count-vectorizer

This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.

It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization

Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.

NLP based Classification Model that predicts a person's personality type as one of the 16 Myers Briggs personality types. Extremely challenging project dealing with correlation between human psychology and casual writing styles and handling heavily imbalanced classes. Check the app here - https://mb-predictor-motetuzs5q-uc.a.run.app/

Fake News Detection System for detecting whether news is fake or not. The model is trained using "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. Link for dataset: https://arxiv.org/abs/1705.00648.

A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomial-naive-bayes,logistic regression,svm,decision trees to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer,TFIDF Vetorizer,WordnetLemmatizer. It is implemented usi…

💉 Vaccine Sentiment Classifier is a deep learning classifier trained on real world twitter data, that distinguishes 3 types of tweets: Neutral, Anti-vax & Pro-vax.

Natural Language Processing Recipes

Scrapped tweets using twitter API (for keyword ‘Netflix’) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard

Text Mining project about Sentiment Analysis of Drugs Reviews.

Application of Machine Learning Techniques for Text Classification and Topic Modelling on CrisisLexT26 dataset.

Built a movie recommender system with Streamlit and deploy in Heroku Platform.

Fake News Detection using Decision Tree Classification Machine Learning model.

Short Stories Recommendations.

my exercises of course natural language processing datacamp

A simple Sklearn based example to demonstrate the working of TF-IDF.

Spam Classifier project for my end-of-semester project for Intro to AI class. We were a group of four people. I worked on all the Naive Bayes models.

Movie Recommendation - provides user with the top choices of movie he/she wanted to watch based on their current choice

A machine learning system that takes a comment and classifies it as offensive or non-offensive (neutral). This system will be trained in a data set with comments in which the tags (insult or non-insult) are known. Classification algorithms used: Naive Bayes, SVM, Random Forest.

A machine learning model that predicts tags for a given question and body.