XG-Boost 101: Used Cars Price Prediction

4.6
17件の評価
提供:
Coursera Project Network
このガイド付きプロジェクトでは、次のことを行います。

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ダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

あなたが開発するスキル

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

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  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

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

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