Applying SVM on these feature sets • SET 1:Review text, preprocessed one converted into vectors using (BOW) • SET 2:Review text, preprocessed one converted into vectors using (TFIDF) • SET 3:Review text, preprocessed one converted into vectors using (AVG W2v) • SET 4:Review text, preprocessed one converted into vectors using (TFIDF W2v) Procedure • We will work with 2 versions of SVM Linear kernel RBF kernel • While working with linear kernel, used SGDClassifier’ with hinge loss because it is computationally less expensive. • While working with ‘SGDClassifier’ with hinge loss and trying to find the AUC score, used CalibratedClassifierCV • Similarly, like kdtree of knn, While working with RBF kernel it's better to reduce the number of dimensions. We used min_df = 10, max_features = 500 and consider a sample size of 40k points.