XG-Boost 101: Used Cars Price Prediction

Overview

Title:

XG-Boost 101:

Used Cars Price Prediction

Description:

Dive into the realm of machine learning with our hands-on project where you'll learn to predict used car prices using three different algorithms:

Multiple Linear Regression, Random Forest Regression, and XG-Boost. Ideal for car dealerships, this project will enhance your understanding of the factors influencing used car prices.

By the end of this project, participants will:

• Gain insights into the application of AI and Machine Learning in the automotive industry.
• Explore the theory and practical application of the XG-Boost Algorithm.
• Learn to import and use key Python libraries, manage datasets, and conduct Exploratory Data Analysis.
• Utilize data visualization tools like Seaborn, Plotly, and Word Cloud to interpret data better.
• Prepare data for modeling by standardizing and splitting into training and testing sets.
• Develop skills in building, training, and evaluating models with XG-Boost, Random Forest, and Multiple Linear Regression using Scikit-Learn.
• Assess model performance through various Key Performance Indicators (KPIs).

Note:

This course is optimized for learners in North America.

Efforts are underway to bring this learning experience to other regions.

Provider:

Coursera

Categories:

Artificial Intelligence Courses, Python Courses, Machine Learning Courses, scikit-learn Courses, Data Visualization Courses.

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Taught by

Ryan Ahmed

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Subjects