How to Interpret Machine Learning Models with Python -- Part 1 (easy)

In this article, I will try to interpret the Linear Regression, Lasso, and Decision Tree models which are inherently interpretable. I will analyze global interpretability -- which analyzes the most important feature for prediction in general and local interpretability -- which explains individual prediction results. Machine learning models are used in applications such as fraud and risk detection in bank transactions, voice assistants, recommendation systems, chatbots, self-driving cars, social network analysis, etc. However, sometimes it is difficult to interpret them because the algorithm represents a black box(e.g. So we need additional techniques to analyze black box decisions.