Movie Recommender System

This project is a movie recommender system built using machine learning techniques. It leverages a pre-trained model to suggest movies based on user preferences. The system features a user-friendly interface built with Streamlit, making it easy to interact with the recommender system.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Files
• Contributing
• License

Overview

The Movie Recommender System utilizes machine learning to provide personalized movie recommendations. The system is designed to suggest movies that align with user preferences based on historical data and a trained model. The frontend is implemented using the Streamlit library, which allows for an interactive and intuitive user experience.

Features

• Personalized Recommendations: Get movie suggestions based on your input preferences.
• Streamlit Interface: Easy-to-use web interface for interacting with the recommender system.
• Pre-trained Model: Use a machine learning model that has been trained and saved for predictions.
• Simple Setup: Quick installation and setup process.

Installation

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

1. Clone the Repository:

   git clone https://github.com/rjsandeepkumawat/movie_recommender.git
   cd movie_recommender

2. Create a Virtual Environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies:

   Install the required libraries using the requirements.txt file:

   pip install -r requirements.txt

Additional Pickle file:

pickle.dump(similarity,open('similarity.pkl','wb'))

Usage

To run the Movie Recommender System, follow these steps:

1. Ensure you are in the project directory.

2. Run the Streamlit App:

   streamlit run app.py

3. Open the App:

   After running the command, Streamlit will provide a local URL (e.g., http://localhost:8501) where you can view and interact with the movie recommender system.

4. Interact with the Interface:

   Follow the instructions on the Streamlit interface to input your preferences and receive movie recommendations.

Files

• app.py: The main script for running the Streamlit application. It contains the code for the frontend interface and the logic to interact with the recommender model.
• model.pkl: The serialized machine learning model used for making recommendations. This file should be in the same directory as app.py for the application to function correctly.
• requirements.txt: A file listing all the necessary Python packages required to run the application. It can be used with pip to install dependencies.
• similarity.pkl: This file should be in the same directory as app.py for the application to function correctly.
• app_background.jpg: This File is used for background in application you can choose your own by changing image path in app.py

Contributing

Contributions are welcome! If you have suggestions or improvements, please fork the repository and submit a pull request. For significant changes, open an issue first to discuss what you would like to change.