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.

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

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

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

Web interactive streamlit dashboard for credit scoring interpretation

Audio Pattern Recognition project - Music Genres Classification

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.

a machine learning training project

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

Simpsons Members Recognizer Supervised Machine Learning Algorithm.

KNN을 이용한 주가 데이터 분류

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

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.

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.

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

Scripts developed for the "Knowledge Extraction and Machine Learning" (ECAC) class "To Loan or Not To Loan" data mining case study / Kaggle competition.

Second assignment of Artificial Intelligence course held by Professor Andrea Torsello of Ca' Foscari University of Venice, spam detectors with SVM classification using linear, polynomial of degree 2, RBF kernels and Naive Bayes and k-NN