Retour à XG-Boost 101: Used Cars Price Prediction
Avis et commentaires pour d'étudiants pour XG-Boost 101: Used Cars Price Prediction par Coursera Project Network
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À propos du cours
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. Â
- 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.
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par Md. M I C
•18 mars 2021
par Satyajit N
•22 févr. 2021
par Gregory G J
•14 janv. 2021
par F 1 B
•9 août 2022
par Paúl A A V
•10 mars 2021
par Shadi Q
•14 juil. 2022
par Akash S C
•29 mai 2021
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