Final Year project based upon Network Intrusion Detection System

The objective of this project is to classify whether upcoming product will have positive or negative Sentiment.

Text classification on job description dataset

Sentiment Analysis of Movie Reviews is either positive or negative review, the dataset which is used is "IMDB Dataset of 50K Movie Reviews" and the machine learning algorithm which I used in this is Logistic Regression , Random Forest and LinearSVC.

Object Detection Techniques for the Vehicle Detection

This repository contains a number of experiments with Multi Lingual Transformer models (Multi-Lingual BERT, DistilBERT, XLM-RoBERTa, mT5 and ByT5) focussed on the Dutch language.

Application of various text classification algorithms on multiple datasets.

This project involves the implementation of efficient and effective LinearSVC on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.

In this project I intend to predict customer churn on bank data.

Arabic_Dialect_Identification_NLP-AIM-Task

Implementation of various Machine Learning and Deep Learning models for Sentiment Analysis on the 'Sentiment Labelled Sentences Data Set' by University of California, Irvine.

Fake news detection using TF-IDF vectorization and LinearSVC

Erdos Institute Bootcamp project analyzing cuisines by recipe ingredient lists.

Developed a project which detects the news either as fake or real. GPT2 transformer model is used to predict the sentiment and genre of news. Classifier Machine Learning models and Hugging Face Transformer-Based language models are used to classify the news

Scraping data through Instagram and using the data to build a predictive model

A mini ML project of feature and model selection on breast cancer data

Fake news related to the coronavirus pandemic has now become a huge problem since false information can lead to worry and concerns regarding the disease. It is not possible to perfectly detect fake news unless the news has been labelled fake or real. Therefore, I have taken this issue as my problem and have developed a project that can detect fake news regarding Covid19 pandemic with the help of the dataset from Kaggle containing Covid19 public media information. I have also used different machine learning classifiers to check which classifier is best suited for the detection

NLP pipeline for detecting fake vs. real news headlines using traditional ML models and fine-tuned Transformers (DistilBERT).

A comprehensive machine learning project for detecting fraudulent job postings, leveraging NLP (TF-IDF) and a LinearSVC classifier to enhance online job platform security.

Part of an internal project for my internship

Implementation of Drug database with LinearSVC, BernoulliNB, MultinomialNB, LogisticRegression, Perceptron and MLPClassifier models

NLP Classification and Clustering with spam SMS dataset

This repository houses a Streamlit web application for fake news detection. The app allows users to input a news article and predicts whether it is likely fake or real based on its content. It provides options to select different vectorizers (TF-IDF or Bag of Words) and classifiers (Linear SVM or Naive Bayes) to customize the prediction model.

Dartmouth COSC 274: Machine Learning models for Amazon Reviews dataset

A robust fake news detection system leveraging machine learning models (SVM and Random Forest) to identify political misinformation. Includes preprocessing, training, and evaluation scripts with datasets available for download.

The aim is to create a classifier that indicates whether a requested transaction is genuine or fraudulent.

Movie genre classification in NLP using multinomial navie bayes classification and linear support vector classification.

This project was built within 24h by the team Augusteam for the DevHacks 2022 Climate Change hackathon sponsored by Systematic and it won the third place worth 500€

Deep-learning is hard bro !, so made a binary classifier in machine learning that uses HSV Color Space to Look out for Features

Machine Learning clasificación con SKLearn