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.
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Updated
Nov 22, 2023 - HTML
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.
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.
Repository of the python scripts for the CS competition held in Kaggle obtaining the 4th place
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
Built a Recommender System is a system that seeks to predict or filter preferences according to the user’s choices.
🍿 Movie Recommendation App with Flask
An anime recommendation engine that allows us to recommend anime based on a given anime title or a given user using Pyspark
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 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.
A recommendor system implemented using the Collaborative Filtering Algorithm.
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.
IMDB Movie Recommendation Engine. Uses jaccard similarity of genres, and title similarity
A recommendation engine to recommend similar apparel products based on a given query product.
Personalized smoking recommendations based on Collaborative Filtering.
Virtual library to hold user books and book recs
Implementation tasks for multiple algorithms to process massive data. The algorithms are written in Python.
The project is a system that recommends books for users based on their preferences entered into the system. Data is based on GoodReads data source. The system also uses a decision tree as a method for choosing the best books for the user.
This project is a part of "Unsupervised Machine Learning” curriculum as capstone projects at AlmaBetter School
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