' :\\data\\ \\raw\\comp os ms-windows misc\\ - ']dtype='| 'print(len(data filenames) data target_names ['alt atheism''comp graphics''comp os ms-windows misc''comp sys ibm pc hardware''comp sys mac hardware''comp windows ''misc forsale''rec autos''rec motorcycles''rec sport baseball''rec sport hockey''sci crypt''sci electronics''sci med'sci space''soc religion christian''talk politics guns''talk politics mideast''talk politics misc''talk religion misc'we can choose among training and test sets as followstrain_data sklearn datasets load_mlcomp(" news- ""train"mlcomp_root=mlcomp_dirprint(len(train_data filenames) test_data sklearn datasets load_mlcomp(" news- ""test"mlcomp_root=mlcomp_dirprint(len(test_data filenames) for simplicity' sakewe will restrict ourselves to only some newsgroups so that the overall experimentation cycle is shorter we can achieve this with the categories parameter as followsgroups ['comp graphics''comp os ms-windows misc''comp sys ibm pc hardware''comp sys ma hardware''comp windows ''sci space'train_data sklearn datasets load_mlcomp(" news- ""train"mlcomp_root=mlcomp_dircategories=groupsprint(len(train_data filenames) clustering posts you must have already noticed one thing real data is noisy the newsgroup dataset is no exception it even contains invalid characters that will result in unicodedecodeerror we have to tell the vectorizer to ignore themvectorizer stemmedtfidfvectorizer(min_df= max_df= stop_words='english'charset_error='ignore'vectorized vectorizer fit_transform(dataset data