https://github.com/kwonnayeon/team-projects-archive

A collection of team projects
https://github.com/kwonnayeon/team-projects-archive

catboost exploratory-data-analysis machine-learning recommendation-systems tabnet team-project

Last synced: about 2 months ago
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A collection of team projects

• Host: GitHub
• URL: https://github.com/kwonnayeon/team-projects-archive
• Owner: KwonNayeon
• License: mit
• Created: 2024-08-02T00:13:06.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-07T14:34:41.000Z (5 months ago)
• Last Synced: 2024-08-08T16:47:45.412Z (5 months ago)
• Topics: catboost, exploratory-data-analysis, machine-learning, recommendation-systems, tabnet, team-project
• Language: Python
• Homepage:
• Size: 1020 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Team Projects Archive

This repository contains various projects developed by the team, showcasing different analytical and modeling techniques. The current projects include job recommendation modeling and data analysis tasks. Due to contractual obligations with the organization that held the competition, dataset files related to these projects are not included.

## Projects Overview

### 1. JobCare Recommendation

This project focuses on creating a job recommendation system using machine learning, specifically leveraging TabNet and CatBoost.

- **File:** `Jobcare_Recommendation_Modeling_TabNet_CatBoost.py`

  - This script demonstrates the use of TabNet and CatBoost models for job recommendations. It includes data preprocessing, feature engineering, model training, and evaluation.

  **Team Members:** Nayeon Kwon, Minkyeong Sim, Minkyeong Kim, Younghoon Yoo, Hyunwoo Im

### 2. LPoint Data Analysis

This project involves analyzing data related to LPoint, including summary statistics and exploratory data analysis (EDA).

- **Files:**

  - `LPoint_Data_Analysis_KR_Summary.pdf`

    - A summary report detailing the key findings and insights from the data analysis.

  - `lpoint_eda.py`

    - A Python script for performing exploratory data analysis on the LPoint dataset.

  **Team Members:** Nayeon Kwon, Minkyeong Sim, Minkyeong Kim

## Getting Started

### Prerequisites

Before running the scripts, make sure you have the following packages installed:

- `pytorch_tabnet`

- `catboost`

- `bayesian-optimization`

- `numpy`

- `pandas`

- `scikit-learn`

- `torch`

You can install these packages using pip:

```bash

pip install pytorch_tabnet catboost bayesian-optimization numpy pandas scikit-learn torch

```

## License

This project is licensed under the [MIT License](LICENSE). See the [LICENSE.txt](LICENSE) file for details.