Code for paper 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'
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Updated
Jul 23, 2022 - Python
Code for paper 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'
A parallel implementation of local outlier factor based on Spark
Implementation of feature engineering from Feature engineering strategies for credit card fraud
Machine learning algorithm to detect fraudulent credit card transactions
Package implements a number local outlier factor algorithms for outlier detection and finding anomalous data
Detect outliers with 3 methods: LOF, DBSCAN and one-class SVM
An implementation of a density based outlier detection method - the Local Outlier Factor Technique, to find frauds in credit card transactions. For detecting both local and global outliers.
Insight Data Science DS.2019C.TO project
Anomaly Detection
Geospatial-temporal analysis using Holoviews, along with Pandas to combine various types of data in sensible ways to describe common daily routines for GASTech employees and identify up to twelve unusual events or patterns in the data.
The project is about outlier detection with different methods same as FastVOA, Kmeans, DBScan or LOF, conducted on KDD dataset.
Deriving the Local Outlier Factor Score
Identify fraudulent credit card transactions.
Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.
Comparison of various anomaly detection algorithms using scikit-learn and visualization through Plotly Dash
Anomaly detection using unsupervised method is a challenging one. Isolated Random Forest and Local Outlier Factor are the most promising one. They detect outlier with highest recall possible.
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