An anomaly is a data point or a set of data points in our dataset that is different from the rest of the dataset. It may either be a too large value or a too small value. Anomalies describe many critical incidents like technical glitches, sudden changes, or plausible opportunities in the market. Anomalies are a very small fraction of the entire dataset. In this project, we look at how autoencoders can be used to detect anomalies.
This jupyter notebook explains how one can create an Autoencoder to detect Anomalies.
- Pyhton 3.x
- TensorFlow 2.x
- Pandas
- Numpy
- Matplotlib
The dataset used for this explanation is ECG5000 available here link. The dataset contains ECG readings. This dataset contains the labels in the first column and the rest of the columns contain the features.
Here it is considered as a non time series problem where we have to detect anomalies in the dataset.
I have written an article explaining this at hello ML available here.
You can simply download the jupyter notebook and run it. Feel free to make changes and execute them to understand the concepts better.
You can make a contribution to it by making a pull request to it.