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
