<jupyter_start><jupyter_text>[CTR Prediction: Predict if a user is likely to click an advertisement](https://www.kaggle.com/c/avazu-ctr-prediction)<jupyter_code>df = sqlContext.read.csv('s3n://sparkdemonstration/1M.csv', header=True, inferSchema=True)
df.printSchema()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14429530201342283, 'fraction_numerical': 0.006711409395973154, 'mean_word_length': 8.34375, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '140030', 'license': 'MIT', 'n_tokens_mistral': 115, 'n_tokens_neox': 108, 'n_words': 16, 'path': 'classification/lab-ctr-intro.ipynb', 'repo_name': 'tejaswiniallikanti/upgrad-mlcloud', 'size': '298'}