Building the Model

Explore how to build anomaly detection models using PyCaret by creating and assigning models like the local outlier factor to datasets. Understand how to separate inliers from outliers, evaluate model effectiveness through skewness analysis, visualize results with scatter plots and UMAP, and save models for future use. This lesson equips you to handle anomaly detection practically and effectively in Python.

We'll cover the following...

Creating and assigning the model
Inliers and outliers
Evaluating the model
Plotting the model
Saving the model
Implementation

	Channel	Region	Fresh	Milk	Grocery	Frozen	Detergents_Paper	Delicassen	Anomaly	Anomaly_Score
0	Retail	Other	12669	9656	7561	214	2674	1338	0	1.107687
1	Retail	Other	7057	9810	9568	1762	3293	1776	0	1.027102
2	Retail	Other	6353	8808	7684	2405	3516	7844	0	1.398439
3	Horeca	Other	13265	1196	4221	6404	507	1788	0	1.200384
4	Retail	Other	22615	5410	7198	3915	1777	5185	0	1.164052
5	Retail	Other	9413	8259	5126	666	1795	1451	0	1.184313
6	Retail	Other	12126	3199	6975	480	3140	545	0	1.130491
7	Retail	Other	7579	4956	9426	1669	3321	2566	0	1.013751
8	Horeca	Other	5963	3648	6192	425	1716	750	0	1.201904
9	Retail	Other	6006	11093	1881	1159	7425	2098	0	1.053333

1.Introduction to Machine Learning

2.Regression

3.Classification

4.Clustering

Project

5.Anomaly Detection

6.Natural Language Processing

7.Deploying a Machine Learning Model

8.Conclusion

9.Appendix

Building the Model

Creating and assigning the model