Uninterruptable Power Supply (UPS) systems have become essential to modern industries that require continuous power supply to manage critical operations. Since a failure of a single battery will affect the entire backup system, UPS systems providers must replace any battery before it runs dead. In this regard, automated monitoring tools are required to determine when a battery needs replacement. Nowadays, a primitive method for monitoring the battery backup system is being used for this task. This thesis presents a classification model that uses data mining cleansing and processing techniques to remove useless information from the data obtained from the sensors installed in the batteries in order to improve the quality of the data and determine at a given moment in time if a battery should be replaced or not. This prediction model will help UPS systems providers increase the efficiency of battery monitoring procedures. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2013.