Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them

Last update: Apr 03, 2022

Overview

Sleep-stage-classification-using-ML-algorithms

Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them.

Description of Files

Sleep_Stage_Classification.ipynb - Code for Sleep Stage Classification
Report.pdf - Report
.mat files - Data and Features

Contributors

Aadharsh Aadhithya A

Anirudh Edpuganti

Chaitanya Reddy

Pillalamarri Akshaya

Owner

Anirudh Edpuganti

AI enthusiast...

GitHub Repository

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Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them

Related tags

Overview

Sleep-stage-classification-using-ML-algorithms

Description of Files

Contributors

Owner

Anirudh Edpuganti

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning

Conducted ANOVA and Logistic regression analysis using matplot library to visualize the result.

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色、简单易用、通用、自动化、灵活。

Nixtla is an open-source time series forecasting library.

A machine learning toolkit dedicated to time-series data

Learning --> Numpy January 2022 - winter'22

PySpark + Scikit-learn = Sparkit-learn

Apple-voice-recognition - Machine Learning

This project has Classification and Clustering done Via kNN and K-Means respectfully

A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize

An easier way to build neural search on the cloud

All-in-one web-based development environment for machine learning

Bodywork deploys machine learning projects developed in Python, to Kubernetes.

CD) in machine learning projectsImplementing continuous integration & delivery (CI/CD) in machine learning projects

Python package for stacking (machine learning technique)

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

WAGMA-SGD is a decentralized asynchronous SGD for distributed deep learning training based on model averaging.

Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。它的特点包括: 效果出色、简单易用、通用、自动化、灵活。