As a data scientist what predictive models do you have to use most frequently?

That may depend upon the specific analysis requirement, however, certain ones are more popular than others. Predictive Modeling is an essential part of Data Science. It is one of the final stages of data science where you are required to generate predictions based on the historical data. In order to get an in-depth insight inside data and make decisions that will drive the businesses, we need predictive modeling.

Predictive modeling makes use of statistics to forecast the outcomes. Data Science and Predictive Modeling, therefore, share the common background of statistics.

Decision trees – Multiple variable analysis. Produced by algorithms that identify various ways of splitting data into branch-like segments. They help in understanding the route one may take for decision making.

Regression (linear and logistic) – Perhaps the most popular amongst the statistical methods. Regression analysis estimates relationships among variables, finding key patterns in large and diverse data sets, and how they relate to each other.

Neural networks are used to solve complex pattern recognition problems and are used for analyzing large data sets.

Besides these, there other models like – Random Forest, Ridge Regression, K-nearest Neighbors, Time series, etc.