Phony News Classifier is a repository which contains analysis of a natural language processing application i.e fake news classifier with the help of various text preprocessing strategies like bag of words,tfidf vectorizer,lemmatization,Stemming with Naive bayes and other deep learning RNN (LSTM) and maintaining the detailed accuracy below

TFIDF being the most basic and simple topic in NLP, there's alot that can be done using TFIDF only! So, in this repo, I'll be adding the blog, TFIDF basics, wonders done using tfidf etc.

Movie Recommendation System based on machine learning concepts

Posts/Feeds recommendation engine based on content based and collaborative filtering methods

Hire the Perfect candidate. HackerEarth Competitions solution.

An NLP model to detect fake news and accurately classify a piece of news as REAL or FAKE trained on dataset provided by Kaggle.

Machine learning approach for fake news detection using Scikitlearn

Python-based web application, Flask platform, utilizes a powerful Content-Based Filtering Algorithm to provide personalized recommendations excercises

In this project we are comparing two approaches for movie recommendation for a new user or existing user based on their age, gender, occupation.

For our final project, our group chose to use a dataset (from Kaggle) that contained medical transcriptions and the respective medical specialties (4998 datapoints). We chose to implement multiple supervised classification machine learning models - after heavily working on the corpora - to see if we were able to correctly classify the medical sp…

Data consists of tweets scrapped using Twitter API. Objective is sentiment labelling using a lexicon approach, performing text pre-processing (such as language detection, tokenisation, normalisation, vectorisation), building pipelines for text classification models for sentiment analysis, followed by explainability of the final classifier

Learned to detect fake news with Python. We took a political dataset, implemented a TfidfVectorizer, initialized a PassiveAggressiveClassifier, and fit our model. We ended up obtaining an accuracy of 92.82% in magnitude.

Use Key NLP techniques to classify news articles into categories: Bag_of_Words (tf-Idf), word embeddings and BERT language model

The aim - is to develop a model that will give accurate predictions for the customer's test sample, but the training sample for is not given. It should be collected by parsing

Fake new detection using text classification as real or fake news segments. Required installations - Python 3.8, NLTK, Scikit-Learn, Jupyter. Text cleaning, tokenization, vectorization, classification model generation and evaluation.

Detecting 'FAKE' news using machine learning.

Fake news classifier model

Penerapan TF-IDF Vectorizer dan Passive Aggressive Classifier dalam pendeteksian berita palsu dengan Python.

Fake News Prediction System using logistic regression, stopwords, nltk