Machine Learning with Scikit-learn

This blog provides an overview of how to build a Machine Learning model with details on various aspects such as data pre-processing, splitting the training and testing data, regression/classification, and finally model evaluation. Machine Learning (ML) is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions. ML systems are trained rather than explicitly programmed. It provides efficient tools for data analysis, data pre-processing, model building, model evaluation, and much more. So in this blog we will implement various ML models with the help of Scikit learn(sk-learn), which is a simple open-source Machine Learning library.