Logistic Regression 101: US Household Income Classification

ofrecido por
Coursera Project Network
En este Proyecto guiado gratis, tú:

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.

Demuestra esta experiencia práctica en una entrevista

Clock2 Hours
BeginnerIdeal para principiantes
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Requerimientos

Basic python programming and mathematics.

Habilidades que desarrollarás

Deep LearningMachine LearningPython ProgrammingArtificial Intelligene(AI)classification

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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