Anomaly Detection Example with Kernel Density in Python

   The Kernel Density estimation is a method to estimate the probability density function of a random variables. We can apply this model to detect outliers in a dataset.

   In this tutorial, we'll learn how to detect the outliers of regression data by applying the KernelDensity class of Scikit-learn API in Python. The tutorial covers:

1. Preparing the data
2. Anomaly detection with KernelDensity
3. Testing with Boston housing dataset
4. Source code listing

We'll start by loading the required libraries for this tutorial.

Anomaly Detection Example with K-means in Python

   The K-means clustering method is mainly used for clustering purposes. I experimented to apply this model for anomaly detection and it worked for my test scenario. Technically, we can figure out the outliers by using the K-means method. However, it is better to use the right method for anomaly detection according to data content you are dealing with.

   In this tutorial, we'll learn how to detect outliers for regression data by applying the KMeans class of Scikit-learn API in Python. The tutorial covers:

• The K-Means algorithm
• Preparing the data
• Anomaly detection with K-means
• Testing with Boston housing dataset
• Source code listing
  

We'll start by loading the required libraries for this tutorial.

Introduction to Anomaly Detection Methods with Python

   Anomaly detection can be done by applying several methods in data analysis. I explained my previous tutorials on how to detect anomalies in a dataset by applying methods like Isolation Forest, Elliptical Envelope, One-Class SVM,  DBSCAN, and Gaussian Mixture.

   We applied the classes provided by Scikit-Learn API for these models. The sample dataset is created randomly by using create_blob() function and anomalies are detected by using each methods. Both data and the result are visualized in a plot to confirm visually. The Python source codes are provided for all tutorials.

   I summarised the above mentioned anomaly detection methods in this tutorial. Here, we'll briefly address the following topics.

1. What is anomaly detection
2. Isolation Forest Method
3. Local Outlier Factor Method
4. Elliptical Envelope Method
5. One-Class SVM Method
6. Gaussian Mixture Method

   Let's get started.

Anomaly Detection Example with Gaussian Mixture in Python

   The Gaussian Mixture is a probabilistic model to represent a mixture of multiple Gaussian distributions on population data. The model is widely used in clustering problems. In this tutorial, we'll learn how to detect anomalies in a dataset by using a Gaussian mixture model.
 
  The Scikit-learn API provides the GaussianMixture class for this algorithm and we'll apply it for an anomaly detection problem. The tutorial covers:

1. Preparing the dataset
2. Defining the model and anomaly detection
3. Source code listing

Anomaly Detection Example with DBSCAN in Python

   The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. The main principle of this algorithm is that it finds core samples in a dense area and groups the samples around those core samples to create clusters. The samples in a low-density area become the outliers. We'll focus on finding out those outliers in this tutorial.
 
   The Scikit-learn API provides the DBSCAN class for this algorithm and we'll use it in this tutorial. The tutorial covers:

1. Preparing the dataset
2. Defining the model and anomaly detection
3. Source code listing

We'll start by loading the required libraries for this tutorial.

from sklearn.cluster import DBSCAN
from sklearn.datasets import make_blobs
from numpy import random, where
import matplotlib.pyplot as plt

Anomaly Detection Example with One-Class SVM in Python

   A One-class classification method is used to detect the outliers and anomalies in a dataset. Based on Support Vector Machines (SVM) evaluation, the One-class SVM applies a One-class classification method for novelty detection.
   In this tutorial, we'll briefly learn how to detect anomaly in a dataset by using the One-class SVM method in Python. The Scikit-learn API provides the OneClassSVM class for this algorithm and we'll use it in this tutorial. The tutorial covers:

1. Preparing the data
2. Defining the model and prediction
3. Anomaly detection with scores
4. Source code listing

We'll start by loading the required libraries for this tutorial.

from sklearn.svm import OneClassSVM
from sklearn.datasets import make_blobs
from numpy import quantile, where, random
import matplotlib.pyplot as plt