The Interquartile Range (IQR) method is an objective, non-parametric statistical technique used to identify and isolate outliers in a dataset based on data dispersion. It is a foundational cornerstone of data cleaning pipelines because, unlike the Z-score method, it does not assume a normal distribution and remains highly robust when dealing with heavily skewed real-world data like salaries, real estate prices, or transaction volumes. 1. The Mathematical Framework (The 1.5 × IQR Rule)
The IQR measures the spread of the middle 50% of your data. It creates an analytical “fence” around your data, flagging any data points that fall outside these boundaries.
Lower Outliers Normal Data Range Upper Outliers <———–|——————-|——————-|———–> Lower Bound Median Upper Bound (Q1 - 1.5*IQR) (Q3 + 1.5*IQR) The mathematical formula functions via four main steps: Ultimate Guide to Data Cleaning Techniques – Kaggle
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