This Jupyter notebook demonstrates image segmentation using Lazy Snapping and K-Means Clustering. It showcases how these algorithms can partition an image into segments based on pixel intensity and user-defined masks.

Iris flower classification using k-Nearest Neighbors (k-NN) algorithm in Python. Simple, effective ML model for species prediction.

This project is a simple Python application I created as part of my learning journey. I plan to expand this repository with more complex projects in the future.

the code uses KNN, Gaussian Naive Bayes & SVM to classify images. It preprocesses, normalizes data, applies PCA , computes accuracy, precision etc. It evaluates k-NN using Euclidean distance & cosine similarity, visualizing results with line plots, 3D scatter plots, & confusion matrices to demonstrate classifier performance.

This project simulates server performance data (CPU, Memory, Network Traffic) for multiple servers across time intervals and applies K-Nearest Neighbors (KNN) for similarity analysis and prediction.

This project implements a movie recommendation system using the K-Nearest Neighbors (KNN) machine learning algorithm.

A movie recommendation engine

Forecasting COVID-19 Cases

Classification of IRIS Dataset using various distance metrics.

Distance functions implemented using Apple's Accelerate library.

Fast, type-safe utilities for vector embedding comparison and search.

A K Nearest Neighbors classifier developed from scratch for self-learning purposes. Accuracy is off the charts, since we have full control on the algorithm.

Implementation of K-Nearest Neighbors algorithm rebuilt from scratch using Python. Comparison to the Sci-Kit Learn implementation included.

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.

Encoding celebrity dataset with LBP and SIFT and finding matches of given dataset. Option for finding your celebrity lookalike.

Simple statistical functions that are useful for exploratory spatial data analysis (ESDA) on-the-fly in JavaScript

Raku package for the computation of various distance functions.