Logistic regression applied to driver's alertness prediction | |
| Author | Touhami, Mohamed Karim |
| Call Number | AIT RSPR no.IM-14-05 |
| Subject(s) | Automobile drivers Machine learning |
| Note | A Research Study submitted in partial fulfillment of the requirements for the degree of Master of Science in Information Management, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Research studies project report ;no. IM-14-05 |
| 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. |
| Year | 2014 |
| Corresponding Series Added Entry | Asian Institute of Technology. Research studies project report ; no. IM-14-05 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Guha, Sumantha; |
| Examination Committee(s) | Vatcharaporn Esichaikul;Chutiporn Anutariya; |
| Scholarship Donor(s) | Telecom SudParis; |
| Degree | Research Studies Project Report (M.Sc.) - Asian Institute of Technology, 2014 |