We may earn an affiliate commission when you visit our partners.
Ryan Ahmed
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
Read more
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
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/l72xlq/xg
Two deals to help you save
Save money when you learn. Here are two deals and offers that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until July 14
Coursera Plus Annual
Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.
Up to
40%
off
Get offer
Valid until August 30
Google AI App Builder
Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind
Take
100%
off
Get offer
What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches Machine Learning algorithms relevant to banking and finance professionals
Teaches a modern Machine Learning algorithm, XG-Boost, used by professionals
Develops understanding of Multiple Linear Regression necessary for use in industry
Covers data analysis techniques, such as data standardization, pivotal for ML modeling
Requires some background in programming and data analytics
Assumes learners have foundational knowledge of Multiple Linear Regression, which may not be included in this course
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Hands-on xg-boost for price prediction
According to learners, this is a highly practical and concise project-based course, ideal for those seeking a quick, hands-on application of machine learning, particularly XG-Boost, for used car price prediction. Students praise its clear, step-by-step guidance and effective coverage of EDA and model evaluation, making it excellent for portfolio building. However, some find the theoretical depth of XG-Boost limited, feeling it's more a guided coding exercise than a comprehensive '101'. Additionally, the North America-centric dataset may be a minor drawback for learners in other regions.
Dataset is primarily relevant to North America, limiting broader applicability.
"The dataset being North America-centric made it less directly applicable to my region, which was a bit of a disappointment."
"The regional data constraint was also a minor issue."
Opinions vary on whether it's suitable for complete beginners or too basic for experienced learners.
"Perfect for a beginner in machine learning who wants a quick, guided project."
"If you're a complete beginner, you might struggle. If you already know ML, it's too basic."
"Good course for a quick introduction. It covers the basics well and the instructor is clear."
Ideal for quick skill development and portfolio enhancement.
"A perfect 1.5-hour crunch project."
"Short, sweet, and to the point. Great for portfolio building."
"Perfect for a beginner in machine learning who wants a quick, guided project."
"A good quick project, especially for understanding data preprocessing and model evaluation metrics."
Provides excellent hands-on experience applying ML models.
"Absolutely fantastic! This project is a gem for anyone looking to quickly grasp XG-Boost application. The step-by-step guidance is super clear, and the hands-on coding makes it very practical."
"Exactly what I needed – a practical, project-based approach to XG-Boost. The dataset is good, and the way it compares different regression models is helpful."
"Very hands-on and practical. If you want to see XG-Boost in action for a real-world problem, this is a good starting point."
"I learned a lot about applying XG-Boost to a real dataset. Definitely useful for building a portfolio or understanding the workflow."
Focuses on implementation over deep algorithmic theory.
"I was hoping for a bit more theoretical depth on XG-Boost itself, beyond just the practical application."
"I found the explanation of XG-Boost a bit superficial. It jumps straight into code without fully explaining the 'why' behind certain parameters or the algorithm's mechanics."
"Disappointed with the depth. The title '101' implied a solid introduction, but it felt more like a guided coding exercise without much conceptual reinforcement."
"The explanation for XG-Boost is a bit superficial. It's more of a guided lab than a true '101' course on the algorithm's theory. I needed to supplement with external resources."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in XG-Boost 101: Used Cars Price Prediction with these
activities:
Review Linear Algebra
Show steps
Helps ensure that students have the necessary background in linear algebra for this course.
Browse courses on
Linear Algebra
Show steps
-
Review the fundamentals of linear algebra, including vectors, matrices, and linear equations.
-
Solve practice problems to test your understanding.
-
Complete online tutorials or watch videos on linear algebra.
Introduction to Machine Learning
Show steps
Provides a comprehensive overview of machine learning concepts and algorithms.
View
Machine Learning, revised and updated edition...
on Amazon
Show steps
-
Read the book's chapters on regression and XG-Boost.
-
Complete the practice exercises and examples in the book.
-
Discuss the book's concepts with classmates or online forums.
Learn about XG-Boost
Show steps
Helps students build a deeper understanding of XG-Boost and its applications.
Browse courses on
XG-Boost
Show steps
-
Follow online tutorials or watch videos on XG-Boost.
-
Complete practice exercises using XG-Boost.
-
Participate in online forums or discussion groups related to XG-Boost.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build Regression Models
Show steps
Provides students with hands-on experience in building and evaluating regression models.
Browse courses on
Regression Models
Show steps
-
Use Python libraries such as Scikit-Learn to implement regression models.
-
Experiment with different model parameters and hyperparameters.
-
Evaluate the performance of the models using metrics such as R-squared and mean absolute error.
Attend AI Workshop
Show steps
Exposes students to the latest advancements in AI and machine learning from industry experts.
Browse courses on
Artificial Intelligence
Show steps
-
Research and identify relevant AI workshops in the area.
-
Register for and attend the workshop.
-
Actively participate in the workshop and ask questions.
Predict Used Car Prices
Show steps
Allows students to apply their knowledge of XG-Boost and regression models to a real-world problem.
Browse courses on
Machine Learning
Show steps
-
Gather and clean data on used car prices.
-
Train and evaluate an XG-Boost model to predict used car prices.
-
Create a dashboard or visualization to present the results.
Help Peers with XG-Boost
Show steps
Helps students reinforce their understanding of XG-Boost by teaching and helping others.
Browse courses on
XG-Boost
Show steps
-
Answer questions and provide guidance to peers on XG-Boost.
-
Create study materials or resources to share with others.
-
Lead a study group or workshop on XG-Boost.
Contribute to XG-Boost
Show steps
Allows students to contribute to the development of XG-Boost and gain practical experience in open source software.
Browse courses on
XG-Boost
Show steps
-
Identify a specific area or feature in XG-Boost to contribute to.
-
Fork the XG-Boost repository and create a new branch.
-
Make your changes and submit a pull request.
Review Linear Algebra
Show steps
Helps ensure that students have the necessary background in linear algebra for this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the fundamentals of linear algebra, including vectors, matrices, and linear equations.
- Solve practice problems to test your understanding.
- Complete online tutorials or watch videos on linear algebra.
Introduction to Machine Learning
Show steps
Provides a comprehensive overview of machine learning concepts and algorithms.
View
Machine Learning, revised and updated edition...
on Amazon
Show steps
Show steps
Show steps
- Read the book's chapters on regression and XG-Boost.
- Complete the practice exercises and examples in the book.
- Discuss the book's concepts with classmates or online forums.
Learn about XG-Boost
Show steps
Helps students build a deeper understanding of XG-Boost and its applications.
Browse courses on
XG-Boost
Show steps
Show steps
Show steps
- Follow online tutorials or watch videos on XG-Boost.
- Complete practice exercises using XG-Boost.
- Participate in online forums or discussion groups related to XG-Boost.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build Regression Models
Show steps
Provides students with hands-on experience in building and evaluating regression models.
Browse courses on
Regression Models
Show steps
Show steps
Show steps
- Use Python libraries such as Scikit-Learn to implement regression models.
- Experiment with different model parameters and hyperparameters.
- Evaluate the performance of the models using metrics such as R-squared and mean absolute error.
Attend AI Workshop
Show steps
Exposes students to the latest advancements in AI and machine learning from industry experts.
Browse courses on
Artificial Intelligence
Show steps
Show steps
Show steps
- Research and identify relevant AI workshops in the area.
- Register for and attend the workshop.
- Actively participate in the workshop and ask questions.
Predict Used Car Prices
Show steps
Allows students to apply their knowledge of XG-Boost and regression models to a real-world problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather and clean data on used car prices.
- Train and evaluate an XG-Boost model to predict used car prices.
- Create a dashboard or visualization to present the results.
Help Peers with XG-Boost
Show steps
Helps students reinforce their understanding of XG-Boost by teaching and helping others.
Browse courses on
XG-Boost
Show steps
Show steps
Show steps
- Answer questions and provide guidance to peers on XG-Boost.
- Create study materials or resources to share with others.
- Lead a study group or workshop on XG-Boost.
Contribute to XG-Boost
Show steps
Allows students to contribute to the development of XG-Boost and gain practical experience in open source software.
Browse courses on
XG-Boost
Show steps
Show steps
Show steps
- Identify a specific area or feature in XG-Boost to contribute to.
- Fork the XG-Boost repository and create a new branch.
- Make your changes and submit a pull request.
Career center
Learners who complete XG-Boost 101: Used Cars Price Prediction will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. This course can help you build a foundation in machine learning by teaching you how to use XG-Boost, Random Forest, and Multiple Linear Regression to predict used car prices. This skill is essential for Machine Learning Engineers, who need to be able to use machine learning to solve real-world problems.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help you build a foundation in data analysis by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Data Analysts, who need to be able to use data to make informed decisions.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help you build a foundation in data science by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Data Scientists, who need to be able to use data to make informed decisions.
Statistician
Statistician
Statisticians are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help you build a foundation in statistics by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Statisticians, who need to be able to use data to make informed decisions.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business data to identify trends and patterns. This course can help you build a foundation in business analysis by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Business Analysts, who need to be able to use data to make informed decisions.
Actuary
Actuary
Actuaries are responsible for using mathematical and statistical models to assess risk. This course can help you build a foundation in actuarial science by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Actuaries, who need to be able to use data to make informed decisions.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data to identify trends and patterns. This course can help you build a foundation in financial analysis by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Financial Analysts, who need to be able to use data to make informed decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical models to analyze financial data. This course can help you build a foundation in quantitative analysis by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Quantitative Analysts, who need to be able to use data to make informed decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and statistical models to improve business operations. This course can help you build a foundation in operations research by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Operations Research Analysts, who need to be able to use data to make informed decisions.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risk. This course can help you build a foundation in risk analysis by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Risk Analysts, who need to be able to use data to make informed decisions.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, building, and deploying software applications. This course can help you build a foundation in software engineering by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Software Engineers, who need to be able to use data to make informed decisions.
Computer Scientist
Computer Scientist
Computer Scientists are responsible for researching and developing new computer technologies. This course can help you build a foundation in computer science by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Computer Scientists, who need to be able to use data to make informed decisions.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining data pipelines. This course can help you build a foundation in data engineering by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Data Engineers, who need to be able to use data to make informed decisions.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. This course can help you build a foundation in database administration by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Database Administrators, who need to be able to use data to make informed decisions.
Data Architect
Data Architect
Data Architects are responsible for designing and building data architectures. This course can help you build a foundation in data architecture by teaching you how to use machine learning algorithms to predict used car prices. This skill is essential for Data Architects, who need to be able to use data to make informed decisions.
For more career information including salaries, visit:
OpenCourser.com/course/l72xlq/xg
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
XG-Boost 101: Used Cars Price Prediction .
Provides a comprehensive overview of statistical learning methods, covering topics such as linear models, tree-based methods, and support vector machines.
Provides a comprehensive guide to building and deploying machine learning models using Python libraries such as scikit-learn, Keras, and TensorFlow. It practical resource for anyone looking to gain hands-on experience with machine learning.
Save
Provides a practical guide to building and deploying predictive models. It covers topics such as data preparation, model selection, and evaluation.
Provides a comprehensive guide to deep learning using the Python programming language. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a gentle introduction to data science, covering topics such as data cleaning, analysis, and visualization. It great resource for anyone looking to get started with data science.
Provides a comprehensive guide to Bayesian analysis using the Python programming language. It covers topics such as Bayesian inference, Bayesian modeling, and Bayesian computation.
Save
Provides a comprehensive guide to econometrics. It covers topics such as linear regression, time series analysis, and forecasting.
Provides a comprehensive guide to natural language processing using the Python programming language. It covers topics such as text preprocessing, text classification, and text generation.
Provides a comprehensive guide to machine learning using the R programming language. It covers topics such as data preparation, model selection, and evaluation.
Provides a comprehensive guide to causal inference in statistics. It covers topics such as graphical models, causal effects, and counterfactuals.
Provides a comprehensive guide to Bayesian statistics. It covers topics such as Bayesian inference, Bayesian modeling, and Bayesian computation.
Provides a very basic introduction to machine learning, covering topics such as supervised and unsupervised learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l72xlq/xg
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser