In this project, we're going to build a spam filter for SMS messages using the multinomial Naive Bayes algorithm. Our goal is to write a program that classifies new messages with an accuracy greater than 80% — so we expect that more than 80% of the new messages will be classified correctly as spam or ham (non-spam).
To train the algorithm, we'll use a dataset of 5,572 SMS messages that are already classified by humans. The dataset was put together by Tiago A. Almeida and José María Gómez Hidalgo, and it can be downloaded from the The UCI Machine Learning Repository. The data collection process is described in more details on this page, where you can also find some of the papers authored by Tiago A. Almeida and José María Gómez Hidalgo.
We will now start by reading in the dataset
import pandas as pd
sms_spam = pd.read_csv('/Users/ememobongekpenyong/Desktop/DQ/SMSSpamCollection', sep='\t', header = None, names = ['Label', 'SMS'])
print(sms_spam.shape)
sms_spam.head()
(5572, 2)
Label | SMS | |
---|---|---|
0 | ham | Go until jurong point, crazy.. Available only ... |
1 | ham | Ok lar... Joking wif u oni... |
2 | spam | Free entry in 2 a wkly comp to win FA Cup fina... |
3 | ham | U dun say so early hor... U c already then say... |
4 | ham | Nah I don't think he goes to usf, he lives aro... |
sms_spam['Label'].value_counts(normalize=True)
ham 0.865937 spam 0.134063 Name: Label, dtype: float64
We can see that about 87% of the messages are ham, and the remaining 13% are spam. This sample looks representative, since in practice most messages that people receive are ham.
Once our spam filter is done, we'll need to test how good it is with classifying new messages.
To test the spam filter, we're first going to split our dataset into two categories:
A training set, which we'll use to "train" the computer how to classify messages. A test set, which we'll use to test how good the spam filter is with classifying new messages.
We're now going to split our dataset into a training and a test set, where the training set accounts for 80% of the data, and the test set for the remaining 20%.
# Randomize the dataset
data_randomized = sms_spam.sample(frac=1, random_state=1)
# Calculate index for split
training_test_index = round(len(data_randomized) * 0.8)
# Training/Test split
training_set = data_randomized[:training_test_index].reset_index(drop=True)
test_set = data_randomized[training_test_index:].reset_index(drop=True)
print(training_set.shape)
print(test_set.shape)
(4458, 2) (1114, 2)
We'll now analyze the percentage of spam and ham messages in the training and test sets. We expect the percentages to be close to what we have in the full dataset, where about 87% of the messages are ham, and the remaining 13% are spam.
training_set['Label'].value_counts(normalize=True)
ham 0.86541 spam 0.13459 Name: Label, dtype: float64
test_set['Label'].value_counts(normalize=True)
ham 0.868043 spam 0.131957 Name: Label, dtype: float64
The results look good! We'll now move on to cleaning the dataset.
To calculate all the probabilities required by the algorithm, we'll first need to perform a bit of data cleaning to bring the data in a format that will allow us to extract easily all the information we need.
We'll begin with removing all the punctuation and bringing every letter to lower case.
# Before cleaning
training_set.head()
Label | SMS | |
---|---|---|
0 | ham | Yep, by the pretty sculpture |
1 | ham | Yes, princess. Are you going to make me moan? |
2 | ham | Welp apparently he retired |
3 | ham | Havent. |
4 | ham | I forgot 2 ask ü all smth.. There's a card on ... |
# After cleaning
training_set['SMS'] = training_set['SMS'].str.replace('\W', ' ')
training_set['SMS'] = training_set['SMS'].str.lower()
training_set.head()
Label | SMS | |
---|---|---|
0 | ham | yep by the pretty sculpture |
1 | ham | yes princess are you going to make me moan |
2 | ham | welp apparently he retired |
3 | ham | havent |
4 | ham | i forgot 2 ask ü all smth there s a card on ... |
Let's now move to creating the vocabulary, which in this context means a list with all the unique words in our training set.
training_set['SMS'] = training_set['SMS'].str.split()
vocabulary = []
for sms in training_set['SMS']:
for word in sms:
vocabulary.append(word)
vocabulary = list(set(vocabulary))
It looks like there are 7,783 unique words in all the messages of our training set.
len(vocabulary)
7783
We're now going to use the vocabulary we just created to make the data transformation we want.
word_count_per_sms = {unique_word: [0] * len(training_set['SMS']) for unique_word in vocabulary}
for index, sms in enumerate(training_set['SMS']):
for word in sms:
word_count_per_sms[word][index] += 1
pd.set_option('display.max_columns', 500)
word_counts = pd.DataFrame(word_count_per_sms)
word_counts.head()
cann | kerala | harlem | project | chikku | hanuman | dependents | value | scammers | subpoly | asking | tree | tor | 400mins | what | ffffffffff | elaborate | attempt | shouldn | honey | 31p | cl | flash | careful | adult | 07090201529 | menu | plaza | flung | costs | newspapers | delivered | selection | skateboarding | adding | jeans | 08002988890 | 2wu | coming | amount | 26th | positive | skilgme | recovery | gifts | showers | jazz | eurodisinc | weigh | converter | 1 | diff | categories | video | sold | shocking | invention | yup | whom | havbeen | theory | regard | 09061701851 | customersqueries | windy | aeroplane | mine | haiz | afraid | predictive | ujhhhhhhh | astne | clarification | expected | atleast | ave | charged | ringtone | autocorrect | 5we | congrats | torture | drove | gives | manege | speechless | thankyou | motherfucker | toaday | videosounds | bajarangabali | compensation | simply | tkts | successfully | elama | pudunga | 07808726822 | 08002986906 | confuses | dolls | celebration | fold | wise | shipping | sura | 1450 | ctargg | argh | apologise | situations | 23f | silence | arrange | switch | la32wu | merememberin | discussed | kr | bloo | 2hook | madoke | devils | 0870753331018 | chgs | neighbour | m95 | properly | 2bold | 0845 | trivia | pending | halloween | presnts | unsub | check | finally | services | thgt | around | earliest | neighbor | wavering | cyclists | blake | reply | stories | arguing | smells | 7548 | dollars | shoot | such | loving | jokin | drum | handset | upon | priority | moms | shudvetold | position | landline | tampa | warwick | chase | m221bp | shared | off | yahoo | msn | relation | thts | regalportfolio | chatlines | capital | spose | fell | cine | 09050000460 | jacket | fonin | wake | corect | pairs | wicket | returning | shite | instructions | push | suntec | sack | owned | specify | challenge | sandiago | princes | weasels | 2rcv | call | ooooooh | 330 | make | tight | pray | complaint | bcm1896wc1n3xx | 449071512431 | marsms | 1b6a5ecef91ff9 | lim | 25 | attracts | terms | 08719181259 | perform | two | supervisor | txtin | nvq | voicemail | cuppa | knees | downloaded | 09066368470 | application | smth | joy | misss | tacos | personality | suddenly | jaz | prices | flow | neglet | mys | inconvenient | snowman | truffles | fyi | timin | mutai | stopcost | janinexx | strip | 08450542832 | bani | treats | california | ... | tonexs | smiley | stability | 09063442151 | abt | smarter | youre | stolen | modl | useless | dropped | joined | 786 | permissions | shrink | yar | garments | bell | s | formatting | 4ward | collected | mu | prestige | bridal | depressed | noi | scream | subscribe | supose | amigos | inlude | premier | 81010 | wahay | foreign | accident | 80155 | invoices | rcb | goodmate | dorothy | faggy | courageous | keys | suply | 200 | nottingham | 08718730666 | nyt | tips | operator | gigolo | rebtel | ali | golddigger | prakesh | wedlunch | 09061702893 | balance | legs | iraq | naughty | malaria | gender | dozens | 09061744553 | fuckin | here | 1pm | twenty | surly | listn | lyk | _ | 18yrs | cud | contacted | wendy | thanks | avent | 2stop | cheap | movie | items | clubmoby | ijust | labor | afternoons | sometme | humans | honest | bunch | owns | 3rd | grahmbell | complain | tayseer | lotto | thursday | cares | expressoffer | beer | nb | dao | kallis | mathe | ned | hmmross | billing | contacts | l8r | aletter | usmle | shindig | jump | melle | concentrate | ola | bawling | reduce | 09065174042 | trek | panties | nowhere | click | charity | moby | ph | 87131 | fne | renting | senor | chip | busy | scotland | truck | above | rr | warning | base | ryan | iam | worried | heads | toa | cheat | 0906346330 | shake | mobilesvary | shoul | miiiiiiissssssssss | inconsiderate | promoting | 11pm | 3ss | underdtand | window | ji | lanre | children | calld | erupt | wall | sankatmochan | excuse | payee | falls | sha | mother | keeping | impede | 150p | tranquility | hole | beneficiary | yijue | cha | against | nattil | iff | birthday | tank | yourself | everyone | update_now | hubby | wearing | detroit | price | formally | flirtparty | fumbling | is | wright | traveling | stereo | wylie | lancaster | ru | flower | sun | tues | burden | boggy | janarige | suppose | crucify | has | footprints | 07753741225 | hrishi | far | mtmsg18 | hows | weed | taxless | rcv | perf | n9dx | callfreefone | usf | black | tonight | dvg | unicef | sorrows | dodda | msg150p | 1680 | congratulation | marketing | edward | lo | girlfrnd | frwd | avenge | house | restock | guides | village | 4the | settling | pt2 | chad | newsletter | ibn | nytho | anything | worry | |
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5 rows × 7783 columns
training_set_clean = pd.concat([training_set, word_counts], axis = 1)
training_set_clean.head()
Label | SMS | cann | kerala | harlem | project | chikku | hanuman | dependents | value | scammers | subpoly | asking | tree | tor | 400mins | what | ffffffffff | elaborate | attempt | shouldn | honey | 31p | cl | flash | careful | adult | 07090201529 | menu | plaza | flung | costs | newspapers | delivered | selection | skateboarding | adding | jeans | 08002988890 | 2wu | coming | amount | 26th | positive | skilgme | recovery | gifts | showers | jazz | eurodisinc | weigh | converter | 1 | diff | categories | video | sold | shocking | invention | yup | whom | havbeen | theory | regard | 09061701851 | customersqueries | windy | aeroplane | mine | haiz | afraid | predictive | ujhhhhhhh | astne | clarification | expected | atleast | ave | charged | ringtone | autocorrect | 5we | congrats | torture | drove | gives | manege | speechless | thankyou | motherfucker | toaday | videosounds | bajarangabali | compensation | simply | tkts | successfully | elama | pudunga | 07808726822 | 08002986906 | confuses | dolls | celebration | fold | wise | shipping | sura | 1450 | ctargg | argh | apologise | situations | 23f | silence | arrange | switch | la32wu | merememberin | discussed | kr | bloo | 2hook | madoke | devils | 0870753331018 | chgs | neighbour | m95 | properly | 2bold | 0845 | trivia | pending | halloween | presnts | unsub | check | finally | services | thgt | around | earliest | neighbor | wavering | cyclists | blake | reply | stories | arguing | smells | 7548 | dollars | shoot | such | loving | jokin | drum | handset | upon | priority | moms | shudvetold | position | landline | tampa | warwick | chase | m221bp | shared | off | yahoo | msn | relation | thts | regalportfolio | chatlines | capital | spose | fell | cine | 09050000460 | jacket | fonin | wake | corect | pairs | wicket | returning | shite | instructions | push | suntec | sack | owned | specify | challenge | sandiago | princes | weasels | 2rcv | call | ooooooh | 330 | make | tight | pray | complaint | bcm1896wc1n3xx | 449071512431 | marsms | 1b6a5ecef91ff9 | lim | 25 | attracts | terms | 08719181259 | perform | two | supervisor | txtin | nvq | voicemail | cuppa | knees | downloaded | 09066368470 | application | smth | joy | misss | tacos | personality | suddenly | jaz | prices | flow | neglet | mys | inconvenient | snowman | truffles | fyi | timin | mutai | stopcost | janinexx | strip | 08450542832 | bani | ... | tonexs | smiley | stability | 09063442151 | abt | smarter | youre | stolen | modl | useless | dropped | joined | 786 | permissions | shrink | yar | garments | bell | s | formatting | 4ward | collected | mu | prestige | bridal | depressed | noi | scream | subscribe | supose | amigos | inlude | premier | 81010 | wahay | foreign | accident | 80155 | invoices | rcb | goodmate | dorothy | faggy | courageous | keys | suply | 200 | nottingham | 08718730666 | nyt | tips | operator | gigolo | rebtel | ali | golddigger | prakesh | wedlunch | 09061702893 | balance | legs | iraq | naughty | malaria | gender | dozens | 09061744553 | fuckin | here | 1pm | twenty | surly | listn | lyk | _ | 18yrs | cud | contacted | wendy | thanks | avent | 2stop | cheap | movie | items | clubmoby | ijust | labor | afternoons | sometme | humans | honest | bunch | owns | 3rd | grahmbell | complain | tayseer | lotto | thursday | cares | expressoffer | beer | nb | dao | kallis | mathe | ned | hmmross | billing | contacts | l8r | aletter | usmle | shindig | jump | melle | concentrate | ola | bawling | reduce | 09065174042 | trek | panties | nowhere | click | charity | moby | ph | 87131 | fne | renting | senor | chip | busy | scotland | truck | above | rr | warning | base | ryan | iam | worried | heads | toa | cheat | 0906346330 | shake | mobilesvary | shoul | miiiiiiissssssssss | inconsiderate | promoting | 11pm | 3ss | underdtand | window | ji | lanre | children | calld | erupt | wall | sankatmochan | excuse | payee | falls | sha | mother | keeping | impede | 150p | tranquility | hole | beneficiary | yijue | cha | against | nattil | iff | birthday | tank | yourself | everyone | update_now | hubby | wearing | detroit | price | formally | flirtparty | fumbling | is | wright | traveling | stereo | wylie | lancaster | ru | flower | sun | tues | burden | boggy | janarige | suppose | crucify | has | footprints | 07753741225 | hrishi | far | mtmsg18 | hows | weed | taxless | rcv | perf | n9dx | callfreefone | usf | black | tonight | dvg | unicef | sorrows | dodda | msg150p | 1680 | congratulation | marketing | edward | lo | girlfrnd | frwd | avenge | house | restock | guides | village | 4the | settling | pt2 | chad | newsletter | ibn | nytho | anything | worry | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | ham | [yep, by, the, pretty, sculpture] | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | ham | [yes, princess, are, you, going, to, make, me,... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | ham | [welp, apparently, he, retired] | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | ham | [havent] | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | ham | [i, forgot, 2, ask, ü, all, smth, there, s, a,... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 7785 columns
We're now done with cleaning the training set, and we can begin creating the spam filter. The Naive Bayes algorithm will need to answer these two probability questions to be able to classify new messages: $$ P(Spam | w_1,w_2, ..., w_n) \propto P(Spam) \cdot \prod_{i=1}^{n}P(w_i|Spam) $$$$ P(Ham | w_1,w_2, ..., w_n) \propto P(Ham) \cdot \prod_{i=1}^{n}P(w_i|Ham) $$
Also, to calculate P(wi|Spam) and P(wi|Ham) inside the formulas above, we'll need to use these equations:
$$ P(w_i|Spam) = \frac{N_{w_i|Spam} + \alpha}{N_{Spam} + \alpha \cdot N_{Vocabulary}} $$$$ P(w_i|Ham) = \frac{N_{w_i|Ham} + \alpha}{N_{Ham} + \alpha \cdot N_{Vocabulary}} $$Some of the terms in the four equations above will have the same value for every new message. We can calculate the value of these terms once and avoid doing the computations again when a new messages comes in. Below, we'll use our training set to calculate:
* P(Spam) and P(Ham)
* NSpam, NHam, NVocabulary
We'll also use Laplace smoothing and set $\alpha = 1$.
# Isolating spam and ham messages first
spam_messages = training_set[training_set['Label'] == 'spam']
ham_messages = training_set[training_set['Label'] == 'ham']
# P(Spam) and P(Ham)
p_spam = len(spam_messages)/len(training_set)
p_ham = len(ham_messages)/len(training_set)
# N_spam
n_words_per_spam_message = spam_messages['SMS'].apply(len)
n_spam = n_words_per_spam_message.sum()
# N_ham
n_words_per_ham_message = ham_messages['SMS'].apply(len)
n_ham = n_words_per_ham_message.sum()
# N_vocabulary
n_vocabulary = len(vocabulary)
# Laplace smoothing
alpha = 1
Now that we have the constant terms calculated above, we can move on with calculating the parameters $P(w_i|Spam)$ and $P(w_i|Ham)$. Each parameter will thus be a conditional probability value associated with each word in the vocabulary.
The parameters are calculated using the formulas:
$$ P(w_i|Spam) = \frac{N_{w_i|Spam} + \alpha}{N_{Spam} + \alpha \cdot N_{Vocabulary}} $$$$ P(w_i|Ham) = \frac{N_{w_i|Ham} + \alpha}{N_{Ham} + \alpha \cdot N_{Vocabulary}} $$# Initiate parameters
parameters_spam = {unique_word:0 for unique_word in vocabulary}
parameters_ham = {unique_word:0 for unique_word in vocabulary}
# Calculate parameters
for word in vocabulary:
n_word_given_spam = spam_messages[word].sum() # spam_messages already defined in a cell above
p_word_given_spam = (n_word_given_spam + alpha) / (n_spam + alpha*n_vocabulary)
parameters_spam[word] = p_word_given_spam
n_word_given_ham = ham_messages[word].sum() # ham_messages already defined in a cell above
p_word_given_ham = (n_word_given_ham + alpha) / (n_ham + alpha*n_vocabulary)
parameters_ham[word] = p_word_given_ham
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) /opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 2896 try: -> 2897 return self._engine.get_loc(key) 2898 except KeyError: pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 'cann' During handling of the above exception, another exception occurred: KeyError Traceback (most recent call last) <ipython-input-14-f3bdb650f628> in <module> 5 # Calculate parameters 6 for word in vocabulary: ----> 7 n_word_given_spam = spam_messages[word].sum() # spam_messages already defined in a cell above 8 p_word_given_spam = (n_word_given_spam + alpha) / (n_spam + alpha*n_vocabulary) 9 parameters_spam[word] = p_word_given_spam /opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key) 2978 if self.columns.nlevels > 1: 2979 return self._getitem_multilevel(key) -> 2980 indexer = self.columns.get_loc(key) 2981 if is_integer(indexer): 2982 indexer = [indexer] /opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 2897 return self._engine.get_loc(key) 2898 except KeyError: -> 2899 return self._engine.get_loc(self._maybe_cast_indexer(key)) 2900 indexer = self.get_indexer([key], method=method, tolerance=tolerance) 2901 if indexer.ndim > 1 or indexer.size > 1: pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 'cann'