There are a lot of classifiers to choose from but how to understand which classifier to use and when? Is there any common approach or logic I should follow ?

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@Anisha, There is no specific logic for choosing a classifier but following are some preferrable classification technique which is commonly used depending on your requirement:

**Decision Tree**-Decision tree is one of the most popular tools for classification and prediction. It is a flowchart of a tree structure where each note represents a test, each branch represents the outcome of a test and each terminal node hold a class label.

**Rule-based classifiers**-The term rule-based classification can be used to refer to any classification scheme that makes use of IF-THEN rules for class prediction.

** SMO (SVM)**-A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, this algorithm outputs an optimal hyperplane which categorizes new examples.

** Naive Bayes**-Naive Bayes predicts membership probabilities for each class such as probability that given record or data point belongs to a particular class .

**Neural Network**s-A neural network consists of units (neurons), arranged in layers, which convert an input vector into some output.

For more details you may visit:https://scikit-learn.org/stable/tutorial/machine_learning_map/