This application features a GUI for classifying user-input text as spam or ham using a Naive Bayes algorithm for machine learning.

Train model using your own dataset and use it to predict the label for a given text. Additionally, it identify if the text is likely to be spam or irrelevant.

This cloud recommendation system suggests similar services based on use cases. Powered by a TF-IDF backend in Flask and a React frontend, it provides accurate and user-friendly recommendations for cloud services.

This repository contains a machine learning project for email spam detection. It includes data preprocessing, model training, evaluation, and deployment using Python and scikit-learn.

CV Score Pro Enhancing job matching using machine learning, CV Score Pro streamlines the job application process by comparing CVs with job descriptions to generate a compatibility score. Empower your job search with smart, efficient, and effective matching.

This model will tell you weather mail is spam or not

Spam Mail Prediction using Machine Learning

Machine Learning | Deep Learning

This repository explores the correlation between news headlines' textual embeddings and their political orientation. Using clustering and transformer-based embeddings, the goal is to classify news sources based on headline content. Key features include clustering visualizations, BERT embeddings, and comparisons between K-Means, Spectral, and DBSCAN

CSIT349 - Applied AI Course Final Project

The scope of this project is to classify fake and true news. After performing an analysis on the dataset using two different vectorizers and two machine learning algorithms, the results are conveyed in the form of accuracy score and confusion matrices.

This repository houses a Streamlit web application for fake news detection. The app allows users to input a news article and predicts whether it is likely fake or real based on its content. It provides options to select different vectorizers (TF-IDF or Bag of Words) and classifiers (Linear SVM or Naive Bayes) to customize the prediction model.

Magic-XML — is a modern web application developed for the convenient and swift transformation of data from XML files into CSV format. The application leverages the power of FastAPI to ensure high performance in request processing, as well as utilizes machine learning algorithms and natural language processing for efficient analysis

Restaurant Recommendation Application

Content: Null value check, 1st & 2nd level text cleaning, pipelining Tfidf & Logistic Regression

To build a model to accurately classify a piece of news as REAL or FAKE.

The author implemented logistic regression and support vector machine for topic labelling and applied two feature extractions, Bag-of-Words (CountVectorizer) and TF-IDF (TfidfVectorizer), after which the results for both methods were analyzed.

This project is to build sentiment analysis models capable of classifying text data into three categories: Positive, Negative, and Neutral sentiments.

Using Multinomial Naive Bayes, this code snippet illustrates a text classification task. It starts by loading training and testing data sets containing text descriptions and the associated genres.

Through NLP techniques, we will develop chatbots capable of engaging in meaningful conversations, analyze sentiments from customer reviews, or even automatically summarize large bodies of text.