11. A system comprising: one or more processing modules; and one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: training a machine learning algorithm to create a predictive model; for each record of a set of distinct records, using the predictive model to calculate a probability of the record being accessed; for each record of the set of distinct records, if the probability of the record being accessed as calculated is greater than a threshold value, then placing the record in a first database cluster H; for each record of the set of distinct records, if the probability of the record being accessed as calculated is not greater than the threshold value, then placing the record in a second database cluster L; receiving a request from a requester for at least one record of the set of distinct records; and presenting the at least one record from the set of distinct records to the requester in response to the request; wherein training the machine learning algorithm comprises: for each record in the set of distinct records, inputting a training feature vector associated with the record into the machine learning algorithm, the training feature vector associated with the record comprising a list of characteristics of the record; for each record in the set of distinct records, inputting a cost vector associated with the record into the machine learning algorithm, the cost vector associated with the record configured to train the machine learning algorithm to reduce a probability of a false negative prediction for the record; and iteratively operating the machine learning algorithm on each record in the set of distinct records to create the predictive model.
11. A system comprising: one or more processing modules; and one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: training a machine learning algorithm to create a predictive model; for each record of a set of distinct records, using the predictive model to calculate a probability of the record being accessed; for each record of the set of distinct records, if the probability of the record being accessed as calculated is greater than a threshold value, then placing the record in a first database cluster H; for each record of the set of distinct records, if the probability of the record being accessed as calculated is not greater than the threshold value, then placing the record in a second database cluster L; receiving a request from a requester for at least one record of the set of distinct records; and presenting the at least one record from the set of distinct records to the requester in response to the request; wherein training the machine learning algorithm comprises: for each record in the set of distinct records, inputting a training feature vector associated with the record into the machine learning algorithm, the training feature vector associated with the record comprising a list of characteristics of the record; for each record in the set of distinct records, inputting a cost vector associated with the record into the machine learning algorithm, the cost vector associated with the record configured to train the machine learning algorithm to reduce a probability of a false negative prediction for the record; and iteratively operating the machine learning algorithm on each record in the set of distinct records to create the predictive model. 20. The system of claim 11 wherein the cost vector associated with the record represents an estimate of cost incurred when the record is placed in an incorrect database cluster of the first database cluster H or the second database cluster L.