Conclusion
In this project, we build a music recommendation application that combines the Intel Edison to collect sensor data, Amazon Web Service(AWS) to deal with data collected and build machine-learning model and Spotify API to add appropriate music to user’s playlist based on the prediction result.
More detailed, the Edison collects luminous intensity by a luminous sensor, the position and speed by a GPS sensor, the traffic condition by HERE API and weather by OpenWeatherMap API with the position we collected by GPS. At the same time, the data is uploaded to Kinesis and applied on machine learning model. The prediction result of the model will be used on Spotify API to recommend the appropriate songs.
Our project can help users enjoy the traveling in car and not concern about what songs to play. Also in some way it can react positively to make the travel safer and more comfortable. It is an amazing combination of technology and art and will make life better.
More detailed, the Edison collects luminous intensity by a luminous sensor, the position and speed by a GPS sensor, the traffic condition by HERE API and weather by OpenWeatherMap API with the position we collected by GPS. At the same time, the data is uploaded to Kinesis and applied on machine learning model. The prediction result of the model will be used on Spotify API to recommend the appropriate songs.
Our project can help users enjoy the traveling in car and not concern about what songs to play. Also in some way it can react positively to make the travel safer and more comfortable. It is an amazing combination of technology and art and will make life better.