XG-Boost 101: Used Cars Price Prediction

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In this Guided Project, you will:
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:

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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