Applied Bayesian Inference on Products

This is the repo in accordance with these three Medium articles:

1. Applied Bayesian Inference with PyMC3 pt.1
2. Applied Bayesian Inference with PyMC3 pt.2
3. Applied Bayesian Inference with PyMC3 and Bambi pt.3

In part 1, I introduce modeling the conditional world via Python/PyMC3 from a contrived coin flip example. In part 2, I dive deeper into Bayesian Analysis with a dataset from Kaggle. StockX's 2019 Sneaker dataset can be found here: https://www.kaggle.com/hudsonstuck/stockx-data-contest. In part part 3, I take the skills learned so far to build ML models based on Bayesian estimation to predict streams for Spotify's Top 200 songs. The dataset can be found here: https://www.kaggle.com/sashankpillai/spotify-top-200-charts-20202021

Part 1 Contents:

1. Thinking Bayes
2. Probabalistic Programming

Part 2 Contents:

1. Introduction
2. Exploratory Data Analysis & Data Cleaning
3. Modeling & Analysis
4. Group Comparison
5. Conclusion

Part 3 Contents:

1. Introduction
2. Exploratory Data Analysis & Data Cleaning
3. The Simple Regression
4. Robust Regression and Out-of-Sample Prediction
5. Multiple, Hierarchical, and Generalized Linear Models
6. Model Compare
7. Conclusion