https://github.com/floressek/haggle_comp

This repository serves as the central hub for our collaborative data science project based on a Kaggle competition. Our team of data scientists is working together to tackle a real-world problem by exploring data, engineering features, and building robust predictive models.
https://github.com/floressek/haggle_comp

kaggle-competition kaggle-dataset

Last synced: 6 months ago
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This repository serves as the central hub for our collaborative data science project based on a Kaggle competition. Our team of data scientists is working together to tackle a real-world problem by exploring data, engineering features, and building robust predictive models.

• Host: GitHub
• URL: https://github.com/floressek/haggle_comp
• Owner: Floressek
• License: mit
• Created: 2025-02-14T18:53:21.000Z (8 months ago)
• Default Branch: master
• Last Pushed: 2025-02-16T18:02:00.000Z (8 months ago)
• Last Synced: 2025-03-26T10:36:13.460Z (6 months ago)
• Topics: kaggle-competition, kaggle-dataset
• Language: Jupyter Notebook
• Homepage:
• Size: 213 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Data Science Team Project on Kaggle

This repository serves as the central hub for our collaborative data science project based on a Kaggle competition. Our team of data scientists is working together to tackle a real-world problem by exploring data, engineering features, and building robust predictive models.

## Project Overview

Our project leverages a Kaggle dataset to explore various data science methodologies including data cleaning, exploratory data analysis (EDA), feature engineering, and model development. The primary goal is to derive actionable insights and build a model that performs well on unseen data. This repository contains all the necessary code, notebooks, documentation, and resources to facilitate our team-based approach.

## Repository Structure

- **data/**: Contains both raw and processed datasets, as well as data dictionaries.

- **codes/**: Jupyter notebooks documenting our exploratory analysis, model experiments, and visualization efforts.

- **results/**: Output files including model predictions, evaluation metrics, and performance reports.

- **docs/**: Detailed documentation, project reports, and presentations.

- **.gitignore**: Ensures that unnecessary files (temporary outputs, virtual environments, etc.) are not tracked in the repository.

## Collaboration and Workflow

- **Version Control**: We utilize Git and GitHub to track changes, manage versions, and facilitate collaboration.

- **Branching Strategy**: Our workflow involves using feature branches for new experiments and merging them into the main branch after thorough reviews.

- **Team Contributions**: All team members contribute via pull requests and code reviews to maintain high code quality and ensure consistency across the project.

- **Documentation**: Comprehensive documentation is maintained to ensure reproducibility and ease of knowledge transfer among team members.

## How to Get Started

1. **Clone the Repository**: Use `git clone` to download the repository to your local machine.

2. **Set Up the Environment**: Follow the setup instructions in the README file to install the necessary dependencies using either `conda` or `pip`.

3. **Explore the Notebooks**: Begin with the notebooks in the `notebooks/` directory to understand our initial analysis and experimental approaches.

4. **Review the Codebase**: Dive into the `src/` folder for modular code on data processing and model building.

5. **Collaborate**: Fork the repository, create new branches for your contributions, and submit pull requests for review.

## Future Enhancements

- Further refinement of feature engineering and model tuning.

- Integration of additional Kaggle datasets and external data sources.

- Exploration of advanced machine learning techniques and ensemble methods.

- Deployment of the best-performing model as a web service for real-time predictions.

## Conclusion

This GitHub repository encapsulates our team's collaborative efforts in tackling a challenging Kaggle project. By combining rigorous data analysis, state-of-the-art machine learning techniques, and a robust version control system, we aim to produce a high-quality model that delivers significant insights and real-world impact.