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
25 évaluations
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.
- 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.
Meilleurs avis
Filtrer par :
1 - 5 sur 5 Avis pour XG-Boost 101: Used Cars Price Prediction
par Md. M I C
•18 mars 2021
Very engaging and clear explanation. One of the best guided projects.
par Satyajit N
•22 févr. 2021
Excellent Course
par Gregory G J
•14 janv. 2021
Thumbs Up!
par Paúl A A V
•10 mars 2021
Nice
par Akash S C
•29 mai 2021
Not worth the money! Way short and simple introduction to XGBoost for the price of a full month course on Coursera.
Coursera Footer
© 2022 Coursera Inc. Tous droits réservés.
