You are building a machine learning model to predict daily temperatures based on hourly temperature data that is continuously uploaded. Initially, you randomly split the dataset into training and test sets and applied transformations to these datasets separately. During testing, your model achieved an accuracy of 97%. However, after deploying the model to a production environment, its accuracy dropped to 66%. What steps can you take to improve your model's accuracy in production?