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

Movie Recommendation System based on machine learning concepts

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

Intents-Based Chatbot with Streamlit

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.

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

Hire the Perfect candidate. HackerEarth Competitions solution.

Fake News Prediction System using logistic regression, stopwords, nltk

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

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

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

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

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

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.

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.

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

Detecting 'FAKE' news using machine learning.

Fake news classifier model

This webapp helps to find the inaccurate information around the world through news

Program that can take in large amounts of .csv files in the same directory and trains a deep learning neural network model based on the best/worst students evaluated by the user. Second program then utilizes the model from the first program to produce a projected report for each individual student inputted.

Detect FAKE news using sklearn

Part of an internal project for my internship

Movie recommendation system using TfidfVectorizer, NearestNeighbors

emotion analysis project - Python & Streamlit & Machine Learning

An innovative system for filtering and categorizing movie reviews

SMS Spam Classifier is a machine learning project that classifies SMS messages as either spam or not spam (ham).

No description provided.

A Simple conversational chatbot built using NLU concepts. The project uses reddit comments taken from 2015, which has about 1.7 billiion interactions.

This projects aims to recommend movies to the user based on high similarity scores among them.