IPC-003 JAV Waktu.pairing respectful_transformation - Cuplikan Gratis dan Subtitle Bahasa Indonesia srt.

GQR-24 Penipu Warga Malam Mencarikorban di Jalan

18 Nov 2009

CRPD-326 Pertemuan Menarik antara Siswa dan Guru di Sekolah Ladies

19 Nov 2009

MVSD-104 Pelembutan Indah dengan Senyum Yū YosLensu

19 Nov 2009

BLK-002 Pengalaman Intens Menaiki Roller Coaster Bersama Itsuki Mana

19 Nov 2009

RKI-045 Seorang ahli dalam seni menggunakan lidah dengan keahlian luar biasa dan penuh dedikasi.

19 Nov 2009

SPRD-337 Penyenang Mata Tatkala Siister Tua Menyembunyikan Rahasia

18 Nov 2009

SOUL-025 Rektor Perempuan Terkemuka, Shizuko Miyauchi

19 Nov 2009

TYOD-066 Gerakan Tak Terkontrol seperti Bungkol

19 Nov 2009

KISD-031 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() ```

19 Nov 2009

MVSD-105 Dia menenggak minuman dengan cepat, sambil tersenyum kepada teman-temannya.

19 Nov 2009