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**List Of Most
Widely Used Predictive Models**

One of the major problems that are associated with this data explosion is that, analyzing & managing large volumes data would be presenting a huge problem for the analysts. This is where Data Science comes into the game of play. Data Science isn’t just capable of mining & interpreting with Big Data but is also capable of making accurate predictions out of Big Data. The prediction process in Data Science is helping the enterprises to accurately predict their customer demand, accurately forecast threats & risks, better performing marketing campaigns, increase revenue, etc.

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Now,
let’s look at the list of most widely used **Predictive
Models** in Data Science.

**Decision Trees**

When it comes to analyzing multiple variables & make predictions out of it then, Decision Trees would be the best approach to be used. This technique makes use of advanced algorithms that would help in splitting data into branch-like segments. This technique of splitting the data into subsets is done based on categories of input variables. This is considered as the most reliable approach to make accurate business decisions.

**Regression (linear and logistic)**

When it comes to analyzing the relation among data variables, uncovering the data patterns, finding the relation between the data sets from large volumes of Big Data, the best approach to be used would be Regression technique.

**Neural Networks**

Neural Networks are one of the most advanced concepts in AI & Deep Learning technologies. When it comes to handling pattern recognition problems and analysing large data sets using Neural Networks would be the ideal approach.

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