Mastering Logistic Regression Analysis: Theory and Practice with Real
World Datasets
Learn with case studies on Advertisement Dataset, Diabetes Dataset, Credit Risk using Logistic Regression in R Studio. Unlock the potential of logistic regression analysis in our comprehensive course. Explore real-world datasets and learn feature scaling techniques. Master model fitting, evaluation, and dimension reduction methods. Gain practical skills for predictive modeling and risk assessment.
Offer ends in:
What you'll get
- 4+ Hours
- 1 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Theoretical foundations of logistic regression analysis.
- Techniques for preprocessing and scaling features in real-world datasets.
- Model fitting and interpretation using logistic regression.
- Analysis of classifier coefficients and confusion matrices for model evaluation.
- Dimension reduction methods to improve model performance.
- Strategies for reducing false positives and setting appropriate thresholds.
- Plotting ROC curves and calculating the area under the curve for model assessment.
- Practical application of logistic regression to advertisement, diabetes, and credit risk datasets.
- Splitting datasets into training and testing sets for model validation.
- Mastery of logistic regression analysis for predictive modeling and risk assessment in various domains.
- Know in detail about logistic regression analysis and its benefits
- Know about the different methods of finding the probabilities and Understand about the key components of logistic regression
- Learn how to interpret the modeling results and present it to others
- Know how to interpret logistic regression analysis output produced by R
Content
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MODULE 1: Essentials Training
Courses No. of Hours Certificates Details Logistic Regression & Supervised Machine Learning with R 4h 14m ✔
Description
Introduction: This course provides a thorough introduction to logistic regression analysis, a powerful statistical technique widely used in predictive modeling. Participants will gain a solid understanding of the theoretical underpinnings of logistic regression and its applications in various domains.
Advertisement Dataset: Participants will work with a real-world advertisement dataset, exploring its structure and characteristics. They will learn about feature scaling techniques to preprocess the data and prepare it for logistic regression modeling. Through practical exercises, participants will fit logistic regression models to the advertisement dataset and analyze classifier coefficients to understand the impact of predictor variables.
Diabetes Dataset: In this section, participants will delve into logistic regression analysis using a diabetes dataset. They will learn how to build logistic regression models, perform dimension reduction, and evaluate model performance using confusion matrices. Participants will also explore techniques for reducing false positives and plot ROC curves to assess model accuracy.
Credit Risk: Participants will analyze a credit risk dataset, focusing on variables such as loan amount, applicant income, and credit history. They will learn how to split the dataset into training and testing sets, apply logistic regression models, and assess credit risk based on model predictions.
Reference Files: To reinforce learning and provide additional resources, participants will have access to reference files containing supplementary materials and datasets used throughout the course.
By the end of the course, participants will have gained practical skills in logistic regression analysis and be able to apply them to real-world datasets for predictive modeling and risk assessment.
Requirements
- Students or anyone taking this course should have some familiarity with R. There are no basic skills required to take this course.
Target Audience
- Anyone who is interested in modeling data and estimate the probabilities of given outcomes.
Offer ends in:
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