Movie_Recommdation_System

Overview

This is a web-based Movie Recommendation System that suggests movies similar to a selected title using a machine learning approach. The system is built using Python, Flask, and JavaScript, and leverages cosine similarity for recommendations. It also includes a user-friendly frontend with an integrated search feature.

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Features

• Select a Movie: Users can choose a movie from a dropdown list.
• Recommendations: Displays a list of top recommended movies based on the selected title.
• Search Bar: Allows users to search for movies dynamically.
• Responsive UI: Built with modern HTML, CSS, and JavaScript for a seamless experience.

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Technologies Used

• Backend: Flask
• Frontend: HTML, CSS, JavaScript
• Data Processing: Python (Pandas, NumPy, NLTK, Scikit-learn)
• Storage: Pickle files for saving data and similarity matrix

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Setup and Installation

Prerequisites

Ensure you have the following installed:

• Python 3.7+
• pip (Python package manager)
• Git

Steps

1. Clone the repository:

   git clone <repository_url>
   cd <repository_folder>

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask app:

   python app.py

4. Access the application in your browser at http://127.0.0.1:5000.

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How It Works

1. The system reads movie data from tmdb_5000_movies.csv and tmdb_5000_credits.csv.
2. Relevant features like genres, keywords, cast, and crew are extracted and processed.
3. Tags are created by concatenating these features.
4. Text data is preprocessed and vectorized using CountVectorizer.
5. Recommendations are computed using cosine similarity between the vectorized tags.

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File Structure

Movie-Recommendation-System/
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├── static/
│   ├── style.css       # Styling for the web application
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├── templates/
│   ├── index.html      # Main HTML file
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├── app.py              # Flask application code
├── tmdb_5000_movies.csv # Dataset: Movie details
├── tmdb_5000_credits.csv # Dataset: Cast and crew details
├── list_of_movies.pkl   # Pickled list of movies
├── similarities.pkl     # Pickled similarity matrix
├── requirements.txt     # Python dependencies
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└── README.md            # Documentation (this file)

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Usage

1. Start the Flask app and navigate to the homepage.
2. Select a movie from the dropdown or use the search bar to find a movie.
3. Click the "Get Recommendations" button to see similar movies.

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Example

Input: Spider-Man 2

Output:

• Spider-Man
• Spider-Man 3
• The Amazing Spider-Man
• The Dark Knight
• Iron Man

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Contributing

Contributions are welcome! Feel free to fork the repository, make changes, and submit a pull request.

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License

This project is licensed under the MIT License.

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