0. Data preprocessing

0.1 Import library 

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

0.2 Import dataset 

email = pd.read_csv('emails.csv')
email
text spam
0 Subject: naturally irresistible your corporate... 1
1 Subject: the stock trading gunslinger fanny i... 1
2 Subject: unbelievable new homes made easy im ... 1
3 Subject: 4 color printing special request add... 1
4 Subject: do not have money , get software cds ... 1
... ... ...
5723 Subject: re : research and development charges... 0
5724 Subject: re : receipts from visit jim , than... 0
5725 Subject: re : enron case study update wow ! a... 0
5726 Subject: re : interest david , please , call... 0
5727 Subject: news : aurora 5 . 2 update aurora ve... 0

5728 rows × 2 columns

email.head()
text spam
0 Subject: naturally irresistible your corporate... 1
1 Subject: the stock trading gunslinger fanny i... 1
2 Subject: unbelievable new homes made easy im ... 1
3 Subject: 4 color printing special request add... 1
4 Subject: do not have money , get software cds ... 1 
len(email)

5728

0.3 Check if any null value 

email.isna().sum()

text    0
spam    0
dtype: int64
email.shape

(5728, 2)
email.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5728 entries, 0 to 5727
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   text    5728 non-null   object
 1   spam    5728 non-null   int64 
dtypes: int64(1), object(1)
memory usage: 89.6+ KB

0.4 Cleaning the text 

import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
corpus = []
for i in range(0, len(email)):
  e_mail = re.sub('[^a-zA-Z]', ' ', email['text'][i])
  e_mail = e_mail.split()
  ps = PorterStemmer()
  e_mail = [ps.stem(word) for word in e_mail if not word in set(stopwords.words('english'))]
  e_mail = ' '.join(e_mail)
  corpus.append(e_mail)

[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\patel\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!

corpus[2509]

'subject enron mid year perform manag process enron mid year perform manag process begun process requir select suggest review provid perform relat feedback may also request provid feedback fellow employe need access perform manag system pep http pep enron com question direct pep help desk follow number u option europ option canada canada employe e mail question perfmgmt enron com log pep enter user id password provid log immedi prompt chang secur password user id password user id wkamin password welcom'

0.5 Creating the Bag of Words model 

from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer()
X = cv.fit_transform(corpus).toarray()
y = email['spam']

len(X)

5728
X.shape

(5728, 25607)
len(y)

5728

0.6 Splitting the dataset into the Training set and Test set 

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 2509)

1. Training the Naive Bayes model on the Training set

1.1.1 GaussianNB 

from sklearn.naive_bayes import GaussianNB
GN_classifier = GaussianNB()
GN_classifier.fit(X_train, y_train)

GaussianNB()

1.1.2 Score 

GN_score = GN_classifier.score(X_test,y_test)
GN_score

0.9528795811518325
y_GN_pred = GN_classifier.predict(X_test) #Predicting the Test set results

1.2.1 MultinomialNB 

from sklearn.naive_bayes import MultinomialNB
MN_classifier = MultinomialNB()
MN_classifier.fit(X_train, y_train)

MultinomialNB()

1.2.2 Score 

MN_score = MN_classifier.score(X_test,y_test)
MN_score

0.9842931937172775
y_MN_pred = MN_classifier.predict(X_test) #Predicting the Test set results

2. Making the Confusion Matrix

2.1 GaussianNB 

from sklearn.metrics import confusion_matrix
GN_cm = confusion_matrix(y_test, y_GN_pred)
print(GN_cm)

[[849  26]
 [ 28 243]]

2.2 MultinomialNB 

from sklearn.metrics import confusion_matrix
MN_cm = confusion_matrix(y_test, y_MN_pred)
print(MN_cm)

[[862  13]
 [  5 266]]

3. Compare Both models 

models = pd.DataFrame({"GaussianNB": GN_score,
                           "MultinomialNB": MN_score
                           },
                          index=[0])

models.T.plot.bar(title="Comapre different models",
                      legend=False)
plt.xticks(rotation=0);

4. SAVE MODEL 

import pickle
pickle.dump(MN_classifier,open("Email_spam_naive_bayes_MN.pkl","wb"))