Outlier Detection and Handling with Python: Techniques and Examples

Introduction:

Outliers are data points that are significantly different from other data points in a dataset. Outliers can occur due to various reasons, such as measurement errors, data entry errors, or even genuine extreme values. Outliers can cause problems in data analysis and machine learning models, as they can skew results and lead to inaccurate predictions. Therefore, it’s essential to identify and handle outliers appropriately. In this blog post, we’ll explore various outlier detection and handling techniques using Python and provide examples to demonstrate their effectiveness.

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Case Study

We’ll be using the Boston Housing Prices dataset from the Scikit-learn library for our case study. The dataset contains information about the housing prices in Boston based on various features such as crime rate, number of rooms, and distance to employment centers.

1. Visualizing Outliers

The first step in identifying outliers is to visualize the data using box plots, scatter plots, or histograms. For example, we can use a box plot to visualize the distribution of the…