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https://github.com/aarnasi/ml-scikit-learn

Traditional machine learning (ML) demonstrations for data analysis and predictive modeling.

https://github.com/aarnasi/ml-scikit-learn

Science Score: 13.0%

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    Low similarity (9.4%) to scientific vocabulary

Keywords

classfication clustering linear-regression recommender-system
Last synced: 6 months ago · JSON representation

Repository

Traditional machine learning (ML) demonstrations for data analysis and predictive modeling.

Basic Info
  • Host: GitHub
  • Owner: aarnasi
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 47.9 KB
Statistics
  • Stars: 0
  • Watchers: 3
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
classfication clustering linear-regression recommender-system
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

ml-scikit-learn

Welcome to the ML Scikit-Learn Samples repository! This project provides a collection of scripts demonstrating various machine learning algorithms and techniques using the Scikit-learn library.

Introduction

Scikit-learn is a powerful and easy-to-use library for machine learning in Python. This repository contains a variety of example projects and tutorials to help you get started with machine learning using Scikit-learn. Each example is designed to be self-contained and easy to understand, making it perfect for learning and experimentation.

Examples

Here are some of the key examples included in this repository:

  1. Classification:

    • Decision Trees
    • Logistic Regression
    • Support Vector Machines
    • K-Nearest Neighbour

  2. Regression:

    • Simple Linear Regression
    • Multiple Linear Regression
    • Non Linear Regression

Installation

git clone https://github.com/aarnasi/ml-scikit-learn-samples.git cd ml-scikit-learn-samples pip install -r requirements.txt

Owner

  • Name: sinni
  • Login: aarnasi
  • Kind: user

Software Engineer | AI Enthusiast | GPU Programmer | Distributed Systems Architect

GitHub Events

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  • Push event: 1
Last Year
  • Push event: 1

Dependencies

requirements.txt pypi
  • numpy >=1.25.2
  • pandas >=2.1.0
  • scikit-learn >=1.5.0
  • sklearn >=0.0