In this project I analyze fictitious recruiting data for two separate tasks - prediction and explanation. In both tasks, the models that are used aim to identify which candidates will be hired based on a series of input variables. In the prediction tasks, a multilayer perceptron neural network model is trained with the goal of optimizing prediction performance. While the neural network predicts accurately 'off the shelf', the second task in this project is to use an alternative model that aids interpretation of the factors that influence hiring decisions for this company. For the explanation task, a multiple logistic regression model is trained to predict hiring decisions but also to be able to enable a close examination of the relationships between the input variables on the outcome.