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Codecademy

Linear Regression in Python

via Codecademy

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Overview

Learn how to fit, interpret, and compare linear regression models in Python.

In this course, you'll learn how to fit, interpret, and compare linear regression models in Python. This is useful for research questions such as:

- Can I predict how much a customer will spend at a store based on attributes such as age, income, and location?
- What is the relationship between a person's income and other attributes such as education level and years of experience?

This course requires some prior experience with Python, including experience with Pandas and basic data manipulation, summary statistics, and hypothesis testing.

Syllabus

  • Simple Linear Regression: Learn how to fit and interpret a simple linear regression model.
    • Lesson: Introduction to Linear Regression
    • Article: Linear Regression with a Categorical Predictor
    • Quiz: Simple Linear Regression
    • Project: Linear Regression at Codecademy
  • Multiple Linear Regression: Learn how to build and interpret linear regression models with more than one predictor.
    • Lesson: Multiple Linear Regression
    • Article: Matrix Representation of Linear Regression
    • Lesson: Interactions and Polynomial Terms
    • Article: Log Transformations (And More)
    • Quiz: Multiple Linear Regression
    • Project: Algerian Forest Fires
  • Choosing a Linear Regression Model: Learn how to choose the best linear regression model for a particular research question.
    • Lesson: Choosing a Linear Regression Model
    • Quiz: Choosing a Linear Regression Model
    • Project: Craigslist Analysis
  • Linear Regression Implementation: Learn about the differences between scikit-learn and statsmodels with respect to fitting linear regression models.
    • Article: Linear Models in scikit-learn vs. statsmodels

Taught by

Sarai Fernandez

Reviews

4.4 rating at Codecademy based on 33 ratings

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