New York city taxi fare prediction | |
| Author | Reddy, Sheelam Sairaghuveer |
| Call Number | AIT RSPR no.ICT-19-10 |
| Subject(s) | Data mining Machine Learning Local transit--Fares |
| Note | A research submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Communication Technologies, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Research studies project report ; no. ICT-19-10 |
| Abstract | People of New York take hundreds of thousands of taxi rides per day. We are using the Kaggle competition “New York City Taxi Fare Prediction” dataset to make a Machine learning model that can predict the Taxi fare price. The approximate taxi fare prediction will help people of New York City to make their plans in advance and save money time. In recent years machine learning is very good and quite accurate about the predictions. In this research study, we are able to predict the fare of a taxi by building different models the help of applying different machine learning techniques such as AdaBoost, Classification and regression(CART), CatBoost, Deep neural networks, LightGBM.Root mean square error was the evaluation matrix for checking the accuracy of the models. Among them, LightGBM has given the best accuracy |
| Year | 2019 |
| Corresponding Series Added Entry | Asian Institute of Technology. Research studies project report ; no. ICT-19-10 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information and Communication Technology (ICT) |
| Chairperson(s) | Teerapat Sanguankotchakorn; |
| Examination Committee(s) | Guha, Sumanta;Bohez, Erik L.J.;Nicole, Olivier; |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship; |
| Degree | Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 2019 |