# Outlier Detection

## What is Outlier Detection?

Outlier detection, also known as anomaly detection, is the process of identifying data points that deviate significantly from the expected pattern or distribution of the data. Outliers can be the result of noise, errors, or genuinely unusual observations. Detecting outliers is important for improving data quality, identifying data entry errors, detecting fraud, and discovering novel patterns in data.

## Example of Outlier Detection

Suppose we have a dataset containing information about the heights of people in a population, and we want to detect potential outliers that might indicate measurement errors or exceptionally tall or short individuals.

Here’s a Python code example using the `pyod` package:

``````import numpy as np
import matplotlib.pyplot as plt
from pyod.models.knn import KNN

# Generate sample data with one outlier
data = np.random.normal(170, 5, size=(100, 1))
data = np.append(data, [], axis=0)

# Fit a k-Nearest Neighbors outlier detector
knn = KNN(n_neighbors=5, contamination=0.01)
knn.fit(data)

# Predict the outliers
outlier_predictions = knn.predict(data)

# Plot the data and the detected outlier
plt.scatter(range(len(data)), data, c=outlier_predictions, cmap=plt.cm.coolwarm)
plt.xlabel('Index')
plt.ylabel('Height (cm)')
plt.title('Outlier Detection using k-Nearest Neighbors')
plt.show()
``````

In this example, we generate a sample dataset containing the heights of people in a population, with one exceptionally tall individual (210 cm). We use the k-Nearest Neighbors outlier detection method from the `pyod` package to detect the outlier. The plot shows the data points with their indices, and the detected outlier is highlighted in a different color.