qda

andersjohansson/orgqda

Qualitative data analysis using org-mode

openqda/openqda

Free open collaborative data analysis software

junyanyao/ISLR_Python

Introduction to Statistical Learning with Application in R[This repo converts the lab solutions and exercise in python]

MaxenceGiraud/MachineLearningAlgos

Personal reimplementation of some ML algorithms for learning purposes

dohliam/thematic-analysis

A simple data parser to aid in the process of Qualitative Data Analysis with multimodal data

Chaoukia/Probabilistic-Graphical-Models

Probabilistic graphical models home works (MVA - ENS Cachan)

novinsh/multivariate-project

Sign Language Digit Classification

Escapist-007/ML_Projects

The projects are part of the graduate-level course CSE-574 : Introduction to Machine Learning [Spring 2019 @ UB_SUNY] . . . Course Instructor : Mingchen Gao (https://cse.buffalo.edu/~mgao8/)

itsatefe/Pattern-Recognition

Statistical Pattern Recognition (classic machine learning)

xinyexu/STATS-415

Introduction to Data Mining

agn-7/gaussian_naive_bayes_classifier

A simple 1-dimensional Gaussian Naïve Bayes Classifier.

agn-7/machine_learning_projects

Simple Machine Learning and Data Science projects as tutorials.

AliOsamaHassan/Binary-Classification-Models-On-Skin-Dataset

Binary Classification Models On Skin Dataset

AshutoshGanguly123/Classification-Algorithms

Code for the chapter published in the Statistical learning book by Elsevier publication.

m-pana/avila

Analysis of the Avila bible dataset from the UCI repository using several machine learning algorithms.

shahjui2000/ML_codes

Pseudo-Inverse, Gradient-Stochastic-Steepest Descent, Logistic Regression and LDA-QDA

swilliamc/Capstone2

Heart Failure Prediction for Harvard University Professional Certificate in Data Science Capstone Project, 2nd Capstone Project using R programming

thalisrocha/Intro-Supervised-Learning

ViRoLam/EEG_BCI

EEG classification project. Uses a variety of classifiers and methods to parse EEG data.

aghaPathan/ML-Classifier-Comparision

This repository contains Jupyter Notebook file containing the code to compare different sklearn classifiers on a dataset. Then it saves the output .png results in the working folder.

AhmadBsk/Boiled-egg-problems

Boiled egg problems (Solve with LDA, QDA, Naive Bayes Classifiers, decision tree, pruned decision tree)

AshutoshGanguly1/Classification-Algorithms

Code for the chapter published in the Statistical learning book by Elsevier publication.

HaleyEgan/Haiti-Earthquake-Disaster-Relief

The goal of this disaster relief project was to test supervised learning algorithms on imagery data collected after the 2010 Haiti earthquake, in order to locate and provide aid to survivors as quickly as possible.

jazaoo13/LDA_QDA_Iris_Dataset

Implementation of LDA and QDA classifiers using the Iris dataset. Includes data normalization, training, testing, and accuracy measurement.

JuanIMartinezB/Machine-learning-Classification

Hotel Booking Cancelations Prediction notebook. Machine learning techniques: supervised learning and classification using Logistic Regression, K-NN, LDA and QDA

mobiiin/statistical-learning

statistical learning course by dr. Mohammad zade at Sharif Uni. of Tech

mtopacio35/STA-135-Project

analysis of PimaIndiansDiabetes dataset in R, utilizing multivariate data techniques of QDA, PCA, factor analysis

Neversole/Flight-Carrier-Prediction

This project uses a machine learning QDA model to predict the carrier of commercial airline flights.

q-maze/DisasterResponse

Comparison of several different models for identification of refugee locations following the 2010 Haiti Earthquake.

ShrayanRoy/Bankruptcy-Prediction

Project of a coursework - Multivariate Analysis (M.Stat Semester 2) under the supervision of Prof. Swagata Nandi. (Project Group : Adrija Saha, Shrayan Roy, Sampurna Mondal)

SujayTalanki/BreastCancerClassificationInR

In this project, I use several different classification algorithms to predict whether a patient has breast cancer or not. This project uses K-fold cross validation, logistic regression, LDA, QDA, SVM, and model tuning techniques to achieve a 96% accuracy rate. This project was completed via R Markdown and LaTex.

Yoonyoung-Cho/HR_Data_predicting_employee_resignition_2019

2019.12.12 개인 프로젝트. 직원의 퇴사를 예측하고 퇴사 이유 및 해결방안 제시

Zauverer/exce_Linear_Discriminant_Analysis

Linear Discriminant Analysis

martintony4all/Statistical-Machine-Learning

Statistical machine learning

shoaib555/Credit-Card-Fraud-Detection

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Here, the aim is to analyze the dataset and detect the fradulent transactions.