Linear regression shows us how we can use data to predict the value of an outcome. This course covers the structure of a linear regression model, how to interpret it, how to determine if a model is appropriate, and how to use the model to predict values of new data.
In this course, you’ll learn to create single and multiple linear regressions, identify the different types of predictors, and identify a cost function for linear regression. You’ll also learn how to interpret regression parameters, how to check linear regression fit, and how to apply linear regression models.
You will use tools such as scikit-learn, statsmodels, pandas, NumPy and matplotlib.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll combine your new skills to complete a project to predict insurance costs.
- Describing a linear regression model
- Constructing a linear regression model and evaluating it based on the data
- Interpreting the results of a linear regression model
- Using a linear regression model for inference and prediction
Linear Regression Modeling in Python [5 lessons]
- Define the advantages of a linear regression model
- Identify the intercept, coefficients, and error of a linear regression model
- Create a simple linear regression
- Create a multiple linear regression
- Identify different types of predictors
- Identify a cost function for linear regression
Projects in this course
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