How to Fit Data with Noise: Focusing on RANSAC

Hello everyone! Today, I'm going to guide you through the fascinating world of data analysis with the topic, "How to Fit Data with Noise?" If you've ever dealt with data analysis, you know this is a common problem. Let me share an exciting story from my early days learning data analysis and introduce you to a fantastic solution. Let's dive right in! First of all, what do you think is the first thing you encounter when starting data analysis? That's right, 'noise'. When I was first learning data analysis, I was often baffled by the strange outlier values in my data sets. Whether it was a scientific experiment where an outlier measurement would stand out or analyzing economic data with sudden market changes showing completely different patterns, noise was always a challenge. Such data often ruins the overall results. I remember a time when a couple of outliers completely messed up my economic data analysis. "If only these outliers weren't there..." I'm sure you've had similar experiences.