🚕 New York City TCL trips

📖 Introduction

The goal of this project is to predict the time of a NYC taxi trip. The data is provided by the NYC Taxi and Limousine Commission.

🚀 Pipelines

The pipeline of the entire process is:

📡 Data engineering

The data engineering pipeline is designed to download the data from the NYC Taxi and Limousine Commission, split into train, validation and test sets and create the target.

⚛️ Data science

The data science pipeline is designed to train the model. The model is a scikit-learn pipeline with a Gradient Boosting model. The process is:

• Merge both airport variables
• Create datetime features
• Create the scikit-learn pipeline: feature imputer, column transformer and the gradient boosting model using HistGradientBoosting
• Train the model

🛸 Model inference

Predict the trip duration using the scikit-learn pipeline.

🧪 Model evaluation

Evaluate the performance model. To do so:

• Compute metrics
• Plot the histograms of the predictions

📊 Prepare for dashboard

Prepare data for the dashboard.

🖥️ Kedro

Overview

This is your new Kedro project, which was generated using kedro 0.19.1.

Take a look at the Kedro documentation to get started.

Rules and guidelines

In order to get the best out of the template:

• Don't remove any lines from the .gitignore file we provide
• Make sure your results can be reproduced by following a data engineering convention
• Don't commit data to your repository
• Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

How to install dependencies

Declare any dependencies in requirements.txt for pip installation.

To install them, run:

pip install -r requirements.txt

How to run your Kedro pipeline

You can run your Kedro project with:

kedro run

How to test your Kedro project

Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests as follows:

pytest

You can configure the coverage threshold in your project's pyproject.toml file under the [tool.coverage.report] section.

Project dependencies

To see and update the dependency requirements for your project use requirements.txt. You can install the project requirements with pip install -r requirements.txt.

Further information about project dependencies

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: context, 'session', catalog, and pipelines.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run pip install -r requirements.txt you will not need to take any extra steps before you use them.

Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to ignore notebook output cells in git

To automatically strip out all output cell contents before committing to git, you can use tools like nbstripout. For example, you can add a hook in .git/config with nbstripout --install. This will run nbstripout before anything is committed to git.

Note: Your output cells will be retained locally.

Package your Kedro project

Further information about building project documentation and packaging your project