Member-only story
Outliers — those data points that deviate significantly from the rest of the dataset — can be both a blessing and a curse in data science. They have the potential to reveal hidden insights but can also skew results if not handled properly. In this blog, we’ll explore:
✅ What outliers are
✅ How to detect them
✅ How to manage them effectively
If you don’t have membership, Read full article here
🔍 What Are Outliers?
An outlier is a data point that is significantly different from other observations. It can be much higher or lower than the rest of the dataset. Outliers can occur due to:
- Measurement errors
- Data corruption
- Natural variability in data
- Rare occurrences (e.g., fraudulent transactions in banking)
🎯 Types of Outliers
📌 Univariate Outliers — Extreme values in a single variable.
📌 Multivariate Outliers — Unusual combinations of values across multiple variables.
📌 Global Outliers — Data points that are far removed from all other points.
📌 Contextual Outliers — Values that are considered outliers in a specific context (e.g., seasonal trends).
📌 Collective Outliers — A group of values that together behave differently from the norm.
