code
gds_env
| code | gds_env | |
|---|---|---|
| 1 | 1 | |
| 874 | 126 | |
| - | - | |
| 0.0 | 7.8 | |
| 3 months ago | 16 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
code
-
An "Improvement Prediction" error in R Shiny
The following below is my code (I did reference DataProfessor's code here while making my code). Not really sure what my error is and how to fix.
gds_env
What are some alternatives?
Shiny_Desktop_App - Deploy your R Shiny app(s) locally on Windows
AMAYARA-Lab - The アマヤラ Lab project provides a ready-to-use Jupyter Lab environment to help out with Android malware analysis using YARA rules.
python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
ydata-quality - Data Quality assessment with one line of code
FinMind - Open Data, more than 50 financial data. 提供超過 50 個金融資料(台股為主)，每天更新 https://finmind.github.io/
machine_learning_complete - A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
fastpages - An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
reinforcement_learning_course_materials - Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University
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.
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.