- Decision Tree Classifier
- Random Forest
- Extra Tree
- Naive Bayes
- Logistic Regression
- Multi-Layered Perceptron (MLP)
- used CIC 2017 and 2018 Datasets
- 19Lac Big training data samples
- 800K Validation data samples
If you can find the pattern for expected or "normal" data, then you can also find those data points that don't fit the pattern. Companies in industries as diverse as financial services, healthcare, retail and manufacturing regularly employ a variety of data science methods to identify anomalies in their data for uses such as fraud detection, customer analytics, cybersecurity and IT systems monitoring. Anomaly detection can also be used to eliminate outlier values from data sets for better analytics accuracy.