Python packages for data science stack
As the field of data science develops and becomes more well-known, data scientists and analysts will have a wider range of tools to choose from. Even though libraries like scikit-learn, pandas, NumPy, and matplotlib are the backbone of the PyData Stack, it is important to learn and become proficient with new libraries and packages if you want to move up in a data-related field. Five Python ecosystem packages have been made in the last few years, and they are becoming very important in the fields of machine learning and data science course . Here's what's in these packages: 1. SHAP The need to find ways to reduce the bad effects of machine learning models is driving more and more people to be interested in explainable AI (XAI). The conclusions that machine learning algorithms come up with are likely tainted by biases that reinforce people's beliefs. 2. UMAP In 2018, Leland McInnes and his colleagues devised a method called "Uniform Manifold Approximation and Projectio