This project employs emotion detection in textual data, specifically trained on Twitter data comprising tweets labeled with corresponding emotions. It seamlessly takes text inputs and provides the most fitting emotion assigned to it.

Recognition of Persomnality Types from Facebook status using Machine Learning

A simple language detection project that uses MultinomialNB model. It has a local application that made with Streamlit.

This project used machine learning concept to predict disease on their symptoms

This repository contains introductory notebooks for Naive bayes algorithm

SMS spam detection using machine learning

Sentiment analysis of Election USA 2020

Implementation of Drug database with LinearSVC, BernoulliNB, MultinomialNB, LogisticRegression, Perceptron and MLPClassifier models

Disease prediction from patient sentiment using TF-IDF and Multinomial Naive-Bayes

Analyzing the satistics & results of the Canadian Premier League - Dashboards and Stat Tables - MNB - Random Forest

Natural Language Processing (NLP) and web scrapping. The multinomial Naive Bayes classifier is used for the classification.

Rating: (6/10) The project uses Python libraries and APIs to analyze Reddit data, predict user input, suggest new titles based on cosine similarity, calculate combined scores, and output the best suggestion.

Web-app that uses Machine Learning to Predict if SMS message is ether Span or Not Span

Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.

🚀 Developed a Python-based ML model for SMS and Email spam detection using NLP. Achieved high accuracy and precision! 📧🤖🔍 #MachineLearning #NLP #SpamDetection