Predictive Analytics on Accident Data Using Rule Based and Discriminative Classifiers

Abstracting useful information from a big data has always been a challenging task. Data mining is a powerful technology with great potential to extract knowledge based information from such data. Prediction can be done with past and related records in different fields. Risk and safety have always been an important consideration in the field of aircraft. Prediction of accident in aircraft will save life and cost. This paper proposes an accident prediction system with huge collection of past records by applying effective predictive data mining techniques like Decision Tree (DT) and Support Vector Machine (SVM) which have a greater capacity to handle huge and noisy data that are used to predict accidents with more accuracy. The methods used, prove to handle noisy, unrelated and missing data. The prediction results are tabulated and ranges between 85% to 90%.