How to build a model from Mechanical Turk results
Amazon Mechanical Turk will notify you when your results are ready and you will finally have a labelled dataset. In some cases, a few records might not have achieved any consensus, so could either improve your task instructions or, if the remaining dataset is big and statistically distributed enough to generate a useful model, simply discard them.
Amazon Mechanical Turk and other crowdsourcing platforms can be very useful in helping you to build your machine learning model from an unlabeled dataset.
Other solutions could involve unsupervised learning techniques, such as clustering and neural networks, which are pretty good at identifying patterns and structures in unlabelled data. However, for most tasks, they are still far behind human intelligence. “Low-tech” solutions involving real humans will probably bring much higher accuracy, with an acceptable trade-off between cost, complexity, and speed.