ECON 487: Data Science for Strategic Pricing

This repository organizes course materials by concept. Each directory pairs the original R Markdown file with a Jupyter notebook rewritten in Python.

Directory Overview

• r_fundamentals – Basic Python commands, graphics, indexing, data loading, and summaries using the Auto data set.
• demand_elasticity – Log transformations, simple regressions, and elasticity calculations for orange juice.
• intertemporal_and_cv – Linear regression with cross‑validation.
• regularization_and_trees – LASSO regression and decision tree examples.
• gradient_boosting – Gradient‑boosted tree regression on synthetic data.

Running the Notebooks

1. Launch Jupyter and open the .ipynb files inside each directory.

This repository organizes course materials by concept. Each directory pairs the original R Markdown file with a Jupyter notebook rewritten in Python.

Directory Overview

• r_fundamentals – Basic Python commands, graphics, indexing, data loading, and summaries using the Auto data set.
• demand_elasticity – Log transformations, simple regressions, and elasticity calculations for orange juice.
• intertemporal_and_cv – Linear regression with cross‑validation.
• regularization_and_trees – LASSO regression and decision tree examples.
• gradient_boosting – Gradient‑boosted tree regression on synthetic data.

Running the Notebooks

1. Launch Jupyter and open the .ipynb files inside each directory.

This repository organizes course materials by concept. Each directory pairs the original R Markdown file with a Jupyter notebook converted from it. Notebooks rely on an R kernel.

Directory Overview

• r_fundamentals – Basic R commands, graphics, indexing, data loading, and summaries using the Auto data set.
• demand_elasticity – Box plots, log transformations, regressions, and demand elasticities for orange juice.
• intertemporal_and_cv – Demand estimation with intertemporal substitution and cross‑validation.
• regularization_and_trees – LASSO regression, elasticity matrices, and regression tree models.
• gradient_boosting – Gradient‑boosted trees with cross‑validation and feature importance analysis.

Running the Notebooks

1. Install R and IRkernel for Jupyter.
2. Launch Jupyter and open the .ipynb files inside each directory.

The data files required by each notebook are stored alongside the notebooks.