🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).

NTHU EE6550 Machine Learning slides and my code solutions for spring semester 2017.

This repository contains the Iris Classification Machine Learning Project. Which is a comprehensive exploration of machine learning techniques applied to the classification of iris flowers into different species based on their physical characteristics.

This project aims to predict diabetic patients using three different classification algorithms: Logistic Regression, Support Vector Classifier, and Random Forest Classifier. The project is implemented using Python and leverages scikit-learn, a popular machine learning library.

A web app for visualizing Binary Classification Results using Streamlit module in Python deployed on Heroku.

Unsupervised and supervised learning for satellite image classification

Build and evaluate various machine learning classification models using Python.

Predictions for the English Premier League season

This repository provides a cancer classification model using Support Vector Classifier (SVC). The model aims to classify cancer cases into benign or malignant based on various features obtained from medical examinations.

Project made in Jupyter Notebook with "News Headlines Dataset For Sarcasm Detection" from Kaggle.

This project implements the Support Vector Machine (SVM) algorithm for predicting user purchase classification. The goal is to train an SVM classifier to predict whether a user will purchase a particular product or not.

Classification ML models for predicting customer outcomes (namely, whether they're likely to opt into email / catalog marketing) depending on customer demographics (age, proximity to store, gender, customer loyalty duration) as well as sales and shopping frequencies by department

Interactive ML web application will allow users to choose classification algorithm, let them interactively set hyper-parameter values, and Input Image.

Sentiment Analysis is NLP technique used to determine the sentiment expressed in a piece of text, which can be positive, negative, or neutral. SVC is a powerful machine learning model that can be used for this purpose due to its effectiveness in handling high-dimensional data.

Intro to Machine Learning Final Project

I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The r…

Intro to Machine Learning Assignment 2

Assignments from Applied Machine Learning Class (UTD BUAN-6341)

Integrative Biomechanical and Clinical Features Predict In-Hospital Trauma Mortality