Akashkumar9508/Movie_recommendation-

Movie Recommendation System

Representation of Web-App

# #Integrating Model into a Web Platform with Streamlit

Objective:

Implement a movie recommendation system on the web using machine learning techniques.

Framework Used :

Leveraged Streamlit, a Python library for building interactive web applications.

Key Components:

- Frontend Design:

Designed a user-friendly interface with input fields for movie titles.

- Backend Processing:

Integrated pre-trained recommendation model using Streamlit's Python integration.

- User Interaction:

Enabled real-time movie recommendations based on user input.

Benefits:

- Streamlined development process with Streamlit's intuitive APIs.

- Enhanced user experience through interactive movie suggestions.

- Facilitated iterative improvements based on user feedback.

Outcome:

- Engaging and responsive web application showcasing machine learning capabilities.

- Demonstrated potential for personalized recommendations in a user-friendly format.

Next Steps**:

- Continuously refine and optimize the recommendation algorithm.

- Explore additional features and enhancements based on user interactions and feedback.

We have to create Virtual Envoriment then istall all packages.

Run the command "Python run streamlitapp"