Back to XG-Boost 101: Used Cars Price Prediction
Learner Reviews & Feedback for XG-Boost 101: Used Cars Price Prediction by Coursera Project Network
35 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. Â
- 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.
Top reviews
Filter by:
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 Burhanettin
•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.
Coursera Footer
© 2023 Coursera Inc. All rights reserved.
