How to Tackle Air Quality Prediction Using Machine Learning

The use of statistical methods to estimate or predict the behavior of a phenomenon in the future has been common practice in many disciplines such as health care, trading, auto insurance and customer relationship management. The goal of these methods is not necessarily the prediction of the exact outcome, but a determination of the likelihood of different outcomes, since in that case one can prepare accordingly. Machine learning has further empowered such methods, allowing them to become even more accurate by using more data and additional computation. Predictive models require lots of data, which can be problematic since storing such data is expensive and transferring such data can create a significant load on networks. What if there was a method that would allow for the training of predictive models without the need to transfer needed data in its original raw form.