🏆 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.

Build and evaluate various machine learning classification models using Python.

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.

Predictions for the English Premier League season

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.

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.

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 regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.

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

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

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

Unsupervised and supervised learning for satellite image classification

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

Integrative Biomechanical and Clinical Features Predict In-Hospital Trauma Mortality

Intro to Machine Learning Final Project

Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.

Intro to Machine Learning Assignment 2

Development and comparison of 12 machine learning models to predict autism as well as a discussion of the process.

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

Using a support vector machine to classify emails

This repository contains a notebook that examines the performance of various classification models on the Kaggle dataset: https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data. The best performing model was a Random Forest Classifier with 86.67% accuracy.

A machine learning project to predict diabetes using a Support Vector Classifier model. It includes data preprocessing, model training, evaluation, and a Flask web application for real-time predictions.

PROJECT NAME: Exploration and analysis of publicly available data: suspension of parking bays in Dublin City Council.

Diabetes Predictor Web App Predict diabetes in patients using classification models such as Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machines. It is deployed in a Flask web application on AWS Elastic Beanstalk.

Intro to Machine Learning Assignment 3

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This is just a theoretical Machine Learning Model that will analyze the data and determine where the stroke can occur.

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