# Predictive Modeling: Implementing Predictive Models Using Visualizations

Predictive Analytics    |    Intermediate
• 12 videos | 41m 5s
• Includes Assessment
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

• After completing this video, you will be able to list the benefits of feature selection and the general types of feature selection algorithms.
• 3.  Predictive Models
After completing this video, you will be able to recall the different types of predictive models that can be implemented and the features of each.
• 4.  Scatter Plots
In this video, you will learn how to create scatter plots and how they can help you make predictions.
• 5.  Pearson's Correlation
In this video, you will learn how to define Pearson's correlation measures and specify the possible ranges for Pearson's correlation.
• 6.  Boxplot
After completing this video, you will be able to recognize the anatomy of a boxplot.
• 7.  Boxplot Using Python
In this video, you will create and interpret box plots using Python.
• 8.  Crosstab Using Python
Learn how to use crosstabs to visualize categorical variables.
• 9.  Statistical Concepts for Predictive Models
Upon completion of this video, you will be able to describe statistical concepts used for predictive modeling.
• 10.  Tree-Based Method
Learn how to apply tree-based methods for regression and classification.
• 11.  Best Practices for Predictive Modeling
Upon completion of this video, you will be able to describe the best practices for implementing predictive modeling.
• 12.  Exercise: Implement Boxplots and Scatter Plots
In this video, you will learn how to create boxplots, scatter plots, and crosstabs using Python.

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