Playground with machine learning methods using python sklearn.
This is more like a playground project that provides basic examples to evaluate and test classifiers using a popular dataset. We explore machine learning methods using the python sklearn package.
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Clone the repository and enter the project directory.
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Create your virtual environment and install the required dependencies:
virtualenv -p `which python3` venv
source venv/bin/activate
pip install
- Make sure you have a python framework like Anaconda. Then use
pythonwto run the examples.
In the src folder:
Test the versions of the installed libraries:
pythonw check_versions.py
Inspect the dataset:
pythonw inspect-dataset.py
Train some algorithms and evaluate them:
pythonw evaluate-algos.py
Browse the src folder for more examples.
If you're having any problem, please raise an issue on GitHub.
PRs accepted. Some general guidelines:
- Write a concise commit message explaining your changes.
- If applies, write more descriptive information in the commit body.
- Refer to the issue/s your pull request fixes (if there are issues in the github repo).
- Write a descriptive pull request title.
- Squash commits when possible.
Before your pull request can be merged, the following conditions must hold:
- All the tests passes (if any).
- The coding style aligns with the project's convention.
- Your changes are confirmed to be working.
Small note: If editing the Readme, please conform to the standard-readme specification.
The project is licensed under the Apache 2.0 license.