Se analiza un universo de documentos y se describen documentos similares entre sí utilizando Python y SK-Learn

A Turkish NLP tool built as a computer project. Used: Python 3, Word2Vec, Natural Language Processing Techniques, Linux Bash Script.

NLP, flask & Docker deployed in Heroku in a funny API where you can find similarities with your comments and Simpsons & South Park characters

Comparando cuentos de Manuel Rojas utilizando TF-IDF.

Machine Learning problems solutions with different types of algorithms.

Created a movie recommender engine based on cosine similarity

Evaluating Descriptive Answer using Cosine-Similarity (Machine Learning) Algorithm.

A machine learning model to recommend movies.This model is completely build in python using cosine similarity.This type of recommendation system, takes in a movie that a user currently likes as input. Then it analyzes the contents,popularity etc of the movie to find out other movies which have similar content.

A subreddit recommendation engine using selected network link prediction and community detection algorithms to predict subreddit forum groups a particular user is likely to comment on.

An NLP summary generator website using word vectors and cosine similarity

Data Mining course project

Here we predict the outcomes of each games of the world cup which includes the group stages and the knockout stages .The datasets for this included a lot of attributes about each player , each team , their history, their strengths , their ratings etc.As a side quest we also predicted player ratings and found similar players to a given player.

Recommendation System for Spotify songs recommend 10 songs similar to the song you like

Forecasting COVID-19 Cases

recommendation system

Content-based Movie Recommender System

Coursework for CS 5154 - Information Retrieval. Dual level (grad & undergrad) course introducing information storage and retrieval with unstructured data. Includes concepts such as tf-idf, cosine similarity, relevance-based evaluation, text classification, clustering etc.

The project utilizes data analysis to recommend books based on user reviews for a given input book. Additionally, it retrieves top-rated books based on customer ratings.