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What are Classification and Prediction?
Classification is a data mining method used to predict team membership for data instances. In classification, there is a target categorical variable, including income bracket. For example, it can be a division into three classes or categories such as high income, middle income, and low income.
The data mining model analyzes a huge set of records, each record including data on the target variable and a set of input or predictor variables. For example, consider the excerpt from a data set present in the table.
Excerpt from Data Set for Classifying Income
Assume that the researcher causes like to be adequate to categorize the income brackets of persons not present in the database depending on other characteristics related to that person, including age, gender, and occupation. This task is a classification task. It is very nicely appropriated to data mining methods and techniques.
The algorithm would proceed approximately as follows. First, consider the data set including both the predictor variables and the (previously classified) target variable, income bracket.
In this method, the algorithm understands which combinations of variables are related to which income brackets. For instance, older females can be related to the high-income bracket. This data set is known as the training set.
Examples of classification tasks in business and research involve −
It can be deciding whether a specific credit card transaction is fraudulent.
It can be locating a new candidate into a specific track about certain requirements.
It can be used to check whether mortgage software is a good or bad credit risk.
It can be investigating whether a specific disease is a display.
It can be determining whether or not certain financial or personal behavior denotes a possible terrorist threat.
Prediction is the same as classification, except that for prediction, the results are misrepresented in the future.
Examples of prediction tasks in business and research involve −
It can be predicting the value of a stock three months into the future.
It can be predicting the percentage increase in traffic deaths next year if the speed limit is raised.
It can be predicting the winner of this fall’s baseball World Series, depending on a similarity of team statistics.
It can be predicting whether a specific molecule in drug discovery will start a profitable new drug for a pharmaceutical organization.
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