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.

Implementation of PCA with KNN Clustering

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

Application of PCA in facial recognition

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

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.

A Python implementation of PCA algorithm from scratch using numpy

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

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

Codes and Project for Machine Learning

SDS course assignments

ML Classification Algorithm to predict Approval or Decline of a Loan

A newspaper articles classification system based on theme/topic using BERT (HuggingFace)

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.

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.

This project involves reducing testing time for car configurations. The tasks include removing columns with zero variance, checking for null values, applying label encoding, performing dimensionality reduction, and using XGBoost to predict testing time.

Reduce the curse of dimensionality

- Graph Based Feature Selection is a new approach of reducing the dimensionality of a dataset using a Graph Based approach. - The apporach tries to generate a Kruskal's minimum spanning tree of a graph where the features of the dataset are the vertices and the correlation among them are the weights of the edges. -The edges having weights greater…