Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

A tool for clustering images using deep learning features and visualizing the results in organized grids.

An implementation of the K-Nearest Neighbors algorithm from scratch using the Python programming language.

A robust classifier for few-training-data problem based on a distributionally robust optimization framework

k-NN-based mapping of cells across representations to transfer labels, embeddings, and expression values.

Web interactive streamlit dashboard for credit scoring interpretation

Audio Pattern Recognition project - Music Genres Classification

Simpsons Members Recognizer Supervised Machine Learning Algorithm.

This program is a real-time face recognition system that uses OpenCV and k-Nearest Neighbors (k-NN) to detect and label faces from a webcam feed.

a machine learning training project

Machine Learning tasks and mini projects based on my learning in a Datascience bootcamp in Udemy

I am partaking in research with my professor Dr. Boxiang Dong at Montclair State University in using deep learning techniques for anomaly detection. This project is to help with that research, specifically in implementing Machine Learning classifiers and more.

KNN을 이용한 주가 데이터 분류

Predicting company bankruptcy using various machine learning models. The dataset is sourced from Kaggle: Company Bankruptcy Prediction.

Benchmarks of collaborative filtering techniques and optimizing K-NN and SVD for recommendation accuracy

A k-Nearest Neighbors (k-NN) Classifier for the Iris Flower Dataset implemented in Python using NumPy and SciPy. This project calculates distances between new and training samples, finds the k nearest neighbors, and predicts the types of new samples. Accuracy is evaluated against known labels.

An ML-driven Starbucks drink recommender that learns user preference to serve tailored recommendations.

🔢 A self-introduction to machine learning. Simple application that recognises handwritten/mouse drawn digits from 0-9.