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Logistic Regression 101: US Household Income Classification

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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.

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Discover "Logistic Regression 101: US Household Income Classification," a comprehensive course in Personal Development led by expert instructors. Dive into Logistic Regression and XG-Boost models using U.S. Census data to predict income levels. Learn essential Python libraries, data visualization, and model evaluation techniques over 120 minutes. Ideal for North American learners, with a Starter subscription available on Coursera, this foundational course equips you with practical skills in data analysis and machine learning.