<jupyter_start><jupyter_text>Scikit Datasets  Breast cancer dataset<jupyter_code>from sklearn.datasets import load_breast_cancer

breast_cancer_dataset = load_breast_cancer()
breast_cancer_dataset.keys()
print(breast_cancer_dataset.DESCR)
breast_cancer_dataset.feature_names
breast_cancer_dataset.data.shape
breast_cancer_dataset.target_names
breast_cancer_dataset.target.shape
df_features = pd.DataFrame(breast_cancer_dataset.data, columns=breast_cancer_dataset.feature_names)

df_target = pd.DataFrame(breast_cancer_dataset.target, columns=["cancer"])
df = pd.concat([df_features, df_target], axis=1)
df.head()
df.shape<jupyter_output><empty_output><jupyter_text>Boston housing datasetSource: https://www.kaggle.com/prasadperera/the-boston-housing-dataset<jupyter_code>from sklearn.datasets import load_boston

boston_dataset = load_boston()
boston_dataset.keys()
print(boston_dataset.DESCR)
boston_dataset.feature_names
boston_dataset.data.shape
boston_dataset.target.shape<jupyter_output><empty_output><jupyter_text>Email newsgroup datasetSource: https://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups<jupyter_code>from sklearn.datasets import fetch_20newsgroups

fetch_20_train = fetch_20newsgroups(subset='train')
fetch_20_train.keys()
print(fetch_20_train.DESCR)
fetch_20_train.data[0:2]
fetch_20_train.target_names
fetch_20_train.target<jupyter_output><empty_output><jupyter_text>Digits datasetSource: http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits<jupyter_code>from sklearn.datasets import load_digits

digits_dataset = load_digits(n_class=10)
digits_dataset.keys()
print(digits_dataset.DESCR)
digits_dataset.data.shape
digits_dataset.target
import matplotlib.pyplot as plt

plt.imshow(digits_dataset.images[1], cmap='Greys')
plt.show()
plt.imshow(digits_dataset.images[6], cmap='Greys')
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10298826040554962, 'fraction_numerical': 0.012273212379935965, 'mean_word_length': 10.867088607594937, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, 'https://': 2, 'lorem ipsum': 0, 'www.': 1, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '600256', 'license': 'MIT', 'n_tokens_mistral': 708, 'n_tokens_neox': 677, 'n_words': 75, 'path': '02. ML Datasets.ipynb', 'repo_name': 'jamwine/Machine-Learning', 'size': '1874'}