Solved by verified expert:Write a classification script named ‘classifier.py’ that trains on reviews_train.txt and achieves the maximum possible accuracy on reviews_test.txt. You can use any type of classifier that you want.DT Notepad”””A simple script that demonstrates how we classify textual data with sklearn.”””from sklearn.feature_extraction.text import CountVectorizerfrom sklearn.metrics import accuracy_scorefrom sklearn.tree import DecisionTreeClassifier#read the reviews and their polarities from a given filedef loadData(fname): reviews= labels= f=open(fname) for line in f: review,rating=line.strip().split(‘t’) reviews.append(review.lower()) labels.append(int(rating)) f.close() return reviews,labelsrev_train,labels_train=loadData(‘reviews_train.txt’)rev_test,labels_test=loadData(‘reviews_test.txt’)#Build a counter based on the training datasetcounter = CountVectorizer()counter.fit(rev_train)#count the number of times each term appears in a document and transform each doc into a count vectorcounts_train = counter.transform(rev_train)#transform the training datacounts_test = counter.transform(rev_test)#transform the testing data#train classifierclf = DecisionTreeClassifier()#train all classifier on the same datasetsclf.fit(counts_train,labels_train)#use hard voting to predict (majority voting)pred=clf.predict(counts_test)#print accuracyprint (accuracy_score(pred,labels_test))
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