Data Cleaning (Classic and LLMs) — Epoch Club 18

This repository contains all the materials from the Epoch Club session on Practical Data Cleaning and Preprocessing with Python, held on December 11, 2025. It includes code notebooks, a challenge description, and a supporting slide deck used during the session.

Session Video

You can watch the video of the session on YouTube as soon as it is released.

Features

• Hands-on Data Cleaning Notebook: A step‑by‑step Jupyter notebook demonstrating real‑world data preprocessing techniques.
• Challenge Task: A Challenge.md file describing an applied data cleaning exercise for practice or homework.
• Reference PDF: A PDF titled “Data Preprocessing Masterclass — From Tabular Basics to LLM Pipelines” summarizing concepts, workflows, and best practices.

Repository Structure

<root>
├── Challenge.md                                      # Data cleaning challenge description
├── Data Preprocessing Masterclass_ From Tabular Basics to LLM Pipelines.pdf  # Session slides
├── data_cleaning_tutorial.ipynb                      # Main hands-on tutorial notebook
└── README.md                                         # This file

Getting Started

1. Clone this repository:

   git clone https://github.com/Ryan-PG/ec-18-data-preprocessing-classic-and-llms.git
   cd ec-18-data-preprocessing-classic-and-llms
   

2. Set up a Python environment:

   • Create and activate a virtual environment (recommended), or use Kaggle or Google Colab to run the notebook in the cloud.

3. Open the notebook:

   jupyter notebook data_cleaning_tutorial.ipynb
   

   or upload the notebook to Kaggle or Colab and run all cells there.

4. Tackle the challenge:

   • After going through the notebook, open Challenge.md and attempt the challenge using the same or a new notebook.

Using in Kaggle, Colab, and HF Spaces

• Kaggle: Create a new Notebook, upload data_cleaning_tutorial.ipynb, attach your dataset, and run cells sequentially.
• Google Colab: Open the notebook from GitHub or upload it directly; mount Google Drive if you need local files.

Contributing

Contributions, improvements to the notebook, or additional challenge ideas are welcome. Open an issue or submit a pull request to enhance the data cleaning workflows or add new preprocessing examples.

License

This repository is licensed under the MIT License. You are free to use, modify, and share the materials in accordance with the license terms.

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Thank you for exploring and contributing to this Epoch Club data cleaning repository!