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Machine Learning Model For Predicting Price of Processors Using Multivariate Linear Regression by Anderson Squinzani is a document available to read on EtoBox.

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The paper presents a machine learning model utilizing multivariate linear regression to predict processor prices based on attributes such as clock speed, cores, and cache memory. It discusses the importance of data science in making informed purchasing decisions for CPUs, and emphasizes the use of data visualization and exploratory analysis to enhance understanding. The model aims to assist consumers in selecting cost-effective processors tailored to their needs, while suggesting potential future improvemen

Author

Anderson Squinzani

Language

EN