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Predictive Modeling: Implementing Predictive Models Using Visualizations

Predictive Modeling: Implementing Predictive Models Using Visualizations


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore how to work with feature selection, general classes of feature selection algorithms, and predictive modeling best practices. Discover how to implement predictive models with scatter plots, boxplots, and crosstabs using Python.



Expected Duration (hours)
0.7

Lesson Objectives

Predictive Modeling: Implementing Predictive Models Using Visualizations

  • 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
  • Course Number:
    it_mlfupddj_02_enus

    Expertise Level
    Intermediate