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Back to XG-Boost 101: Used Cars Price Prediction

Learner Reviews & Feedback for XG-Boost 101: Used Cars Price Prediction by Coursera Project Network

4.7
stars
27 ratings

About the Course

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 of 7 Reviews for XG-Boost 101: Used Cars Price Prediction

By Md. M I C

Mar 18, 2021

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

By Satyajit N

Feb 22, 2021

Excellent Course

By Gregory G J

Jan 14, 2021

Thumbs Up!

By F 1 B

Aug 9, 2022

perfect

By Paúl A A V

Mar 10, 2021

Nice

By Shadi Q

Jul 14, 2022

Extremely simplified project. Definetely not good for the intermediate or advanced learners. It's good if you really have no clue about XGBoost but it doesn't allow you to go through the original paper from Chen and understand it.

By Akash S C

May 29, 2021

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