Project Domain:

Data Science / Machine Learning / Web Development / Recommendation Systems

Technologies Used:

• Backend: PHP / Java / Node.js

• Frontend: HTML, CSS, Bootstrap, JavaScript

• ML Tools: Python (Pandas, Scikit-learn, Surprise, or TensorFlow/Keras for deep learning)

• Database: MySQL / MongoDB / PostgreSQL

• APIs (Optional): TMDB API or IMDB dataset

Project Objective:

To create a machine learning-based movie recommendation system that suggests movies to users based on their interests, watch history, or user similarity, similar to Netflix or Amazon Prime's recommendation engines.

Key Features:

1. User Login & Profiles:
   Users can create accounts and store watch preferences or ratings.

2. Movie Search & Watchlist:
   Users can browse/search movies and add them to their favorites or watch history.

3. Personalized Recommendations:

   • Based on user preferences (ratings, watch history)

   • Based on similarity to other users or movies

4. Trending & Popular Movies:
   Option to view top-rated, most-watched, or currently trending movies.

5. Admin Panel:
   Admins can manage movie data, upload CSVs, and retrain the recommendation model.

Types of Recommendation Models:

1. Content-Based Filtering:

Recommends movies similar to ones the user has liked in the past.

• Based on genres, tags, descriptions, actors, directors

• Uses TF-IDF or cosine similarity

2. Collaborative Filtering:

• Recommends movies based on the behavior of similar users

• Techniques: Matrix Factorization, KNN, SVD (using the Surprise library)

3. Hybrid Model (Advanced):

Combines both content-based and collaborative filtering for better accuracy.