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# Step 1: Import Libraries and Loading Data import pandas as pd df = pd.read_csv('music.csv') # Step 2: Shuffle the data df = shuffle(df) # Step 3: Drop unnecessary columns df = df.drop(['Path'], axis=1) # Step 4: Split the data into features and labels test = df.iloc[:500, :] # First 500 rows for testing train = df.iloc[500:, :] # Remaining rows for training # Step 4 : Separate Labels and Features train_labels = train['Class'].tolist() test_labels = test['Class'].tolist() train = train.drop(['Class'], axis=1) test = test.drop(['Class'], axis=1) # Step 6 : Importing Classifer from sklearn.metrics import accuracy_score, log_loss from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier from sklearn.naive_bayes import GaussianNB from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis train_samples, test_samples, train_labels, test_labels = train_test_split(X, y, test_size=0.2, random_state=42) # Step 7: Setting Up Classifiers classifiers = [ KNeighborsClassifier(3), SVC(kernel="rbf", C=0.025, probability=True), DecisionTreeClassifier(), RandomForestClassifier(), AdaBoostClassifier(), GradientBoostingClassifier(), GaussianNB(), LinearDiscriminantAnalysis(), QuadraticDiscriminantAnalysis() ] # Step 8: Train and evaluate each classifier for clf in classifiers: clf.fit(train_samples, train_labels) res=clf.predict(test_samples) acc = accuracy_score(test_labels, res) print (clf.__class__.__name__+" Accuracy: "+str(acc))
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