Sentiment analysis on tweets using Logistic Regression, Decision Trees, KNN, Random Forest, XG Boost, SVM, and NLP (RoBERTa)

Approximation of Fermi surface of 2D Hubbard model by support vector machine.

Files relevant for my bachelor thesis on different automatic emotion recognition approaches

The Operator Splitting QP Solver

Culled from the UCI Machine Learning Repository, the Dry Bean Dataset (licensed under CC BY 4.0) provides valuable insights into bean classification and is a valuable resource for machine learning enthusiasts.

A webpage application that can record attendance using the attendee's face. This application provides essential tools to manage attendance during runtime conveniently.

Chemometrics library for data fusion, model training and prediction of data from multiple sensor sources.

A machine learning project to predict diabetes using a Support Vector Machine (SVM) model. The project utilizes the Pima Indians Diabetes Database to train and evaluate the model, providing performance metrics such as accuracy, precision, recall, and F1-score.

A Solana explorer for humans

Zuma is a ready to go distro for running Solana nodes simply, securely, and economically.

ML-algorithms from scratch using Python. Classic Machine Learning course.

Code for classifying breast cancer tumors using machine learning. Includes preprocessing, visualizations, and models like Logistic Regression, Decision Tree, and Random Forest. Evaluated with accuracy, precision, recall, and F1-score. Clone, install dependencies, and run the Jupyter notebook for full analysis.

Python scripts with solutions for different Machine Learning tasks

This repository contains machine learning programs in the Python programming language.

This repository contains my machine learning models implementation code using streamlit in the Python programming language.

Machine Learning No-Code Environment

High-performance, Composable framework for Fully On Chain Games and Autonomous Worlds

ML Projects with py in an end-to-end pipeline in ML refers to a complete workflow that integrates various stages of a project, from data ingestion and preprocessing to model training, evaluation, and deployment, ensuring a seamless process.

This project focuses on analyzing the survival of passengers on the Titanic using various machine learning techniques. The primary goal is to predict whether a passenger survived the disaster based on features such as age, sex, class, and more.