Spam Mail Detection Using Machine Learning Project

Spam Mail Detection Using Machine Learning Thesis Topics

We provide a flexible platform for scholar were constant thesis writing and editing can be carried as per scholars wish. Our writers carefully carry out the framework of thesis writing work right from scratch. We have numerous top ranked writers so you can select your preferred thesis writer under machine learning and complete it with deadline.

1. Spam Email Detection Using Machine Learning Integrated in Cloud

Keywords:

Emails, Spam, Decision Tree, Naive Bayes, KNN, Random Forest, SVM, Machine Learning Algorithms

            To find the spam in email our study proposes a hybrid approach to ML methods. The proposed hybrid method for boosting and bagging of ML based multinomial DT, NB, KNN, RF and SVM. To boost classification accuracy, we used concurrent grouping of weak classifiers. To detect spam in email first we have to gather the datasets then we preprocess the data that involves tokenization, stemming words and removing stop words, retrieve and pick features and then classify the data. We used correlation-based approach for feature selection process.    

2. Score based Support Vector Machine for Spam Mail Detection

 Keywords:

Machine learning, Ham

            Emails send to an unauthorised servers can lead to spam and these spam can reduce our internet connection and takes important details like password, credit card details, and can interfere with search outcome on computers. Our paper proposes the ML method SVM to classify the mail as spam or ham by utilizing the proposed dataset. Our proposed model achieves best accuracy when compared to existing methods. 

3. Detecting ham and spam emails using feature union and supervised machine learning models

Keywords:

Spam detection, Features extraction, Machine learning classifiers, Term frequency, Sampling

            Our study uses the characters from the text mails to predict the mail is spam or normal. To get an increased accuracy of spam mail detection we have to use multiple features. We use the methods like ML and DL and the effect of data resampling has been examined. RF and LR can perform best accuracy rate.

4. Content-based Spam Email Detection Using N-gram Machine Learning Approach

Keywords:

N-grams.

            Spam filter can detect the mail by malicious or undesired and blocks them from receiving into user inboxes. Our paper uses various features such as word2vec, work n-grams, character n-grams, and a combination of variable length n-grams. We also use different ML methods to train the retrieved features such as SVM, DT, LR and Multinomial NB. Our SVM provides the higher accuracy rate. 

5. Email spam detection using bagging and boosting of machine learning classifiers

Keywords:

Email spam, text mining, J48algorithm, spam filtering, correlation-based feature selection, bagging, boosting

            Our paper aims to detect the email spam by building an ensemble system by utilizing bagging and boosting of ML methods. We used the dataset Ling-spam corpus. To detect the spam email by bagging we use the ML based Multinomial NB and J48 Decision Tree classifier and the adaboost technique to classify the weak classifiers into strong. We have done three various experimentations by using the methods to detect the email spam.

6. The Comparison of Machine Learning Methods for Email Spam Detection

Keywords:

E-mail Filtering

            Technologies have given new chance and various ways for us to interfere. They also bought new path and technique for cyber-attacks. We overcome the general type of cyber-attacks cause via email. ML methods can be used to detect the malicious email. We have been tested supervised ML techniques such as RF, SVM, DT, NB and KNN. We work with some real data that contains emails in various phishing and bulk emails.

7. Comparative Analysis for Email Spam Detection Using Machine Learning Algorithms

Keywords:

Spam email identification, Logistic regression

            Due to the major internet increase and email security issues our paper uses four supervised ML methods proposed for spam and ham email classification namely NB, SVM, LR and Random Forest classifier. Our paper experiments these four methods achieved on our prepared feature sets on two various datasets to select the best one with increased accuracy and decrease overfitting and underfitting for spam detection. RF achieves the better outcome.  

8. E-mail Spam Detection Using Machine Learning – KNN

Keywords:

Spam Classification

            Our study debates on how ML method with google updated on Collab that can examine and prevent all spam and phishing emails. Email spam filter is an effective that one message can out of thousand can allowed to pass through. Various ML methods can be utilized to identify spam but our paper uses KNN to improve outstanding. We will use spam classification methods to determine how these can come to findings and classify them as spam or not is known as spam detection.   

9. Detection of Email Spam using Machine Learning Algorithms: A Comparative Study

Keywords:

Accuracy, Decision Tree

            Many mails were accepted daily and some of them were irrelevant to us and can cause harm to our system. This can be achieved by spam detection and it is procedure of classify the mail as spam or not. We have to detect the spam to deliver the relevant data and reject the spam emails. We have to compare the analysis by using ML methods and the ML methods were compared to the metrics accuracy and precision. SVM gives the better performance. 

10. Comparative Results of Spam Email Detection Using Machine Learning Algorithms

Keywords:

Supervised Learning

            Spam mail contains malware includes link for phishing websites that can lead to steal or loss of data. Our paper explores five machine learning methods in python language by utilizing scikit-learn library and we have to compare their achievements against publicly contained spam email corpuses. Our argued methods are SVM, Random Forest, Logistic Regression, Multinomial Naive Bayes and Gaussian Naive Bayes.