"The local outlier factor is based on a concept of a local density, where locality is given by nearest neighbors, whose distance is used to estimate the density. By comparing the local density of an object to the local densities of its neighbors, one can identify regions of similar density, and points that have a substantially lower density than their neighbors. These are considered to be outliers."

In this tutorial, we'll learn how to detect anomaly in a dataset by using the Local Outlier Factor method in Python. The Scikit-learn API provides the LocalOutlierFactor class for this algorithm and we'll use it in this tutorial. The tutorial covers:

- Preparing the dataset
- Defining the model and prediction
- Anomaly detection with scores
- Source code listing