A platform enables sharing diverse knowledge, but similarly worded questions are common. We use NLP techniques to identify duplicate questions, enhancing user experience by making it easier to find high-quality answers.

A macro to get free metal shards in Horizon Forbidden West using Logitech G Hub

A silly script I made for testing face recognition that adds a pink bow at my head

Minecraft bot for equations when shooting an arrow

Sentiment analysis identifies sentiment in text data, like reviews or tweets. We use BoW to represent each document as a word frequency vector and logistic regression for classification.

VAITP - Vulnerability Attack and Injection Tool for Python

Generate random kits in Java MC

This project is a SMS spam classifier which detect whether the SMS is spam or ham using the multinomial Naive Bayes algorithm along the side of BOW/TF-IDF in NLP

Vectorization Techniques in Natural Language Processing Tutorial for Deep Learning Researchers

Sentiment Analysis on Movies Reviews

Using NLP and a smart chatbot, this project gauges customer sentiments online, offering customization and real-time feedback. Employing TF-BOW-LDA and ML models, it empowers e-commerce decisions, culminating in an NLP course at uOttawa in 2023.

The project aimed to classify Gutenberg texts accurately. Employing advanced NLP methodologies, it covered collection, preprocessing, feature engineering, and model evaluation for literary work classification. as part of the University of Ottawa's 2023 NLP course.

The Bow Framework

Four conversional chatbots created using NLTK, BOW, TF-IDF, cosine similarity and one using OpenAI pre-trained model, all in Python

The Bow Framework

Word Grouping by List Category

This is a Flask-based personality detection project with features including real-time prediction, Reddit mining chatbot, NER tag identification. Personality detection is important for understanding human behavior, improving communication and relationships, and even enhancing mental health treatment.

Create features when hit player with bow.

The goal is to classify the given texts into 4 subjects i.e, maths, physics, chemistry, and biology. Methods used: tokenization, lemmatization, stop words, word clouds, bag of words, neural networks, transformers and BERT. Tools: NLTK, sklearn and PyTorch.