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email_spam_detection.py
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email_spam_detection.py
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# -*- coding: utf-8 -*-
"""EMAIL_SPAM_DETECTION.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1BPPF1OcNjhM4NuFtMbWrqkW-x5OcUW8w
# **EMAIL SPAM DETECTION WITH MACHINE LEARNING**
#Download **Dataset** here [EMAIL SPAM DETECTION](https://drive.google.com/file/d/19Yz5vBQ3T174xeSOOqVzw3rWWm39stsf/view?usp=share_link)
### Knowing about the Dataset
**Importing the Required Libraries**
"""
# Numpy Library for Numerical Calculations
import numpy as np
# Pandas Library for Dataframe
import pandas as pd
# Matplotlib and for Plottings
import matplotlib.pyplot as plt
# Pickle Library for Saving the Model
import pickle
# RE Library for Regular Expression
import re
# NLTK Library for Natural Language Processing
import nltk
nltk.download('stopwords') # Downloading the Stopwords
# Stopwords for removing stopwords in the Text
from nltk.corpus import stopwords
# PorterStemmer for Stemming the Words
from nltk.stem.porter import PorterStemmer
# CountVectorizer for Bagging of Words and Vectorizing it
from sklearn.feature_extraction.text import CountVectorizer
# Train_Test_Split for splitting the Dataset
from sklearn.model_selection import train_test_split
# Decision Tree Classifier, Random Forest Classifier and Multinomial Naïve Bayes are Models
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import MultinomialNB
# Accuracy Score and Confusion Matrix is for Analysis of Models
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
"""**Reading informations in the Dataset**"""
from google.colab import drive
drive.mount('/content/drive')
spam = pd.read_csv("/content/drive/My Drive/Oasis Infobyte/Data Science - Internship/Email-Spam-Detection/spam.csv", encoding='ISO-8859-1')
"""**Checking for null values in Data**"""
spam.isnull().sum()
"""**Checking the First Five Values in the Data**"""
spam.head()
"""**Checking the Last Five Values in the Data**"""
spam.tail()
"""**Taking the required Columns in the Dataset**"""
spam = spam[['v1', 'v2']]
spam.columns = ['label', 'message']
spam.head()
"""**Dimensions of the Dataset**"""
spam.shape
"""**Checking for the classes in the Data**"""
spam.groupby('label').size()
"""### Plotting
**Plotting the Label in the Dataset**
"""
spam['label'].value_counts().plot(kind='bar')
"""### NLP
**Preprocessing the Text in the Dataset**
"""
ps = PorterStemmer()
corpus = []
for i in range(0, len(spam)):
review = re.sub('[^a-zA-Z]', ' ', spam['message'][i])
review = review.lower()
review = review.split()
review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
review = ' '.join(review)
corpus.append(review)
# Printing the first 5 values in the corpus list
corpus[1:6]
"""**Creating Bag of Words Model**"""
cv = CountVectorizer(max_features = 4000)
X = cv.fit_transform(corpus).toarray()
Y = pd.get_dummies(spam['label'])
Y = Y.iloc[:, 1].values
"""### Data Modeling
**Splitting the Dataset into Training and Testing Set**
"""
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.20, random_state=42)
"""### Model Building
**Creating the Models**
"""
# Model 1 - Random Forest Classifier
model1 = RandomForestClassifier()
model1.fit(X_train, Y_train)
# Model 2 - Decision Tree Classifier
model2 = DecisionTreeClassifier()
model2.fit(X_train, Y_train)
# Model 3 - Multinomial Naïve Bayes
model3 = MultinomialNB()
model3.fit(X_train, Y_train)
"""**Prediction**"""
pred1 = model1.predict(X_test)
pred2 = model2.predict(X_test)
pred3 = model3.predict(X_test)
"""### Model Testing
**Testing the Model**
"""
# Model 1 - Random Forest Classifier
print("Random Forest Classifier")
print("Confusion Matrix: ")
print(confusion_matrix(Y_test, pred1))
print("Accuracy: ", accuracy_score(Y_test, pred1))
print("--------------------------------")
# Model 2 - Decision Tree Classifier
print("Decision Tree Classifier")
print("Confusion Matrix: ")
print(confusion_matrix(Y_test, pred2))
print("Accuracy: ", accuracy_score(Y_test, pred2))
print("--------------------------------")
# Model 3 - Multinomial Naïve Bayes
print("Multinomial Naïve Bayes")
print("Confusion Matrix: ")
print(confusion_matrix(Y_test, pred3))
print("Accuracy: ", accuracy_score(Y_test, pred3))
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Y_test, pred3)
import seaborn as sns
sns.heatmap(cm, annot=True)
report1 = classification_report(Y_test, pred1)
print("Classification Report for RFC \n", report1)
report2 = classification_report(Y_test, pred2)
print("Classification Report for DTC \n", report2)
report3 = classification_report(Y_test, pred3)
print("Classification Report for MNB \n", report3)
"""**Best Model is Multinomial Naïve Bayes**
### Saving Models
Saving all the Models
"""
filename = "RFC.pkl"
pickle.dump(model1, open(filename, 'wb'))
filename = "DTC.pkl"
pickle.dump(model2, open(filename, 'wb'))
filename = "MNB.pkl"
pickle.dump(model3, open(filename, 'wb'))
print("Saved all Models")