Grocery Dataset Classification with Deep Learning in Keras and Tensorflow.

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.

This project is a Movie Recommendation System that suggests movies to users based on their input of a favorite movie. It uses Cosine Similarity and TF-IDF Vectorizer to compute similarity between movies based on features like genres, keywords, cast, crew, and more.

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 🚀✉️📱

Sentiment analysis on the IMDB dataset using Bag of Words models (Unigram, Bigram, Trigram, Bigram with TF-IDF) and Sequence to Sequence models (one-hot vectors, word embeddings, pretrained embeddings like GloVe, and transformers with positional embeddings).

Train data collection interface for TTW backend.

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.

Many countries speak Arabic; however, each country has its own dialect, the aim of this project is to build a model that predicts the dialect given the text.

Explore the Indonesian presidential campaign of 2024 through advanced text classification. This project transforms tweets into insights on national resilience using cutting-edge machine learning models and text preprocessing techniques. Dive into the intersection of politics and data science!

Built MultinomialNB, Logistic Regression, Random Forests and LSTM with the TF-IDF vectorizer for fake and real news classification. Also performed K-means unsupervised algorithm with PCA and t-SNE.

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.

We watch and read a lot of news daily. These news have a great impact on our lives and on the society as a whole. It can generate positive or negative impact on a person and can even shake the entire system of the country. So our model, thus, uses natural language processing and classifies the news headlines into positive, negative or neutral im…

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.