Course Content

ABSTRACT

 

Email is the most used source of official communication method for business purposes. The usage of the email continuously increases despite of other methods of communications. Automated management of emails is important in the today's context as the volume of emails grows day by day. Out of the total emails, more than 55 percent is identified as spam. This shows that these spams consume email user time and resources generating no useful output. The spammers use developed and creative methods in order to fulfill their criminal activities using spam emails. Email Spam has become a major problem nowadays, with Rapid growth of internet users, Email spams is also increasing. People are using them for illegal and unethical conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and can also seek in into your system. Creating a fake profile and email account is much easy for the spammers, they pretend like a genuine person in their spam emails, these spammers target those peoples who are not aware about these frauds. So, it is needed to Identify those spam mails which are fraud.

So we proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression ,Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier,  KNN and SVC  to predict an email is Spam or Real based on email data entered by the user in the front end.

INTRODUCTION

Email or electronic mail spam refers to the “using of email to send unsolicited emails or advertising emails to a group of recipients. Unsolicited emails mean the recipient has not granted permission for receiving those emails. “The popularity of using spam emails is increasing since last decade. Spam has become a big misfortune on the internet. Spam is a waste of storage, time and message speed. Automatic email filtering may be the most effective method of detecting spam but nowadays spammers can easily bypass all these spam filtering applications easily. In the past few years, spam emails are increased in a tremendous amount which becomes big trouble for the internet. Intruder sends spam mail to genuine users to get sensitive information about the user, his personal information and tries to violate his information. Recently many peoples use email as a form of communication. To protect the user from this threat emails should classify as spam (Malicious) or ham (Good) mail. For that proper classification of mails is required. In the past few years, the application of machine learning in different fields is increased because of the capability of handling a large amount of data and the availability of necessary tools. Machine learning is an application of artificial intelligence that helps systems to learn automatically from experiences and use it without specific programming. Machine learning extract features from the data to generate the model, hence helping computers to make educated guesses about unseen data with a significant amount of accuracy. Using machine learning we can predict the outcome of an application or software before explicitly programmed. Hence the machine learning approach is very useful in email classification.

Now-a-days, communication through email has become one of the cheapest and easy ways for the official and business users due to easy availability of internet access. Most of the people prefer to use email to share important information and to maintain their official records. But just like the two sides of coin, many people misuse this easy way of communication by sending unwanted & useless bulk emails to others. These unwanted emails are spam emails that affect the normal user to face the problems like excessive usage of their mailbox memory and filtration of useful email from unwanted useless emails. So, there is the need of some autonomous approach that filters the excessive data of emails in the form of spam emails.

So we proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression ,Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier,  KNN and SVC  to predict an email is Spam or Real based on email data entered by the user in the front end.

 

 

 

 

 

 

 

 

 

 

Objective

The main aim of this project is to predict whether an email is spam or real using machine learning techniques, NLP techniques and machine learning algorithms like Logistic Regression, Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier, KNN and SVC with good accuracy based on email data entered by the user in the front end.

 

 

 

 

 

 

 

 

 

 

 

 

Problem Statement

 

The unwanted emails are spam emails that affect the normal user to face the problems like excessive usage of their mailbox memory and filtration of useful email from unwanted useless emails. So, there is the need of some autonomous approach that filters the excessive data of emails in the form of spam emails. The spammers use developed and creative methods in order to fulfill their criminal activities using spam emails. Email Spam has become a major problem nowadays, with Rapid growth of internet users, Email spams is also increasing. People are using them for illegal and unethical conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and can also seek in into your system.

 

 

 

 

 

 

Proposed System:

 

We proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression ,Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier,  KNN and SVC  to predict an email is Spam or Real based on email data entered by the user in the front end.