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 collaborative filtering approaches for recommender systems, comparing user-based and item-based methods, using neighborhood similarity to predict ratings, address sparsity, and enable scalable precomputed item similarity.

Explore content-based recommender systems that use item features like genre, actors, and director to tailor recommendations, address privacy and cold-start issues, and limit exploration to user's existing content preferences.

Explore context aware recommender systems that use time, mood, location, and weather to tailor predictions, expanding from two-dimensional to three-dimensional modeling, with privacy considerations and Python implementations.

Explore user-based collaborative filtering with cosine similarity on rating vectors to find similar users and recommend movies. Implement with Python and pandas using pivot tables to build the ratings matrix.

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 recommender using matrix factorisation, from data preparation and normalization to predicting ratings and generating top movie recommendations with merged movie info.

Explore how the k-nearest neighbors algorithm performs non-parametric classification by majority vote among the five nearest points, using distance measures and an odd k for two-class problems.

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

Implement item-based collaborative filtering in Python using cosine similarity, create the item similarity matrix, and generate the top 10 most similar movies to a given title.

Build an item-based collaborative filtering model with matrix factorization to generate predicted ratings for a recommendation website, using a transposed user item matrix and id-based title retrieval.

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.

Learn to build a content-based recommender system using movie genres as features, train a random forest classifier to predict user ratings, evaluate with confusion metrics, and generate top recommendations.

Build a content-based movie recommender by one-hot encoding genres and applying a nearest neighbors model to return movies matching the user's selected genres.

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.

Learn to create a Django project and app, configure settings and database, run the development server, and manage migrations.

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.

Create and customize a Django admin interface by registering movie and review models, configuring list display, filters, and search, and securing access with a createsuperuser login.

Create Django views for listing and detailing reviews and movies. Wire URL patterns, render templates, and connect the app to the main URLs for a working movie reviews site.

Add and organize Django templates for the movie reviews site, creating app-specific folders, configuring template paths, and building base, list, and detail templates to display movies, reviews, and ratings.

Build a Django forms framework-based movie review form using a model and a text area widget with validation. Handle submissions via a view with cleaned data and reverse redirects.

Design a mobile-friendly website using Bootstrap in a Django app, integrating templates, static files, and navigation with responsive components like buttons and dropdown menus.

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

Load pre-generated csv data into a Django movie recommendation database by building load scripts for movies, ratings, and users with pandas read_csv.

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.