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Final Year project based upon Network Intrusion Detection System
Text classification on job description dataset
Object Detection Techniques for the Vehicle Detection
The objective of this project is to classify whether upcoming product will have positive or negative Sentiment.
Application of various text classification algorithms on multiple datasets.
Implementation of various Machine Learning and Deep Learning models for Sentiment Analysis on the 'Sentiment Labelled Sentences Data Set' by University of California, Irvine.
Arabic_Dialect_Identification_NLP-AIM-Task
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.
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.
In this project I intend to predict customer churn on bank data.
❓ Question Or Not Classifier (Great For Beginners NLP Practice)
Erdos Institute Bootcamp project analyzing cuisines by recipe ingredient lists.
Scraping data through Instagram and using the data to build a predictive model
Fake news detection using TF-IDF vectorization and LinearSVC
NLP Classification and Clustering with spam SMS dataset
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€
Dartmouth COSC 274: Machine Learning models for Amazon Reviews dataset
Implementation of Drug database with LinearSVC, BernoulliNB, MultinomialNB, LogisticRegression, Perceptron and MLPClassifier models
A mini ML project of feature and model selection on breast cancer data
Movie genre classification in NLP using multinomial navie bayes classification and linear support vector classification.
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
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
Part of an internal project for my internship
In this part I'm working on Maternal Health Risk Prediction
DevStack Solution Internship Program "Data Science Internship" Task-1 on Fake news detecting system using python and machine learning
The aim is to create a classifier that indicates whether a requested transaction is genuine or fraudulent.
An end-to-end plagiarism classification model deployed in AWS SageMaker.
Build, train and compare performances of multiple binary classification machine learning model techniques to detect credit card fraudulent transactions.
A Breast Cancer Prediction Model
A flask app to prdict iris flower using LinearSVC deployed on heroku
This project aims to determine the likelihood of a company facing bankruptcy, a crucial aspect of financial analysis and investment decision-making.
Machine Learning clasificación con SKLearn
Application of LSTM on imdb dataset for sentiment classification problem