机器学习和预测建模之间的区别

1. 机器学习：
它是计算机科学的一个分支，除了需要明确编程之外，还利用认知掌握策略来对其结构进行编程。换句话说，这些机器被正确地识别为随着经验更好地开发。

2. 预测建模：
它是一种数学方法，它利用统计数据和过去的趋势来预测未来。它旨在处理提供的统计数据，以在事件触发后得出最终结论。换句话说，它利用以前的特征并将它们应用于未来。例如，如果购买者从电子商务网站购买智能手机，他可能会立即对其附加组件着迷。几年后，他可能会成为智能手机电池的可靠客户。目前，他购买竞争对手智能手机口音的可能性显然很暗淡。

机器学习和预测建模之间的区别：

S.No.	Machine Learning	Predictive 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.