Classification comes under the category of supervised learning that it learns from a given set of inputs and makes predictions on unseen data. Supervised learning has two main parts which are

• Regression (for predicting numerical values)
• Classification (classifying the categories)

Lets us understand classification by taking a real-life example, suppose we have given a medical record of the patient and we have to predict that the given patient has blood sugar or not. 1 represents the patient has blood sugar while 0 represents the patient does not have blood sugar.

There are mainly three types of classification which are

Application of classification are

• Image classification and video classification is perhaps the best application of classification. Almost every industries i.e medical and agriculture using this. Some of them are lung cancer classification, real-time activity classification, medical imaging classification, and plant weeds classification.
• In the financial sector we are using classification applications to find loan defaulters, credit scoring, loan distribution i.e. whether a customer has to give loan or not. Many companies like RedCarpet and KreditBee uses classification to give loans to the customer.
• In the Sports industry whether a particular team is going to win a match or not based on the previous track records.
• Text classification and sound classification is used to classify text and sound. Sound classification is used by marine engineers.

Various Classification algorithms are

Here there are few algorithm which can applied to regression problem as well we call it CART (Classification and Regression) algorithm

• SVM
• Decision Tree
• KNN
• Neural Network
• Gradient Boosting
• Random forest

Conclusion

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