PyPI package for MFA-based estimation in complex-valued linear inverse problems.
Python image compression comparison using DCT, wavelet, lossless, and fractal-style methods across multiple resolutions with MSE, PSNR, compression ratio, and runtime metrics.
From-scratch implementation of Linear Regression in Python for educational purposes.
mplementation of "Improving the Imperceptibility of Pixel Value Difference and LSB Substitution Based Steganography Using Modulo Encoding" — a combined PVD + LSB + modulo encoding scheme with CTR_DRBG key-dependent embedding, including baseline comparisons and experiment framework.
Comparison of Extreme Learning Machine and MLP on the classic IRIS flower dataset.
Disease Forecasting in a Tropical Context: A Comparative Evaluation of Model Performance and Generalizability for Dengue Fever and Influenza in Vietnam
This project builds and optimizes a model on a dataset using Ridge regression and polynomial features. Model accuracy is enhanced through regularization and polynomial transformations. Grid search and cross-validation are used to find the best parameters, and the model's performance is evaluated.
This repo compares four predictive models—Linear Regression, ARIMA, XGBoost, and LSTM—to forecast Coca‑Cola FEMSA stock closing prices using Python and five years of historical data.
The Recommender-System project is a machine learning-based application designed to predict user preferences and provide personalized recommendations. It leverages various algorithms, such as collaborative filtering and content-based filtering, to analyze user data and suggest relevant items. The project also includes a CI/CD pipeline for automating
This project provides a tool to compare two images using various similarity metrics, including histograms, structural similarity index (SSIM), mean squared error (MSE), mean absolute error (MAE), feature matching, and image hashing.
Реализация модели линейной регрессии без использования специальных библиотек для задачи прогнозирования цены автомобиля в зависимости от его пробега; регуляризация; MSE, R-squared statistic; визуализация функций потерь и предсказаний цены по данной выборке
Library for MSE to bootstrap ASGI/WSGI application
Measures and metrics for image2image tasks. PyTorch.
Cryptography library for MicroService Encryption
Built a custom adam scheduler using gradient clipping, LR scheduling, momentum updates, with two different loss functions
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