XG-Boost 101: Used Cars Price Prediction

via Coursera

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.