Music Recommendation using Machine Learning
There are few people alive right now who haven’t heard of Netflix and Spotify. Both these global brands have grown exponentially in the past few years, and have now become one of the most widely recognised brands of all time. One of the most significant things both of them have in common, other than being a streaming service, is that they both have amazing recommendation software. Netflix’s recommendation has built a name for itself, giving customers exactly what they want, time and time again. Seeing the popularity that such a system has, other tech giants such as Amazon and Google are also working on their own recommendations application. In this Machine Learning project, we will be taking a look at how to build such an app on our own.
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The goal of such a recommendation system would be to provide personalized content by rightly identifying what the user wants. This means that computers will have to think the way humans do; analysing each of the user’s last choices to predict what they would like in the future. As we have already seen, whenever there is a need for a machine to mimic human behavior, we have to utilize Machine Learning techniques. Therefore, in this project, we will be relying on Machine Learning and Neural Networking techniques to build the ultimate music recommendation system.
- Basics of Data Segmentation
- Python Programming
- Data Analysis
- Machine Learning Fundamentals
- Neural Networking
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- Music recommendation systems are mainly of three types. Content-based systems utilises past user choice to predict songs and recommend them. Collaborative systems predict songs based on what other similar users have previously listened to. Hybrid systems utilise both user data and similar user data to predict songs the user will appreciate.
- In this project, we will build a music recommendation system using real datasets.
- We will utilise a dataset sourced from outside called Million Songs Dataset which contains two files: triplet_file and metadata_file.
- The triplet_file has within it information regarding the singer or band’s name, the title of the song and how long the song lasts.
- Meanwhile, the metadat_file will contain information regarding the year of release and the name of the album, and also a reference id for the song.
- The Million Songs Dataset has been sourced from various websites across the web and includes several different kinds of music.
- The first part of the project is integrating this data, and building a data pipeline.
- The Python library pandas will be used to integrate both these datasets.
- Start by defining the files we will be integrating, and mention their address. Afterward, define columns within the pandas file for each input from the datasets.
- Next, combine both sets of data and create one large dataset that has all the information we need.
- Now, we need to transform this data and analyse it. For this, we must first create a subset of this data, merge the song and artist name and take a look at how many times that particular song has been listened to.
- Following this, we will list the songs in the order of popularity. This data transformation greatly simplifies the dataset, making it easier to understand and manipulate.
- Next, split this into two datasets, one for training and the other for testing. To do this, we can make use of the train_test_split function from the scikit-learn library. Around 20% of the dataset will be kept for testing.
- Finally, test the model and after a few iterations, the system will be able to successfully recommend the right songs.
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Music Recommendation using Machine Learning