Includes top ten must know machine learning methods with R.
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Updated
Oct 6, 2024
Includes top ten must know machine learning methods with R.
Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.
This repository contains the Iris Classification Machine Learning Project. Which is a comprehensive exploration of machine learning techniques applied to the classification of iris flowers into different species based on their physical characteristics.
Fault diagnosis of some critical and non-critical faults in electric drives using anomaly detection.
An Open MPI implementation of the well known K-Nearest Neighbors (Machine Learning) classifier.
Just a simple implementation of K-Nearest Neighbour algorithm.
PCA(Principle Component Analysis) For Seed Dataset in Machine Learning
Syracuse University, Masters of Applied Data Science - IST 707 Data Analytics
This project is using Strava's API to download and process my workout data.
This project involves detecting iris species using the k-nearest neighbors (KNN) algorithm in Jupyter Notebook. The iris species detection task is a classic problem in machine learning, where the goal is to classify iris flowers into different species based on their measurements.
This project focuses on predicting heart disease using the K-Nearest Neighbors (KNN) classification algorithm implemented in a Jupyter Notebook. It aims to provide a tool that can assist in early detection and diagnosis of heart disease based on given input features.
Fraud detection
This repository contains a Python implementation of a K-Nearest Neighbors (KNN) classifier from scratch. It's applied to the "BankNote_Authentication" dataset, which consists of four features (variance, skew, curtosis, and entropy) and a class attribute indicating whether a banknote is real or forged.
This is a Python - based application that predicts diseases based on the symptoms inputted by the user using machine learning (KNN classifier algorithm).
k-Nearest Neighbors Algorithm with p-adic Distance
Static and Dynamic Analysis of android malware using various different machine learning algorithms
Portfolio
I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The r…
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