This repository is my sandbox for practicing product analysis, experimentation, and statistical evaluation. Using synthetic or real product usage data, I explore A/B testing, retention modeling, feature impact analysis, and other techniques that help drive product decisions.
- Execute rigorous A/B testing to estimate treatment effects.
- Analyze user retention patterns and factors influencing churn.
- Model conversion funnels, time-to-event, and growth metrics.
- Practice causal inference: difference-in-differences, regression discontinuity designs.
- Build dashboards and visualizations supporting hypothesis-driven product decisions.
| Folder / File | Description |
|---|---|
data/ |
Raw or simulated datasets (events, sessions, conversions, cohorts) |
scripts/ |
Reusable modules for modeling, simulation, and metrics pipelines |
README.md |
Overview and documentation for this repository |
-
A/B Testing & Significance Analysis
- Compute test statistics, p-values, and confidence intervals.
- Adjust for multiple hypothesis testing or sequential tests.
-
Retention & Churn Modeling
- Kaplan-Meier curves, survival analysis, cohort-based retention visuals.
- Identify behavioral predictors of churn via logistic regression.
-
Conversion Funnel & Time-To-Event Modeling
- Visualize funnel drop-offs and time lag between stages.
- Use hazard models for time-to-conversion dynamics.
-
Causal Analysis Approaches
- Define treatment/control groups; simulate assignment.
- Apply difference-in-differences and regression discontinuity where appropriate.
-
Feature Impact Modeling
- Use regression or uplift models to estimate lift from new features or campaigns.