CAS in Applied Data Science — Final Project

This repository contains materials for a CAS in Applied Data Science final project, including Jupyter notebooks, minimal local datasets (kept out of version control), and the project poster. The goal is to make the work easy to understand, review, and reproduce.

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🧭 Overview

• Notebooks document the end-to-end workflow: exploration, feature engineering, modeling, and evaluation.
• Poster includes the final presentation artifact and any supporting files.
• Data is stored locally under data/ and excluded from version control to keep the repo lean and compliant with data policies.

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📁 Repository Structure

cas_ads_project/
├─ Notebooks/        # Analysis and modeling notebooks
├─ Poster/           # Poster and related source files
├─ data/             # Local data only (ignored by git)
│  └─ README.md      # Where to get data / how to regenerate it
├─ requirements.txt  # Python dependencies
└─ README.md

Data policy: The data/ directory is intentionally not tracked in version control. Keep raw, interim, and processed files here. Use data/README.md to explain where data comes from and how to obtain or recreate it (e.g., links, scripts, or generation steps).

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▶️ Working With the Notebooks

• Use a recent Python 3 environment and install the packages listed in requirements.txt.
• Open the notebooks in your preferred Jupyter interface.
• Run notebooks top-to-bottom. If a notebook expects files, place them under data/ and adjust any paths if needed.
• For heavy or external datasets, provide a small sample and describe the full source in data/README.md so others can test quickly.

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📊 Poster / Report

The Poster folder contains the final poster and, when applicable, its editable source (e.g., presentation file or LaTeX). Include brief notes on how the poster was generated or exported so that it can be reproduced.

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📬 Contact

Questions or suggestions can be opened as issues or shared with @giovamau on GitHub.