In some implementations, the machine learning model may utilize one or more of an artificial neural network, na?ve Bayes classifier algorithm, k-means clustering algorithm, support vector machine algorithm, linear regression, logistic regression, decision trees, random forest, nearest neighbors, and/or other approaches. Machine learning component 112 may utilize training techniques such as supervised learning, semi-supervised learning, unsupervised learning, reinforcement learning, and/or other techniques.

In supervised learning, the model may be provided with known training dataset that includes desired inputs and outputs (e.g., the input/output pairs described herein), and the model may be configured to find a method to determine how to arrive at those outputs based on the inputs. The model may identify patterns in data, learn from observations, and make predictions. The model may make predictions and may be corrected by an operator—this process may continue until the model achieves a high level of accuracy/performance. Supervised learning may utilize approaches including one or more of classification, regression, and/or forecasting.

Semi-supervised learning may be similar to supervised learning, but instead uses both labelled and unlabeled data. Labelled data may comprise information that has meaningful tags so that the model can understand the data (e.g., the input/output pairs described herein), while unlabeled data may lack that information. By using this combination, the machine learning model may learn to label unlabeled data.

Machine learning component 112 may be configured to store the trained machine learning model. In some implementations, the trained machine learning model may be stored in electronic storage 128.