XG-Boost 101: Used Cars Price Prediction

4.6
17 個評分
提供方
Coursera Project Network
在此指導項目中,您將:

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.

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

  • Artificial Intelligence (AI)
  • Python Programming
  • Machine Learning
  • regression

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

審閱

來自XG-BOOST 101: USED CARS PRICE PREDICTION 的熱門評論

查看所有評論

常見問題

常見問題

還有其他問題嗎?請訪問 學生幫助中心