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 offers a complete K-Nearest Neighbors (KNN) tutorial, guiding you from core theory to hands-on practice. Learn to implement KNN from scratch with NumPy, apply it using scikit-learn, and explore visualizations, datasets, and Jupyter notebooks to fully understand, test, and optimize the algorithm.

Fault diagnosis of some critical and non-critical faults in electric drives using anomaly detection.

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.

An Open MPI implementation of the well known K-Nearest Neighbors (Machine Learning) classifier.

Just a simple implementation of K-Nearest Neighbour algorithm.

No description provided.

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 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.

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 regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.

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.

This is a Python - based application that predicts diseases based on the symptoms inputted by the user using machine learning (KNN classifier algorithm).

Data Science Project: SpaceX Falcon 9 First Stage Landing Prediction.

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.

No description provided.

Static and Dynamic Analysis of android malware using various different machine learning algorithms

Portfolio

CMS Hospital Rating with exploratory data analysis, data visualization, and applied machine learning predictive models, such as KNN, SVM, and Random Forest.

Classify the motion capture from hand postures through supervised learning models

k-Nearest Neighbors (KNN) used for an Etherium Blockchain classification problem

Collection of some classical Machine learning Algorithms.

Classifying the different types of water based on analysis and used various Machine Learning algorithms to solve this usecase

A machine learning project to predict telecom customer churn using classification models. It includes data preprocessing, visualization, model evaluation, hyperparameter tuning (GridSearchCV/RandomizedSearchCV), and final model deployment using Streamlit. Dataset: Telco Customer Churn.

Aplicação web onde você consegue treinar um modelo de Machine Learning para classificar uma pessoa como do sexo masculino ou feminino com base em seu nome.

Simpsons Members Recognizer Supervised Machine Learning Algorithm.

Performance Comparison of two different distance metrics in K - Nearest Neighbors