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AI Doctor Kit

by binginagesh Updated: Nov 2, 2021

AI Doctor

IDE ENV AI Doctor

Libraries for Development Environment

notebookby jupyter

Jupyter Notebook star image 8966 Version:v7.0.0a2

License: Others (Non-SPDX)

Jupyter Interactive Notebook

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notebookby jupyter

Jupyter Notebook star image 8966 Version:v7.0.0a2 License: Others (Non-SPDX)

Jupyter Interactive Notebook
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EDA

Libraries needed for exploratory data analysis and visualization

pandasby pandas-dev

Python star image 33259 Version:v1.4.1

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 33259 Version:v1.4.1 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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matplotlibby matplotlib

Python star image 15355 Version:v3.5.1

License: No License (null)

matplotlib: plotting with Python

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matplotlibby matplotlib

Python star image 15355 Version:v3.5.1 License: No License

matplotlib: plotting with Python
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numpyby numpy

Python star image 20101 Version:v1.22.3

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

Python star image 20101 Version:v1.22.3 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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seabornby mwaskom

Python star image 9320 Version:v0.11.2

License: Permissive (BSD-3-Clause)

Statistical data visualization in Python

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seabornby mwaskom

Python star image 9320 Version:v0.11.2 License: Permissive (BSD-3-Clause)

Statistical data visualization in Python
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Modelling

Libraries needed for building models

scikit-learnby scikit-learn

Python star image 49728 Version:1.0.2

License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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scikit-learnby scikit-learn

Python star image 49728 Version:1.0.2 License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python
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imbalanced-learnby scikit-learn-contrib

Python star image 5660 Version:0.9.0

License: Permissive (MIT)

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

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imbalanced-learnby scikit-learn-contrib

Python star image 5660 Version:0.9.0 License: Permissive (MIT)

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
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imbalanced-learnby wqw547243068

Python star image 0 Version:Current

License: Others (Non-SPDX)

Python module to perform under sampling and over sampling with various techniques.

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imbalanced-learnby wqw547243068

Python star image 0 Version:Current License: Others (Non-SPDX)

Python module to perform under sampling and over sampling with various techniques.
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xgboostby dmlc

C++ star image 22464 Version:v1.6.0rc1

License: Permissive (Apache-2.0)

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

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xgboostby dmlc

C++ star image 22464 Version:v1.6.0rc1 License: Permissive (Apache-2.0)

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
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Group Name 4

Kit Solution Source

buildwithai-hackathonby nagi1995

Jupyter Notebook star image 0 Version:Current

License: No License (null)

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buildwithai-hackathonby nagi1995

Jupyter Notebook star image 0 Version:Current License: No License

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Deployment Information

Please add your kit solution or prototype source repository in this section.

The simplest installation procedure would be to use anaconda (Can be downloaded from https://www.anaconda.com/). Create an virtual environment using conda create --name activate clone the GitHub repository git clone https://github.com/nagi1995/buildwithai-hackathon.git cd buildwithai-hackathon Use the below command to download necessary libraries for running the code pip install -r requirements.txt

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