Anomaly detection is the identification of data points, items, observations or events that do not conform to the expected pattern of a given group. These anomalies occur very infrequently but may signify a large and significant threat such as cyber intrusions or fraud. Anomaly detection is heavily used in behavioral analysis and other forms of analysis in order to aid in learning about the detection, identification and prediction of the occurrence of these anomalies.
About Dataset: Dataset I have used for this notebook is labelled and reason for using this dataset is that I have to compare the result of algorithms. Due to confidentiality issues, features from V1 to V28 have been transformed using PCA and there is no missing value in the dataset.
- Exploratory Data Analysis
- Features transformation
- Features selection
- Isolation Forest
- Local Outlier Factor