Logistic Regression 101: US Household Income Classification

제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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.

Clock2 Hours
Beginner초급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

  • Deep Learning
  • Machine Learning
  • Python Programming
  • Artificial Intelligene(AI)
  • classification

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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