Logistic Regression and Machine Learning in R for Data Science: End-to-End Case Studies and Applications

This article shows how logistic regression can be applied within data science using R to build interpretable and effective classification models for domains such as customer analytics, healthcare, and financial risk assessment.

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Article outline

1. Introduction to Logistic Regression in Data Science

• Explanation of logistic regression as a core supervised learning algorithm in data science.

• Its role in binary and multi-class classification.

• Why logistic regression remains widely used: interpretability, speed, and probability outputs.

2. Applications of Logistic Regression in Data Science

• Customer Analytics: predicting churn or customer retention.

• Healthcare Analytics: identifying the presence of disease from patient metrics.

• Financial Services: credit scoring and fraud detection.

• Marketing Analytics: predicting campaign responses.

3. Mathematical Foundation of Logistic Regression

• The logistic (sigmoid) function for mapping predictors to probabilities.

• Coefficients interpreted as log-odds and odds ratios.

• Model estimation via Maximum Likelihood Estimation.

• Role of regularization (L1/L2) in modern machine learning contexts.

4. Setting Up the R Environment

• Required packages: stats, caret, ggplot2, pROC, ResourceSelection, ROCR.

• Preparing environment for modeling, diagnostics, and visualization.

5. Preparing Data for Logistic Regression in R

• Overview of data preprocessing steps: splitting into train/test, scaling, encoding categorical variables.

• Explanation of why preprocessing is important in machine learning workflows.

6. Building Logistic Regression Models in R

• Using glm() with family = binomial for logistic regression.

• Extracting and interpreting coefficients.

• Calculating odds ratios for feature interpretability.

7. Model Evaluation and Diagnostics

• Confusion matrix, accuracy, precision, recall, F1-score (caret).

• ROC curve and AUC (pROC).

• Precision–Recall curves.

• Hosmer–Lemeshow goodness-of-fit test (ResourceSelection).

• Visualizing odds ratios with coefficient plots.

8. Case Study 1: Customer Churn Prediction

• End-to-end example with data preparation, logistic regression model fitting, evaluation, and interpretability.

9. Case Study 2: Healthcare Disease Classification

• Application of logistic regression to healthcare context, highlighting interpretability for medical decision-making.

10. Case Study 3: Financial Credit Risk Assessment

• Logistic regression applied to financial credit scoring, with evaluation metrics and risk insights.

11. Comparison with Other Machine Learning Models

• Benchmarking logistic regression against Decision Trees (rpart) and Random Forests (randomForest).

• Discussion of trade-offs: interpretability vs. predictive power.

12. Advantages and Limitations in Data Science Applications

• Strengths: simplicity, interpretability, probabilistic outputs.

• Weaknesses: linear log-odds assumption, limited capacity for non-linear patterns.

• Best practice scenarios for logistic regression use.

13. End-to-End R Script

• A unified R script covering preprocessing, logistic regression training, diagnostics, evaluation, and model comparison across the case studies.

14. Conclusion

• Summary of logistic regression’s relevance in machine learning pipelines.

• Emphasis on balancing interpretability and predictive accuracy in data science practice.

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