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Logistic Regression Applied to Driver’s Alertness Prediction

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dc.contributor.advisor Guha, Sumanta
dc.contributor.author Touhami, Mohamed Karim
dc.contributor.other Esichaikul, Vatcharaporn
dc.contributor.other Anutariya, Chutiporn
dc.date.accessioned 2015-05-13T18:00:12Z
dc.date.available 2015-05-13T18:00:12Z
dc.date.issued 2014-02
dc.identifier.other AIT RSPR no.IM-14-05
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/790
dc.description 27 p. en_US
dc.description.abstract Talking on the phone or being distracted by events outside the car clearly can lead to the loss of the driver’s alertness or vigilance. To predict whether the driver is alert or not has therefore became a main issue for cars Companies such as Ford. This research study is based on the prediction challenge proposed by the company Ford via the website Kaggle.com. The objective is to be on the top 5 on the Kaggle Leaderoard where participants are ranked according to the score their model achieved. Many binary prediction model or binary classifiers can be used for such challenges. A further investigation on the data shows that linear models are the most adapted. Logistic regression with feature engineering have been performed on the data to come out with a final classifier. This model clearly achieve a better score than the winner of the competition and use only 4 variables. en_US
dc.description.sponsorship Telecom SudParis en_US
dc.publisher AIT en_US
dc.title Logistic Regression Applied to Driver’s Alertness Prediction en_US
dc.type Research report en_US


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