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The Big Data analytics are a logical analysis of very large scale datasets.The data analysis enhances an organization and improve the decision makingprocess. In this article, we present Airline Delay Analysis and Prediction toanalyze airline datasets with the combination of weather dataset. In thisresearch work, we consider various attributes to analyze flight delay, forexample, day-wise, airline-wise, cloud cover, temperature, etc. Moreover, wepresent rigorous experiments on various machine learning model to predictcorrectly the delay of a flight, namely, logistic regression with L2regularization, Gaussian Naive Bayes, K-Nearest Neighbors, Decision Treeclassifier and Random forest model. The accuracy of the Random Forest model is82% with a delay threshold of 15 minutes of flight delay. The analysis iscarried out using dataset from 1987 to 2008, the training is conducted withdataset from 2000 to 2007 and validated prediction result using 2008 data.Moreover, we have got recall 99% in the Random Forest model.

Empirical Study on Airline Delay Analysis and Prediction