This project was created for the class: Design and Analysis of Algorithms, an elective class for the Masters in Science in Data Science, offered by ITESO university.
Install all the dependencies stated in the requirements.txt file, just run the following command in terminal:
pip install -r requirements.txt
Or you can manually install one by one using the name and version in the file.
- Autoregressive Feature Generation (autoregressive_features)
- Hadamard Product for Feature Generation (hadamard_features)
- Genetic Programming for Symbolic Operations for Feature Generation (symbolic_features)
- Timeseries Block Folds without filtration (t_folds)
- Classifier model: Logistic Regression with Elastic Net Regularization (logistic_net)
- Classifier model: Least Squares Support Vector Machines (ls_svm)
- Classifier model: Artificial Neural Net Multilayer Perceptron (ann_mlp)
- Genetic Algorithms Optimization (genetic_algo_optimization)
- Plotly visualizations of results (visualizations.py)
- Machine Learning Models Performance Metrics (model_evaluation)
B.Eng in Financial Engineering, M.Sc in Data Science candidate, Juan Francisco Muñoz-Elguezabal
GNU General Public License v3.0
Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.
For more information in reggards of this project, please contact [email protected]