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30 Αυγ 2024 · Outliers, deviating significantly from the norm, can distort measures of central tendency and affect statistical analyses. The piece explores common causes of outliers, from errors to intentional introduction, and highlights their relevance in outlier mining during data analysis.
24 Απρ 2023 · In this blog post, we explored various outlier detection and handling techniques using Python and provided examples to demonstrate their effectiveness.
11 Οκτ 2023 · Learn how to effectively detect and handle outliers in Python with visualization techniques, statistical methods, and robust strategies. Improve your data pipelines and machine learning models.
19 Οκτ 2023 · Pandas, the versatile data manipulation library in Python, provides a set of tools for efficiently handling outliers. In this step-by-step guide, we will explore what outliers are, how to detect them, what actions to take when handling them, and how to leverage pandas along the way.
22 Νοε 2020 · Tutorial on univariate outliers using Python. This first post will deal with the detection of univariate outliers, followed by a second article on multivariate outliers. In a third article, I will write about how outliers of both types can be treated.
14 Φεβ 2021 · Outliers are observations that deviate strongly from the other data points in a random sample of a population. In two previously published articles, I discussed how to detect different types of outliers using well-known statistical methods.
17 Ιουλ 2023 · ∘ Boxplot. · Detecting outliers. ∘ Z-score method. · Treating outliers. ∘ Capping outliers. ∘ Trimming outliers. · References: While there may be variations in its definition, an outlier is typically characterized as a data point that falls well outside the expected range for a given variable or population (Osborne & Overbay, 2004).
