Linear Discriminant Decision Tree classifier implemented in Python

This project provides a comprehensive framework for evaluating classification models and selecting the best algorithm based on performance metrics. It demonstrates the importance of hyperparameter tuning and model comparison in machine learning workflows.

The purpose of this study is based on the available data, it was predicted after analyzes whether class is Seker, Barbunya, Bombay, Cali, Dermosan, Horoz or Sira.

EC447: Pattern Recognition and Machine Learning Course Project

The code for Quantized Fisher Discriminant Analysis (QFDA)

This includes some of the basic widely used ML algorithm from scratch.

CS385 homework. Logistic regression and LDA from scratch.

The code for Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)

A mobile application that diagnoses Parkinson’s disease patients using hand drawings

Supervised Learning Algorithms

Predict the gesture based on PoseNet keypoints

My implementation of homework 3 for the Machine Learning class in NCTU (course number 5088).

Contains our pattern recognition project files, which is about performing a dimensional reduction using the KLDA technique and performing a classification by employing a probabilistic approach.

Deciphering the composition of recycling material at collection point

A collection of machine learning algorithm implementations

Using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on same dataset and analyzing the best one

Predicting that the patient is suffering from Heart Disease or Myocardial Infarction (MI) based on various parameters.

Datamining concepts

The main purpose of the project is to make size-reduce to the independent variable in the dataset via LDA and PCA. Then, the dependent variable is to estimate the status with the logistic regression method.

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well in noisy and contaminated datasets.