Project2 - CS2073 Project 2 Anomaly Detection Anomaly detection(aka outlier analysis is a step in data mining that identifies data points events

Project2 - CS2073 Project 2 Anomaly Detection Anomaly...

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CS2073 Project 2 Anomaly 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 indicate critical incidents, such as a technical glitch, or potential opportunities, for instance a change in consumer 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 1 to 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) to the code that you are writing for Project2. In Project1, you asked users to enter X- and Y-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.

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