Outlier Detection and Removal using the IQR Method

Outliers can wreak havoc on data analysis and machine learning models. They can lead to incorrect conclusions, biased predictions, and skewed statistical measures. To combat this, we use statistical methods to detect and manage outliers. In this blog post, we will learn the ins and outs of the IQR method

You use this method when your Data is left- or right-skewed.

Left and Right skewd Data

To use this method, you need knowledge of some things:

1. What is a box plot?
2. What is IQR?

What is a box plot?

This is the bloxplot. You can plot a boxplot for any numerical column.boxplot contain percentiles of 25, 50 (median), 75, and 100.

• 25th percentile (also known as the first quartile): This means that 25% of the data values are less than or equal to a particular value.
• 50th percentile (also known as the median): This means that 50% of the data values are less than or equal to a particular value. It’s the middle value when the data is sorted in ascending order.
• 75th percentile (also known as the third quartile): This means that 75% of the data values are less than or equal to a particular value.
• 100th percentile: This means that there is no value in the dataset that is greater than or equal to a particular value. In other words, the value at the 100th percentile is the maximum value in the dataset, and no data point in the dataset exceeds or equals this value.

What is IQR?

The Interquartile Range, or IQR, is a measure of statistical dispersion. It represents the range within which the middle 50% of the data falls. To calculate the IQR, you need to find the difference between the 75th percentile (Q3) and the 25th percentile (Q1).

IQR = Q3 — Q1

To identify outliers using the IQR method, we establish two boundaries:

• Lower Bound: Q1–1.5 * IQR
• Upper Bound: Q3 + 1.5 * IQR

These boundaries help us determine which data points might be outliers.

Any data point that falls below the lower bound (Q1–1.5 * IQR) is considered an outlier. These values are significantly lower than the majority of the dataset and are potential candidates for removal or further investigation.

Conversely, any data point that exceeds the upper bound (Q3 + 1.5 * IQR) is also considered an outlier. These values are much higher than the majority of the dataset and may warrant special attention.

Benefits of the IQR Method

One advantage of the IQR method is that it is robust to skewed data distributions. It identifies outliers based on percentiles, making it less sensitive to extreme values.

Simple and Effective The IQR method is easy to implement and interpret. It provides a clear range within which most data points should fall, making it a valuable tool for data analysis and quality control.

Conclusion: In your data analysis journey, identifying and managing outliers is crucial to ensure the accuracy and reliability of your results. The IQR method offers a robust and straightforward approach to pinpointing potential outliers, helping you make informed decisions about how to handle them in your dataset.

Implementation:

blog_notebooks/Outlier Detection and Removal using the IQR Method at main ·…

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