#  Miz - Movie Recommender System

This is a web-based  Miz - Movie Recommender System that suggests 5 movies to the user based on their preferences. The system is built using HTML, CSS, Flask, and incorporates machine learning techniques to provide accurate recommendations. The dataset used for this project is sourced from TMDB (The Movie Database).

## Features

- User-friendly web interface
- Suggests 5 movies based on user input
- Utilizes TMDB datasets for movie information
- Machine learning algorithms for recommendations
- Developed using Flask for the backend and HTML/CSS for the frontend

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Datasets](#datasets)
- [Machine Learning Model](#machine-learning-model)
- [Contributing](#contributing)
- [License](#license)

## Installation

1. **Clone the repository**
    ```bash
    git clone https://github.com/yourusername/movie-recommender-system.git
    cd movie-recommender-system
    ```

2. **Create a virtual environment**
    ```bash
    python3 -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
    ```

3. **Install the dependencies**
    ```bash
    pip install -r requirements.txt
    ```

5. **Run the application**
    ```bash
    flask run
    ```

    The application will be available at `http://127.0.0.1:5000`.

## Usage

1. **Open your web browser and navigate to the application URL**
    ```plaintext
    http://127.0.0.1:5000
    ```

2. **Enter your movie preferences**
    - The application might prompt for your favorite movie, genre, or other preferences.

3. **Get recommendations**
    - The system will process your input and display 5 movie recommendations.

## Project Structure

```plaintext
movie-recommender-system/
│
├── static/
│   ├── css/
│   │   └── style.css        # CSS files for styling
│   └── js/
│       └── script.js        # JavaScript files (if any)
│
├── templates/
│   ├── index.html           # Main page of the web application
│   └── layout.html          # Layout template
│
├── .env                     # Environment variables
├── app.py                   # Main Flask application
├── model.py                 # Machine learning model script
├── requirements.txt         # List of Python dependencies
├── README.md                # Project README file
└── data/
    └── tmdb_dataset.csv     # TMDB dataset file

Datasets

The dataset used in this project is from TMDB (The Movie Database). The dataset includes various attributes about movies, such as title, genre, ratings, and more. This data is used to train the machine learning model and provide recommendations.

Machine Learning Model

The recommender system uses collaborative filtering and content-based filtering techniques to suggest movies. The model is trained on the TMDB dataset to learn patterns and preferences based on user input. The recommendation engine processes the user's preferences and generates a list of 5 movies that match the user's taste.

Contributing

Contributions are welcome! If you would like to contribute to this project, please follow these steps:

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

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Feel free to modify the README file as per your project's specifics, such as additional features, detailed usage instructions, or specific contributions guidelines.