020 12:27
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This is a spam messages filter is based on naive Bayes Classifier.
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| 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) | |
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