Insights in the Freelance Market

A data science and machine learning project focused on analyzing patterns in the freelance market. This repository contains code for segmenting clients and freelancers, predicting engagement success, and estimating project budget variance. The goal is to uncover actionable insights and optimize strategies for talent and project management.

📖 Contents

• Introduction
• Data
• Methods
• Results
• Conclusion
• Requirements
• Installation
• How to Run
• Data Access
• How to Cite

🎯 Introduction - TO BE UPDATED SOON

📁 Data - TO BE UPDATED SOON

🔬 Methods - TO BE UPDATED SOON

📈 Results - TO BE UPDATED SOON

🏁 Conclusion - TO BE UPDATED SOON

💻 Requirements

• Operating System
  • macOS
  • Linux
  • Windows (limited testing carried out)
• Python 3.12.x
• Required core libraries: environment.yaml

⚙ Installation

Step 1: Install Miniconda

Installation guide: https://docs.conda.io/projects/miniconda/en/latest/index.html#quick-command-line-install

Step 2: Clone the repository and change the current working directory

git clone https://github.com/Symfa-Inc/upwork-insights.git
cd upwork-insights

Step 3: Set up an environment and install the necessary packages

chmod +x make_env.sh
./make_env.sh

🚀 How to Run - TO BE UPDATED SOON

🔐 Data Access - TO BE UPDATED SOON

All essential components of the project, including the curated dataset and trained models, have been made publicly available:

• Dataset: https://zenodo.org
• Models: https://zenodo.org

🖊️ How to Cite - TO BE UPDATED SOON

Please cite our paper if you found our data, methods, or results helpful for your research:

Danilov V., Vinogradov M., Pudlik D., Galuza S. (2025). PAPER TITLE. Journal Title. DOI: TO.BE.UPDATED.SOON