Msc thesis on data analytics, DIT 2019
├── LICENSE <- Legal notice details
├── Makefile <- Makefile with 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 Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a YYY/MM/DD date (for
│ ordering), and a short `-` delimited description, e. g.,
│ `2018/10/20-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Thesis report (LaTeX)
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
└── src <- Source code for use in this project.
├── init.py <- Makes src a Python module │ ├── data <- Scripts to download or generate data │ └── make_dataset.py │ ├── features <- Scripts to turn raw data into features for modeling │ └── build_features.py │ └── models <- Scripts to train models and then use trained models to make │ predictions ├── predict_model.py └── train_model.py
This project needs a number of dependencies to work:
- Install Anaconda python. See instrucioons for your OS at project's website.
- Create python environment using:
- Install ffmpeg libraries. See instrucionts for your OS at project's website.
From projects root folder:
- Update '''.env''' file with a proper path for the '''path.root''' variable
- '''make data''' will download and pre-process the dataset.
- '''make features''' will compute features (MFCC, roll_off ...)
- '''make train name=MODEL''' will train the indicated model.
Author: Víctor Santiago González Contact: [email protected]
Project based on the cookiecutter data science project template. #cookiecutterdatascience