This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms.

Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.

𝗙𝗶𝗿𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻🔥using Machine Learning Algorithm with python🐍, GoogleColab & database taken from 𝗨𝗖𝗜 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆

Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.

Analisys of the dataset Heart Failures clinical records from UCI using different rebalancing techiniques and different models

Machine learning project on Distinguish between the presence and absence of cardiac arrhythmia and its classification in one of the 16 groups.

Classification of default of credit card clients from the UCI dataset using machine learning techniques

A machine learning web application that predicts the likelihood of credit card default using a Random Forest model trained on the UCI Credit Card dataset. Built with FastAPI for the backend and a clean HTML/CSS frontend,Fully containerized with Docker for easy deployment and demo-ready for showcasing model predictions.

BankNotePredictorUsingFastApi uses ML to predict whether a banknote is real or fake, powered by FastAPI and a trained Random Forest model

Adaptive Weighted voting Aggregation for Ensemble of classifiers

Early-stage diabetes risk prediction dataset simple UI example for data mining lesson

This project predicts Customer Lifetime Value (CLV) for e-commerce. It aims at forecasting the revenue a business can expect from a customer over time. I did an explatory analysis. From Linear Regression to Neural Networks, explore how different models perform in predicting CLV.

⚓ ML system for submarine threat detection using SVM to classify sonar signals as mines or rocks. Features GridSearchCV optimization, sklearn pipelines, and comprehensive evaluation metrics for underwater anomaly recognition.

A machine learning project for letter recognition using SVM, KNN, and Decision Tree, Random Forest, and Naive Bayes algorithms. Includes data preprocessing, model training, evaluation, and visualization, and research report.

Summer 2025 project on baseline ML algorithms for T2D detection using UCI Diabetes dataset

Training Higgs Dataset with Keras - https://doi.org/10.5281/zenodo.13133945

Machine learning regression model predicting 1985 automobile prices. Lasso model achieves 91.7% R² with superior generalization over XGBoost. Handles extreme multicollinearity (VIF 16,676→8.36), data leakage detection, and outlier treatment through PCA and domain-driven feature engineering.

AN ADVANCED APPROACH TO RECOGNIZE HUMAN ACTIVITIES VIA DEEP LEARNING

Using data to help us choice high quality wine

I walk you though what an entire machine learning cycle looks like for a binary classification problem. For this walkthrough, we are utilizing UCI's Iranian Churn dataset