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Improving the Quality of Prediction of Traffic Accident Based on GPS-trackers Using Clusterisation

Student: Andrei Bulanov

Supervisor: Nikolay Pilnik

Faculty: Faculty of Economic Sciences

Educational Programme: Applied Economics (Master)

Final Grade: 7

Year of Graduation: 2019

Nowadays there are many articles about car accidents, but very few of them cover the topic of predicting the probability of getting into an accident based on the driver's behavior on the road. At first, an attempt was made to solve this problem using interpretable methods. Such as logistic regression and data clustering. Further, the quality of the prediction was increased using more advanced machine learning methods, such as random forest, adaboost, and xgboost. Models were based on data from gps trackers which were installed in the insurance company. Unfortunately high predictive power was not achieved using interpreted methods, but more complex methods allowed us to significantly improve the prediction accuracy.

Full text (added May 31, 2019)

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