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Coded in python. This allows the user to which restaurants offer delicious food and are highly rated by the customers. Dataset is taken based on survey by people who went to the restaurant and gave their opinion about it.
Meal recommendation app, created during HackaTUM, that uses a hybrid recommendation system and combines React, FastAPI, and speech technologies for an enhanced, accessible user experience.
In this work, a small search engine for animal adoption was implemented. The focus is on web page scraping, that set of methodologies used to automate the collection of information from Internet sites.
In this project I used NLP to analyze a dataset containing each episode from the hit show "The Office" with my findings I used TF-IDF and the Cosine Similarity to build a recommendation engine based on whether or not 'Micheal' and 'Dwight' appeared in the episode.
In this repository you will find all I learned in Machine Learning course from Stanford University. You can access to my completion certificate by clicking on the following link https://coursera.org/share/460d85edfd557a066eabc50320eb7749
The Book Recommender System is a collaborative filtering-based approach that suggests personalized book recommendations based on user preferences and similarities. The system provides a user-friendly interface through Streamlit for an enhanced user experience.
Este es un trabajo en grupo para la asignatura de Ingeniería del Software hecho por Xabier Bahillo, Nicolás Martín, Alain Fernández y Asier Oyanguren. Representa una web de recomendaciones de películas respecto a valoraciones recientes y valoraciones de usuarios con gustos parecidos.
In this Project I have made two types of Book Recommendation System. One is simple popularity based which recommends books depending on the book-ratings given by the users. Another type implemented in this project is content based recommendation using TF-IDF technique of NLP.