Airline delays play a very significant role in influencing the global economy and also in individual carrier performance. Thus, delay prediction is an important area of research as minimizing delay can efficiently solve these issues. We used the time series dataset provided by U.S. Department of Transportation and conducted classification and regression analysis. In this study, we employed only PySpark for our data processing and analysis, based on the Apache Spark technology of the big data framework. We used five machinelearning models for regression analysis and achieved an RMSE of 20.30. Additionally, we used seven machine-learning models for classification with an accuracy of 94.39%. We further compare the performance measures of several machine learning models for different types of delay.