Machine Learning Projects: Recommendation system website

Explore how recommender systems filter information to personalize ads and product recommendations, and examine major methods using the movieland movie ratings dataset.

Explore efficient recommendation techniques and the features and potential of different approaches, including three types of systems, such as content-based and filtering recommender systems, and discuss frontier in content-based techniques.

Explore matrix factorization and singular value decomposition to predict missing movie ratings using latent features in a user-by-movie matrix. Transform missing values to zero and select k by cross-validation.

Build a user-based collaborative filtering system with kNN and cosine similarity on a user-movie rating matrix to recommend unseen movies.

learn to build an item-based collaborative filtering model with k-nearest neighbors to recommend similar movies by computing distances between ratings, merging data to reveal movie titles, and selecting nearest neighbors.

Build a context aware recommender in python by extending content-based models with a time dimension and hourly user preferences. Learn to merge datasets, normalize genres, and generate top movie recommendations.

Build a movie recommendation website with Python and Django, covering models, admin, views, templates, and forms; import datasets and integrate a prebuilt machine learning model for recommendations.

Learn how Django, a Python web framework, handles dynamic, database-driven websites by routing requests to views, querying models, and rendering HTML with templates.

Define Django models for a movie reviews app with tables movies and reviews, including title, average rating, rating choices, and user foreign key, and migrate database with makemigrations and migrate.

Implement login, logout, and sign up pages using Django, with forms, templates, and views that manage authentication and redirect to the homepage.

Create the recommendation view, urls, and template to render suggestions by username, extend the movie list template, and set up templates and models for future neighbor-based suggestions.

Implement a nearest neighbors model for an item-based recommendation system, building a pivot table and a sparse user-movie matrix, then render personalized movie suggestions.

Address the cold-start problem in recommendation systems by using three approaches: wait for user reviews, apply content-based or context-aware filtering, or recommend by popularity based on average ratings.

Choose a database based on expected traffic and size; sqlite suits low to medium traffic, while mysql or postgres handle higher loads, and secure production with environment secret keys.