This repository has several Machine Learning (ML) models. These are created in different python files and explained in Jupyter Notebooks, where you can find the explanation of how they work and, why are there differents functions or classes, and the performance (results) of the models through diffents metrics.
Several libraries have been used to create this project, which are specified in the file requirements.txt inside the docs folder.
The main objectives of this proyect are the following:
- Test models for various purposes.
- Provide a base or template to create a model.
- Share how machine learning works and what can it do!
The models created in this repository have been tested in order to work well. However, you will be able to see the results in the Jupyter Notebooks provided.
To replicate this repository, read the next sections and make sure you meet all the prerequisites and follow all installation steps.
In this section you can see a prerequisites list of things you will need to use the examples or fork the repository.
- Python 3.10.*
- Libraries mentioned in requirements.txt
- Jupyter Notebooks (usually installed with Anaconda or conda)
- IDE (Visual Studio Code preferably)
We have used a virtual enviroment to work isolated from the global operative system. This allow us to use versions other than those installed globally on the system.
There are a variety of commonly used virtual enviroments used in Python with differents advantages and disadvantajes. We chose to work with venv, one of the most used.
To create it, follow the next steps:
- Download and install Python from python.org.
- Locate yourself inside the src/ folder.
- Execute the following command in a shell adding a name to the virtual enviroment:
python -m venv <venv> .\venv\Scripts\activate pip install --upgrade pip pip install -r ../docs/requirements.txtIn this section you can see where to find the original datasets used in this proyect.