Manas Rahman's starred repositories
AJAX-Movie-Recommendation-System-with-Sentiment-Analysis
A content-based recommender system that recommends movies similar to the movie the user likes and analyses the sentiments of the reviews given by the user
recommendation
Recommendation System using ML and DL
systems
Merlin Systems provides tools for combining recommendation models with other elements of production recommender systems (like feature stores, nearest neighbor search, and exploration strategies) into end-to-end recommendation pipelines that can be served with Triton Inference Server.
Group_Recommendation_Syatem_GcPp_clustering
A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.
Movie-Recommendation-System
Searching and recommending movies using pandas , numpy , scikit-learn and jupyter notebook
Recommendation-System
This repo deals with recommendation system notebooks implemented in python.
movie_recommender_system
A movie recommendation system utilizing a Graph Neural Network (GNN) framework implemented in Jupyter Notebook
Recbole-Debias
RecBole-Debias is a toolkit built upon RecBole for reproducing and developing debiased recommendation algorithms.
Cosmetics-Chemical-Composition-Analysis-and-Visualization
Choosing cosmetics can be tough and risky. Instead of guessing, let's use data science to predict suitable products. This notebook creates a content-based recommendation system by processing ingredient lists of 1,472 Sephora cosmetics with word embedding, and visualizes similarities using t-SNE and Bokeh.
Recommendation-System-On-IMDB
This repository contains a Jupyter notebook that demonstrates the creation of a content-based movie recommendation system using Natural Language Processing (NLP) in Python.
Recommandation-Syatem-
In this notebook, I will attempt at implementing a few recommendation algorithms (content based, popularity based and collaborative filtering) and try to build an ensemble of these models to come up with our final recommendation system.
Book-Recommendation-and-Search-Systems-Leveraging-NLP-for-Enhanced-User-Experience
A Jupyter notebook project that builds a data-driven book recommendation system using Goodreads data, analyzing user preferences and interactions to suggest personalized book recommendations.
aicamp-newscorp-052024
Contains notebook and slides on the talk "Building End-to-End Recommendation System with Vertex AI"
Netflix-Movie-and-Series-Recommendation-System
This notebook serves as a comprehensive guide to building and refining content-based recommendation systems, with the ultimate aim of improving user experience through personalized movie suggestions.
Movie-Recommendation-System
The Movie Recommendation System is a content-based recommendation engine that suggests movies similar to a selected movie based on their metadata, such as genres, cast, crew, and keywords. The backend processing and model development are implemented in Jupyter Notebook, while the frontend interface is built using PyCharm and Streamlit. This project
MovieLens-RecSys
A comprehensive recommendation system project using the MovieLens 32M dataset, featuring data cleaning, preprocessing, exploratory data analysis, and model development to provide personalised movie recommendations. This repository includes scripts, notebooks, and tools for building and evaluating various recommendation algorithms.
Medicine-recommendation-ML
• Medicine Recommendation System using Supervised Learning classification Algorithm Trained ML model and developed WEB APP that recommends medicines based on patient symptoms. The machine learning model was trained in Jupyter Notebook with a dataset split of 70 percent for training and 30 percent for testing.
flight-recommendation
The repository contains programs to recommend users on the best flights available between two given airports. Idea is that users can enter all the reqquirements in plain text and the system should be able to fetch the recommended flights based on that.
Deep-learning-recommendation-system
This repository contains my notebooks on deep learning recommendation systems.
recommendation-systems
Notebooks to learn recommendation algorithms
Movie-Recommendation-System
The Movie Recommendation System is a user-friendly tool designed to provide personalized movie recommendations based on a user's input. Built using Python and integrated into a Jupyter Notebook environment, this system allows users to enter the name of a movie and receive a list of recommended films that are similar or relevant.
Book_Recommendation_System
Using Jupyter Notebook, Data- Preprocessing, model Building, model evaluation, model training can be done, recommendation algorithms namely Content-based filtering and collaborative filtering are used.
EnsembleGroove-Music-Recommendation-System
EnsembleGroove is a music recommendation system that utilizes powerful ensemble learning algorithms, including Random Forest, XGBoost, LightGBM, and CatBoost.
E-commerce-Product-Recommendation-System-
An E-commerce Product Recommendation System using Jupyter Notebook and pandas is designed to suggest products to users based on their preferences and behavior. Using Amazon's dataset, which includes customer ratings, reviews, and product information,