Repositorio com códigos relacionados a pesquisa de TCC sobre desempenho dos algoritmos Naive Bayes, RL e SVM para classificação de revisões.

A NLP project about a brief application of tfidf vectorizer for text feature extraction and pipeline and feature union to chain multiple steps into one

Performed text classification using KNN, SVC and Decision-Tree algorithms to predict the author.

Performed text preprocessing, clustering and analyzed the data from different books using K-means, EM, Hierarchical clustering algorithms and calculated Kappa, Consistency, Cohesion or Silhouette for the same.

Multi label classification. Dataset is from Kaggle(https://www.kaggle.com/badalgupta/stack-overflow-tag-prediction)

Predict tags on StackOverflow with linear models - Week 1 assignment of Coursera's Natural Language Processing course from the Advanced Machine Learning Specialization.

Different NLP Models on Kaggle Dataset

This is one of the Projects which was done in interest to learn the difference between the different classification algorithm and derive a solid conclusion from that. It scrap sthe data from youtube and related to six different classes and then by using different classification algorithm it classifies them.

Python natural language pre-processing scripts

Python code to perform sentiment analysis of twitter data. Used TermFrequencyInverseDocumentFrequency(TFIDF) model along with Machine Learning algorithms to make predictions

A text can be assigned more than one label

Classify the given genetic variations/mutations based on evidence from text-based clinical literature

Some videos have more impact than the others resulting in higher memorability scores for such videos. Using various ML algorithms, such memorability scores are predicted.

This repository consists of machine Learning models, deep learning models and some NLP tasks such as Topic Modelling, Sequence generation, Sentiment analysis, Recommendation System

Predicting Tags for Stack Overflow

Worked on an Natural Language Processing problem statement wherein based on the commentary of a given over, you need to predict whether the batsman scored a six/four or there was a wicket taken.

In this project, a TFID Passive aggressive classifier is designed to detect fake news.