datascience-template VS code

Compare datascience-template vs code and see what are their differences.

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datascience-template code
1 1
19 876
- -
0.0 0.0
over 1 year ago 3 months ago
Jupyter Notebook Jupyter Notebook
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

datascience-template

Posts with mentions or reviews of datascience-template. We have used some of these posts to build our list of alternatives and similar projects.

code

Posts with mentions or reviews of code. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing datascience-template and code you can also consider the following projects:

An-Introduction-to-Statistical-Learning - This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

Shiny_Desktop_App - Deploy your R Shiny app(s) locally on Windows

video-super-resolution-youtube

gds_env - A containerised platform for Geographic Data Science

homemade-machine-learning - 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource

handson-ml - ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.

ydata-quality - Data Quality assessment with one line of code

snowpark-python-demos - This repository provides various demos/examples of using Snowpark for Python.

machine_learning_complete - A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.

ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.

project-atlas-sao-paulo - A project for the development of rich geospatial data from the city of São Paulo for use in Machine Learning models.