Final project based on text classification by emotions using machine learning models and BERT.

flask website that automatically assigns multiple relevant tags to a Stackoverflow question

This project develops a machine learning model to classify individuals as healthy, having rheumatoid arthritis (RA), or systemic lupus erythematosus (SLE) using RNA-Seq gene expression data. The project also identifies significant genes as potential biomarkers, leveraging SGDClassifier and XGBoost models.

이화여자대학교 컴퓨터공학과 19-2,20-1 캡스톤프로젝트

This project focuses on building a fraud detection model for credit card transactions using a dataset containing transactions made by European cardholders in September 2013. We are working with a highly unbalanced dataset and the challenge lies in effectively detecting fraudulent transactions while minimizing false positives.

A sentiment analysis project using linear support vector machine with stochastic gradient descent method. Built to analyze whether the tweet sentiment are happy or sad.

Here you can explore different projects around Machine Learning Algorithms, Enjoy ☺️

Data Project

Fake reviews Detection using SGD Classifier

implementation of basic ML algorithms

The implications of hate speech have received considerable attention from the common public and society as a whole. There has been a rising concern over the effects of hate speech and offensive language. However, much of this attention is focused on the critical presentation and evaluation of arguments in favor of and against the ban on hate speech, as opposed to previous conceptual analysis tasks. The general concept of hate speech goes beyond legal texts and sentences, in fact, it goes beyond the legal definition of hate speech. The analysis is done using many well-known conceptual analysis methods that are different from analytic philosophy. The main motive of this analysis is to dispel the myth that evil emotions and malicious rationale are a fundamental part of the nature of human beings.

This project performs analysis and experimentation with classifiers in Python using the MNIST dataset, which consists of images of handwritten digits.

The online payment fraud analysis project follows several step approach from data preprocessing through model evaluation, result comparison and final model selection, using transaction patterns to identify fraud indicators including account draining, suspicious transfers, and balance inconsistencies.

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 150.

Sentiment analysis using Amazon customer reviews

A financial institution wants to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI on the due date.

SILOKA-AI (Sistem Klasifikasi Laporan Kampus AI)