Anomaly Detection Using PyCaret!!!

Getting Started with Anomaly Detection!!

If you are not familiar with PyCaret. I suggest you to first go through the below link before moving on from here.

Complete Guide to PyCaret.

Also, Check out our Article on:

Reading Data

from pycaret.datasets import get_dataget_data('index')

Scrolling down we can find datasets available for the Classification Modelling.

import pycaretfrom pycaret.anomaly import *data = get_data('anomaly')

We will use the anomaly data

Setting up the PyCaret environment

ano = setup(data = data)

After this press enter and you will get results as shown below.

Creating Models


Model ID for Anomaly Models.

| ID | Name |
| ‘abod’ | Angle-base Outlier Detection |
| ‘iforest’ | Isolation Forest |
| ‘cluster’ | Clustering-Based Local Outlier |
| ‘cof’ | Connectivity-Based Outlier Factor |
| ‘histogram’ | Histogram-based Outlier Detection |
| ‘knn’ | k-Nearest Neighbors Detector |
| ‘lof’ | Local Outlier Factor |
| ‘svm’ | One-class SVM detector |
| ‘pca’ | Principal Component Analysis |
| ‘mcd’ | Minimum Covariance Determinant |
| ‘sod’ | Subspace Outlier Detection |
| ‘sos | Stochastic Outlier Selection |

Plot a Model


Predict Model

prediction = predict_model(model,data = data)

Save Models

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