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Difference between Machine Learning and Predictive Modelling

  • Last Updated : 16 Nov, 2020

1. Machine Learning :
It is a branch of computer science which makes use of cognitive mastering strategies to program their structures besides the need of being explicitly programmed. In different words, those machines are properly recognized to develop better with experience.

2. Predictive Modelling :
It is a mathematical approach which makes use of statistics and past trends for the future prediction. It targets to work upon the furnished statistics to attain an end conclusion after an event has been triggered. In other words, it makes use of previous traits and applies them to future. For example, if a purchaser purchases a smartphone from an e-commerce website, he would possibly be fascinated in its add-ons immediately. He would possibly be a viable customer for smartphone battery a few years down the line. Currently, possibilities of him shopping for the accent of a competitor smartphone are noticeably bleak.



Difference between Machine Learning and Predictive Modelling :

S.No.Machine LearningPredictive Modelling
1.To solve complex problems it uses various ML models.To predict future outcomes, it uses past data.
2.They have the tendency to adapt themselves and learn from experiences.They do not have the tendency to adapt to the data.
3.No need to explicitly programmed.To process data, they need to be programmed the system manually.
4.To deal with a particular problem, their models are smart enough to adapt and update.They don’t have smart models which can take decision by themselves.
5.It is a data- driven approach.It is a use case driven approach.
6.It does not require a huge amount of historical data to process task.It requires a high amount of historical data to process a particular task, i.e. to predict future outcomes.
7.To solve a problem, it requires a detailed description of the problem.To solve a problem, it does not requires a detailed description of the problem.
8.It uses various models, algorithms and learnings to deal with a problem such as Rule-based machine learning, SVM, ANN, etc.It also uses different algorithms and learnings to deal with a problem such as KNN, Random forests, Neural Networks, etc.

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