Natural Language Processing to predict future words in text prediction app

In this project the Turkey Student Evaluation dataset which in question consists of feedback from students who attended multiple courses at Gazi University, Ankara used.To analyze the questions we used unsupervised learning methods, which involve finding patterns or groupings in a given dataset without any labeled output variable.

The project is mainly to demonstrate the performance in terms of convergence for Random Initialisation and K++ for K-Means Algorithm.

MDS-FIB Algorithms, Data Structures and Databases (ADSDB) Subject 2024-25 Q1, Data-Science End-to-End project path

K means implementation from scratch

Crosslingual Information Retrieval on the europarl dataset.

This is the implementation of SIGIR - 2005 paper on Iterative translation disambiguation for cross-language information retrieval

Artificial Intelligence: Apply the principles of Unsupervised Learning.

Unsupervised Multi-modal Emotion Recognition System based on Momentum Contrast and Dual-reconstruction

Brain Tumor Detection using Color Segmentation with KMeans Clustering ⚠️

This project aims to provide an unsupervised lightweight solution to estimate the count of various different category of Vehicles. By implementing a novel Locality Sensitive Hashing based sketch.

Content based image retrieval roughly based on the paper "Multimedia Retrieval through Unsupervised Hypergraph-based Manifold Ranking" by Pedronette et al.

[AISTATS 2022] Gap-Dependent Unsupervised Exploration for Reinforcement Learning

Repositori ini berisi dua project analisis data menggunakan metodologi CRISP-DM. Project pertama meneliti tren penjualan Walmart dengan Supervised analysis menggunakan algoritma Naive Bayes Gaussian dan K-Nearest Neighbors. Project kedua mengeksplorasi faktor sosio-ekonomi antar negara dengan Unsupervised analysis.

Clustering the phylogenetic tree of Covid-19 sequences with Biopython and MUSCLE

Unsupervised domain adaptation using Adaptiope dataset and PyTorch

This repository contains an implementation of a Recommendation System designed to provide recommendations using two main approaches: Content-Based Filtering (CBF) and Collaborative Filtering (CF). The system can be applied in a variety of use cases.

This repository if for creating auto-encoders easily. The main focus of the auto-encoders on this page is for genetic and spectral data analysis but likely could be used for any high dimensional data

This is a code implementation of the paper Invariant Information Clustering for Unsupervised Image Classification and Segmentation

无监督说话人聚类算法比较