ML model for spam detection using Naive Bayes & TF-IDF. Achieved 0.98 accuracy. Utilized Scikit-learn, Numpy, nltk. Implements NLP concepts. Explore precise spam classification effortlessly. #MachineLearning #SpamDetection 🚀✉️📱

Fact Extraction and Verification

Named Entity recognition and emotion mining on Apple Macbook reviews.

A simple web app that uses a trained sequential neural net to predict the rating of a hotel review.

The recommender framework goes about as a friend in need and channels the melodies that are reasonable for that client at that point. It likewise expands the client's fulfilment by playing fitting tune at the correct time, and, in the interim, limit the client's work.

Program to analyze transfer loss across domains using TF-IDF vectors with Chi squared into logistic regression model.

SMS Spam prediction using classification algorithms.

Text Processing performed on the Apple Macbook for feature extraction

Sistema de recomendación de películas basado en contenido. Utilizando TF-IDF y la similitud del coseno. La data fue extraída, transformada y analizada para el entrenamiento del modelo. Disponibilizandolo junto con la data limpia para futuras consultas, a través del despliegue con FastAPI y Render.

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

Application of Machine and Deep Learning to Sentiment Analysis

Predict emotions (happiness, anger, sadness) from WhatsApp chat data using machine learning and deep learning models. Includes text normalization, vectorization (TF-IDF, BoW, Word2Vec, GloVe), and model evaluation.

This is my Machine Learning model created with PassiveAggressiveClassifier to detect a news as Real or Fake depending on it's contents.

Predict whether a DonorsChoose.org project proposal submitted by a teacher will be approved.

Project showing the sentiment analysis of text data using NLP and Dash.

The goal of this project is to use Netflix data (7787,12) to classify and group movies and shows into specific clusters. We will utilize techniques such as K-means clustering, Agglomerative clustering and content-based recommendation systems to analyze the data and provide personalized suggestions to consumers based on their preferences.

Building a basic spam classifier with Tf-IDF Vectorizer and Naïve Bayes model

Using text analytics to understand cultural patterns in philosophical texts. Exploring gender, author, region, and time-period differences, and extracting key philosophical concepts.