We can use the following methods to screen the outliers:

**Linear models:** Linear models like logistic regression can be trained to screen the outliers. In this way, the model collects the subsequent outlier it meets.

**Boxplot:** The box plot depicts the allocation of the data and its changeability. Box plot includes lower and upper quartiles; therefore, the box fundamentally stretches the Inter-Quartile Range(IQR). The main reason for using the box plot is to identify the outliers in the data.

**Proximity**-based models: K-means clustering is the example of this kind of model, where data points form various or âkâ clusters based on the features like distance or similarity.

**Probabilistic and Statistical models**: We can use statistical models like exponential distribution and normal distribution for identifying the variations in the allocation of the data points. If we found any data point outside the distribution scope, then we can render it an outlier.