This repository contains Jupyter notebooks and Python scripts that cover foundational concepts and practical implementations of NLP preprocessing techniques. Each topic is accompanied by clear explanations and code examples using the Natural Language Toolkit (NLTK) library.

Simple Python Implementation of Stemmer and Lemmatizer

Modern NLP pipeline for Portuguese news articles: preprocessing, tokenization, stemming, lemmatization, POS tagging, and exploratory analysis with spaCy, NLTK, and Python.

ML model

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

This is a basic search engine written in Python for ranking documents based on the proximity of query terms, featuring an indexing and ranked retrieval system for textual data.

This is a conversion of the PyElly NLP tool from Python 2.7 code into Python 3.8 code.

The project involved natural language processing (NLP) techniques, tokenization, stemming, and the application of the TF-IDF vectorization method.

my data to day mapping work where i use NLP techniques and pandas majorly on scrapped data

Data Preprocessing and Feature Extraction

Basic Content Based Recommendation System using Cosine Similarity

The static model of movie recommendation system using tmdb data set of movies

NLP Pre-processing using : Normalization , Tokenization, Lemmatization and Stemming

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

Spam Detection System using NLTK - Bag of Words - Multinomial Naive Bayes

A multi-class classification problem where the objective is to read a question posted on the popular reference website, StackOverflow and predict the primary topics it deals with, i.e. tags which the question will be associated with.

Indexing and Retrieval Models

Extract text content from an HTML page, process it, and extract unique words from the processed text. This notebook utilizes various text processing techniques including cleaning, normalization, tokenization, lemmatization or stemming, and stop words removal.