1. A method to rank search results comprising: accessing, using one or more processors within a network-based system, historical transaction data; selecting, using the one or more processors, a plurality of predictive features that are predictive of an item being sold over the network-based system, selecting the plurality of predictive features includes using a regression model to determine the plurality of predictive features with the highest correlation to items being sold, wherein the selecting includes selecting a category related feature, a seller related feature, and an item related feature; creating, using the one or more processors, a training data set including extracting the plurality of predictive features from the historical transaction data; creating, using the one or more processors, a prediction model based on the training data set to predict a probability that an item offered for sale by an item listing published on the network-based system will be sold; receiving a search request on the network-based system; generating, using the one or more processors, a list of search results based on the search request; ranking, using the one or more processors, the list of search results using the prediction model; and presenting, using the one or more processors, the ranked search results.