Abstract
The project goal is to showcase the benefits and powerfulness of Bayesian statistics. Here, I introduce you four scenarios, weaving one or more Bayesian subtopics, such as Bayesian Inference , Bayesian Classification, Bayesian Survival Analysis, et al.
- Scenario 1: An investor uses Bayesian methods to update beliefs on stock volatility amid market uncertainty, simulating “what-if” scenarios for portfolio risk.
- Scenario 2: A retailer forecasts customer churn and spending using Bayesian survival to optimize retention campaigns, turning data into personalized ‘survival odds’ visuals.
- Scenario 2+: one step further: A retailer want to model purchase behavior across regions to tailor promotions, visualizing group-level and individual-level belief updates.
- Scenario 3: A telecom firm classifies user risk of churning using Bayesian methods, visualizing “belief updates” to prioritize interventions like targeted offers.
- Scenario 4: A streaming service refines genre recommendations via simulation-based inference, interactively exploring how user listening data “evolves” latent preferences.
These scenarios are structures as narrative stories to guide the portfolio’s layout:
- starting with a real-world problem introduction,
- moving through analysis steps,
- incorporating interactive visualizations
- ending with insights and reproducibility notes.
Each scenario uses Python for code analysis (e.g., PyMC or Stan via pymc or cmdstanpy), public datasets and GitHub for hosting code/notebooks alongside a dynamic webpage(i.e, via Streamlit or Dash embedded in HTML for interactivity)
The detailed roadmap of each scenarios are presented in the table below. For each, I’ve outlined 5-7 key steps with descriptions, including where interactive visualizations fit in. This layout ensures a logical flow: problem → method → visualization → results → reflection.
- 🚗Roadmap - Scenario 1
- 🚌Roadmap - Scenario 2
- 🚖Roadmap - Scenario 3
- 🚛Roadmap - Scenario 4
Data