From 2994b80253f8be0e3d7067210a5ad9702021afcf Mon Sep 17 00:00:00 2001
From: Jayesh023 <142317927+Jayesh0048@users.noreply.github.com>
Date: Thu, 8 Jan 2026 17:34:27 +0530
Subject: [PATCH] Add outlier detection tutorial notebook

This notebook provides an introduction to outlier detection, explaining the concept of outliers and demonstrating a method using Z-scores.
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 outlier_detection.ipynb | 20 ++++++++++++++++++++
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 create mode 100644 outlier_detection.ipynb

diff --git a/outlier_detection.ipynb b/outlier_detection.ipynb
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+# Outlier Detection Tutorial
+
+This notebook explains what outliers are and how to detect them using simple methods.
+
+An outlier is a data point that is very different from other values in a dataset.
+Outliers can affect model performance and data analysis.
+
+import numpy as np
+import pandas as pd
+
+data = pd.DataFrame({
+    "values": [10, 12, 13, 14, 15, 100]
+})
+
+from scipy import stats
+
+data["z_score"] = stats.zscore(data["values"])
+data
+
+#Z-score measures how far a value is from the mean.