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Ml-AlgoithmsKit

Implementation of standard ML Algorithms from scratch using Numpy and Pandas libraries. New algorithm implementations will be frequently updated in the repo.

Algorithms Implemented:

• Linear Regression
• Logistic Regression
• Multi-Layered Perceptron
• K-Means Clustering
• KNN (K-Nearest Neighbors)
• Decision Tree

Installation: Building the Library using PyProject.toml file

Clone this directory:

git clone https://github.com/sandeshkatakam/ML-AlgorithmsKit.git

Navigate to the cloned library dir and open the terminal from that path

python3 -m pip install --upgrade build

The above commands upgrades the build tools neccessary for building the .whl for the library

python3 -m build

You will recieve the following message from the terminal:

Successfully built MLAlgorithmsKit-0.0.1.tar.gz and MLAlgorithmsKit-0.0.1-py3-none-any.whl

Once completed, will generate these .whl and .tar.gz files

dist/
├── MLAlgorithmsKit-0.0.1-py3-none-any.whl
└── MLAlgorithmsKit-0.0.1.tar.gz

Now the package is built and can be installed using pip.
Follow this command to install the package:

pip install MLAlgorithmsKit-0.0.1-py3-none-any.whl

Usage:

You can start using the Package and follow the below examples to get started:

Example usage of Package:(Notebook Links)

• Decision Trees:
• K Nearest Neighbours:
• K Means Clustering:
• Linear Regression
• Logistic Regression:
• Neural Network (or MLP):

Contributing

Contributions are very much welcomed!
Further manyo ther Classical Machine Learning Algorithms can be added to the library!!