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

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

- 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…

Reduce the curse of dimensionality

Tutorial- data Pre-processing

Clustering NBA Players Based on Performance

Application of PCA in facial recognition

ML Homeworks using ML tools

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

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

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.

SDS course assignments

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 focused on applying machine learning to build a clustering model to segment and analyze customer characteristics in the airline industry based on LRFMC scores using K-Means and suggest business strategy recommendations based on the results.

Codes and Project for Machine Learning

Introduction to Machine Learning project with the goal of improving the classification performance on a dataset by optimizing the number of features and weak learners.

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

Dimensionality Reduction Techniques and NLP

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