DataS-DHSC/tech-club

Materials for Statistics and Data Science (SDS) Tech Club sessions

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362heavy/Liver_Cirrhosis_Stage_Detection_System

This repository contains a system for detecting the stage of liver cirrhosis using historical patient data. It employs machine learning to analyze key medical indicators and classify patients into three distinct stages. 🦠📈

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mahaseva/Mobile_Phone_Pricing_Prediction_System

# Mobile Phone Pricing Prediction SystemThis project predicts the price range of mobile phones based on their specifications. Using a dataset with features like battery power and RAM, we classify phones into four price categories. 🛠️📱

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SaketJha-323/Mobile_Phone_Pricing_Prediction_System

Mobile

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Param-Sahu/Exit_Poll_and_Vote_Prediction_Model

The objective is to analyze voter behavior based on demographic and opinion-based variables and build a classification model that can predict which party a voter will vote for. This model is used to simulate an exit poll.

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RezaSaadatyar/Machine-Learning-with-MATLAB

This repository contains a MATLAB-based Machine Learning Software (MLS) offers advanced biomedical signal processing with an intuitive GUI for analyzing EEG, ECG, and EMG. Features include noise filtering, feature extraction, dimensionality reduction, and customizable machine learning algorithms for tailored classification and analysis.

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arnaldog12/Machine_Learning

Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

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Yousuf1733/Titanic-Dataset-Analysis

Exploratory data analysis of the Titanic dataset, uncovering insights on passenger survival rates based on gender, age, and class. Includes data cleaning, visualization, and findings.

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StarlangSoftware/Classification-Cy

Machine learning library for classification tasks

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TatevKaren/data-science-popular-algorithms

Data Science algorithms and topics that you must know. (Newly Designed) Recommender Systems, Decision Trees, K-Means, LDA, RFM-Segmentation, XGBoost in Python, R, and Scala.

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AsadiAhmad/LDA

Dimension Reduction with LDA

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sawallesalfo/Machine_Learning_Journey

Language: R - Size: 1.27 MB - Last synced at: about 1 month ago - Pushed at: over 3 years ago - Stars: 4 - Forks: 1

Vikascd/Netflix-Recommender-System

Sistema de recomendación de títulos de Netflix basado en contenido. Incluye filtros por título, género y tipo de contenido (películas o series) con interfaz interactiva en Jupyter Notebook.

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paulbrodersen/somnotate

Automated polysomnography for experimental animal research

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StarlangSoftware/Classification

Machine learning library for classification tasks

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AsadiAhmad/Image-Classification-LDA-and-PCA

Image Classification with Perceptron and LDA and PCA dimension reduction

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je-suis-tm/machine-learning

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

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drkocoglu/Patter_Recognition_Class

Pattern Recognition - ECE - TTU - Spring 2021

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StarlangSoftware/Classification-CS

Machine learning library for classification tasks

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kbasu2016/Autism-Detection-in-Adults

This is a binary classification problem related with Autistic Spectrum Disorder (ASD) screening in Adult individual. Given some attributes of a person, my model can predict whether the person would have a possibility to get ASD using different Supervised Learning Techniques and Multi-Layer Perceptron.

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invcble/Alzheimer-s-Classification-using-OASIS-Dataset

Machine Learning models for Alzheimer’s Classification

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sushant1827/Human-Activity-Recognition-with-Smartphones

Kaggle Machine Learning Competition Project : To classify activities into one of the six activities performed by individuals by reading the inertial sensors data collected using Smartphone.

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BahAilime/breast-cancer-ml

🦠 Breast cancer survival prediction (notebook + streamlit)

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JEFworks/MUDAN

Multi-sample Unified Discriminant ANalysis

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StarlangSoftware/Classification-CPP

Machine learning library for classification tasks

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gmrandazzo/QStudioMetrics

A Comprehensive Software for Data Mining and Multivariate Analysis

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ozturkfemre/classification

R | Classification Project

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weihan-zhao/knotweed_fieldwork

The documents under this repository are the codes for analysis of knotweed population performance across ranges and environmental drivers of trait variation

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dehaoterryzhang/Iris_Classification

Iris classification with Python Scikit-learn :blossom:

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Kimiarfaie/Student-Performance-Prediction

Predicting students' success or risk of dropping out. Coursework for Data Science course at UGR.

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bunnythewiz/ML-Algorithms-from-Scratch

A collection of foundational machine learning algorithms implemented from scratch.

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StarlangSoftware/Classification-Py

Machine learning library for classification tasks

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nirab25/Insurance-Claim-Fraud-Detection

Insurance claim fraud detection using machine learning algorithms.

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HoganHPH/RSLDA-in-Python

A Python implementation of RSLDA (paper "Robust Sparse Linear Discriminant Analysis").

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aavalose/portfolio-classification

NBA classification model

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StarlangSoftware/Classification-Js

Machine Learning Library for Classification Tasks

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utsav507/titanic-data-analysis

Data Analysis and Predictive Analysis of Algorithms on the Titanic Dataset

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AliAmini93/Fault-Detection-in-DC-microgrids

Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.

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stabgan/Linear-Discriminant-Analysis

We used LDA in this project to expand the capabilities of our Logistic Regression Classifier in both Python and R

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T-Fernandes/Machine_Learning

Portfolio of machine learning projects

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jElhamm/Linear-Discriminant-Analysis-Data-Mining

"This repository contains implementations of Linear Discriminant Analysis (LDA) algorithms for data mining tasks. Linear Discriminant Analysis is a dimensionality reduction technique used to find a linear combination of features that characterizes or separates classes of data."

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zyxsjdy/Computer-Vision-and-Pattern-Recognition-CSP21-IMPERIAL

Coursework for Computer Vision and Pattern Recognition at IMPERIAL, 2022

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RachitaGurudev/Election-Exit-Poll-Prediction

News channel CNBE wants to analyze recent elections. This survey was conducted on 1525 voters with 9 variables. Model is built to predict which party a voter will vote for on the basis of the given information, to create an exit poll that will help in predicting overall win and seats covered by a particular party.

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marinafajardo/prevendo-customer-churn

Prevendo Customer Churn em Operadoras de Telecom

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prasanth5reddy/CSE-569-Fundamentals-of-Statistical-Learning

CSE 569, Fall 2019 Fundamentals of Statistical Learning Course at ASU

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mohamedhassan279/Face-Recognition Fork of MostafaGalal1/Face-Recognition

Face Recognition Using several dimensionality reduction techniques along with KNN as a classification algorithm

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Vivek-Tate/Human-Activity-Patterns-Recognition

This Human Activity Recogisition analyses human activity patterns using smartphone sensor data from the UCI Human Activity Recognition dataset. It involves outlier detection, correlation analysis, and structural graph analysis. DBSCAN clustering is applied, followed by LDA for dimensionality reduction, to visualise and interpret activity clusters

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tommykwok722/Lead-Prediction-System

Develop a Lead Prediction System to enhance marketing efforts by accurately identifying prospective customers.

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hidiryuzuguzel/Dimensionality-Reduction-and-Single-Multi-layer-perceptrons

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SCUS3/Classification-Model-Evaluation

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.

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camilobetanieto/StatisticalLearning

Analysis of student performances of the Open University Learning Analytics dataset by using logistic regression and various classifiers.

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krawczyk22/Facial-attendance-system

Facial recognition system using the fusion of the PCA and LDA algorithms to verify students' attendance in real-time.

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PrusWielki/adv-machine-learning-proj-1

LDA, QDA and NB in Python from scratch

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soumyadeepghoshGG/CS-GO-Round-Win-Predictor

Counter-Strike: Global Offensive round winner predictor based on models trained with snapshots of data across different rounds.

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junlulocky/RobustFisherLDA

An implementation of [robust version] of Fisher Linear discriminant analysis

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YoussefAboelwafa/Face-Recognition

Face Recognition Project using PCA and LDA Algorithms for Dimensionality Reduction

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arnab-007/Classification-of-surface-EMG-signals-for-improved-control-of-prosthetic-fingers

This repository contains codes for feature extraction and subsequent classification of surface electromyogram (EMG) signals.

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nakshatrasinghh/Machine-Learning

Making Machine Learning Algorithms Easy to Comprehend

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fabtdt/hit_song_predictor

Hit song predictor

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saireddythfc/code-academy

Intermediate Machine learning course with example projects

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Honey28Git/Machine-Learning

The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.

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Honey28Git/Predictive-Modelling

Using Linear Regression, Logistic Regression and Linear Discriminant Analysis Models to make accurate predictions for different datasets.

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enesozi/ML-course-HW2

Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA)

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MohEsmail143/face-recognition

Face Recognition using PCA & LDA dimensionality reduction, then classification using KNN.

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Andrewwango/femda

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.

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ShantamGupta/Prediction-of-Coronary-Heart-Disease

Prediction of Coronary Heart Disease(CHD) using South African Heart Disease Data

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fikrianggara/Multivariate-Analysis-on-R

Source code written in R to implement multivariate analysis methods. Covering principal component analysist, factor analysist, clustering, manova, and so on.

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hmtalha786/Topic-Modeling

Extract the dominant topics from the given text input

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sudarshan-koirala/Dimensionality-Reduction

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

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rahulptel/aobd17

Size: 1.05 MB - Last synced at: over 1 year ago - Pushed at: about 7 years ago - Stars: 2 - Forks: 3

n8tlmps/credit-risk-assessment

evaluating credit default rate using statistical machine learning methods

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himansh18131018/flower-classification-using-machine-learning-algorithm

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nihaljn/machine-learning

A collection of machine learning algorithm implementations

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rajvi-patel-22/Linear-Discriminant-Analysis-of-a-32x32-grayscale-image-for-image-compression.

Discriminant analysis methods can be good candidates to address such problems. These methods are supervised, so they include label information. The goal is to find directions on which the data is best separable. One of the very wellknown discriminant analysis method is the Linear Discriminant Analysis. Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting (curse of dimensionality) and also reduce computational costs. Pertaining to our problem, we are given a [32 X 32] binary image as input and the goal is to apply LDA technique to transform the features into a lower dimensional space, which maximizes the ratio of the between-class variance to the within-class variance, thereby guaranteeing maximum class separability between two classes in our case with the minimal loss.

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vinayshanbhag/math

linear algebra,math concepts and applications

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liquidsunset/evolution_linear_machine

Language: Java - Size: 2.37 MB - Last synced at: over 1 year ago - Pushed at: over 8 years ago - Stars: 0 - Forks: 0

abhishek2602/Advanced-Machine-Learning

Advanced Machine Learning

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GiuDF1102/Language-Indentification-2023

Italian Language Detection from Utterance Embeddings: A Comparative Study of SVM, Gaussian Models, Logistic Regression, and GMM. Made for the Machine Learning and Pattern Recognition course at Politecnico di Torino.

Language: Python - Size: 6.9 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 2

rfhussain/Marketing-Data-Analysis-of-Bank-with-Machine-Learning

An assignment project which analyzes the demographic and marketing campaign related information of client records to predict if he/she will subscribe to the product or not

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theablemo/ML-assignments

This repository contains the practical assignments for the Machine Learning course taught by Dr.Mohsen Ansari in Spring semester of 2023 at Sharif University of Technology

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likarajo/glass_type

Identification of the type of glass based on their oxide content i.e. Na, Fe, K, etc using Dimensionality Reduction with Principal Component Analysis and Linear Discriminant Analysis

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shrinath305/PCA-and-LDA-Linear-discriminant-analysis

Dimension reduction using PCA and LDA

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Ayantika22/LDA-Linear-discriminant-Analysis--clustering-visualization-for-Iris-dataset

Linear discriminant Analysis clustering Visualization for IRIS dataset

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Ayantika22/LDA-Linear-discriminant-Analysis-for-Wine-Dataset

LDA(Linear discriminant Analysis) for Wine Dataset in machine learning

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santoshgunashekar/Machine-Learning

The implementation of different machine learning algorithms during the internship

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Chaoukia/Probabilistic-Graphical-Models

Probabilistic graphical models home works (MVA - ENS Cachan)

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Ayantika22/Linear-discriminant-Analysis-LDA-for-Wine-Dataset

Linear discriminant Analysis(LDA) for Wine Dataset of Machine Learning

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HCYENDLURI/Comparing-6-Classifiers-for-Sepsis-Dataset

To Detect Sepsis Disease using six Classifiers on clinical data

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prathmachowksey/Fisher-Linear-Discriminant-Analysis

Implementation of Fisher Linear Discriminant Analysis in Python

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timothygmitchell/Empirical_Study_of_Ensemble_Learning_Methods

Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning

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Divya171997/Residential_Properties-Applied_Statistics

The dataset contains information regarding residential properties which were collected by the US Census Service, the period 2006 to 2010.

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AScick/Machine_Learning_Project

Bunch of exercises computed during the Machine Learning for Finance course.

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ellapav/support_vector_machine

support vector machine applied to separable data

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KritikaAggarwal15/data-science-framework

This is a combined framework which has all the EDA functions including bivariate and univariate analysis and classification models. EDA Functions can help to analyze the Data before running any ML model.

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bilge-karaca/NLP_with_Disaster_Tweets

Predicting which tweets are about real disasters. Using Bag-of-Words, TF-IDF Vectors, Naive Bayes, Linear Discriminant Analysis, Truncated SVD, custom tokenizer, lemmatization, GridSearchCV.

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sayarghoshroy/Image-Classification-Formulations

Insights and Analysis - Using Various Deep Learning Architectures on Image Classification Datasets

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kk289/Predicting-Credit-Defaults

Machine Learning: Group Project

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gionanide/Speech_Signal_Processing_and_Classification

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

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tugrulhkarabulut/Gaussian-Discriminant-Analysis

Gaussian Discriminant Analysis introduction and Python implementation from scratch

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hiroyuki-kasai/ClassifierToolbox

A MATLAB toolbox for classifier: Version 1.0.7

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