A Streamlit demo to interactively visualize Uber pickups in New York City

Last update: Dec 28, 2022

Related tags

Overview

Streamlit Demo: Uber Pickups in New York City

A Streamlit demo written in pure Python to interactively visualize Uber pickups in New York City.

View the live app

Check out the live app at share.streamlit.io/streamlit/demo-uber-nyc-pickups. This demo is hosted with Streamlit sharing - the best way to deploy, manage, and share your Streamlit apps. Get an invite at streamlit.io/sharing.

Run this demo locally

pip install --upgrade streamlit
streamlit run https://raw.githubusercontent.com/streamlit/demo-uber-nyc-pickups/master/streamlit_app.py

Questions? Comments?

Please ask in the Streamlit community.

Owner

Streamlit

The fastest way to build custom ML tools

GitHub Repository

A machine learning toolkit dedicated to time-series data

tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti

2.3k Dec 29, 2022

Forecast dynamically at scale with this unique package. pip install scalecast

🌄 Scalecast: Dynamic Forecasting at Scale About This package uses a scaleable forecasting approach in Python with common scikit-learn and statsmodels

158 Jan 03, 2023

Katana project is a template for ASAP 🚀 ML application deployment

Katana project is a FastAPI template for ASAP 🚀 ML API deployment

100 Dec 26, 2022

Management of exclusive GPU access for distributed machine learning workloads

TensorHive is an open source tool for managing computing resources used by multiple users across distributed hosts. It focuses on granting

131 Dec 12, 2022

moDel Agnostic Language for Exploration and eXplanation

moDel Agnostic Language for Exploration and eXplanation Overview Unverified black box model is the path to the failure. Opaqueness leads to distrust.

1.2k Jan 04, 2023

Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type)

(intron I nterrogator and C lassifier) intronIC is a program that can be used to classify intron sequences as minor (U12-type) or major (U2-type), usi

4 Jul 26, 2022

Confidence intervals for scikit-learn forest algorithms

forest-confidence-interval: Confidence intervals for Forest algorithms Forest algorithms are powerful ensemble methods for classification and regressi

272 Dec 01, 2022

Anytime Learning At Macroscale

On Anytime Learning At Macroscale Learning from sequential data dumps (key) Requirements Python 3.7 Pytorch 1.9.0 Hydra 1.1.0 (pip install hydra-core

8 Mar 29, 2022

PROTEIN EXPRESSION ANALYSIS FOR DOWN SYNDROME

PROTEIN-EXPRESSION-ANALYSIS-FOR-DOWN-SYNDROME Down syndrome (DS) is a chromosomal disorder where organisms have an extra chromosome 21, sometimes know

1 Jan 20, 2022

Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

2 Jan 18, 2022

nn-Meter is a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices

A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

241 Dec 26, 2022

scikit-learn: machine learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started

3 Dec 16, 2022

Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies

Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies. We have amassed a dataset of millions of rows of high-frequency market data dating back to 2018 w

4 Sep 22, 2022

STUMPY is a powerful and scalable Python library for computing a Matrix Profile, which can be used for a variety of time series data mining tasks

STUMPY STUMPY is a powerful and scalable library that efficiently computes something called the matrix profile, which can be used for a variety of tim

2.5k Jan 06, 2023

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

DoWhy | An end-to-end library for causal inference Amit Sharma, Emre Kiciman Introducing DoWhy and the 4 steps of causal inference | Microsoft Researc

5.6k Jan 07, 2023