/Travel-Time-Prediction

Trip duration prediction - An important task to look forward for cab service providers.

Primary LanguageJupyter Notebook

Travel-Time-Prediction

The purpose of this modelling is to accurately predict the trip duration of taxi's. To make predictions we will use several algorithms, tune the corresponding parameters of the algorithm by analysisng each parameter against RMSE and predict the trip duration. To make our prediction we use RandomForest Regressor, LinearSVR and LinearRegression. We improved accuracy by tuning hyperparameters and RandomForest gave best accuracy of 83%.

Prerequisites

You need to install :

  • python
  • numpy
  • matplotlib
  • sklearn
  • scipy
  • pip
  • jupyter Notebook

Executing the files

  • python filename.py
  • using IDLELib

Built with

  • ActiveState Python
  • Jupyter Notebooks
  • Microsoft Power BI

Authors

  • Mohammed Khursheed Ali Khan

Acknowledgements

  • Kaggle platform
  • NYC limousine Open Data
  • RPubs
  • University of California, San Diego
  • Harrison