Final Year project based upon Network Intrusion Detection System
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Updated
Jul 10, 2019 - Jupyter Notebook
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
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.
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.
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.
In this project I intend to predict customer churn on bank data.
Fake news detection using TF-IDF vectorization and LinearSVC
Part of an internal project for my internship
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
Implementation of Drug database with LinearSVC, BernoulliNB, MultinomialNB, LogisticRegression, Perceptron and MLPClassifier models
Erdos Institute Bootcamp project analyzing cuisines by recipe ingredient lists.
Dartmouth COSC 274: Machine Learning models for Amazon Reviews dataset
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 fa…
A mini ML project of feature and model selection on breast cancer data
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