Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.

SDLDpred - Symptom-based Drugs of Lifestyle-related Diseases prediction

Football Match Analysing Tool

Color Detection using K-means clustering algorithm to detect and recognize dominant color from images.

Lung Cancer Detection with SVM uses the Support Vector Machine algorithm to detect lung cancer from medical images and patient data. This project covers data preprocessing, feature extraction, model training, and evaluation, aiming to provide a reliable tool for early detection and timely diagnosis.

Clustering high-dimensional data with Minkowski distance

K-means clustering algorithm using MapReduce.

CS4051 - Information Retrieval Course Assignment

This repository contains solutions to common mapper and reducer problems in Hadoop using Python

his repository contains an implementation for eliminating backdoor triggers embedded in images, particularly addressing poison label attacks such as Trojan, BadNets, and Blend.

This is a complete web app for Resume and Personality prediction all in one app😁😁. Resume Analysis uses Pyresparer and other libs, Personality depends on ocean(5types) using Kmeans model.🙏

Progressive Ensemble K-means clustering

A code for DICOM image segmentation using K-Means

A python library for quantum machine learning and quantum deep learning, built on top of qiskit and pennylane

Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

Color Fusion is about fusing the colors of one image into another image based on latter image. The underlying algorithm to acheive this effect is K Means. Other than that a web based interface is also provided to interact with the program running on backend for color fusion using flask

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

Ameliorating Performance of Random Forest using Data Clustering | Research for a novel approach for binary class classification problem

CBERTdp is a strategy to speed up the clssification task by clustering BERT embeddings using different methods in order to use K-Means and the Dot-Product to obtaint the prediction results