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Final project based on text classification by emotions using machine learning models and BERT.
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.
Here you can explore different projects around Machine Learning Algorithms, Enjoy ☺️
flask website that automatically assigns multiple relevant tags to a Stackoverflow question
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.
Data Project
Klasifikasi Biner dan Multikelas untuk dataset penjualan pada sebuah supermarket
Fake reviews Detection using SGD Classifier
Supervised and unsupervised algorthimn analysis on APS Failure at Scania Trucks Dataset
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.
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.
Fake News Detection using SGD Classifier