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

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

A chatbot is a conversational assistant that assists you with information via chat. This chatbot gives a response in both speech and text.

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.

A chatbot which can recommend itineraries, flights and hotels

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

Tokenization, Stemming, Lemmatization, Bag of words, TF-IDF

Stock Sentimental analysis Classifier using News Headlines

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

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 …

Recommend similar apparel searched by user on Amazon.

Using NLP or prediction of stack overflow posts using linear models for multi-class classification

Oasis Infobyte Internship Data Science task-4

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

I trained a natural language processing (NLP) model to classify restaurant reviews as either positive or negative sentiment.

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.