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In Artificial Intelligence, classification is the process of labelling a set of data (structured or unstructured) into different
classes or groups where we can assign a label to each class. For example, cities in India have different coloured dustbins
for different types of waste: green coloured dustbins for biodegradable waste, blue dustbins for non-biodegradable
or plastic waste, yellow dustbins for paper waste and red dustbins for metallic waste. Hence, we classify the waste into
different categories while also labelling each category.
In machine learning, a predictive classification model tries to approximately map the function from input variables to
discrete output variables. The main goal is to determine which class/category the new data will belong to. For example,
heart disease detection is a classification problem. There are only two classes in this case—a patient has heart disease or
does not have heart disease. In this case, the classifier needs training data to understand how a given input variable is
related to the class. Once the classifier is properly trained, it can be used to determine whether a particular patient has
heart disease or not.
Task
Form a group of 5 students. Each group should think and come up with one use case from the classroom
environment or their home/society, where they would like to apply classification algorithm to solve the problem.
Real Life Applications of Classification
In classification, our data is categorized into a preferred and distinct number of classes while assigning label to each
class. Applications of classification in real life include:
• speech recognition
• handwriting recognition
• face tagging as done by Facebook
• detecting fraudulent transactions in banks
• predicting whether advertisements on a website will be clicked or not
• product classification
• document classification
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