{"id":750,"date":"2019-09-13T22:19:00","date_gmt":"2019-09-13T19:19:00","guid":{"rendered":"https:\/\/mse-mse.com\/?page_id=750"},"modified":"2019-09-13T23:31:16","modified_gmt":"2019-09-13T20:31:16","slug":"maritime-logistics","status":"publish","type":"page","link":"https:\/\/mse-mse.com\/ms\/maritime-logistics\/","title":{"rendered":"Maritime Logistics"},"content":{"rendered":"
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.<\/p>\n
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.<\/p>\n
C ++, XGBoost, Python (Pandas, Scikit-learn, Keras libraries)<\/p>\n
71% of the project was completed (three of four stages).<\/p>","protected":false},"excerpt":{"rendered":"
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…<\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"yoast_head":"\n