CS2073 Project 2Anomaly Detection Anomaly detection (aka outlier analysis) is a step in data mining that identifies data points, events,and/or observations that deviate from a dataset’s normal behavior. Anomalous data can indicatecritical incidents, such as a technical glitch, or potential opportunities, for instance a change inconsumer behavior. Machine learning is progressively being used to automate anomaly detection.In Project 2, you will build on top of the linear curve fit code that you developed as part of Project 1to perform anomaly detection on a dataset that has been provided to you. In terms of reusing the code that you developed as part of Project1, you have two options 1.Copy/Paste the analysis part of the code only (not the user inputs and program outputs) tothe code that you are writing for Project2. In Project1, you asked users to enter X- andY-values. In this project, you will read data from a file. 2.Save the analysis part of the code you developed in Project1 in a separate file as a function.Then call the function in your code for Project2. I will be covering this in our class next week.