What is Predictive Analysis?

The goal of predictive analysis is to use current data to make predictions about future data. Essentially, you are using current and historical data to find patterns and predict the likelihood of future outcomes. Like in inferential analysis, your accuracy in predictions is dependent on measuring the right variables. If you aren’t measuring the right variables to predict an outcome, your predictions aren’t going to be accurate. Additionally, there are many ways to build up prediction models with some being better or worse for specific cases, but in general, having more data and a simple model generally performs well at predicting future outcomes. All this being said, much like in exploratory analysis, just because one variable may predict another, it does not mean that one causes the other; you are just capitalizing on this observed relationship to predict the second variable. Prediction the future is hard. There aren’t easy ways to gauge how well you predicted an event until that event has passed, so evaluating different approaches or models is a challenge. However, with predictive analysis we can analyze what happened in the past, and make inferences from it. Common things that use predictive analysis are determining the upcoming weather, the outcomes of sports events, and the development of diseases. If there have been rain clouds in the area, chances are it will rain soon. If the Cardinals have had a really good season so far, chances are they will go to the World Series. If a mole on a patient is similar to another patient who has cancer, chances are this patient has cancer as well. These all seem simple predictions to make, but in reality they are much more complicated, and require detailed machine learning models.