EMAIL SPAM DETECTION USING MACHINE LEARNING

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score

#loading the data from csv file spam = pd.read_csv('/content/spam.csv',encoding='latin-1') print(spam)

#replace the null values with a null string spam = spam.where((pd.notnull(spam)),'')

#printing the first 5 rows of df spam.head()pam = spam.where((pd.notnull(spam)),'')

#checking the number of rows and columns in df spam.shape

#Label spam mail as 0; ham mail as 1; spam.loc[spam['v1'] == 'spam', 'v1',] = 0 spam.loc[spam['v1'] == 'ham', 'v1',] = 1

x = spam['v2'] y = spam['v1']

print(x) print(y)

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=3)

print(x.shape) print(x_train.shape) print(x_test.shape)

#transform the text data to feature vectors that can be used as input in Logistic Regession feature_extraction = TfidfVectorizer(min_df=1,stop_words='english',lowercase=True) x_train_features = feature_extraction.fit_transform(x_train) x_test_features = feature_extraction.transform(x_test)

#convert y_train and Y-test values as integer y_train = y_train.astype('int') y_test = y_test.astype('int')

print(x_train)

print(x_train_features)

model = LogisticRegression()

#training the Logistic Regression model with the training data model.fit(x_train_features,y_train)

#prediction on training data Prediction_on_training_data = model.predict(x_train_features) accuracy_on_training_data = accuracy_score(y_train,Prediction_on_training_data) print('Accuracy on training data : ', accuracy_on_training_data)

#prediction on test data Prediction_on_test_data = model.predict(x_test_features) accuracy_on_test_data = accuracy_score(y_test,Prediction_on_test_data) print('Accuracy on test data : ', accuracy_on_test_data)

#Building a predictive system input_mail = ["I've been searching for the right words to thank your this breather. I promise i wont take your help for granted and will fulfil my promise. You have been wonderful and a blessing at all times"]

#convert text to feature vectors input_data_features = feature_extraction.transform(input_mail)

#making prediction prediction = model.predict(input_data_features) print(prediction)

if (prediction[0]==1): print('Ham mail') else: print('Spam mail')