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IoT Energy Efficiency Management Kit

by jeffrey220204 Updated: Jan 9, 2022

Welcome to our Sustainable Energy Hackathon.

Visualization

data visualization

CBoardby TuiQiao

JavaScript star image 2910 Version:Current

License: Permissive (Apache-2.0)

An easy to use, self-service open BI reporting and BI dashboard platform.

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CBoardby TuiQiao

JavaScript star image 2910 Version:Current License: Permissive (Apache-2.0)

An easy to use, self-service open BI reporting and BI dashboard platform.
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cubesviewerby jjmontesl

JavaScript star image 419 Version:v2.0.2

License: Others (Non-SPDX)

Explore and visualize analytical datasets

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cubesviewerby jjmontesl

JavaScript star image 419 Version:v2.0.2 License: Others (Non-SPDX)

Explore and visualize analytical datasets
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incubator-supersetby apache

Python star image 31662 Version:0.38.0

License: Permissive (Apache-2.0)

Apache Superset is a Data Visualization and Data Exploration Platform

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incubator-supersetby apache

Python star image 31662 Version:0.38.0 License: Permissive (Apache-2.0)

Apache Superset is a Data Visualization and Data Exploration Platform
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Analysis

Data Analysis

grafanaby grafana

TypeScript star image 53798 Version:v9.4.0-beta1

License: Strong Copyleft (AGPL-3.0)

The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.

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grafanaby grafana

TypeScript star image 53798 Version:v9.4.0-beta1 License: Strong Copyleft (AGPL-3.0)

The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
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numpyby numpy

Python star image 22621 Version:1.24.1

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

Python star image 22621 Version:1.24.1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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pandasby pandas-dev

Python star image 36783 Version:1.5.2

License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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pandasby pandas-dev

Python star image 36783 Version:1.5.2 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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Labelling

Data labelling

seabornby mwaskom

Python star image 10314 Version:0.12.2

License: Permissive (BSD-3-Clause)

Statistical data visualization in Python

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seabornby mwaskom

Python star image 10314 Version:0.12.2 License: Permissive (BSD-3-Clause)

Statistical data visualization in Python
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label-studioby heartexlabs

Python star image 11847 Version:1.7.0

License: Permissive (Apache-2.0)

Label Studio is a multi-type data labeling and annotation tool with standardized output format

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label-studioby heartexlabs

Python star image 11847 Version:1.7.0 License: Permissive (Apache-2.0)

Label Studio is a multi-type data labeling and annotation tool with standardized output format
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VoTTby microsoft

TypeScript star image 3961 Version:v2.2.0

License: Permissive (MIT)

Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.

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VoTTby microsoft

TypeScript star image 3961 Version:v2.2.0 License: Permissive (MIT)

Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
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ML-Learning

ML-labelling

labelboxby Labelbox

JavaScript star image 1686 Version:1

License: Permissive (Apache-2.0)

Labelbox is the fastest way to annotate data to build and ship computer vision applications.

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labelboxby Labelbox

JavaScript star image 1686 Version:1 License: Permissive (Apache-2.0)

Labelbox is the fastest way to annotate data to build and ship computer vision applications.
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ml-annotateby planbrothers

Python star image 79 Version:Current

License: Permissive (MIT)

Use ML-Annotate to label data for machine learning purposes

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ml-annotateby planbrothers

Python star image 79 Version:Current License: Permissive (MIT)

Use ML-Annotate to label data for machine learning purposes
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auto-sklearnby automl

Python star image 6704 Version:v0.14.7

License: Permissive (BSD-3-Clause)

Automated Machine Learning with scikit-learn

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auto-sklearnby automl

Python star image 6704 Version:v0.14.7 License: Permissive (BSD-3-Clause)

Automated Machine Learning with scikit-learn
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automl

auto ML

Auto-PyTorchby automl

Python star image 1912 Version:v0.2.1

License: Permissive (Apache-2.0)

Automatic architecture search and hyperparameter optimization for PyTorch

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Auto-PyTorchby automl

Python star image 1912 Version:v0.2.1 License: Permissive (Apache-2.0)

Automatic architecture search and hyperparameter optimization for PyTorch
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tpotby EpistasisLab

Python star image 8916 Version:0.11.7

License: Weak Copyleft (LGPL-3.0)

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

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tpotby EpistasisLab

Python star image 8916 Version:0.11.7 License: Weak Copyleft (LGPL-3.0)

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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h2o-3by h2oai

Jupyter Notebook star image 6124 Version:jenkins-master-6093

License: Permissive (Apache-2.0)

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.

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h2o-3by h2oai

Jupyter Notebook star image 6124 Version:jenkins-master-6093 License: Permissive (Apache-2.0)

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.
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Deployment Information

https://github.com/kandikits/automl-starter

This sections has the instructions to install the kit. For the Auto ML kit source, the deployment instructions are: 1. Clone the automl-starter from the source: https://github.com/kandikits/automl-starter 2. Install the required libraries by 'pip install -r requirements.txt' 3. Navigate to the 'automl-classification-pycaret.ipynb' and open and run each cells

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