This Jupyter Notebook demonstrates the implementation of a K-Nearest Neighbors (KNN) algorithm using the concept of nearest neighbors without using direct classifiers. It also includes exploratory data analysis (EDA) and comparison of three classifiers.

Classification of IRIS Dataset using various distance metrics.

Practice Material

🌼 Classify the different species of the Iris flower.

Classification model to categorize clothing items into distinct classes

DTW(Dynamic Time Warping) & Subsequence-DTW Python Module

🪓 Predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset

Repository on Approximate Bayesian Computation and the different distance metrics which can be implemented.

Distance metrics are one of the most important parts of some machine learning algorithms, supervised and unsupervised learning, it will help us to calculate and measure similarities between numerical values expressed as data points

Python 3 library for Multi-Criteria Decision Analysis based on distance metrics, providing twenty different distance metrics.

Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai

PyTorch implementations of the beta divergence loss.

The Python 3 library for Multi-Criteria Decision Analysis.

KNN using brute force and ball trees implemented in Python/Cython

Similarity and distance measures for clustering and record linkage applications in R