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Volver a XG-Boost 101: Used Cars Price Prediction

Opiniones y comentarios de aprendices correspondientes a XG-Boost 101: Used Cars Price Prediction por parte de Coursera Project Network

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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....

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1 - 7 de 7 revisiones para XG-Boost 101: Used Cars Price Prediction

por Md. M I C

18 de mar. de 2021

por Satyajit N

22 de feb. de 2021

por Gregory G J

14 de ene. de 2021

por F 1 B

9 de ago. de 2022

por Paúl A A V

10 de mar. de 2021

por Shadi Q

14 de jul. de 2022

por Akash S C

29 de may. de 2021