📷 Generates Text Analytics using Bag of Words. Upload your data and it will suggest the relevant Newsgroups for you.

Repository for the lectures taught in the course named "Natural Language Processing" at the University of Guilan, Department of Computer Engineering.

🍰 A library for creating n-grams, skip-grams, bag of words, bag of n-grams, bag of skip-grams.

VIP Machine Learning Exercises and Practices

A chatbot which can recommend itineraries, flights and hotels

Oasis Infobyte Internship Data Science task-4

Text classification is the task of assigning a set of predefined categories to free text. Text classifiers can be used to organize, structure, and categorize pretty much anything. For example, new articles can be organized by topics, support tickets can be organized by urgency, chat conversations can be organized by language, brand mentions can …

Analyse of election machine texts from the Finnish parliament elections 2015

Notebooks to tackle the Kaggle Spooky Author competition. The challenge is to accurately identify the author from their sentences.

Different takes at creating a content based movie recommendation system using MovieLens dataset. One approach focuses on finding the correlation between different attributes to recommend movie. Another approach make use of the bag of word model along with machine learning algorithms.

Trained and optimized a Classification Machine Learning model to predict the grammatical flow of email using state of the art techniques : 1. Word2Vec 2. tf-idf 3. bag-of-words. The models used include Logistic Regression and Support Vector Mechanics with 250-300 features.

Classify comments on categories - "Toxic", "Severe Toxic", "Obscene", "Threat", "Insult", "Identity Hate", "Any of the Above", "None of the Above".

Performed Twitter Sentiment Analysis on 10 years of Twitter Data using bag of words approach, and predicted stock market trends using Time Series Neural Networks.

This repository contains implementations of text classification using a Rule-Based Classifier and Bag of Words model, as well as word embeddings using the Skip-gram model of Word2Vec. It includes detailed preprocessing steps, model training, and relevant references.

Computer Vision multilabel classification with different techniques: (KNN,PCA,LDA,BOVW,SIFT,VGG16)

Code and projects developed in the VCOM subject throughout the semester (MIEIC 4th year, 2nd semester).

Movie Recommendation using Content Filtering (Cosine Similarity) with Flask web application

Stock Sentimental analysis Classifier using News Headlines

Basic Language Models , Bag of Words, Ngram Models Etc NLP modelling and associated tasks