Q1 Classifier Performance ComparisonQ1a Analyze the data set Social_Network_Ads.csv and create the plot with correct titles on axes:Q1b Use the following classifiersNave BayesLogistic RegressionDecision TreesKNNSupport Vector MachineRandom ForestFor each classifier showThe classifier boundary for training and testPrintout your 1st name on all graphs Q1c Compare the confusion matrix in the following table for the above data setTPTNFPFNAccuracyNave BayesLogistic RegressionDecision TreesKNNSupport Vector MachineRandom ForestQ2 Principal Component AnalysisSummarize how the PCA algorithm works using the following link and recreate the code for the IRIS data set.https://plot.ly/ipython-notebooks/principal-component-analysis/Q3 Review the material on PCA in the following and visually describe how PCA works (use snapshots)http://setosa.io/ev/principal-component-analysis/Q4 LDA Explain how LDA differs from PCAhttps://sebastianraschka.com/Articles/2014_python_lda.htmlQ5 Compare accuracy of LDA vs PCA techniques using the dimensionality reduction on Wine data. The dataset is attached.Q5 Dimensionality Reduction on Wine data.zipQ1 Classifier Comparison on Social Ads Network.zipHW Classifier Comparison.docx
