This project was developed for Microsoft Engage 2022. It is a laptop recommendation system built using Streamlit and Python. The system leverages cosine similarity to provide users with personalized laptop recommendations based on their preferences.
- User-Friendly Interface: Developed with Streamlit for an interactive and intuitive user experience.
- Cosine Similarity: Utilizes cosine similarity to recommend laptops that best match the user's criteria.
- Efficient Recommendations: Quickly processes user input to provide relevant laptop suggestions.
In this project, I also demonstrate the role of various algorithms used in recommendation engines for web-streaming apps like Netflix and audio-streaming apps like Spotify. Here are some of the algorithms highlighted:
Role in Recommendation Engine:
- Content-based filtering recommends items similar to those the user has interacted with in the past.
- Uses item features and user profiles to match preferences.
A. Open the Streamlit application in your web browser. B. Input your laptop preferences. C. Receive personalized laptop recommendations based on cosine similarity.
For any questions or feedback, please feel free to contact me at abhir.mirikar@gmail.com