Artificial intelligence that studies historical data, identifies patterns and makes predictions about the future that can better inform business decisions.
keywords : Historical, Predict, forecast
1.For actionable insights, accurate forecast.
2.Aids strategic decision - making.
Predictive analytics models can be categorized into two main types:
1.classification model (Binary or multiclass)
Note : If you want to predict a categorical or discrete output value.
2.regression model (linear or nonlinear)
Note : if want to predict a continuous or numerical output value.
Classification models are used to predict a categorical or discrete output value based on one or more input variables. For example, you can use a classification model to predict whether an email is spam or not based on its sender, subject, and content. Classification models can be binary or multiclass, depending on the number of possible output values. Binary classification models have only two output values, such as yes or no, while multiclass classification models have more than two output values, such as red, green, or blue. Some of the common classification models are decision trees, k-nearest neighbors, support vector machines, and neural networks.
Regression models are used to predict a continuous or numerical output value based on one or more input variables. For example, you can use a regression model to predict the sales revenue of a product based on its price, features, and customer feedback. Regression models can be linear or nonlinear, depending on the relationship between the input and output variables. Linear regression models assume a straight line relationship, while nonlinear regression models can capture more complex and curved relationships. Some of the common regression models are simple linear regression, multiple linear regression, polynomial regression, and logistic regression.