/Movie_Recommended_system

Primary LanguageJupyter Notebook

Content-Based Movie Recommendation System

Synopsis

The Content-Based Movie Recommendation System is a machine learning project designed to recommend movies to users based on their preferences. By analyzing the content features of movies (such as genre, director, cast, etc.), the system can suggest movies similar to those the user has liked in the past. This approach focuses on finding items with similar content rather than relying on user interaction data.

Features

  • Content Analysis: Analyzes various features of movies, including genres, directors, actors, and plot descriptions.
  • Similarity Computation: Uses similarity metrics to find movies that are similar to those the user likes.
  • User Interaction: Allows users to input their favorite movies and receive recommendations based on content similarity.
  • Scalability: Designed to handle a large dataset of movies efficiently.

Installation

  1. Clone the repository: 
    git clone https://github.com/username/Content_Based_Movie_Recommendation_System.git
  2. Navigate to the project directory: 
    cd Content_Based_Movie_Recommendation_System
  3. Install the required dependencies: 
    pip install -r requirements.txt

Usage

  1. Run the main script to start the recommendation system: 
    python main.py
  2. Follow the prompts to input your favorite movies and receive recommendations.

Dependencies

  • Python 3.x
  • Pandas
  • Numpy
  • Scikit-learn
  • NLTK

Data

The dataset used for this project includes movie details such as titles, genres, directors, cast, and plot descriptions. Ensure you have a dataset in a compatible format (e.g., CSV) placed in the data directory.

How it Works

  1. Data Preprocessing: The system preprocesses the movie data, extracting relevant features for analysis.
  2. Feature Extraction: Various features such as genres, directors, and plot keywords are extracted and transformed into numerical representations.
  3. Similarity Calculation: The system calculates the similarity between movies using techniques like cosine similarity.
  4. Recommendation: Based on the computed similarities, the system recommends movies that are most similar to the user's favorite movies.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

Acknowledgments

  • This project was inspired by various content-based recommendation systems and machine learning resources.