This repo contains implementation of IP2Vec model which is used for learning similarities between IP Addresses

Regression, Classification, Clustering, Dimension-reduction, Anomaly detection

Fisher's LDA is a dimensionality reduction and classification method maximizing class separability by finding linear discriminants that optimize the ratio of between-class to within-class variance.

Data Mining and Wrangling Mini Project 3 - August 25, 2021

in this project, logistic regression, KNN, classification trees, random forests and neural network were used.

Reduce the curse of dimensionality

Dimensionality Reduction Techniques and NLP

A Python library for easy and effective feature reduction in machine learning and data science. It includes various techniques to streamline your feature selection process with FeatureReductor.

Application of PCA in facial recognition

This repository contains Pattern Recognition and Machine Learning programs in the Python programming language.

This work involves two subtasks: assessing clustering results using all input variables and applying PCA for dimensionality reduction to improve understanding of multi-dimensional problems.

Find codes to various dimentionality reduction techniques here!

Задача классификации (Оценка занятости помещения на основе многомерных сенсорных узлов) / Classification task. (Based Occupancy Estimation Using Multivariate Sensor Nodes)

Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.

Codes and Project for Machine Learning

This code is a part of a research project. It aims to identify the impact of the dimentionality reduction techniques on the accuracy and performance of machine learning based intrusion detection systems in IoT environments.

The pupose of this work is to create a model that helps predict the unsubscription (churn) of a given customer or a group of customers according to their age, gender, salary etc... using the provided data.

1st year master project: Projection of a 10-dimentional dataset into 2 or 3 dimentions using the Levenberg–Marquardt optimization algorithm, which was implemented.

This project intends to show the ways we can perform dimensionality reduction techniques on our data.