Anomaly Detection Exercises

Goals

Understand continuous variable probabilistic methods for identifying outliers
Work with discrete variable probabilistic methods using RESTful and time series methods.
Use entropy as a quick way to identify fields that may have anomalies.
Use statistical properties to flag the data points that deviate from the expected.
Using density based clustering, like DBSCAN, we can design the model such that the data points that do not fall into a cluster are the anomalies.*

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
.gitignore		.gitignore
README.md		README.md
c_p_methods_zillow.ipynb		c_p_methods_zillow.ipynb
continuous_probabilistic_methods.ipynb		continuous_probabilistic_methods.ipynb
discrete_anomaly_detection.ipynb		discrete_anomaly_detection.ipynb
time_series_anomaly_detection.ipynb		time_series_anomaly_detection.ipynb
wrangle_zillow.py		wrangle_zillow.py