This repository provides Python implementations for Non-negative Matrix Factorization (NMF) using the Multiplicative Update (MU) algorithm. Two initialization methods are supported: random initialization and Non-negative Double Singular Value Decomposition (NNDSVD). NMF is a matrix factorization technique used in various fields, including topic mod

Проведение бинарной и многоклассовой классификаций эмоций людей на фотографиях

Twitter Fake News Detection, Traffic Sign Classification, Clustering Synthetic Data

These examples provide an introduction to Data Science and classic Machine Learning using NumPy, pandas, Matplotlib, and scikit-learn. They are taken, with some changes, from the book "Python Data Science Handbook: Essential Tools for Working with Data", Second Edition, written by Jake VanderPlas and published by O'Reilly Media in 2023.

Drug classification model deployed with Docker and Flask

The project offers a metaheuristic approach based solution that aims to optimize classification accuracy. I tried to use genetic algorithms in this context to find the best classification parameters. However, it did not support my deep learning classification project using the same dataset.

Linear Regression Hands-on Implementation

This repository focuses on credit card fraud detection using machine learning models, addressing class imbalance with SMOTE & undersampling, and optimizing performance via Grid Search & RandomizedSearchCV. It explores Logistic Regression, Random Forest, Voting Classifier, and XGBoost. balancing precision-recall trade-offs for fraud detection.

The Deep Learning project for Pima Indian Diabetes dataset. For more details, you can read my report and read.me

Projects accompanying the course Advanced ML at ETH Zurich, 2020.

Machine learning project aimed at predicting house rent prices based on various features.

University of Tehran-Artificial Intelligence Spring 2021

This repository implements a Linear Regression model to analyze and predict numerical data. It includes steps for data preprocessing, visualization, model training, and evaluation.

Test task for students assosiation

With approximately 7000 lines of code, both Supervised and Unsupervised Learning types will be explored and discussed.

Decision_tree module for C++ and Python