Classification and Auto-Tagging of Stack Exchange Questions

Simple Codes

Logistic regression model to sort Hogwarts students into their perspective houses based on their performance in classes.

Own implementation of SVM classifier solving dual optimization problem

Recognise Handwritten Digits MNIST data set using Neural Networks and Multi class Classification for Logisitc Regression

Base de dados que será utilizada para treinamento de uma rede perceptron.

A multi-class classification problem where the objective is to read a question posted on the popular reference website, StackOverflow and predict the primary topics it deals with, i.e. tags which the question will be associated with.

Tag Prediction Model for the Doubt Asking Platform. Suggests tags based on the user input question and question description.

Classifying a tweet as positive, neutral, or negative sentiment using Natural Language Processing (CBOW approaches) and Traditional Machine Learning Algorithms.

Cancer prediction system using machine learning algorithm and training the model on different gene expression of cancer

Contains models implemented from scratch and a project implemented from end-to-end

this project utilizes Python for the screening of resumes. It involves data cleaning, visualization, and machine learning techniques to categorize resumes into different job categories.The project achieves high accuracy using a machine learning algorithm, showcasing its effectiveness in automating the resume screening process.

Using ML Classification to predict customer segmentation groups

Реализация метода опорных векторов для классификации данных

ResumeRevealer is an advanced tool designed for HR professionals, recruiters, and hiring managers to streamline the process of resume parsing and candidate evaluation. It offers a comprehensive solution to extract valuable insights from diverse resume formats, standardize job titles, and mine detailed skills from project descriptions.

This GitHub repository provides an implementation of the paper "MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network" . MAGNET is a state-of-the-art approach for multi-label text classification, leveraging the power of graph neural networks (GNNs) and attention mechanisms.

Using NLP or prediction of stack overflow posts using linear models for multi-class classification

This is a Machine learning project for screening of resumes based on the type of job and the content with the help of NLP techniques.

Binary Classification Models with pySpark in Apache Spark