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Predictive maintenance model for customer satisfaction, utilizing feature engineering algorithms: simulated annealing and relief. Developed during a hackathon at the Deutsche Telekom.

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#hack4bestcx

This is the repository I have worked with during the hackathon #hack4bestcx at the Deutsche Telekom. The task was to predict customer satisfaction from a dataset including both technical telecommunication data and the contract types of the customers. Given the high dimensionality of the data, I have focused mainly on feature engineering and implemented two different algortihms to find the most helpful features for a prediction: simulated annealing and relief. The data and the results are censored due to the non-disclosure agreement.

Repositories Used

https://github.com/PadraigC/FeatSelTutorial.git

https://github.com/kennethleungty/Simulated-Annealing-Feature-Selection.git

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Predictive maintenance model for customer satisfaction, utilizing feature engineering algorithms: simulated annealing and relief. Developed during a hackathon at the Deutsche Telekom.

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