Logistic Regression 101: US Household Income Classification

提供方
Coursera Project Network
在此指导 项目中,您将:

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
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

Deep LearningMachine LearningPython ProgrammingArtificial Intelligene(AI)classification

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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