Spam Mail Detection Using Machine Learning Project

Detecting spam mails using machine learning (ML) is an essential application for managing the performance and privacy of email interaction. Custom research paper under spam mail detection are written specifically as per your requirements. Topic selection can be done by our researchers or your own topic can also be developed. Best written work will be provided for research proposal in all areas of machine learning as our team will expend all their knowledge for your paper. The appropriate outline of the work along with its methodology to be used will be clearly explained for your projects.

The following is a literature process that we implement to construct a spam mail prediction using ML:

  1. Objective Definition

            We state the main aim of our project is to design a ML model that classifies emails as spam and not-spam based on their content.

  1. Data Collection
  • Public Datasets: There are several suitable datasets for spam prediction, but we utilize the famous “Spambase” dataset from the UCI ML repository.
  • Own Dataset: When we process to a huge set of emails by ensuring the user privacy we manually label them and develop a dataset.
  1. Pre-processing of Data
  • Text Cleaning: We eliminate special characters, URLs, numbers and transform entire text to lowercase in our project.
  • Tokenization: For creating single words and tokens we segment the email content.
  • Stopword Removal: The words which don’t give particular information we remove those general words.
  • Stemming/Lemmatization: Decreasing words to their base and root format for our work.
  1. Feature Extraction
  • Bag of Words: We convert the text data into vectors based on frequency of the word.
  • TF-IDF: Returning the necessary terms in our email that is corresponding to their frequency throughout all emails.
  • Word Embedding: Methods such as Word2Vec or GloVe support us but it is excessive for our basic project.
  1. Model Selection and Development
  • Naive Bayes: Due to its simplicity and effectiveness we frequently make the first option for the classification task in this model.
  • Random Forest: For better capturing the non- linear patterns we wake use of this.
  • Support Vector Machine (SVM): When with a linear kernel we select this framework which provides efficient outcomes.
  • Logistic Regression: To get productive results we choose this model for high-dimensional inadequate datasets.
  • Deep Learning (DL): If we focus for more advanced solutions we study using models such as RNNs and LSTMs.
  1. Training the Framework
  • For training and testing subsets we divide the dataset.
  • We instruct our selected model by the training data.
  1. Model Evaluation
  • Accuracy: We measure the whole impact of our model by accuracy.
  • Precision, Recall, F1-score: To interpret the adjustment between false positives and negatives we utilize these essential metrics for unstable datasets like spam prediction.
  • Confusion Matrix: By this we offer a explained failure of detection outcomes.
  1. Optimization & Hyperparameter Tuning
  • To adapt model parameters for better robustness we utilize the grid search method for systematic tuning.
  1. Deployment
  • We combine our model into an email system to autonomously categorize incoming emails.
  • For huge-scale mechanisms we study deploying the framework as a microservice by techniques like Docker.
  1. Feedback Loop
  • To constantly develop our model we let users give remark when an email was misclassified.
  • Regularly we retrain our model with fresh data and user review.
  1. Conclusion & Future Improvement
  • We report our detections, challenges and possible domains for enhancements.
  • Our future work consists:
  • To get more accurate spam detection we utilize DL approaches.
  • When the latest spam methods arrives we update the model in real-time.
  • Combining our model with other features such as sender name and email metadata.


  • Privacy Concerns: We often incognito and make sure the data security because email data is highly susceptible.
  • Imbalanced Data: The spam prediction datasets usually have lots of ham (non-spam) than spam emails. So we consider stable methods and validation metrics that assist in this.
  • Feature Engineering: For designing the features we study from email metadata such as subject, sender and the time sent.

Spam prediction is a vintage ML issue and using the appropriate technique, we potentially build an efficient outcome which specifically decrease the number of spam emails in a user’s inbox. By using the correct methodology and proper source code we carry out the simulation part and give proper explanation about its working process.

Spam Mail Detection using Machine Learning Ideas

Spam Mail Detection Using Machine Learning Thesis Topics

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  1. Spam Email Detection Using Machine Learning Integrated in Cloud


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.    

  1. Score based Support Vector Machine for Spam Mail Detection


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. 

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


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.

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



            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. 

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


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.

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


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.

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


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.  

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


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.   

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


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. 

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


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.


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