4 Machine learning techniques for outlier detection in Python

Based on the feedback given by readers after publishing "Two outlier detection techniques you should know in 2021", I have decided to make this post which includes four different machine learning techniques (algorithms) for outlier detection in Python. Here, I will use the I-I (Intuition-Implementation) approach for each technique. That will help you to understand how each algorithm works behind the scenes without going deeper into the algorithm mathematics (the Intuition part) and implement each algorithm with the Scikit-learn machine learning library (the Implementation part). I will also use some graphical techniques to describe each algorithm and its output. At the end of this article, I will write the "Key Takeaways" section which will include some special strategies for using and combining the four techniques.