The goal is to develop a classification model that can accurately differentiate between spam and non-spam messages. This is crucial for applications like email filtering, SMS spam detection, and improving overall user experience by reducing the influx of unwanted or malicious content.

Using Natural Language Processing (NLP) and pandas, numpy, scikit-learn for classification and applying logistic regression as it is a supervised model, lastly NLTK. Pickle library used for saving and running the model anywhere.

Text preprocessing, indexer constructions, and search engines implementation for information retrieval. Performance analysis done by measuring the construction time of indexers.

NLP projects, which I worked on utilising different natural language processing libraries's. 

Fastest JavaScript implementation of the porter2 stemming algorithm

This repository contains projects focused on Arabic Natural Language Processing (NLP)

Library for Stemming Balinese Text Language

Text classification and topic extraction from COVID-19 articles

Extract the stem of a given word.

🍊 📄 Text Mining add-on for Orange3

A suite of Arabic natural language processing tools developed by the CAMeL Lab at New York University Abu Dhabi.

The most popular spellchecking library.

Classification of Spam messages using NLP algorithms: using bag of words, stemming etc ,an Outlier-Robust machine learning approach

ℹ️ Information Retrieval models implemented in Python

This project aims to detect hate speech on Twitter using advanced NLP and machine learning techniques, exploring feature extraction methods like TF-IDF and sentiment analysis, and evaluating models such as Logistic Regression and SVM.

NLP using NLTK package

introduction to NLP

Assessing the impact of different pre-processing techniques for classifying the sentiment of movie reviews