Regression Analysis with Yellowbrick

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En este proyecto guiado, tú:

Evaluate the performance of regression models using visual diagnostic tools from Yellowbrick

Use visualization techniques to steer your machine learning workflow

Clock2 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

Welcome to this project-based course on Regression Analysis with Yellowbrick. In this project, we will build a machine learning model to predict the compressive strength of high performance concrete (HPC). Although, we will use linear regression, the emphasis of this project will be on using visualization techniques to steer our machine learning workflow. Visualization plays a crucial role throughout the analytical process. It is indispensable for any effective analysis, model selection, and evaluation. This project will make use of a diagnostic platform called Yellowbrick. It allows data scientists and machine learning practitioners to visualize the entire model selection process to steer towards better, more explainable models.Yellowbrick hosts several datasets from the UCI Machine Learning Repository. We’ll be working with the concrete dataset that is well suited for regression tasks. The dataset contains 1030 instances and 8 real valued attributes with a continuous target. We we will cover the following topics in our machine learning workflow: exploratory data analysis (EDA), feature and target analysis, regression modelling, cross-validation, model evaluation, and hyperparamter tuning. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, Yellowbrick, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.

Habilidades que desarrollarás

Data ScienceMachine LearningPython ProgrammingData Visualization (DataViz)Scikit-Learn

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Data Exploration

  2. Preprocessing the Data

  3. Pairwise Scatterplot

  4. Feature Importances

  5. Target Visualization

  6. Evaluating Lasso Regression

  7. Visualizing Test Set Errors

  8. Cross Validation Scores

  9. Learning Curves

  10. Hyperparamter Tuning - Alpha Selection

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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