In this exercise, you are required to use support ...

In this exercise, you are required to use support vector machines (SVMs) to prediction incident duration. By this assignment on SVMs, you can get deep understanding of how to use SVMs. The data comes from the national incident management center for towing operations. These data were provided by towing officers, police and Rijkswaterstaat road-inspectors who perform incident handling. The data was collected from 1st May to 13th September 2005 on the region of Utrecht. You can find 1853 registrations of incident in total in the incidentduration.csv. Test_set.csv extract 50% of data and is used to test SVM, while train_set.csv contains the remaining and is used to train SVM. Attributes includes as follows: 1. Incident type (Stopped vehicle, lost load, accident); 2. Kind of vehicles involved (passenger cars, trucks, N/A); 3. Police required (yes, no) 4. Track research (yes, no) 5. Ambulance required (yes, no) 6. Fire brigades required (yes, no) 7. Repair service required (yes, no) 8. Tow truck required (yes, no) 9. Road inspector (yes, no) 10. Lane closer (yes, no) 11. Road repair required (yes, no) 12. Fluid to be cleaned (yes, no) 13. Damage of road equipment (yes, no) 14. Number of vehicles (Involved Single, two, more) 15. Type bergings task (onb, CMI, CMV) 16. By the week (workdays, weekend) 17. Start and end time (during peek hour, off peek hour) 18. Duration (short, long) Build prediction models with SVM to complete the following tasks. A. Use train_set.csv as train data set to build a model, and test on test_set.csv. Report the accuracy of train model and test model you get. (Suggestions: In data preprocess, you can deal with independent variables which are nominal variables by using one-hot representation, so that the corresponding value of each feature is guaranteed to be 0 or 1. It is easy to implement with get_dummies(data) function in the pandas package.) B. Use incidentduration.csv to build a model using 10-fold cross validation. Report the accuracy of train model and test model you get. C. Build a prediction model again after Feature Reduction (Keep 80% variance). Report the accuracy of train model and test model you get. D: Which model gives the highest accuracy on the test set？ Why？ Give you explanation.