Machine Learning Algorithms

Faces recognition project using Support Vector Machines (SVM) and Principal Component Analysis (PCA). It utilizes the Labeled Faces in the Wild (LFW) dataset, employs dimensionality reduction with PCA, and fine‑tunes SVM hyperparameters using RandomizedSearchCV.

Common machine learning algorithm implementations

AIML Projects

Enhancing Patient Care through AI-Driven Disease Prediction

This repository contains a detailed analysis of the Spambase Dataset using different classification algorithms, including Logistic Regression, Logistic Regression with Backward Feature Elimination (BFE), Support Vector Machine (SVM), SVM with Normalized Data, Decision Trees, Random Forest, K-Nearest Neighbors (K-NN), and K-NN with Normalized Data.

Machine Learning Stock Price Predictor Mobile App

Local RAG using LLaMA3

This Repository consists of algorithms related to AI-ML. Few examples include - KNN, Naive Bayes, Decision Trees, etc.

Combat misinformation and fake news by accurately predicting the truth of the article to prevent the spread of harmful information that could lead to confusion, panic, or societal harm.

This repository contains lecturer's dissertation about sentiment analysis web application

Predicting Credit Card Defaults This repository provides a step-by-step tutorial on predicting credit card defaults using machine learning algorithms in Python with scikit-learn. Learn to implement SVM, Random Forest, Decision Trees, k-Nearest Neighbors, and Artificial Neural Networks to forecast default payments for credit card clients.

There are many different types of SVMs in this repository.

Practice Assignments for Data Science Coursework

Implementing SVM's using pandas and sklearn in python

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn

This project analyzes employee attrition data to uncover key factors, predict turnover, and develop strategies for retention, ultimately enhancing organizational stability and performance.

It is my group's middle project on text classification during a student exchange at Asia University, Taiwan. It uses five types of names of articles in PubMed.

Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.