phitrann / applied-data-science

Applied Data Science

This project, named "Applied Data Science", is designed to showcase the integration of various data science techniques and tools to solve real-world problems. It leverages a range of technologies including natural language processing, machine learning models, and data visualization to provide insightful solutions.

Project Structure

The project is structured as follows:

• __init__.py: An initialization script for Python packages.
• cache/: Contains cached models and data for quick access.
• data/: Directory for raw and processed data.
• dependencies.py: Script for managing project dependencies.
• experiments/: Contains experimental scripts and notebooks.
• notebooks/: Jupyter notebooks for exploratory data analysis and prototyping.
• pyproject.toml: Configuration file for project metadata and dependencies.
• README.md: This file, providing an overview of the project.
• requirements.txt: Lists the Python package dependencies for the project.
• src/: Source code for the project's main functionality.
• tests/: Unit and integration tests for the project.

Installation

To set up the project environment, follow these steps:

1. Ensure you have Python 3.8 or higher installed.
2. Clone the repository to your local machine.
3. Install the dependencies by running:

pip install -r requirements.txt

curl -O https://raw.githubusercontent.com/kingabzpro/jobzilla_ai/main/jz_skill_patterns.jsonl
pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly

Usage

To start the application, navigate to the src/applied_data_science directory and run:

streamlit run app.py --server.fileWatcherType none

This will launch the Streamlit application in your default web browser, where you can interact with the provided functionalities.

Features

• PDF Processing: Upload and process PDF documents to extract and analyze content.
• Data Visualization: Interactive charts and graphs to explore data insights.
• Job Recommendation System: Recommends jobs based on the processed data and user inputs.

Contributing

Contributions to the project are welcome! Please refer to the contributing guidelines for more information.

License

This project is licensed under the MIT License - see the LICENSE file for details.