Includes top ten must know machine learning methods with R.
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Updated
Mar 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.
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).
Static and Dynamic Analysis of android malware using various different machine learning algorithms
k-Nearest Neighbors Algorithm with p-adic Distance
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…
Building from scratch simple KNN Classifier without using frameworks built-in functions and applying it on the Pen Digits Dataset.
Combat misinformation and fake news by accurately predicting the truth of the article to prevent the spread of harmful information that could lead to confusion, panic, or societal harm.
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