In artificial intelligence, a prediction refers to the process of putting data into a model and checking the result. In other words, a prediction is not only an estimation of future events, but also a result of model calculation. For example, looking at a picture of a ship and determining its type is called prediction. Also, predicting the future such as weather forecast is also an example of prediction.
AI model predictions enable companies to make highly accurate forecasts about a number of possible outcomes based on historical data. This data can be used to predict probabilities for customer churn, delays in delivery, and daily energy consumptions that are highly related to profit for the business. This provides insights that create tangible business value for the company. For instance, if your model predicts that your customers are likely to churn, your business can start focusing on those customers with an adjusted marketing strategy that can help prevent losing those customers.
Here is an example of predicting the probability of passing graduate school to help you understand better.
Example of predicting the probability of graduate school admission:
[Among the eight variables, the value the user wants to predict is the probability of admission, so the probability of admission is selected as the target variable.]
[After creating the model, you can enter the predictor variables (X values) to predict the predicted value of the target variable.]