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.

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

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

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.

AN ADVANCED APPROACH TO RECOGNIZE HUMAN ACTIVITIES VIA DEEP LEARNING

Using data to help us choice high quality wine

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.

Predicting the red-wine quality using various machine learning algorithms.

a model trying to predict a digit

A jupyter notebook containing the analysis of the UCI Credit Default Dataset

This repository showcases my foundational knowledge in machine learning concepts, based from the Machine Learning series by TechWithTim. It serves as my requirement for aspiring members of the PUP Hygears Programming Team, demonstrating my ability to apply theoretical knowledge to practical implementations.