Text Classification of Legitimate and Rogue Online Privacy Policies: A manual analysis and an experimental procedure

Evaluation of the accuracy of vectorization and text classification methods

IMDB movie review classification using neural network (text-vectorization v/s word-embeddings)

Homeworks and final project for Infosearch course

A content based movie recommender system.

This is a Content-based Movie Recommendation App wherein the user can type in a particular movie that he/she has enjoyed and can get the names and posters of top 5 similar movies.

Tag prediction on Stack Overflow using TensorFlow Keras and Text Vectorization

Naive Bayes classifier with text parser and vectorization libs

A dynamic Movie Recommender web app using ML text vectorization technique to suggest the user with similar kind of movies and their posters.

This project is an unsupervised NLP-based recipe recommender system designed to provide personalized recipe suggestions. The system employs content-based filtering techniques, utilizing cosine similarity to measure the resemblance between user inputs and a database of recipes.

Machine Learning course of Piero Savastano 3: CountVectorizer

Experiments in the field of Sentiment Analysis using ML Algorithms namely Logistic Regression, Naive Bayes along with tfidf, one hot encoding, bag of words vectorization. Different MLP and RNN models viz. LSTM, GRU, Bidirectional LSTM. Lastly, state of the art BERT model

The repository contains notebooks created for collecting and preprocessing the corpus of diary entries and for experiments on creating models for predicting gender, age groups of authors and the time period of text creation.

Discussion about Probabilities, Classification and Zipf's law

Movie Recommender System leverages a content-based approach, suggesting films to users based on the attributes of movies they have previously enjoyed. By analyzing movie metadata such as genre, cast, director, keywords, etc., this project offers personalized recommendations aligned with users' cinematic tastes.

Sentiment Analysis

Performing sentiment analysis on movie reviews using RNN (LSTM) in keras

This project is a Recommendation based project. Similar movies will be recommended based upon the content of the movie. I have used ML algorithm & BOW technique of NLP for our model. An interactive web page is also designed using streamlit library for next level user experience.

Data science and NLP tools developed for my own use.

Successfully established a machine learning model that can accurately classify an e-commerce product into one of four categories, namely "Books", "Clothing & Accessories", "Household" and "Electronics", based on the product's description.