jobme

Find me the most suitable job based on my skillset

As a fresh graduate student from an Intelligent, Interactive and Autonomous Systems (SIIA) Degree At the university of Western Brittany. Let's build a system that will help me find a job.

What is Jobme

Jobme is an AI-based system that suggests various IT stacks/jobs based on the user's skills.

Why Build Jobme

I hold a bachelor degree in computer engineering. After that, I pursued a master degree in Intelligent, Interactive and Autonomous Systems.

During the university, I built strong skills in various fields including back-end development, High Parallel Computing and Machine Learning. The skills set required to work on these domains is wide. In fact, I learnt to code with different programming languages, in different programming paradigms implementing different architectures.

Looking for the first job is a challenge. With a large set of skills a candidate may become distracted and he/she may take a subjective career choice. The aim of this machine learning model is to make a rational decision based on existing data.

Project Organization

├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default mkdocs project; see www.mkdocs.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         jobme and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in 
│
├── script             <- Flask API that exposes a model via an endpoint
│
├── webui              <- Web user interface to interact with the model API             
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── jobme   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes jobme a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    └── plots.py                <- Code to create visualizations

---

Running the project

To run the project in your localhost. It would be easy to clone the dockerize branch. Then, build and run the docker image.

You can interacte with the model via an HTTP request through POSTMAN (or equivalent), or you can directly start the web UI via a npm start command.

STEP 1:

This command will build the docker image described in the dockerfile

docker build -t model_img

STEP 2

Run the built docker image:

docker run --name container_name -p 5000:5000 model_img

This requires to have docker installed in your machine.

If you can't work with containers, you can just install manually the requirements mentioned in requirements_prod.txt file. These requirements will be able to run the development server, but we expect some issues when it comes to run the notebooks.

What to contribute ?

Raise an issue or contact the author via :

[email protected]