Logistic Regression 101: US Household Income Classification

Offered By
In this Guided Project, you will:

Understand the theory and intuition behind Logistic Regression and XGBoost models.

Build and train Logistic Regression and XGBoost models to classify the Income Bracket of US Household.

Assess the performance of trained model and ensure its generalization using various KPIs such as accuracy, precision and recall.

2 Hours
Beginner
No download needed
Split-screen video
English
Desktop only

In this hands-on project, we will train Logistic Regression and XG-Boost models to predict whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually. This data was obtained from U.S. Census database and consists of features like occupation, age, native country, capital gain, education, and work class. By the end of this project, you will be able to: - Understand the theory and intuition behind Logistic Regression and XG-Boost models - Import key Python libraries, dataset, and perform Exploratory Data Analysis like removing missing values, replacing characters, etc. - Perform data visualization using Seaborn. - Prepare the data to increase the predictive power of Machine Learning models by One-Hot Encoding, Label Encoding, and Train/Test Split - Build and train Logistic Regression and XG-Boost models to classify the Income Bracket of U.S. Household. - Assess the performance of trained model and ensure its generalization using various KPIs such as accuracy, precision and recall. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

  • Deep Learning

  • Machine Learning

  • Python Programming

  • Artificial Intelligene(AI)

  • classification

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the problem statement and business case

  2. Import Datasets and Libraries

  3. Exploratory Data Analysis

  4. Perform Data Visualization

  5. Prepare the data to feed the model

  6. Understand the Problem Statement and Business Case

  7. Build and assess the performance of Logistic Regression models

  8. Build and assess the performance of XG-Boost model

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.

Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

Guided Projects are not eligible for refunds. See our full refund policy.

Financial aid is not available for Guided Projects.

Auditing is not available for Guided Projects.

At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.

More questions? Visit the Learner Help Center.