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Dissertation Based on Machine Learning Big dataset, preprocessing the data, analyze – exploratory analysis – data analysis and comparison, evaluation, run time, roc, ft, pt parameters. Contribution – Gap in knowledge 10-15 paper contributions hyperparameter tuning, optimization, apply to optimize ML, Random forest doesn’t have the accuracy, using a combination of ML methods, features for prediction – Show the difference between your work n other recent works Topic Universities using Machine learning to filter student applicants or any such similar


Dissertation
Based on Machine Learning Big dataset, preprocessing the data, analyze – exploratory analysis – data analysis and comparison, evaluation, run time, roc, ft, pt parameters. Contribution – Gap in knowledge 10-15 paper contributions hyperparameter tuning, optimization, apply to optimize ML, Random forest doesn’t have the accuracy, using a combination of ML methods, features for prediction – Show the difference between your work n other recent works Topic Universities using Machine learning to filter student applicants or any such similar



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