Physical activity plays a crucial role in maintaining overall health and reducing the risk of various chronic diseases. Step counting has emerged as a popular method for assessing physical activity levels, given its simplicity and ease of use. However, accurately measuring step counts in free-living environments presents significant challenges, with most activity trackers exhibiting a percent error above 20%. This study aims to address these challenges by creating a machine learning algorithm that leverages activity labels to improve step count accuracy in real-world conditions. Two approaches to balancing data were used:​ one employed a simpler oversampling technique, while the other adopted a more nuanced approach involving the removal of outliers. Models 1 and 2 were trained on each of these uniquely balanced datasets. Model 1 performed much better than Model 2 on testing datasets, but both achieved better than 20% error on new datasets, indicating their potential for more accurate step counting in real-world conditions. Despite challenges such as data imbalance, the study demonstrated the viability of using activity labels to enhance step counting accuracy. Future research should focus on addressing data imbalances and exploring more advanced machine learning techniques for more reliable activity monitoring.