Logistic Regression with R: Build & Predict

位教师：EDUCBA

访问权限由 New York State Department of Labor 提供

3个模块

深入了解一个主题并学习基础知识。

8 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

8 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Differentiate regression vs classification and apply logistic models.
Preprocess datasets, evaluate with confusion matrices and ROC.
Apply logistic regression to healthcare and finance case studies.

您将获得的技能

Dimensionality Reduction
Applied Machine Learning
Model Evaluation
Feature Engineering
Predictive Analytics
Data Manipulation
Performance Measurement
Regression Analysis
Supervised Learning
Logistic Regression
Advanced Analytics
Predictive Modeling
Classification And Regression Tree (CART)
Data Preprocessing
Machine Learning Methods
Statistical Modeling
Risk Modeling
Credit Risk
R Programming
技能部分已折叠。显示 8 项技能，共 19 项。

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作业

12 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有3个模块

Learners completing this course will be able to differentiate regression and classification tasks, apply logistic regression models in R, preprocess raw datasets, evaluate models using confusion matrices, and optimize performance through ROC curves, AUC, and threshold adjustments. They will also gain hands-on experience with real-world applications in healthcare and finance, including diabetes prediction and credit risk assessment.

This course provides a step-by-step approach to mastering logistic regression, starting with foundational concepts and progressing to advanced applications. Learners will benefit from practical datasets, including advertisement, medical, and financial data, ensuring they acquire not just theoretical knowledge but also applied skills. Unique to this course is the integration of both technical depth (feature scaling, dimension reduction, model coefficients) and practical impact (loan approval, risk modeling). By the end, participants will be confident in building, interpreting, and validating supervised machine learning models with logistic regression in R, equipping them with valuable expertise for data science, analytics, and financial decision-making roles.

This module introduces the fundamentals of logistic regression with R, guiding learners through data preparation, feature scaling, model fitting, and coefficient interpretation. Learners will gain the skills to prepare raw data and build a strong base for classification modeling.

涵盖的内容

9个视频4个作业

9个视频总计82分钟

Introduction to Logistic Regression 3分钟
Advertisement Dataset 10分钟
Raw Column 11分钟
Feature Scaling 8分钟
Fitting Logistic Regression Model 7分钟
Classifier Coefficients 11分钟
Classifier Coefficients Continue 9分钟
Make Confusion Matrix 11分钟
Logistic Regression Training Set 11分钟

4个作业总计60分钟

Getting Started with Logistic Regression 10分钟
Preparing Data for Modeling 10分钟
Model Evaluation Basics 10分钟
Graded - Foundations of Logistic Regression 30分钟

This module focuses on applying logistic regression to real-world datasets such as diabetes data, enhancing model performance through dimension reduction, and evaluating advanced metrics including ROC and AUC. Learners will master techniques to optimize classification outcomes.

涵盖的内容

9个视频4个作业

9个视频总计70分钟

Diabetes Dataset 5分钟
Diabetes Dataset - Logistic Rogation Model 9分钟
Making a Model 12分钟
Dimension Reduction 11分钟
Confusion Matrix 5分钟
Reduce Number of False Positives 7分钟
Plot Roc Curve 8分钟
Setting Threshold 7分钟
Area Under Curve 5分钟

4个作业总计60分钟

Logistic Regression with Diabetes Data 10分钟
Enhancing Model Performance 10分钟
ROC & AUC in Logistic Regression 10分钟
Graded - Advanced Logistic Regression Applications 30分钟

This module explores financial applications of logistic regression, including credit risk modeling, loan approval prediction, and dataset management. Learners will develop practical skills to build predictive models for financial decision-making.