{'fraction_non_alphanumeric': 0.012006861063464836, 'fraction_numerical': 0.0, 'mean_word_length': 4.959183673469388, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, '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}}
Accident Localization at the District Heating Network of Kaunas Region using Machine Learning

Machine learning is constantly gaining popularity in real life applications. And one of them is prediction of various real-life events that depend on a huge number of factors that are hard to evaluate. In this article we describe the process of applying XGBoost — one of supervised machine learning methods — to help in prediction and localization of accidents in the district heating network of Kaunas region. We also investigate the importance of the different factors for these events.