Ensuring Predictive Integrity through Diagnostic Replication in R
In predictive modeling, a high R-squared isn't enough. The goal of this project was to perform a rigorous statistical audit on an existing predictive model to determine its reliability, validity, and susceptibility to common statistical biases.
- Model Replication: Re-constructed linear regression models in R to verify initial findings and ensure reproducibility.
- Diagnostic Auditing: Conducted comprehensive checks for:
- Normality & Linearity: Visualizing residuals to ensure the model captures underlying patterns.
- Homoscedasticity: Testing for constant variance to prevent biased standard errors.
- Multicollinearity (VIF): Identifying high correlations between predictors that could inflate variance.
- Outlier Analysis: Utilized Cook’s Distance and Leverage plots to identify influential data points that skewed model results.
This project demonstrates an advanced "Under-the-Hood" understanding of data science:
- Beyond Prediction: Shows the ability to critique a model's foundational assumptions, not just its output.
- R Proficiency: Advanced use of
ggplot2,car, and base R's diagnostic suite for scientific reporting. - Data Integrity: Proves a commitment to "Model Safety"—ensuring that business decisions are based on statistically sound evidence.
- Bias Detection: Identified specific diagnostic failures in the baseline model that led to overfitting.
- Robustness Improvements: Recommended data transformation and variable selection strategies to stabilize predictive accuracy.
- Visual Communication: Created diagnostic dashboards in R to communicate model health to stakeholders.
| Asset | Description |
|---|---|
| 📄 Technical Write-Up (PDF) | Full diagnostic report with statistical interpretations and recommendations. |
| 📊 R Source Code (.R) | Documented R scripts covering data cleaning, modeling, and plotting. |
- Quality Assurance: Validates your ability to act as a "Technical Auditor" for organizational data.
- Reproducible Science: Demonstrates the use of R for transparent and repeatable analysis pipelines.
- Statistical Depth: Moves beyond "Plug-and-Play" machine learning into true inferential expertise.