Actigraphy offers a low-cost alternative to conventional polysomnography (PSG) for screening of sleep-wake patterns. Effective use of actigraphy signals requires reliable methods for detecting sleep-wake states from actigraphy measurements. Hence, there is a growing interest in machine learning methods for training predictive models of sleep-wake states from actigraphy data. Existing work has focused on training a single predictive model for the entire population. However, accounting for individual differences, such as age, biological factors, or lifestyle-related variations, calls for personalized models for reliable identification of sleep-wake states from actigraphy data. This study investigates whether personalized models, trained on individual data, can match the performance of generalized models trained on population data. Using a dataset of 54 individuals, we systematically trained and tested personalized and generalized sleep-wake detectors developed using five commonly used machine learning algorithms. Results of our experiments show that personalized sleep-wake predictors are competitive, in terms of their predictive performance, with their generalized counterparts. Our work demonstrates the feasibility of developing reliable personalized sleep-wake states predictors from actigraphy data. This study lays the groundwork for developing personalized models for sleep-wake states detection that are better equipped to handle individual differences.