What you'll get
  • 4+ Hours
  • 1 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Fundamentals and theory of logistic regression
  • Data preprocessing, scaling, and preparation
  • Fitting models and interpreting results
  • Evaluating models using coefficients, confusion matrices, ROC curves, and AUC
  • Reducing false positives and selecting thresholds
  • Dimension reduction to improve performance
  • Practical applications on advertisement, diabetes, and credit-risk datasets
  • Splitting data for training and testing
  • Interpreting and presenting logistic regression results, including R outputs

Content

Courses No. of Hours Certificates Details
Logistic Regression with R4h 14m✔View Curriculum

Description

This course offers a comprehensive introduction to logistic regression, a powerful statistical technique widely used in predictive modeling and risk assessment. Designed for beginners and data professionals alike, the course blends theory with hands-on exercises using real-world datasets.

Course Highlights

  • Introduction to Logistic Regression: Understand the theoretical foundations, key concepts, and practical applications across different domains.

  • Advertisement Dataset: Explore a real-world advertisement dataset, learn feature scaling, fit logistic regression models, and analyze classifier coefficients to evaluate the impact of predictor variables.

  • Diabetes Dataset: Build logistic regression models, perform dimension reduction, evaluate model performance with confusion matrices, reduce false positives, and plot ROC curves to assess accuracy.

  • Credit Risk Dataset: Analyze credit risk data, split the dataset into training and test sets, apply logistic regression models, and assess risk using model predictions.

  • Reference Materials: Gain access to supplementary files and datasets for practice and reinforcement of learning.

By the end of this course, participants will have the practical skills to apply logistic regression to real-world datasets, build predictive models, and make informed data-driven decisions for risk assessment and analytics.

Requirements

  • Basic familiarity with R programming

  • No prior experience in logistic regression required

  • Understanding of basic statistics and probability concepts

  • Comfort working with datasets 

  • Interest in predictive modeling and data analysis.

Target Audience

  • Students and professionals interested in predictive modeling

  • Data analysts and data scientists looking to apply logistic regression

  • Business analysts aiming to make data-driven decisions

  • Researchers working with probability estimation and classification tasks

  • Professionals in finance, healthcare, marketing, and risk assessment

  • Anyone eager to learn how to model data and estimate outcome probabilities.