Most of the accidents that have been reported in our country is due to lack of concentration of the driver or feeling drowsy by the driver. Fatigue and microsleeping behind the wheel are frequently the cause of major accidents. However, early indicators of weariness can be noticed before a severe scenario emerges, therefore detecting and indicating driver fatigue is still a study issue. The majority of classic sleepiness detection methods are based on behavioral factors, while some are obtrusive and may distract drivers, and others need costly sensors. In this paper, we have designed a Driver Drowsiness Detection System using Python and Dlib models. This method can reduce the number of road accidents also the proposed system does not require any physical contact with the driver, so it is easy to implement. The system can detect facial landmarks, computes Eye Aspect Ratio (EAR) to detect driver's drowsiness based on adaptive thresholding. Machine learning algorithms have been employed to test the effectiveness of the proposed approach.