Recommendation system dataset github. The system is deployed on Heroku for easy access.

Recommendation system dataset github Key Features # In summary, BARS is built with the following key features: Open datasets: BARS collects a set of widely-used public datasets for recommendation research, and assign unique dataset IDs to track different data splits of each dataset. - Saket046/course-recommender Amazon is known not only for its variety of products but also for its strong recommendation system. PythonGitCode Hermes is Lab41's foray into recommender systems. Project Details For this project, we plan to build a basic music recommendation system using the MLlib libraries that are part of the Spark installation. It recommends books based on user input by leveraging similarity scores and displays a list of popular books. We use neural networks to process the images from Fashion Product Images Dataset and the Nearest neighbour backed recommender to generate the final recommendations. Recommender system: Two most popular methods to develop a recommender system are collaborative filtering and content based recommendation systems. ipynb to check the output of CF recommendations based on Content based recommendations. The code is available in our Github repository. Goodbooks-10K: If you’re a book lover or want to build a book recommendation system, the Goodbooks-10K dataset has you covered. g. In our project we are taking into consideration the amazon review dataset for Clothes, shoes and jewelleries and Beauty products. - GitHub - daconjam/Recommender-S This is our academic project for CSP-571 "Data Preparation And Analysis". kaggle. GitHub Gist: instantly share code, notes, and snippets. The system analyzes job titles, key skills, and other relevant data using TF-IDF vectorization and machine learning models. A content-based movie recommendation system built with Python and machine learning. This system leverages different recommendation algorithms to provide personalized suggestions for users looking to explore new content on Netflix. We tested various models like Pure Collaborative, Approximate Nearest Neighbour, K-NN, Naive Bayes and Hybrid Maxtrix Factorization The OTTO session dataset is a large-scale, industry-grade dataset designed to bridge the gap between academic research and real-world applications in session-based and sequential recommendation. Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems. It features anonymized behavior logs from the OTTO webshop and app, supporting both multi-objective (predicting clicks, carts, and orders) and single-objective tasks. Product Recommendation System is a machine learning-based project that provides personalized product recommendations to users based on their browsing and purchase history. Recommendation System application with medical drug dataset picked from https://www. A list of compatible datasets, noting other major repositories containing popular real-world datasets, along with sample code for a range of recommendation tasks. pkl file The file that using . Recommenders is a project under the Linux Foundation of AI and Data. - GitHub - Ganesh2409/Course-Recommendation The goal for this project is to create an LLM based music recommendation system. This project involves developing an advanced system that employs machine learning to analyze soil attributes, weather conditions, and historical data, providing precise crop recommendations for farmers. 🚀 Course Recommendation System is a machine learning-powered web application designed to recommend similar courses from Coursera's vast dataset of over 3,000 courses. Apr 17, 2025 · Personalized game recommendation system using a Steam Platform dataset leveraging 7. This dataset can be freely used for any purpose, including commercial: For example: Creating a tutorial or a course (free or paid) Writing a book Kaggle competitions (as an external dataset) Training an internal model at any company Knowledge-based, Content-based and Collaborative Recommender systems are built on MovieLens dataset with 100,000 movie ratings. gz contains the full dataset while 100k. Cai-Nicolas Ziegler This project is a movie recommendation system that suggests movies based on users' preferences and interactions. By integrating data-driven insights, the system aims to optimize crop selection and enhance agricultural productivity. Food Chicago Entree:: This dataset contains a record of user interactions with the Entree Chicago restaurant recommendation system. This framework offers personalized recommendations while addressing the cold-start problem through vector search. Run collaborative filtering. The dataset contains a set of JSON files that include business information, reviews, tips (shorter reviews), user information and check-ins. The model has been trained using a dataset of 3,000 courses! Find the dataset here 🔗 Link to my Kaggle Notebook here 🔗 Check out the demo of the application on Youtube here 🔗 Use the application live here 🔗 A book recommender system is a tool that suggests books to users based on their interests and reading history. The goal of a recommendation system is to generate meaningful recommendations to a collection of users for items or products that might interest them. This project begins with data collection and a self-growing dataset to ensure that the model will work well in the future and continues This project is a Content-Based Movie Recommendation System built using Python. View on GitHub Books Recommendation System What is a Recomendation System? Recommender systems or Recommendation systems aim to predict users’ interests and recommend product items that quite likely are interesting for them. Includes EDA to uncover genre and yearly trends, with interactive visualizations using Plotly and S A content-based recommendation system built with Flask that suggests Netflix movies and shows based on user input. Jan 8, 2025 · A personalized music recommendation system built using KMeans clustering and PCA on Spotify data. The system is implemented with Flask as the backend and Bootstrap for a responsive, clean UI. Pinterest: This dataset is originally constructed by paper Learning image and user features for recommendations in social networks for evaluating content-based image recommendation, and processed by paper Neural Collaborative Filtering. There are several approaches to building a book recommender system, including collaborative filtering, content-based filtering, and hybrid systems that Feb 14, 2025 · An end-to-end movie recommendation system using the MovieLens 100K dataset. By performing feature extraction on a large dataset of over A machine learning-based music recommendation system utilizes advanced algorithms to analyze user preferences, song features, and historical data. This repository contains two Jupyter notebooks analyzing the TMDB 5000 dataset, plus a Streamlit app for interactive movie recommendations: Movie_Data_Analysis. The data consists of six sub datasets which describes the data with a brief information. ipynb: Predicts movie success (vote average ≥ 7) using Logistic Regression and Naive Bayes. com/jessicali9530/kuc-hackathon-winter-2018. It analyzes movie metadata from TMDB, creates tag embeddings and suggests similar films through a Streamlit app. I have created recommenders able to provide recommendations for specific users, as well as general recommendations based on popularity and movie content. It comes with both explicit ratings (1-10 stars) and implicit ratings (user interacted with the book). ipynb This is jupyter notebook that we use for training our food recommendation model . The The Netflix Movie and Series Recommendation System is a project designed to recommend both movies and series based on the available dataset from Netflix up to 2021. A simple job recommendation system that suggests jobs available based on a given job title using text similarity. The system leverages several machine learning techniques to provide personalized movie recommendations Oct 21, 2022 · Recommendation System resources. . This project leverages machine learning techniques to analyze movie metadata and provide personalized recommendations. The file full_a. Movie_Recommendation_System. This dataset was used for the Winter 2018 Kaggle University Club Hackathon and is now publicly available. This project will implement a collaborative-based filtering method via scikit learn's K-Nearest Neighbours clustering algorithm using the Amazon books dataset. Over 5,000 images of 20 different classes. Our dataset will be the Million Song Dataset, which is a collection of audio features and metadata for one million contemporary popular music tracks. Disease Prediction and Medical Recommendation System 🩺 A machine learning-powered web application that predicts diseases based on user-entered symptoms and provides comprehensive health recommendations including medications, dietary suggestions, and exercise routines. ipynb: Recommends similar movies using cosine similarity. Gist Recommendation and Ratings Public Data Sets For Machine Learning. Users can search for songs by track name and artist name, select their preferred songs from the list, and receive relevant recommendations from the system. - khinvi/steam-game-r Building a Recommendation System for customer using Yelp dataset of restaurants. The dataset used for training and recommendation includes various anime titles, user ratings, and anime features such as genres and synopses. The Crop and Fertilizer Recommendation System is a Python Machine Learning project aimed at recommending optimal crops to farmers based on various soil and environmental factors. The Book-Crossing dataset is a collection of user ratings of books. Introduction We propose and implement a machine learning pipeline that combines content-based and collaborative recommendation methods for a large-scale, personalized song recommendation system. This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. Books-Recommendation-System This is a Recommedation System based project where Top 5 books will be recommended to a user based on his/her preferences. These Recommender systems were built using Pandas operations and by f Multimodal Dataset and Benchmark for Multi-domain and Cross-domain Recommendation System - westlake-repl/NineRec This project is focused on building a movie recommendation system using the MovieLens dataset. More information about the data is available in Ziegler et al. It recommends movies based on the similarity between movies, using metadata like movie descriptions, genres, and keywords. This dataset (ml-latest-small) describes 5-star rating and free-text tagging activity from MovieLens, a movie recommendation service. The system is deployed on Heroku for easy access. The data was compiled by Cai-Nicolas Ziegler of IIF and can be found here. The recommendation model uses pre-trained data for efficient predictions. By inputting key soil parameters (N, P, K, pH), alongside weather data (temperature, humidity, rainfall), the system delivers tailored, region-specific crop suggestions. , genres, synopsis) and recommends animes similar to the one provided by the user. - Ankur744/Crop-an For more information. These Recommender systems were built using Pandas operations and by fitting KNN, SVD & deep learning models which use NLP techniques and NN architecture to suggest movies for the users based on similar users and for queries specific to genre, user, movie, rating The fertilizer recommendation system employs several machine learning algorithms to analyze the dataset and make personalized fertilizer recommendations based on specific crops and environmental conditions. The system utilizes a dataset of job descriptions and employs text preprocessing techniques. The project utilizes various technologies, including Python, Pandas, NumPy, Scikit-learn, TensorFlow, SQL, and To run the files, download the stack overflow dataset and the job-postings dataset from the given link and place into /data/collaborative filtering folder. This is a recommendation engine that recommends 10 courses related to course you search. It provides recommendations based on user preferences and anime content, leve Built a fashion recommendation system using TensorFlow and K-Nearest Neighbors (KNN) algorithm. csv is a subset of 100k users for benchmark purposes. A Medicine Recommendation System in machine learning (ML) is a software application designed to assist healthcare professionals and patients in selecting the most appropriate medication based on various factors such as medical history, symptoms, demographics, and drug interactions - azaz9026/Medicine-Recommendation-System This Crop Recommendation System uses machine learning and the Random Forest Algorithm to guide farmers in selecting the best crops based on an Indian Crop Recommendation Dataset. Employing techniques like content-based filtering, and deep learning models, it provides relevant music recommendations, enhances user engagement and satisfaction on music streaming platforms. The system utilizes collaborative filtering and content-based filtering algorithms to analyze user behavior and generate relevant recommendations. : Improving Recommendation Lists Through Topic Diversification. This repo contains the dataset files, python code and description of our Diet Recommendation System - soumillll/Diet-Recommendation-System This signifies how much significance product recommendation has on order volume and overall sales revenue. With ready-to-use formats, clear A course recommendation system using the Coursera Courses dataset which contains over 3,000 courses. It uses collaborative filtering and content-based filtering techniques to provide recommendations based on data such as ratings, genres, and more Knowledge-based, Content-based and Collaborative Recommender systems are built on MovieLens dataset with 100,000 movie ratings. A product recommender system is a system with the goal of predicting and compiling a list of items that the customer is likely to purchase. The de-facto standard dataset for recommendations is probably the MovieLens dataset (which exists in multiple variations). Machine Learning - Netflix movie recommendation system Data: The dataset contained in this project has 4,303 records with 24 data series. A recommendation system or recommender system, is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item. csv. This project is currently in its very early stages, however the goal of this project is to create an extremely flexible music recommendation system using a chat focused LLM on the frontend to interact with a robust recommendation system on the backend. This python script contains script for using our food recommendation model with the help of gradio package Recommendation System. In this project we built a personalized recommender web app using Yelp dataset of restaurants. This is a movie recommendation system that provides recommendations based on a subset of the MovieLens dataset. They are among the Dec 2, 2024 · You can find this dataset on Amazon’s official registry. The goal of this project is to develop a recommendation system that provides a list of 10 books that are similar to a book that a customer has read. This project aims to improve the overall shopping experience for users This project is a Job Recommendation System built using various machine learning models to predict and recommend relevant jobs based on user profiles and job listings. The movie recommendation system in this repository has the following features: Uses the TMDB 5000 Movie Dataset to provide accurate recommendations Provides recommendations based on movie features such as genre, cast, and crew Supports real-time user input for personalized recommendations Uses machine learning algorithms such as cosine similarity for recommendation Deployed on Heroku, allowing Jan 1, 2010 · The goal of this project is to create a recommendation system that would allow users to discover music based on a given playlist or song that they already enjoy. 79M ratings/reviews using techniques like SVD, cosine similarity, and matrix factorization. The core dataset consists of 11,506 American movies released between 1970 and 2023, with ratings from 11,675 users. Here we are gooing to produce a list of recommendations:based on collaborative filtering method. The model uses the Spotify dataset which contains a variety of features such as acousticness, danceability, energy, instrumentalness, liveness, loudness, speechiness, tempo This project presents a movie recommendation system using both collaborative filtering and vector search techniques to recommend movies to users. This allows researchers to experiment with the datasets and share the results in a consistent way. Built using Python, Scikit-learn, and Streamlit, the app preprocesses course data, applies text vectorization, and leverages cosine similarity to offer personalized recommendations. - Sourik07/Crop-and-Fertilizer-Recommendation-System I developed a fashion recommendation system that utilizes the power of transfer learning using ResNet-50 architecture along with Annoy an optimized K-Nearest Neighbours algorithm to deliver personalized recommendations based on user input. The system employs collaborative filtering, content-based methods, and a hybrid model to generate accurate movie recommendations for over 100,000 users across a diverse film catalog. Citation Please cite the following if you use the data: Recommendation on Live-Streaming Platforms: Dynamic Availability and Repeat Consumption Jérémie Rappaz, Julian McAuley and Datasets There are a plethora of recommender-system datasets, and, more generally, almost every machine learning dataset can be used for recommendation systems, too. This project is a Book Recommendation System built using collaborative filtering techniques. By analyzing the user's past song history and the properties of the music, the system will generate a list of recommended tracks. The system provides personalized recommendations by analyzing user preferences and using KNN, it identifies the 5 most similar fashion items, creating a tailored “closest match” experience for users. These systems can be used by libraries, bookstores, or online retailers to help users discover new books that they might enjoy. It leverages collaborative filtering and NMF-based matrix factorization, includes a dynamic feedback loop for model updates, and features an interactive Streamlit dashboard for analytics and A/B testing. The books data contains all the book titles, ISBNs, author, publisher and year of The Music Recommendation System aims to predict the likelihood that a user will enjoy a song. Additionally, the system employs item-based collaborative filtering to find similar animes based on their features (e. pkl name is the pickle form of our dataset that we use for training food recommendation model Our project focuses on developing a Music Recommendation System using the Spotify Million Playlist Dataset. This project implements an Anime Recommendation System using both content-based and collaborative filtering techniques. It contains 100836 ratings and 3683 tag applications across 9742 movies. We processed approximately two million unique songs to create feature vectors for each of them. It explores how to choose a recommender system for a new application by analyzing the performance of multiple recommender system algorithms on a variety of datasets. Streamlit App: A user-friendly UI hosted on Streamlit to This project implements a comprehensive movie recommendation system using the MovieLens 1M dataset. jzk oiaf dnv uqz bmhiq hlfzee lybag jjaeici cpzrq scvv hfdx qfd kghqqae gjfbebom lacdqws