Untitled

# 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))