<jupyter_start><jupyter_text>Creating Train and Test sets in advance:<jupyter_code>X_train = X[X.ORIGIN == 'train']
X_test = X[X.ORIGIN == 'test']
y_train = y[X.ORIGIN == 'train']
y_test = y[X.ORIGIN == 'test']

for dataset in (X_train, X_test, y_train, y_test):
    print(dataset.shape)<jupyter_output>(5822, 86)
(4000, 86)
(5822,)
(4000,)<jupyter_text>Exploratory Data Analysis Using **pandas_profiling** and **autoviz** for quick statistical analysical and visualization<jupyter_code>report = pandas_profiling.ProfileReport(X)
report.to_file(output_file="report.html")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1480865224625624, 'fraction_numerical': 0.033277870216306155, 'mean_word_length': 7.985074626865671, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '167042', 'license': 'MIT', 'n_tokens_mistral': 257, 'n_tokens_neox': 233, 'n_words': 45, 'path': 'notebooks/caravan-insurance.ipynb', 'repo_name': 'prikmm/MLprojects', 'size': '601'}