# XG-Boost 101: Used Cars Price Prediction

4.7

25 个评分 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

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.

## 您要培养的技能

• Artificial Intelligence (AI)

• Python Programming

• Machine Learning

• regression

## 分步进行学习

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

## 指导项目工作原理 