Build a Music Recommendation Algorithm using KKBox's Dataset

Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

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Project Template Outcomes

  • Understanding the problem statement
  • Data Visualization
  • Inference about data
  • Feature Engineering
  • Outlier treatment
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  • Imputing missing values by removing them
  • Imputing missing values by making missing label
  • Importing the dataset and importing libraries
  • Train and test split for model validation
  • Building Logistic Regression model
  • Building Decision Tree classifier
  • Building Random Forest Classifier
  • Building XGBoost model
  • Making Test predictions using the trained model.
  • Feature Importance

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Project Description

Introduction to Music Recommendation System 

Music is one of the most popular sources of entertainment today. Listening to music has become much easier due to the digital revolution. A few years ago, many users used to listen to a particular artist or band; some used to love specific types of music. However, as the world is getting more and more connected through technology, users have started gaining access to various genres of music on different platforms. Nowadays, the availability of music and music streaming services has increased exponentially, and the public can easily listen to all kinds of music ranging from classical, jazz to pop.

 

Music streaming applications such as Spotify, youtube music, amazon music have features to recommend music to the users based on their listening history and preferences. Such features play a vital role in the business of these streaming services. As the time spent on the platform is directly linked to the growth of the streaming services, appropriate recommendations are essential. The music recommendation system by which the music provider can predict and suggest the right songs based on the characteristics of the music the user has heard over time.

 

Due to the increasing number of songs, artists, and music, it has become challenging to suggest appropriate music pieces to the user. The challenge of a music recommendation system is to build a system that can understand the users’ preferences and offer the songs. Therefore, many music streaming service providers rely on data scientists to use their excellent mathematical tools and develop more efficient recommendation systems.

Overview of Music Recommendation System Project using Machine Learning

We use the KKBOX dataset to build a music recommendation system in this project. This music recommendation app project will walk you through some Machine learning techniques that one can apply to recommend songs to users based on their listening patterns. To predict the chance of a user listening to a piece of music repetitively after the first observable listening event within a particular time. 

Music Recommendation Dataset

The dataset used is from Asia’s leading music streaming service, KKBOX. It holds the world’s most comprehensive Asia-Pop music library with over 30 million tracks. The training data set contains the first observable listening event for each unique user-song pair within a specific time duration. Metadata of each user and song pair is also provided. There are three datasets available.

train.csv: It contains data for different users with attributes such as msno, user_id, song_id, source_system_tab, etc. There are about 7.3 million entries available with 30755 unique user ids.

songs.csv: It contains the data related to songs with attributes such as song_id, song_length, genre_ids, artist_name, etc. The dataset contains about 2.2 million unique song ids.

members.csv: The data is related to users' information over 34403 different users.

Tech Stack for the Music Recommendation Project

Language: Python

Libraries: sklearn, xgboost, pandas, NumPy

An Outline of the Music Recommendation System Source Code

  1. Exploratory data analysis (EDA)

    1. Data visualization

    2. Inference about features

    3. Feature engineering 

  1. Data cleaning (outlier/missing values/categorical)

    1. Outlier detection and treatment

    2. Imputing missing values

      1. Replacing by mode

      2. Removing null values

      3. Making a new label as missing

    3. Converting labeled or string values by numerical values

  1. Model building on training data

    1. Logistic regression

    2. Decision Tree

    3. Random Forest

    4. XGBoost

  1. Model validation

    1. Roc_Auc

  1. Feature importance and conclusion

Learning Takeaways from Music Recommendation System using Machine Learning Project

This project can easily make it to the list of top machine learning projects for beginners because of the simple tools and techniques used to implement a music recommendation system in Python. Here are details of the machine learning tools and techniques used in this project.

Exploratory Data Analysis

The dataset for the music recommender system project has about 3 million rows, and such large-scale data can be easily analyzed using Pandas dataframes in Python. The analysis involves understanding app-user behavior and, more precisely, what makes a user listen to songs again and again. We will achieve this by plotting insightful plots using Python libraries, matplotlib, and seaborn. For this project though, we will be using the first 10,000 rows only.

Data Cleaning

The music recommendation system dataset has a lot of missing values that must be treated mathematically before serving the values as an input to a machine learning model. This project will help you learn three powerful techniques to handle null values in the data. You will also learn how to handle non-numerical data and treat outliers in the dataset. Additionally, you will learn how to perform feature engineering over the dataset and prepare it to apply machine learning algorithms.

Machine Learning Algorithms

The task in this music recommendation system using python project simplifies predicting the value of a target variable which takes value '1' if the user listened to a particular song and '0' if they didn’t. It helps design the recommendation system as songs rows that correspond to the target value = ‘1’ are likely to be heard by the user and should be recommended more often. As the prediction problem falls under the umbrella of binary classification problems, you will explore classification machine learning algorithms: decision tree, logistic regression, XGBoost, and Random forests. After their implementation, you will learn how to compare the performance of different algorithms using statistical scores.

FAQs on Music Recommendation Systems

Here are a few of the most popular questions that one is likely to ask when exploring music recommendation systems.

1) How Music Recommendation works?

The best multi-touch attribution model will be the one that works the best for your dataset. A multi-touch attribution model is a model which changes from business to business, lineage to lineage. It solely depends on which level of touch you are attributing a conversion. Some of the famous multitouch attribution models are: 

 

  • Linear Multi-Touch Marketing Attribution Model

  • U-Shaped Multi-Touch Marketing Attribution Model

  • Time Decay Multi-Touch Marketing Attribution Model

  • W-Shaped Multi-Touch Marketing Attribution Model

2) Which is the best music recommendation algorithm? 

  1. Choose the multi-touch attribution model that you believe will suit your dataset by analyzing which channels were prominent in serving as the point of conversion. 

  2.  If you conclude a probabilistic model works the best, then follow the steps for a Shapely model or any other Position Decay model.

  3.  If you deduce that the first and last steps of the conversion process are the most important, then will follow the steps for a Position Based attribution model. 

  4. Use a programming language like Python to define a function that takes your data as the input. The function must filter the conversions from the data, store the corresponding cookie IDs, aggregate the click counts and then distribute them according to the chosen model.

  5. Once the model has been designed, you can use it to analyze the influence of each channel.

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