The Machine Learning Case Studies gathers a collection of Data Analysis and Machine Learning studies performed under the supervision of Prof. G. Spanakis from Maastricht University. Each Jupyter notebook corresponds to a particular topic with the exception of the 'Flu Madness' Kaggle Solution which incorporates many techniques from Data Analysis and Machine Learning. The main topics covered are: Exploratory Data Analysis, Classification, Regression Techniques, Dimensionality Reduction, Timeseries and more. Each case study is accompanied by deep explanation and a thorough conclusion.
This section lists the major frameworks that the project was built with.
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Hristo Minkov - [email protected]
Codebase Link: https://github.com/icaka98/Machine-Learning-Case-Studies