There are many possible classifiers used but here are some of them:

### Naive Bayes Classification Algorithm
#### Advantages
- It is simple, and its implementation is straightforward.
- The time required by the machine to learn the pattern using this classifier is less.
- It performs well in the case where the input variables have categorical values.
- It gives good results for complex real-world problems.
- It performs well in the case of multi-class classification.

#### Disadvantages
- It assumes independence among feature variables which may not always be the case.
- We often refer to it as a bad estimator, and hence the probabilities are not always of great significance.
- If, during the training time, the model was not aware of any of the categorical variables and that variable is passed during testing, the model assigns 0 (zero) likelihood and thus substitutes zero probability referred to as 'zero frequency.' One can avoid this situation by using smoothing procedures such as Laplace estimation.

### Logistic Regression Classification Algorithm
#### Advantages
- It's a simple model, so it takes very little time for training.
- It can handle a large number of features.

#### Disadvantages
- Although it has the word regression in its name, we can only use it for classification problems because of its range which always lies between 0 and 1.
- It can only be used for binary classification problems and has a poor response for multi-class classification problems

### Decision Tree Classification Algorithm
#### Advantages
- This algorithm allows for an uncomplicated representation of data. So, it is easier to interpret and explain it to executives.
- Decision Trees mimic the way humans make decisions in everyday life.
- They smoothly handle qualitative target variables.
- They handle non-linear data effectively.

#### Disadvantages
- They may create complex trees which sometimes become irrelevant.
- They do not have the same level of prediction accuracy as compared to other algorithms.

### K-Nearest Neighbour Classification Algorithm
#### Advantages
- One can apply it to datasets of any distribution.
- It is easy to understand and is quite intuitive.

#### Disadvantages
- It is easily affected by outliers.
- It is biased towards a class that has more instances in the dataset.
- It is sometimes challenging to find the optimal number for K.
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