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The project reviews academic research, describes effective methods, and implements Python-based models paired with Tableau visualizations. Using the CICIDS2017 dataset, it computes accuracy, F1-score, precision, and recall across AdaBoost, XGBoost, Random Forest, Decision Tree, K-Nearest Neighbor, and Linear Discriminant Analysis. Results show XGBoost performs best overall, and Random Forest achieves the top accuracy and F1-score. Varying feature counts indicates that 35 features improves Random Forest performance. 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All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: |  |\n| --- | --- |\n| Date: | 1st February 2023 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nA Comparative Evaluation of Machine Learning Models and EDA through Tableau Using CICIDS2017  \nDataset  \nAbhay Singh Bangari  \nX21153507  \nAbstract  \nMachine learning is utilized globally in network security, but computers need time to learn. Machine learning can identify many hacker attacks that humans cannot. Business intelligence and machine learning are being studied to strengthen network systems. The research topic is briefly covered in the study. Academic articles on the study topic are examined, and effective research methods are described. In this work, Python is used as a medium to build popular algorithms and Tableau for visualizations. Machine learning models like AdaBoost, XGBoost, Random Forest, Decision Tree, KNearest Neighbor, and Linear Discriminant Analysis. The Canadian Institute for Cybersecurity’s CICIDS2017 dataset is used for in-depth analysis. Performance metrics for all the algorithms are computed, with the help of accuracy, F1-score, precision, and recall. The following investigation revealed that XGBoost is the better-performing algorithm. The random forest model is the bestperforming model in terms of accuracy (98.38%), and F1-score (98.37%) . Contrary to others, AdaBoost and linear discriminant analysis models have been proven to be less effective at preventing intrusions. On testing with different numbers of features in the random forest, it is discovered that the model with 35 features improves its performance.  \n1 Introduction  \nMachine Learning improves most technologies Milenkoski et al. (2015) Modi and Acha (2017) . Many studies have been done to integrate Machine Learning into cyber security Viegas et al. (2016), Buczak and Guven (2015) . Gmail employs Machine Learning to identify spam. Making a country’s digital defense system safer is necessary, but ML can’t help Coelho et al. (2017) . Bayes Net, Multilayer Perceptron (MLP)","cbCaikgZPkVM9CoU","https://ap.wps.com/l/cbCaikgZPkVM9CoU","pdf",1660646,29,"English","# Abstract\n# 1 Introduction","[{\"question\":\"What dataset is used for the analysis in this project?\",\"answer\":\"The project uses the Canadian Institute for Cybersecurity’s CICIDS2017 dataset for in-depth analysis.\"},{\"question\":\"Which machine learning models are evaluated?\",\"answer\":\"The study evaluates AdaBoost, XGBoost, Random Forest, Decision Tree, K-Nearest Neighbor, and Linear Discriminant Analysis.\"},{\"question\":\"What performance metrics are computed for the algorithms?\",\"answer\":\"Performance is measured using accuracy, F1-score, precision, and recall.\"}]","A Comparative Evaluation of Machine Learning Models and EDA through Tableau Using CICIDS2017 Dataset - MSc Research Project Submission Sheet | PDF",73]