Predictive Modeling: Implementing Predictive Models Using Visualizations

Predictive Analytics
  • 12 Videos | 46m 5s
  • Includes Assessment
  • Earns a Badge
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Explore how to work with machine learning feature selection, general classes of feature selection algorithms, and predictive modeling best practices. In this 12-video course, learners discover how to implement predictive models with scatter plots, boxplots, and crosstabs by using Python. Key concepts examined here include the benefits of feature selection and the general classes of feature selection algorithms; the different types of predictive models that can be implemented and associated features; and how to implement scatterplots and the capability of scatterplots in facilitating predictions. Next, you will learn about Pearson's correlation measures and the possible ranges for Pearson's correlation; learn to recognize the anatomy of a boxplot, a visual representation of the statistical five-number summary of a given data set; and observe how to create and interpret boxplots with Python. Then see how to implement crosstabs to visualize categorical variables; learn statistical concepts that are used for predictive modeling; and learn tree-based methods used to implement regression and classification. Finally, you will learn best practices for implementing predictive modeling.

WHAT YOU WILL LEARN

  • list the benefits of feature selection and the general classes of feature selection algorithms
    recall the different types of predictive models that can be implemented and features
    implement scatter plots and describe the capability of scatter plots in facilitating predictions
    define Pearson's correlation measures and specify the possible ranges for Pearson's correlation
    recognize the anatomy of a boxplot
    create and interpret boxplots using Python
  • implement crosstabs to visualize categorical variables
    describe statistical concepts that are used for predictive modeling
    demonstrate the tree-based methods that can be used to implement regression and classification
    describe the best practices for implementing predictive modeling
    implement boxplots, scatter plots, and crosstabs using Python

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 29s
    UP NEXT
  • Playable
    2. 
    Feature Selection Algorithm
    4m 23s
  • Locked
    3. 
    Predictive Models
    4m 13s
  • Locked
    4. 
    Scatter Plots
    3m 37s
  • Locked
    5. 
    Pearson's Correlation
    3m 22s
  • Locked
    6. 
    Boxplot
    2m 22s
  • Locked
    7. 
    Boxplot Using Python
    2m 43s
  • Locked
    8. 
    Crosstab Using Python
    3m 16s
  • Locked
    9. 
    Statistical Concepts for Predictive Models
    4m 37s
  • Locked
    10. 
    Tree-Based Method
    3m 15s
  • Locked
    11. 
    Best Practices for Predictive Modeling
    5m 8s
  • Locked
    12. 
    Exercise: Implement Boxplots and Scatter Plots
    2m 41s

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