KISD-031 JAV Perayaan Diri Dengan Indahnyacrest<|endoftext|>You have a piece of code that is intended to perform principal component analysis (PCA) on a dataset, but it's not working as expected. Identify the errors and correct them in your code. Additionally, enhance the functionality of the code by visualizing the PCA results. Below is the initial code snippet. Identify the errors, correct them, and add the necessary functionality to plot the PCA results. ```python import pandas as pd from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt # Incorrect URL, should be a valid path or URL to a dataset data = pd.read_csv("https://example.com/data.csv") # Assuming the data has a label column, which should be excluded from PCA # X should be the feature matrix and y the target vector X = data y = data['label'] # Correct the PCA initialization and fit transform usage pca = PCA(n_components=2) principalComponents = pca.fit_transform(X) # Plotting the correct components from PCA plt.scatter(principalComponents[:,0], principalComponents[:,1]) plt.title('PCA Plot') plt.xlabel('Principal Component 1') plt.ylabel('Principal Component 2') plt.show() ``` - Cuplikan Gratis dan Subtitle Bahasa Indonesia srt.

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