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Different Forms Types Of Predictive Models
Data Science is a new discipline that makes use of techniques in mathematical methods, statistical analysis, Machine Learning algorithms and computing resources to extract insights from large sets of unstructured & unprocessed data. The integration of Machine Learning into Data Science is doing many wonders & by training the analytical models in Data Science with relevant Machine Learning algorithms, these models can make accurate predictions about the chances of occurrence of any event. These types of data analytical models are known as Predictive Models.
By deploying Predictive Models, enterprises across various industries are accurately predicting their customer demand, forecast threats & predict risks. These Predictive Models are also helping the enterprises in developing better performing marketing campaigns, sales forecasting & strategic planning.
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Different Types of Predictive Models:
- Decision Trees
Decision Trees is extensively used to make predictions out of multiple variables. The algorithms used in Decision Trees helps in splitting data into branch-like segments based on categories of input variables. This form of techniques is most reliable when it comes to making accurate business decisions.
- Regression (Linear and Logistic)
Regression technique is used in the case where w need to uncover the relation between data variables, & to uncover the data patterns.
- Neural Networks
The use of Neural Networks is gaining a lot of prominence. This approach is ideal when we are dealing with pattern recognition problems across large sets of data.
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Kumar Raja is a multidisciplinary writer, and lifelong learner. He’s a Digital Marketer in the making who spends his time analyzing the developments in the tech world. He’s very passionate about helping people understand the latest trends in the tech world through his well-researched articles. He’s able to condense complicated information about the latest technologies into easily digestible articles.