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学生对 Coursera Project Network 提供的 XG-Boost 101: Used Cars Price Prediction 的评价和反馈

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In this hands-on project, we will train 3 Machine Learning algorithms namely Multiple Linear Regression, Random Forest Regression, and XG-Boost to predict used cars prices. This project can be used by car dealerships to predict used car prices and understand the key factors that contribute to used car prices. By the end of this project, you will be able to: - Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry - Understand the theory and intuition behind XG-Boost Algorithm - Import key Python libraries, dataset, and perform Exploratory Data Analysis. - Perform data visualization using Seaborn, Plotly and Word Cloud. - Standardize the data and split them into train and test datasets.   - Build, train and evaluate XG-Boost, Random Forest, Decision Tree, and Multiple Linear Regression Models Using Scikit-Learn. - Assess the performance of regression models using various Key Performance Indicators (KPIs). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....



1 - XG-Boost 101: Used Cars Price Prediction 的 7 个评论(共 7 个)

创建者 Md. M I C

Mar 18, 2021

创建者 Satyajit N

Feb 22, 2021

创建者 Gregory G J

Jan 14, 2021

创建者 F 1 B

Aug 9, 2022

创建者 Paúl A A V

Mar 10, 2021

创建者 Shadi Q

Jul 14, 2022

创建者 Akash S C

May 29, 2021