As part of my continuing data analysis learning journey I thought of trying out past completed Kaggle competition in order to test my skills and knowledge so far . While going through the datasets I came across this Mercedes Green Manufacturing Kaggle competition conducted in sometime in 2017.

Coming from a automotive domain I though this could be a good dataset to apply by data analysis skills. On reading the competition description I could relate to this problem even more closely . The competition is asking, given a set of anonymous categorical and binary variable can you predict the time which the car will take to complete its testing.

As a engineer from this domain I can completely see the importance of such a model . I know how time consuming vehicle testing can be. The process consists of building a prototype car, instrumenting it and then running the required tests . The major bottle neck in car testing occurs during instrumentation phase which requires to de-assemble the car ,fit the required recording instruments and then re-assemble the car.

Another bottle neck during testing is also the availability of testing equipment such as drive cells required to run the test.

All this factors results in man-hours wastage and a increased development time in the vehicle development program. This adds unplanned over-head cost to the company. Hence a model which can predict how much time a car will take to complete a test will help better plan and manage cost and resources.

#stacking #mercedes-benz #xgboost #ensemble-learning #automotive #deep learning