Our Projects:
Maritime Logistics

Realized Tasks:

Building a self-learning forecasting logistics system that allows you to build a variety of schemes for the workload of the hub, build a scheme of calls of vessels at the port and their sequence. The task within the project is divided into four stages.

Implementation path:

To solve the problems of classification and regression, various methods were used, including the logistic regression method, the selected regressors were loaded into the recursive neural network LSTM, and a system was constructed from several recurrent networks.

Tools used:

C ++, XGBoost, Python (Pandas, Scikit-learn, Keras libraries)

Achievements:

71% of the project was completed (three of four stages).

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