This repository contains the code and documentation for the NYC Taxi Trip Duration Prediction project. The project aims to predict the duration of taxi trips in New York City (NYC) using machine learning techniques. Accurately forecasting trip duration can provide valuable insights into urban mobility, traffic patterns, and help optimize the operational efficiency of taxi services.
Link towards Dataset : https://drive.google.com/drive/folders/12-MlD5BYiNFtagMEMdzPxNYKxnH754yx?usp=sharing
The primary goal of this project is to develop a predictive model that can accurately estimate the duration of taxi trips in NYC. By achieving accurate predictions, this project aims to improve route planning, passenger satisfaction, and the overall efficiency of taxi services.
The dataset used for this project typically includes the following information for each taxi trip: pickup and dropoff locations (latitude and longitude), pickup times, passenger counts, and trip durations. This rich dataset allows for an in-depth exploration of factors influencing trip times in the city.
This project holds significance for various stakeholders:
- Taxi Companies: Understanding trip duration helps in efficient dispatching and reduces wait times for customers.
- City Planners: Insights from the data can inform traffic management and urban planning decisions.
- Passengers: Predictive information can enhance the user experience by providing accurate wait and travel times.
To get started with the project, follow these steps:
-
Installation: Clone this repository to your local machine using
git clone https://github.com/your-username/nyc-ai.git. -
Dependencies: Ensure that the necessary libraries, such as NumPy, Pandas, Matplotlib, and Scikit-Learn, are installed. You can install them using
pip install -r requirements.txt. -
Data: Download the NYC taxi trip dataset from [source] and place it in the
data/directory. -
Usage: Follow the instructions in the Jupyter Notebook
nyc_taxi_trip_duration.ipynbto explore the dataset, preprocess the data, and build the predictive model. -
Results: The final model and evaluation results will be saved in the
models/directory.
The project structure is organized as follows:
data/: Directory for storing the dataset.models/: Directory for saving trained models and evaluation results.nyc_taxi_trip_duration.ipynb: Jupyter Notebook containing the code and documentation for the project.requirements.txt: File specifying the required Python libraries and their versions.
Contributions to the project are welcome. If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License.
- [Source of Dataset]
- [Related Paper or Blog Post]
- [Any other acknowledgments]