MOVIE-FY

MOVIE-FY is a web application that recommends movies based on user preferences. The application uses a responsive design and provides users with the ability to search for movies and receive personalized recommendations.

project_recoding.mp4

AI Methodology

MOVIE-FY leverages a Content-Based Filtering technique to recommend movies. This method uses the following approach:

• Data Preprocessing: The movie data is cleaned and preprocessed, focusing on important features like genres, cast, directors, and keywords.
• Vectorization: The textual data (like genres and keywords) is converted into numerical vectors using techniques like TF-IDF (Term Frequency-Inverse Document Frequency).
• Cosine Similarity: The similarity between movies is calculated using the cosine similarity metric, which helps in finding movies that are most similar to a given movie.
• Recommendation: Based on the similarity scores, a list of top N movies similar to the selected movie is generated and presented to the user.

Project Structure

MOVIE-FY/
│
├── data/                      # Directory for storing datasets or any other data files

├── models/                    # Directory for storing models or any backend logic
│   ├── movies.pkl             # Pickle file containing movie data
│   └── similarity.pkl         # Pickle file containing similarity data
│
├── static/                    # Directory for all static files (CSS, JS, Images, etc.)
│   ├── css/                   # Contains all CSS files
│   ├── js/                    # Contains all JavaScript files

│
├── templates/                 # Contains all HTML templates
│
├── .gitattributes             # Git attributes configuration
├── .gitignore                 # Git ignore file for excluding files from version control
├── app.py                     # Main application file
└── README.md                  # Project documentation file'

MOVIE-FY

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/MOVIE-FY.git
   cd MOVIE-FY

2. Install dependencies: Ensure you have Python installed. Then install the required Python packages:

   pip install -r requirements.txt

3. Run the application:

   python app.py

4. Access the application: Open your web browser and go to http://127.0.0.1:5000/.

Usage

• AI-Powered Recommendations: Utilizes Content-Based Filtering and Cosine Similarity for providing personalized movie recommendations.
• Homepage: The homepage provides a starting point for the user to explore the application.
• Recommendation Page: Users can select a movie and get recommendations based on their preferences.
• Movie Page: Detailed information about the selected movie, including related recommendations.

File Structure

• app.py: This is the main Flask application that handles routing and rendering of templates.
• static/css/*.css: These files contain all the custom CSS used for styling the different pages of the application.
• static/js/*.js: These files contain JavaScript used for interactivity and functionality specific to different pages.
• templates/: This directory contains all the HTML files that define the structure and content of the website pages.

Features

• Utilizes Content-Based Filtering and Cosine Similarity for providing personalized movie recommendations
• Responsive design using Tailwind CSS.
• Movie recommendations based on user input.
• A loader animation to enhance user experience during navigation.
• Back to Homepage button for easy navigation.

Contributing

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -a 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.