AML-DS-2021

This is a project for Advanced Machine Learning Course at innopolis university. It contains Seminars coding examples, homework exercises and Course project code.

Prerequisites

Keras >= 2.0.8
TensorFlow >= 2.0
Numpy >= 1.13.3
Matplotlib >= 2.0.2
Seaborn >= 0.7.1
Catboost
PyTorch

All the libraries can be pip installed using pip install -r requirements.txt

Getting Started

Clone this repo (for help see this tutorial).
Navigate to repository folder
Install dependencies which are specified in requirements.txt. use pip install -r requirements.txt or pip3 install -r requirements.txt
Raw Data is being kept here within this repo.
Data processing/transformation scripts are being kept here
To run the repository main code nevigate to scr cd src then run python main.py. Or execute the .ipynb file here

Setup using

cd AML-DS-2021
python -m venv dst-env

Activate environment

Max / Linux

source dst-env/bin/activate

Windows

dst-env\Scripts\activate

Install Dependencies

pip install -r requirements.txt

Setting up

python setup.py

Testing

To run tests, install pytest and unittest using pip or conda and then from the repository root run

pytest tests
#or
python -m unittest discover -s tests/ -p '*_test.py' -v

Repository Structure

├── .gitignore               <- Files that should be ignored by git.
│                               
├── conda_env.yml            <- Conda environment definition
├── LICENSE
├── requirements.txt         <- The requirements file for reproducing the analysis environment, e.g.
│                               generated with `pip freeze > requirements.txt`. Might not be needed if using conda.
├── setup.py                 <- Setup script
│
├── data                     <- Data files directory
│   └── Data1                <- Dataset 1 directory
│
├── notebooks                <- Notebooks for analysis and testing
│   ├── eda                  <- EDA Notebooks directory for
│   │   └── eda1.ipynb       <- Example python notebook
│   ├── features             <- Notebooks for generating and analysing features (1 per feature)
│   └── preprocessing        <- Notebooks for Preprocessing

├── scripts                  <- Standalone scripts
│   └── dataExtract.py       <- Data Extraction script
│
├── src                      <- Code for use in this project.
│   ├── train.py             <- train script
│   └── test.py              <- test script
│
└── tests                    <- Test cases (named after module)
    ├── test_notebook.py     <- Test that Jupyter notebooks run without errors
    ├── test1package         <- test1package tests
        ├── test1module      <- examplemodule tests (1 file per method tested)
        ├── features         <- features tests
        ├── io               <- io tests
        └── pipeline         <- pipeline tests

Contributing to This Repository

Contributions to this repository are greatly appreciated and encouraged.
To contribute an update simply:

Submit an issue describing your proposed change to the repo in question.
The repo owner will respond to your issue promptly.
Fork the desired repo, develop and test your code changes.
Edit this document and the template README.md if needed to describe new files or other important information.
Submit a pull request.

References

Contact

If you would like to get in touch, please contact:

Elizaveta55/DS