A Brief Introduction to 5 Predictive Models in Data Science
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Predictive Modeling in Data Science answers the question “What is going to happen in the future, based on known past behaviors?”
Modeling is an essential part of Data Science and it is mainly divided into predictive and preventive modeling. Predictive modeling is a process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.
Predictive Modeling is also referred to as Predictive Analytics.
Top 5 Predictive Models:
Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer yes or no types.
Clustering Model: This model groups data points into separate groups, based on similar behavior.
Forecast Model: One of the most widely used predictive analytics models, deals in metric value prediction, this model can be applied wherever historical numerical data is available.
Outliers Model: The Outliers model as the name suggests is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.
To find out which predictive model is best for your analysis, you need to do your homework:
- Start by finding what questions you are looking to answer
- What you are expecting to do with that information.
- What data do you need to make that decision?
- How can you gather that data?
- The quality of the data you will collect.
- Errors that might creep in during the data collection process.
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