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VIT Hackathon Sample Submission Kit

by kandikits Updated: Jan 8, 2022

Our solution uses AutoML to predict energy usage. This can be deployed for optimizing energy usage.

Data Exploration

We have created a group for the libraries used for Data Exploration in my solution. The data exploration helps in doing extensive analysis of different data types and in assisting to understand the patterns. Pandas is used in our solution for data manipulation and analysis.

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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pandasby pandas-dev

Python star image 36647 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 36647 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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Machine Learning

We have created a group for Machine Learning which has the libraries used in my solution. The below libraries help in capturing the embeddings for the text. The embeddings are vectorial representations of text with their semantics.

scikit-learnby scikit-learn

Python star image 52681 Version:1.2.0

License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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scikit-learnby scikit-learn

Python star image 52681 Version:1.2.0 License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python
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numpyby numpy

Python star image 22526 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 22526 Version:1.24.1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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label-studioby heartexlabs

Python star image 11773 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 11773 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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Data Labeling

Libraries in this section are used for annotating data for creating training data for machine learning.

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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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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Kit Solution Source

automl-starterby kandikits

Jupyter Notebook star image 0 Version:Current

License: Permissive (MIT)

This repo helps beginners and citizen data scientists to build machine learning models

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automl-starterby kandikits

Jupyter Notebook star image 0 Version:Current License: Permissive (MIT)

This repo helps beginners and citizen data scientists to build machine learning models
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Deployment Information

This is our source code updated in GitHub for our VIT hackathon entry. The below repo is an example. Please replace with your source code from GitHub.

This section has the instructions to install our solution. For our app, 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