Train machine learning regression models using Scikit-Learn library
Understand the theory and intuition behind XG-Boost regression model
Evaluate several trained regression models performance using various Key Performance Indicators (KPIs)
Hello everyone and welcome to this new hands-on project on Scikit-Learn for solving machine learning regression problems. In this project, we will learn how to build and train regression models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests, gradient boosting, and k-means. This project is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your next job interview.
Data Analysis
Machine Learning (ML) Algorithms
Machine Learning
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Understand the Problem Statement
Import Key Libraries and Datasets
Practice Opportunity #1 [Optional]
Perform Data Visualization
Perform Feature Engineering
Understand XG-Boost Algorithm
Train an XG-Boost Regression Model
Evaluate Trained Model Performance
Practice Opportunity #2 [Optional]
Final Capstone Project
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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