Movie Recommendation System

Welcome to the Movie Recommendation System repository! This project leverages advanced data processing and machine learning techniques to provide personalized movie recommendations to users. Whether you're a film enthusiast looking for your next favorite movie or a developer interested in recommendation systems, this project has something for you.

Features

• Personalized Recommendations: Utilizes collaborative filtering and content-based filtering to provide tailored movie recommendations.
• Movie Information: Displays detailed information about each recommended movie, including title, genre, and synopsis.
• Interactive Interface: An easy-to-use web interface built with Streamlit, allowing users to interact with the recommendation system seamlessly.
• Real-time Poster Updates: Randomly changes movie posters on each page refresh to keep the user experience dynamic and engaging.

How It Works

1. Data Collection: Collects movie data from a comprehensive movie dataset, including user ratings and movie metadata.
2. Data Preprocessing: Cleans and preprocesses the data to make it suitable for training machine learning models.
3. Model Training: Trains collaborative filtering and content-based filtering models to predict user preferences.
4. Recommendation Generation: Generates personalized movie recommendations based on user input and model predictions.
5. Web Interface: Provides an interactive web interface using Streamlit, where users can get movie recommendations and see dynamic movie posters.

Installation

To get started with the Movie Recommendation System, follow these steps:

1. Clone the repository:

   git clone https://github.com/Talnz007/Movie_Recomendation_System.git
   cd movie-recommendation-system

2. Install the required packages:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run main.py

Usage

• Getting Recommendations: Simply enter your favorite movies in the input field, and the system will recommend movies based on your preferences.
• Dynamic Posters: Refresh the page to see a new set of random movie posters.
• Explore Movie Details: Click on a recommended movie to see detailed information about it.

Technologies Used

• Python: For data processing and model training.
• Streamlit: For building the interactive web interface.
• Scikit-Learn: For implementing machine learning models.
• Pandas & Numpy: For data manipulation and numerical computations.

Contributing

Contributions are welcome! If you have any ideas or improvements, feel free to submit a pull request or open an issue.

License

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