Aviation data analysis using machine learning

Aviation data analysis plays a vital role in enhancing the safety, efficiency, and profitability of the aviation industry. The growth of aviation data has created an opportunity for data mining techniques to extract valuable information and patterns from large and complex aviation datasets. This paper presents an abstract of an aviation data analysis study that uses data mining techniques to explore and analyse the available aviation data. Data mining is the process of extracting information from large chunks of a dataset. It is also known as information mining. The study aims to extract hidden patterns, relationships, and insights from the data that can help aviation stakeholders to make informed decisions. The data mining techniques applied in this study include clustering, classification, and association rule mining. The study will focus on three main areas of aviation data: flight data, maintenance data, and weather data. It is used in a variety of fields, including medicine, the environment, education, and criminal justice. This research work includes flight crash investigation and analysis. Flight crashes can occur as a result of pilot error, mechanical failure, inclement weather, sabotage, or human error. The research is carried out in order to identify the aboard/ground fatality rate with operators and location, as well as to find similarities between plane crashes. The paper also discusses various data mining techniques employed in aviation data analysis, including clustering, classification, association rule mining, and predictive modeling. Additionally, it explores challenges associated with aviation data, such as data quality, data integration, and privacy concerns. Strategies to address these challenges are discussed, including data preprocessing, feature selection, and anonymization techniques. The research methodology involves the collection of aviation datasets from various reliable sources, followed by data preprocessing and applying appropriate data mining algorithms. The performance of these algorithms is evaluated based on metrics such as accuracy. The results obtained from the analysis demonstrate the efficacy of data mining techniques in aviation data analysis. The insights gained from these techniques contribute to evidence-based decision making, improved safety measures, enhanced operational efficiency, and better customer satisfaction. By leveraging data mining, the aviation industry can achieve improved safety, operational efficiency, and customer experience, leading to enhanced overall performance and competitiveness in the market.

Keywords: Machine Learning, Gradient Boosting, Naïve baye’s , Decision Tree, MLP Classifier ,ML technique’s, evaluation.