<jupyter_start><jupyter_text>Machine Learning<jupyter_code>%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np

from sklearn import datasets
from sklearn.decomposition import PCA<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.05194805194805195, 'fraction_numerical': 0.0, 'mean_word_length': 9.08695652173913, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '559195', 'license': 'Unlicense', 'n_tokens_mistral': 75, 'n_tokens_neox': 74, 'n_words': 21, 'path': 'labs/04_machine-learning/04_PCA-analysis.ipynb', 'repo_name': 'lperezmo/pythonic-science', 'size': '231'}