XG-Boost 101: Used Cars Price Prediction

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In this Guided Project, you will:

Understand the theory and intuition behind XG-Boost Algorithm.

Build, train and evaluate XG-Boost, Random Forest, Decision Tree, and Multiple Linear Regression Models Using Scikit-Learn.

Assess the performance of trained regression models using various Key performance indicators.

2 hours
Intermediate
No download needed
Split-screen video
English
Desktop only

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.

Skills you will develop

  • Artificial Intelligence (AI)

  • Python Programming

  • Machine Learning

  • regression

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the problem statement and business case

  2. Import libraries/datasets and perform Exploratory Data Analysis

  3. Perform Data Visualization - Part #1

  4. Perform Data Visualization - Part #2

  5. Prepare the data before model training

  6. Train and Evaluate a Multiple Linear Regression model

  7. Train and Evaluate a Decision Tree and a Random Forest models

  8. Understand the Theory and Intuition Behind XG-Boost Algorithm

  9. Train and Evaluate a XG-Boost model

  10. Compare models and calculate Regression KPIs

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently Asked Questions

By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.