Prediction. Tempo was at about 122 bpm and had a standard deviation of 33 bpm, artist familiarity was at 61% and had a standard deviation of 16%, most songs were in a major key but the standard deviation was rather wide, loudness was at about -10 dB, and artist hotness was at about 0.43. Did you find this Notebook useful? There are a ton more parameters, but these are the ones that I used, and PyCaret does a great job of automatically detecting information from your data — like picking which features are categorical, and it will confirm that with you in the setup(). About this Competition. Sharma, Hari • December 28, 2019. Here we can see the f1-scores for each feature in our final dataset. Some historic song data from the 1980s was provided - song, genre, artist, position in chart, week in year, date. The algorithm chosen is Random Forest. DJ Khaled boldly claimed to always know when a song will be a hit. Contribute to manasreldin/Song-Popularity-Predictor development by creating an account on GitHub. As you can see, CatBoost was ranked first, having the best RMSE, RMSE, R2. # using a sample of the dataset (you can use any amount), 15 Habits I Stole from Highly Effective Data Scientists, 7 Useful Tricks for Python Regex You Should Know, 7 Must-Know Data Wrangling Operations with Python Pandas, Getting to know probability distributions, Ten Advanced SQL Concepts You Should Know for Data Science Interviews, 6 Machine Learning Certificates to Pursue in 2021, Jupyter: Get ready to ditch the IPython kernel. My inspiration for this project is finding out what it is about a song that I enjoy so much. If a song has appeared on Top 100 BillBoard at least once, then it will be classified as a hit song. Jan 2018; M Nasreldin; Nasreldin, M. (2018). Song hit prediction: Predicting billboard hits using spotify data. Every song in the dataset contains 41 features categorized by audio analysis, artist information, and song related features. Individual h5 files were provided for each song. You can see that some predictions were better than others. Some features that were only missing a reasonable amount we decided to fill in the missing values with the mean. Many data fields were missing and there was no echonest API to fill in data since the API was modified by Spotify. We decided to use BillBoard Top 100 to determine popularity. Do you prefer one over the other? Since Spotify acquired EchoNest, many different features were changed including a simple way to look up song info by ID. The technical features such as tempo, mode, and loudness are about as important as information on the artist such as familiarity, hotness, and identification. For my target, I use the “popularity” measure calculated by Spotify. The 40 trials in which the popularity display appeared were sequenced randomly among the 60 trials. In addition, we may consider using the full dataset to see if we can improve our models. However, it did not have the best MAE, RMSLE, and MAPE, and it was not the fastest. Out of 10,000 songs in our dataset 1192 songs were classified as hot songs. (2016) found that using Twitter posts is useful to predict future charts, when recent music charts are available. Figure1 shows an example of the view counts of Gangnam Style M/V2, which is the most viewed YouTube video in the category “music”. Every artist in the data was uniquely identified by a string, so we decided to do label encoding on them. I used only 1,000 rows instead of the total ~170,000 rows. By signing up, you will create a Medium account if you don’t already have one. Furthermore, the aforementioned platforms measure the popularity in various manners, thus increasing the difficulties in performing generalized and comparable models. Your home for data science. Therefore, you should establish what you mean by success in terms of these metrics. spotify’s song popularity prediction? This Notebook has been released under the Apache 2.0 open source license. By signing up, you will create a Medium account if you don’t already have one. Popular songs secure the lion’s share of revenue. The ability to make accurate predictions of song popular-ity also has implications for customized music suggestions. We wrote python scripts using BeautifulSoup to scrape billboard.com and get all the songs that appeared on the chart from 1958 to 2012. Mechanisms of Action (MoA) Prediction. While DJ Khaled was ill equipped with powerful data science and machine learning tools, he was correct in that certain trends do exist in hit songs. Z/Yen were asked whether PropheZy could predict song popularity from some historic song data. The top 10 artists in 2016 generated a combined $362.5 million in revenue. Add to Collection Using the power of PyCaret [3], you can now test every popular Machine Learning algorithm against one another (or more of them at least). Below are the results of some other songs that our model has predicted as well as the Spotify hotness results to compare them against: Going into this endeavour, we were uncertain if it is even possible to predict, better than random, if a song will be popular or not. iterative-stratification. Tuning saw the AUC score increase from 0.632 to 0.68. Below, is a screenshot of the first few rows along with the first columns: After we eventually pick the best model, we can look at the most important features. For example, we can see that popularity or original is compared side-by-side to theLabel, which is the prediction. The range of confidences for minor lie between -1 and 0 and the range of confidences for major lie between 0 and 1. Twenty songs in each of the subject's top-three genres were presented in random order throughout the experiment. Making predictions of song popularity based on machine learning, often referred to as Hit Song Science, is a problem An interesting trend we can see here is that the actual music aspects of the song are reasonably entangled with artist information. [1] Photo by Cezar Sampaio on Unsplash, (2020), [2] Photo by Markus Spiske on Unsplash, (2020), [4] Yamac Eren Ay on Kaggle, Spotify Dataset, (2021), [5] M.Przybyla, Dataframe Screenshot, (2021), [6] M.Przybyla, SHAP Feature Importance Screenshot, (2021), [7] M.Przybyla, Model Comparison Screenshot, (2021), [8] M.Przybyla, Predictions Screenshot, (2021), [10] Photo by bruce mars on Unsplash, (2018). Screenshot by Author [8]. SMAI 2017 Project. Out of 91 that is not too bad, considering we would be off by up to just a difference of 10 on average. MS in Data Science - SMU. All one million songs came out to about 280 GB. Mechanisms of Action (MoA) Prediction. We were interested in the distribution of hit songs, so we isolated all songs with a hotness value of 1 and graphed the distribution of different features for these songs. Track popularity prediction. Thanks to growing streaming services (Spotify, Apple Music, etc) the industry continues to flourish. Code. As a future improvement, it would be better to have the categorical features that are broken out into one column instead of tens of columns, then as a next step, be fed into the CatBoost model so that target encoding can be applied vs one-hot-encoding — to perform this action, we would confirm or change the key column to be categorical instead, and for any other similar columns. Check your inboxMedium sent you an email at to complete your subscription. for the modelling section. First a search is run using the search endpoint on the API in order to grab the Spotify ID. A Medium publication sharing concepts, ideas and codes. View Presentation-4.pdf from DATA 144 at University of California, Berkeley. Every song has key characteristics including lyrics, duration, artist information, temp, beat, loudness, chord, etc. Previous studies that considered lyrics to predict a song’s popularity had limited success. Head to MachineHack and sign up. A reliable hit predictor could be … Git-hub link : https://github.com/adarsh1001/Song-Popularity-Predictor. research for recommendation and prediction is the use of social networks and social data. The table below shows the results of some of the models that we tried. As we would expect, the familiarity of the artist has a correlation to the hotness value. A grid search was run on XGBoost to further improve the AUC score. The dataset is split in three parts: train (60%), validation (20%) and test (20%). You can see that some predictions were better than others. We present a model that can predict how likely a song will be a hit, defined by making it on Billboard’s Top 100, with over 68% accuracy. Two spreadsheets below provide some positive results, though the test data was sparse. Here is the Python code that you can try testing yourself from importing libraries, reading in your data, sampling your data (only if you want), setting up your regression, comparing models, creating your final model, making predictions, and visualizing feature importance[9]: Using Data Science models to predict a variable can be quite overwhelming, but we have seen how, with a few lines of code, we can compare several Machine Learning algorithms efficiently. Author - Towards Data Science. Methods & Results In addition to using the “song hotttnesss” metric, we can also create our own metric of popularity, which we can define as the number of downloads on iTunes or the number of plays on Spotify. In 2/3 of the trials, during the second listen, the song's popularity was displayed in the 1–5 star scaling system. The top 10 artists in 2016 generated a combined $362.5 million in revenue. 2 Popularity Prediction Problem YouTube provides the statistical information along with the video on the web, and we focus on the video popularity associated with time. However, around 4500 songs were missing this feature, which is almost half of the subset we were using. Predictions. Every Thursday, the Variable delivers the very best of Towards Data Science: from hands-on tutorials and cutting-edge research to original features you don't want to miss. Using the Spotify ID audio features and in depth audio analysis can then be grabbed for a song. We stopped at 2012 since to our most recent songs in the dataset were released in 2012. We can see some interesting trends on the graph above as well. Explore and run machine learning code with Kaggle Notebooks | Using data from Spotify Dataset 1921-2020, 160k+ Tracks The script we developed to map Spotify API data to our training data can be viewed here. Build an ML model — To Predict the popularity of any song by analyzing various metrics in the dataset. Predicting Song Popularity Katherine Lin Rudolf Newman Computer Science Computer Science Northwestern University Northwestern University Evanston, IL 60201 Evanston, IL 60201 katherinelin2016@u.northwestern.edu rudolfnewman2017@u.northwestern.edu Motivation Our project aims to generate an accurate predictor of a song’s popularity. We can see that for tempo there was a range that hot songs commonly used, and there were two peaks within this range at about 100 bpm and 135 bpm. In the screenshot below, I am printing the dataframe with the predictions and the actual values. Another alternative is to use Spotify API to collect our own data. Can you improve the algorithm that classifies drugs based on their biological activity? This significantly increased the importance of this value as we’ll see in the next section. reasons. Review our Privacy Policy for more information about our privacy practices. If there’s one thing I can’t live without, it’s not my phone or my laptop or my car — it’s music. The last prediction was quite poor, while the first two predictions were great. Take a look. Train, Validation and Test. Overall, you can see, even with a small sample of the dataset, we faired pretty well. In summary, we now know how to perform the following to determine song popularity: I want to give thanks and admiration to Moez Ali for developing this awesome Data Science library. I love music and getting lost in it. With these two values, we combined the features to range from -1 for minor to 1 for major. In HSS, approaches that look for social popularity metrics include research on using social media data, for example from Twitter. 1 Goal 2 Data preparation and quick EDA 3 Regression models 4 Comments 5 Improvements (?) Song popularity predictor. For the next steps, I would apply this to an entire dataset, confirm data types, making sure to remove inaccurate models, as well as models that take too long to train. After getting the list of songs that have been on billboard, we go back to our 10,000 songs dataset, and classified them accordingly. The science of hit song prediction has had a controversial history, as early studies such as [1,9] showed that random oracles can not always be outperformed when it comes to predicting hits. Team Members : Bakhtiyar Syed (201525094) Aakash KT (20161202) Saurav … Global consumption 3. Code Input (1) Execution Info Log Comments (1) Cell link copied. arrow_right. Mode is whether the song uses a major or a minor key in its production. Each parameter was tuned, and some values were hypertuned simultaneously. song-popularity-predictor. The cumulative distribution of total view number is in Figure1-a. For this problem, I will be comparing MAE, MSE, RMSE, R2, RMSLE, MAPE, and TT (Sec) — the time it takes for the model to be completed. Song-Popularity-Predictor. Hence, modeling the popularity of all their songs from 2010-2017 will allow us to investigate what separates popular tracks from not-so-popular songs from the most well-known names in music. However, the way the algorithm is trained not would probably not generalize that well since we are just using a sample, so you can expect all of the error metrics to decrease (which is good) significantly, but unfortunately, you will see the training time increase dramatically. Though this value is straightforward with a 0 for minor and a 1 for major, there was also a value named mode_confidence that depicted the probability of the mode selected being accurate. I am not affiliated with any of these companies. Though there is generally more activity in the regions that also produce hits, we can see that the hits are centralized around these specific areas. Increase users 2. The model is fitted with the train set and subsequently, varying the parameters, it is evaluated on the validation set, in order to get the best combination. It consists of 17MB along with data from Spotify from the years 1921 to 2020, including 160,000+ tracks. The two features artist ID and mode were altered to be a better reflection of their properties in the dataset. These are the parameters that I used in the setup() of PyCaret. We have also shown how easy it is to set up different types of data, including data like numeric and categorical. The goal of the best model developed is to predict a song’s popularity based on various features current and historical features. Sr. Data Scientist. predict a song's popularity score using Linear Regression and One Hot Encoding. For the success criteria, I am comparing all of the metrics MAE, MSE, RMSE, R2, RMSLE, MAPE, and TT (Sec), which PyCaret automatically ranks. The duration of the hot songs were at about 200 seconds on average and this duration had a general range of 3 to 4 minutes. The aim of track popularity prediction or Hit Song Science is to apply machine learning techniques in order to capture some information from musical data that would explain the popularity of the respective musical tracks. This is a score from 0 to 100, with more popular tracks having a higher value. I will be discussing the Python library that I used, along with the data, parameters, models compared, results, and code below. The y-axis is in terms of the song hotness Y, where 0 is the lowest score and 1 is the highest score. In other words, our model was tasked with predicting whether a song would make it to Billboard’s 100 most popular song list or not. Machine-learning engineers from the University of Bristol think they might have the master equation to predicting the popularity of a song. An example of this is the artist familiarity field which had only 10 missing values. We trained our data on different models to predict if a song is a hit song or not. As you can see, the top three features are year, instrumentalness, and loudness. Your home for data science. MachineHack backed by its parent Analytics India Magazine has been continuously indulging in helping machine learning and data science community grow to its peak by conducting exciting hackathons and challenging the aspirants.. For example, if time is essential, then you will want to rank that higher, or if MAE is higher you might want to pick Extra Trees Regressor instead to win. The work was supported in part by the National Natural Science Foundation of China (NSFC) Youth Science Foundation under Grant 61802024, the Fundamental Research Funds for the Central Universities under Grant 24820202020RC36, the National Key R&D Program of China under Grant … I am an MS in Data Science student at the University of San Francisco. Thus we can expect the model to use this to predict whether or not a song is a hit. Zangerla et al. (2014) Similarly, the research by Kim et al. The original data in A Million Songs dataset came with a song hotness feature. A song in our data set is considered a hit if it made it to the Billboard Year-End Hot 100 chart at least once during any of the years in the reporting period. Familiarity is on the x-axis and ranges from 0 to 1 as well, describing how ‘familiar’ the artist is based on an algorithm by Echo Nest. A Multimodal End-to-End Deep Learning Architecture for Music Popularity Prediction ... there is still a lack of information to properly assess an accurate estimation of the impact or the popularity of a song within a platform. We believe such GCN-based popularity prediction would give a strong reference to related areas. For example, we can see that popularity or original is compared side-by-side to theLabel, which is the prediction. The main problem with this dataset was the format provided. As this value approaches 1, the hotness of the song also approaches 1 (who’d have thought?). I personally use this product, and what I apply here could be applied to other services as well. Xgboost appears to be the one with the highest accuracy at 0.63 area under the curve (AUC) score, before tuning. Select Hackathons and click on ‘Chartbusters Prediction: Foretell The Popularity Of Songs‘. Some of the benefits of using PyCaret overall, as stated by the developers, is that there is increased productivity, ease of use, and business-ready — all of which I can personally attest to myself. The dataset [4] that I am using is from Kaggle. Check your inboxMedium sent you an email at to complete your subscription. January 2005; Source; DBLP; Conference: ISMIR 2005, 6th International Conference on Music Information Retrieval, London, UK, … Please feel free to check out my profile and other articles, as well as reach out to me on LinkedIn. Audio analysis features: tempo, duration, mode, loudness, key, time signature, section start, Artist related features: artist familiarlity, artist popularity, artist name, artist location, Song related features: releases, title, year, song hotness. Because of this the demo uses a very roundabout way to grab song info. In 2017, the music industry generated $8.72 billion in the United States alone. To answer these questions, we made use of the Million Song Dataset provided by Columbia, Spotify’s API, and machine learning prediction models. Predicting how popular a song will be is no easy task. This project demonstrated the possibility of predicting music hotness, identified trends in popular music, and developed feature extraction tools using Spotify’s API. One of the neat features of PyCaret, is the ability for you to remove algorithms in your compare_models() training — I would start on a small sample of the dataset, and then see which algorithms generally take longer, then remove those when you compare with all of the original data since some of these could take hours to train depending on the dataset. Some feature engineering is then done in order to convert the Spotify data back to a format that is usable for our model. Project by Mohamed Nasreldin, Stephen Ma, Eric Dailey, Phuc Dang. XGBoost provided the best predictions on the training model, with an AUC score of 0.68. For detailed instructions on how to use MachienHack read the below article: HOW TO GET STARTED WITH MACHINE HACK, A-DATA SCIENTIST’S DESTINATION FOR COMPETITIONS and PRACTISE; To participate in the hackathon click here. Automatic Prediction of Hit Songs. The process can be summarized as followed: After collecting the data and cleaning it to be used, we then moved on to data exploration by looking into feature importance, trends in our dataset, and identifying the optimal values for these features. Song popularity predictor Wednesday, December 9, 2020 3:25 PM Reasons: 1. Every Thursday, the Variable delivers the very best of Towards Data Science: from hands-on tutorials and cutting-edge research to original features you don't want to miss. Please feel free to comment down below if you applied this library to a dataset or if you use other techniques. Recently, we concluded our 20th successful edition of Data Science hackathons by announcing the champions for Chartbusters Prediction: Foretell The Popularity Of Songs … I am using the interpret_model() function of PyCaret, which is based on the popular SHAP library. The question of what makes a song popular has been studied before with varying degrees of success. Moving forward, we would like to explore how additional features such as artist location or release date can influence a song’s popularity. DJ Khaled boldly claimed to always know when a song will be a hit. The dataset chosen was the Million Songs Dataset provided by Columbia University and pulled from Echo Nest. Many fields in the dataset were unusable due to old deprecated data. Predicting popular songs can be applied to the problem of predicting preferred songs for a given population. There's a Nirvana song that you ... a model may not be learning an actual relationship between the song features and song popularity because … Song Popularity prediction. Review our Privacy Policy for more information about our privacy practices. We modified the script so that it would produce a csv that we could use to train our models. Take a look. Predicting how popular a song will be is no easy task. Here are all of the features possible below: Here are the most important features using SHAP: All of the columns are used as features, except for the target variable, which is the columnpopularity. Therefore many fields had to be dropped. Predictor is a popular song by Diaz & Parree | Create your own TikTok videos with the Predictor song and explore 0 videos made by new and popular creators. The dataset was too large as well. What do you think about automatic Data Science? Pre-processing was turned text into numbers. I hope you found my article both interesting and useful. Thus, we wanted to find a new way to classify if a song is a hit or not. You can download it easily and quickly. 4. Keep on reading if you would like to learn a tutorial on how to use Data Science to predict the popularity of a song. A Medium publication sharing concepts, ideas and codes. A value of -1 represents 100% confidence that the key is minor and 1 represents 100% confidence that the key is major. This analysis seeks to amend this, utilizing Billboard and Metacritic data to visualize how Grammy nominees and winners for Album of the Year have performed both commercially and critically over the last 20 years. Sings The Future – Song Popularity Prediction. Here are all of the models that I compared: It is important to note that I am just using a sample of the data, so the order of these algorithms may rearrange if you use all of the data if you test this code yourself. For example, n_estimators and learning rate were tuned together as a higher n_estimators value required a lower learning rate to produce optimal results. A script was provided to convert the dataset to mat files to be used with matlab. Spotify Top Writer in Technology and Education. Random predictions would yield a 0.5 AUC score. I will be examining every popular Machine Learning algorithm and pick the best algorithm based on success metrics or criteria — oftentimes, it is some sort of calculated error. Code. We present a model that can predict how likely a song will be a hit, defined by making it on Billboard’s Top 100, with over 68% accuracy.