Viktoria

“I can’t believe how easily and clearly complex material is presented on Dataquest. Things like statistics and programming are not easy to learn. Dataquest explains them more clearly than all other resources. Even beginners can learn easily on Dataquest.”

Viktoria Jorayeva

Consultant, Business Analyst @Fractal

Course overview

Logistic regression and linear regression are very similar, but the two have slightly different objectives. In linear regression, we try to predict losses in insurance claims. In logistic regression, we’re trying to predict categorical outcomes, otherwise known as classification. In other terms, logistic regression is the classification-based equivalent of linear regression.

In this course, you’ll learn the logistic regression method. You’ll learn how to interpret regression parameters, how to evaluate logistic regression models, and how to apply them.

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 skills to complete a project to classify heart diseases.

Key skills

  • Describing a logistic regression model
  • Building a logistic regression model and evaluating it based on the data
  • Interpreting the results of a logistic regression model
  • Using a logistic regression model for inference and prediction

Course outline

Logistic Regression Modeling in Python [5 lessons]

Introduction to Logistic Regression 1h

Lesson Objectives
  • Differentiate classification and regression problems
  • Differentiate success probability, log-odds, and odds
  • Create a simple logistic regression model
  • Identify a cost function for logistic regression

Interpreting the Regression Parameters 1h

Lesson Objectives
  • Create and fit LogisticRegression object
  • Access the important attributes of a LogisticRegression object
  • Interpret logistic regression coefficients

Evaluating Logistic Regression Models 1h

Lesson Objectives
  • Calculate the accuracy of a logistic regression
  • Calculate the sensitivity and specificity of a logistic regression
  • Plot estimated probabilities against observed classes
  • Calculate the positive and negative predictive probability of a logistic regression

Applying Logistic Regression Models 1h

Lesson Objectives
  • Calculate test accuracy of a logistic regression
  • Use split-apply-combine workflow for feature selection
  • Convey the results of a logistic regression to a reader

Guided Project: Classifying Heart Disease 1h

Lesson Objectives
  • Create a logistic regression model from a dataset
  • Evaluate how well the classification model fits the data
  • Interpret the model coefficients
  • Evaluate the predictive power of the logistic regression model

Projects in this course

Guided Project: Classifying Heart Disease

For this project, you’ll assume the role of a medical researcher aiming to develop a logistic regression model to predict heart disease in patients based on their clinical characteristics.

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Aaron

Aaron Melton

Business Analyst at Aditi Consulting

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