Logistic Regression 101: US Household Income Classification

提供方
在此指導項目中,您將:

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
初級
無需下載
分屏視頻
英語(English)
僅限桌面

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.

您要培養的技能

  • Deep Learning

  • Machine Learning

  • Python Programming

  • Artificial Intelligene(AI)

  • classification

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

常見問題

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由於您的工作空間包含適合筆記本電腦或台式計算機使用的雲桌面,因此指導項目不在移動設備上提供。

指導項目授課教師是特定領域的專家,他們在項目的技能、工具或領域方面經驗豐富,並且熱衷於分享自己的知識以影響全球數百萬的學生。

您可以從指導項目中下載並保留您創建的任何文件。為此,您可以在訪問云桌面時使用‘文件瀏覽器’功能。

指導項目不符合退款條件。 請查看我們完整的退款政策

指導項目不提供助學金。

指導項目不支持旁聽。

您可在頁面頂部點按此指導項目的經驗級別,查看任何知識先決條件。對於指導項目的每個級別,您的授課教師會逐步為您提供指導。

是,您可以在瀏覽器的雲桌面中獲得完成指導項目所需的一切。

您可以直接在瀏覽器中於分屏環境下完成任務,以此從做中學。在屏幕的左側,您將在工作空間中完成任務。在屏幕的右側,您將看到有授課教師逐步指導您完成項目。