Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Overview

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License: MIT made-with-python Open Source Love svg1 PRs Welcome contributions welcome Maintenance

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Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

The goal of this project is not to create algorithms that are as streamlined and computationally efficient as possible, but rather to present their inner workings in a clear and usable manner.

Algorithms:

⚙️ Contribution Guidelines

Please go through the whole Contributing Guidelines here.

  • Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.
  • You can only work on issues that you have been assigned to you.
  • If you want to contribute for an existing algorithm, we prefer that you create an issue before making a PR and link your PR to that issue.
  • If you have modified/added code work, make sure the code compiles before submitting.
  • Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
  • Do not update the README.md.

📂 Where to upload the files

  • Your files should be uploaded inside the *code folder into the corresponding language folder (For instance, if you wrote code for an K-Means Implementation, it goes inside the K-Means folder).
  • Under no circumstances create new folders within the language folders to upload your code unless specifically told to do so.
  • Edit the corresponding README.md file to add the link to your code in the corresponding section (GitHub Markdown Guide)
The value of a strong contribution stays beyond everything and gives you satisfaction 👍🌟

📖 Code Of Conduct

You can find our Code of Conduct here.

📝 License

This project follows the MIT License.

😇 Maintainers


Aditya Kumar Gupta

💻 🖋

Paurush Tiwari

💻

Kritika Parmar

💻

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Comments
  • KNN - Base

    KNN - Base

    Hello @kritikaparmar-programmer. I am a GSSOC'21 participant. I would like to work on the issue of the K Nearest Neighbours Algorithm using Python. Please assign this issue to me.

    gssoc21 Level3 Base 
    opened by ananyade2412 21
  • Random Forest - Base

    Random Forest - Base

    I am a GSSoC 21 participant (DISCORD username: smoky237 #5411), I can contribute to the Random Forest algorithm(including bagging concept) with proper implementation and Documentation on a dataset. Kindly assign this work to me @geekquad @kritikaparmar-programmer @MAJOR-BEAST #GSSoc

    gssoc21 Level3 Base 
    opened by ayush237 17
  • Decision Tree - Base

    Decision Tree - Base

    I am a GSSoC 21 participant (DISCORD username: smoky237 #5411), I can contribute to the Decision Trees algorithm with proper implementation and Documentation on a dataset. Kindly assign this work to me @geekquad @kritikaparmar-programmer @MAJOR-BEAST #GSSoc

    gssoc21 Level3 Base 
    opened by ayush237 12
  • C5.0 Decision Tree

    C5.0 Decision Tree

    There is no implementation of the C5.0 Decision Tree in Python available anywhere on the internet.

    Even scikit-learn has C4.0 or C4.5 or CART algorithm implemented under the hood.

    So it would be great to have C5.0 Decision Tree implemented over the same.

    (P.S. - It is not an easy task,)

    gssoc21 Level3 Unassigned 
    opened by khanfarhan10 12
  • Locally Weighted Regression (LOWESS)

    Locally Weighted Regression (LOWESS)

    I am a GSSoC 21 participant. I can contribute to locally weighted regression(LOWESS) with proper implementation and Documentation. Kindly assign this work to me @kritikaparmar-programmer @geekquad @MAJOR-BEAST DISCORD: diksha#6684

    gssoc21 Level3 Base Unassigned 
    opened by dikshajoshi18 11
  • code for gradient descent

    code for gradient descent

    i have written a code for batch gradient descent and have tried to explain the same.please have a look through it and suggest for some changes @ashwani-rathee sir

    opened by Sibasish-Padhy 9
  • Pruning of neural network

    Pruning of neural network

    Pruning of neural network remove the less contributing node(means weight of node which has low weight than others node) from the neural network. It increases the accuracy of neural network models.

    opened by Atul-Kashyap 2
  • Stepwise Regression - Documentation and Implementation

    Stepwise Regression - Documentation and Implementation

    Hey, I am a GSSoC'21 participant and I will like to work on the documentation and implementation of Stepwise Regression. I have already described briefly about this technique in one of my earlier blogs here: https://towardsdatascience.com/the-family-of-unfamiliar-regression-algorithms-ae7eff9e9463

    opened by thisisashwinraj 1
  • Batch-gradient descent

    Batch-gradient descent

    A very helpful optimization technique that is quite time efficient and even reduces space complexity. It proves to come in quite handy when the gradient descent seems to have a relatively fast learning rate or follows non convex optimization. I would like to code the optimization technique and prepare a well documented readme file for it explaining it in detail. Please assign this issue to me under the label of gssoc21.Thank you

    gssoc21 Level3 
    opened by Sibasish-Padhy 13
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Algo Phantoms
Open Source organization focusses on Data Structure and Algorithms
Algo Phantoms
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