In the post-fabrication process for semiconductors, it is critical to predict the yield. This process consists of a series of electrical and physical tests following semiconductor fabrication, tests that generate a significant volume of parametric data. While past research has investigated yield prediction using parametric test data, most studies have difficulty correctly predicting the low and high yield because of the wide range of variables and the large data set. Also, in the case of the packaging yield, prediction is inaccurate as this yield does not directly correlate with the parametric test data. Therefore, this study proposes a framework in which the packaging yield is classified using the parametric test data of the previous step of the packaging test. This study involves three stages. In the first, data preprocessing is conducted due to the large data set. To learn a data mining model using much more data, parametric test data generated in the die level need to be changed into the wafer level. In the second stage, a random forest algorithm is used to select significant variables affecting the packaging yield. Finally, the third stage uses a nonlinear support vector machine (SVM) to classify the low and high yield. Through the three stages, this study demonstrates that this proposed algorithm has a superior performance.