Question: 1 . Build ML Model using python code for K nearest neighbors, Naive Bayesian, Random Forest, and Adaboost for the below onlineuseradvertisement dataset, 2 .
Build ML Model using python code for K nearest neighbors, Naive Bayesian, Random Forest, and Adaboost for the below onlineuseradvertisement dataset,
Performance Evaluation with comments must were Do the prediction for the test data and display the results for the inference. Calculate all the evaluation metrics. Comment on the performance of these models. Answer without comment will not be awarded marks.
FineTuning Hyperparameters. You are required to explore the hyperparameter space for each classifier, utilizing techniques such as grid search or randomized search, to find the optimal combination of parameters that maximizes performance metrics.
Performance Evaluation: After hyperparameter finetuning, evaluate the performance of each classifier using the following evaluation measures: Precision, Recall, Accuracy, Misclassification Rate, F Score
Compare the performance using the evaluation measures and recommend the ML model
Sample Data set:
Age Gender Income Location Device InterestCategory TimeSpentonSite NumberofPagesViewed Click
Male Rural Mobile Sports
Male Suburban Tablet Sports
Male Suburban Tablet Sports
Male Urban Tablet Technology
Female Suburban Mobile Fashion
Female Rural Tablet Technology
Male Urban Mobile Sports
Male Rural Mobile Travel
Male Rural Tablet Technology
Male Rural Desktop Fashion
Male Suburban Desktop Sports
Female Suburban Tablet Technology
Female Rural Tablet Travel
Female Rural Mobile Technology
Female Urban Tablet Fashion
Male Urban Mobile Technology
Male Suburban Tablet Travel
Male Suburban Desktop Travel
Male Urban Desktop Fashion
Male Rural Tablet Travel
Female Suburban Mobile Sports
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