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返回到 XG-Boost 101: Used Cars Price Prediction

學生對 Coursera Project Network 提供的 XG-Boost 101: Used Cars Price Prediction 的評價和反饋

4.6
17 個評分
5 條評論

課程概述

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 - XG-Boost 101: Used Cars Price Prediction 的 5 個評論(共 5 個)

創建者 Md. M I C

2021年3月18日

Very engaging and clear explanation. One of the best guided projects.

創建者 Satya N

2021年2月22日

Excellent Course

創建者 Gregory G J

2021年1月14日

Thumbs Up!

創建者 Paúl A A V

2021年3月10日

Nice

創建者 Akash S C

2021年5月29日

Not worth the money! Way short and simple introduction to XGBoost for the price of a full month course on Coursera.