Regression Analysis in Python
Learning Outcomes
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What’s Included
Course Introduction
Course Introduction
In this lesson, we get a preview of the course ahead.
Visualizing Linear Regression
Visualizing Linear Regression
In this lesson, we learn how to visualize the regression line on a scatter plot.
Regression as a Function
Regression as a Function
In this lesson, we learn how we can use regression values to manually calculate predictions.
The Loss Function
The Loss Function
In this lesson, we learn how to calculate the degree of error in our regression model.
Minimizing the Loss Function
Minimizing the Loss Function
In this lesson, we learn how to find the regression coefficients that respond to the lowest error.
Creating Dummy Variables
Creating Dummy Variables
In this lesson, we learn how to convert categorical variables into numerical variables.
Exploring Variable Relationships
Exploring Variable Relationships
In this lesson, we learn how to build a correlation matrix and correlation heatmap.
Checking for Linear Relationships
Checking for Linear Relationships
In this lesson, we learn how the R score between variables is important for determining which variables to use in our regression model.
Regression with SciKitLearn
Regression with SciKitLearn
In this lesson, we train our regression model in Python.
Inspecting the Distribution of the Errors
Inspecting the Distribution of the Errors
In this lesson, we learn how to check our model errors to ensure that they're normally distributed.
Inspecting the Consistency of the Errors
Inspecting the Consistency of the Errors
In this lesson, we learn how to check if our model errors are consistent.
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