{'fraction_non_alphanumeric': 0.019762845849802372, 'fraction_numerical': 0.001976284584980237, 'mean_word_length': 5.176829268292683, '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}}
Understanding the Customer Perception Using Machine Learning While Booking Flight Tickets

Anyone who buys tickets on a regular basis would have enough time to acquire the best deal. Many airlines adjust ticket prices as part of their revenue management strategy. If demand is expected to outstrip capacity, the airline may have to raise prices. Data was collected over a period on a certain airway, including factors such as time of departure, time of arrival, and airways, in order to estimate the minimum flight rate. To understanding, customer perception using machine learning while booking flight tickets to proposed hybrid machine learning (ML) models will be employed to extract data from these attributes. The regression methodologies for machine learning to estimate prices now are presented in this study. This research work performs the implementation and represents that the proposed algorithm accuracy is very high (98%) and Execution Time (sec) is very less when compare to the existing algorithm.