1597459860
Analysis of My Spotify Streaming History
Spotisis /spo-ti-sis/
noun
The analysis of one’s Spotify streaming history using Python.
I was reading through a lot of data science related guides and project ideas when I came across an article in which the author compared his song choices with his friend’s. I wanted to do something similar, so set out to analyse my own streaming history and compare it with what the world listens to.
Through this, I aim to find out more about my music preferences and how that differs from the world’s genral picks.
I never really put much thought into my music preference before this project — it was always kind of dependent on my mood, and when someone asked me what type of music I like, I had no answer — because it varied from one hour to another.
I’ve split this project into 2 sections:
**Part A **is the analysis of my music streaming history.
- Timeline of my streaming history
- Day preference
- Favorite artist
- Favorite songs
- Spirit of the songs
- Diversity
**Part B **is the comparison of the top 50 songs streamed on my list with the top 50 songs streamed in 2019
The data
Spotify allows every user to request a download of all their streaming history, so Part A is completely dependent on that. They also have an amazing Developer Platform in which the public can use the data available for their own interest. Along with my personal data, I used the audio features option — which breaks down a song and gives it ‘score’ for a number of different attributes. The attributes are as follows:
- Acousticness — A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic
- Danceability — A description of how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
- Energy — Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy.
- Instrumentalness — Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content.
- Liveness — Detects the presence of an audience in the recording.
- Loudness — The overall loudness of a track in decibels (dB). Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
- Speechiness — Speechiness detects the presence of spoken words in a track.
- Valence — A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track.
- Tempo — The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration
- Mode — Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
- Key — The estimated overall key of the track.
The dataset was a little messy, so I used Pandas to clean it up according to my need for each section. The entire code can be found on the GitHub link at the end of this article.
For Part B, I used this dataset from Kaggle.
Before we begin, I just want to say something… Don’t come at me for my music choice!
#python #analysis #data-science #spotify
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Buddha Community
1616069568
Create Music Streaming App Like Spotify
Interested in music application development like Spotify? We at AppClues Infotech help to build online music streaming and podcast apps like Spotify for iOS and Android. Hire our best designers & developers to build your own music streaming app like Spotify with customized features & functionality.
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Email: info@appcluesinfotech.com
Call: +1-978-309-9910
#create music streaming app like spotify #create music streaming app like spotify #create music streaming app like spotify #hire music streaming app developers #cost to make a music streaming app #cost to make an app like spotify
1617952042
A simple process of developing music streaming app like Spotify
Spotify is a music streaming app that allows users to listen to music without downloading. Daniel Ek founded it in 2006. In 2015, its net worth was more than $5 billion. The Spotify company has received $½ billion as a fund from 17 investors. But, currently the additional amount of $500 million has been added to the fund.
Spotify is not the first one to enter the online music service industry. But, it was an innovator by implementing marketing approaches, technology, and subscriptions. Due to these, it has become the preferable music streaming app among users across 170 countries.
The success of Spotify, a music streaming service app
The growth of Spotify is skyrocketing. Let us see the net worth of Spotify now.
In 2011, the Spotify app had 2.5 million subscribers and 500,000 premium users who have registered since its partnership with Facebook’s Open Graph.
In 2012, the number of subscribers had increased and its net worth per month is $20 million.
In 2013, it had 6 million subscribers with 24 million active users. And, it has grown to 15 million subscribers with 45 million free users in 2015.
Since the user base started to rise in 2011, they announced that music streaming services would be limited to 10 hours for a month after listening to unlimited music of 6 months. Later in 2014, this limitation was removed.
In 2020, Spotify hit 155 million subscribers and its net worth is $9.5 billion.
In 2021, the revenue from the Spotify app is expected to reach $10.83 billion.
This clearly shows that the number of subscribers has increased and the app’s revenue is also increasing.
How is Spotify generating revenue?
Do you want which revenue model Spotify follows? They follow a freemium model that is the app is accessible for both free and paid users. Notably, the majority of users stream music via mobile apps.
Paid users can listen to music without any advertisements. Apart from that, premium users can access songs offline and other additional features. They offer many premium options for users. On the other hand, free users encounter ads between every five to six songs.
Also, they adopted several innovative advertising formats to make money out of their app. Available ad formats are listed below.
- Audio ad
- Display
- Homepage takeover
- Branded playlist
- Sponsored session
- Video takeover
- Advertiser page
The success story of Spotify has inspired many entrepreneurs to start a business and jump into the online music streaming market with an app like Spotify.
How to create a music streaming app like Spotify?
Spotify clone app development empowers you to launch the app that suits your business requirements. Here is the process in brief.
Conduct market analysis
Frame your business plan by conducting market research. Also, know your target audience and competitors. This helps you to come up with unique business ideas.
Share your business plan with a mobile app development company/team
Collaborate with a mobile app development company for developing the Spotify clone app. Upon discussing your business idea with them, they help to frame a successful plan to implement.
Nowadays, apps are developed in two different methods. Developing an app from scratch is a time-consuming process and you have to invest more. On the flip side, using the Spotify clone script is beneficial. The pros of this solution are customizable, scalable, ready-to-use, time-effective, and less expensive.
Incorporate the features that are essential for the Spotify clone app development. Make the app design simple and attractive as it gets more attention from users. To make your music streaming app stand out from the other apps, integrate additional features upon analyzing the current market trends.
The robust features to consider while developing the Spotify clone app are listed below.
- Individual profiles
- Social media integration
- Search songs
- Personalized suggestions
- Trending tracks
- Statistics
Also, you can consider adding the following advanced features to your app.
- Push notifications
- Radio stations
- Podcasts
- Behavior tracking
- Membership plans
Deploying your app
Once done with the app design and development, it is ready to deploy. Choose a platform for your app to launch. Before deployment, make sure your app is tested for technical and logical errors. If there is any, fix them and launch the bug-free Spotify clone app.
Post-launch, check the performance of your app regularly. With the collective analysis of the customer’s ratings and reviews to the app, update the version of it accordingly.
Final note
As a pioneer in mobile app development, we offer a world-class Spotify clone app solution that empowers you to start a music streaming service business.
We have 8+ years of expertise in this field. Apart from creating an error-free app, we install the app on a server with hosting capabilities. Most importantly, the source code that we have used to develop the app will be available to you after app deployment.
Associate with us for Spotify clone app development and get your music streaming app.
#spotify clone #spotify clone app #spotify clone script #spotify clone app development #spotify app clone #app like spotify
1622108520
Stateful stream processing with Apache Flink(part 1): An introduction
Apache Flink, a 4th generation Big Data processing framework provides robust **stateful stream processing capabilitie**s. So, in a few parts of the blogs, we will learn what is Stateful stream processing. And how we can use Flink to write a stateful streaming application.
What is stateful stream processing?
In general, stateful stream processing is an application design pattern for processing an unbounded stream of events. Stateful stream processing means a** “State”** is shared between events(stream entities). And therefore past events can influence the way the current events are processed.
Let’s try to understand it with a real-world scenario. Suppose we have a system that is responsible for generating a report. It comprising the total number of vehicles passed from a toll Plaza per hour/day. To achieve it, we will save the count of the vehicles passed from the toll plaza within one hour. That count will be used to accumulate it with the further next hour’s count to find the total number of vehicles passed from toll Plaza within 24 hours. Here we are saving or storing a count and it is nothing but the “State” of the application.
Might be it seems very simple, but in a distributed system it is very hard to achieve stateful stream processing. Stateful stream processing is much more difficult to scale up because we need different workers to share the state. Flink does provide ease of use, high efficiency, and high reliability for the**_ state management_** in a distributed environment.
#apache flink #big data and fast data #flink #streaming #streaming solutions ##apache flink #big data analytics #fast data analytics #flink streaming #stateful streaming #streaming analytics
1597459860
Analysis of My Spotify Streaming History
Spotisis /spo-ti-sis/
noun
The analysis of one’s Spotify streaming history using Python.
I was reading through a lot of data science related guides and project ideas when I came across an article in which the author compared his song choices with his friend’s. I wanted to do something similar, so set out to analyse my own streaming history and compare it with what the world listens to.
Through this, I aim to find out more about my music preferences and how that differs from the world’s genral picks.
I never really put much thought into my music preference before this project — it was always kind of dependent on my mood, and when someone asked me what type of music I like, I had no answer — because it varied from one hour to another.
I’ve split this project into 2 sections:
**Part A **is the analysis of my music streaming history.
- Timeline of my streaming history
- Day preference
- Favorite artist
- Favorite songs
- Spirit of the songs
- Diversity
**Part B **is the comparison of the top 50 songs streamed on my list with the top 50 songs streamed in 2019
The data
Spotify allows every user to request a download of all their streaming history, so Part A is completely dependent on that. They also have an amazing Developer Platform in which the public can use the data available for their own interest. Along with my personal data, I used the audio features option — which breaks down a song and gives it ‘score’ for a number of different attributes. The attributes are as follows:
- Acousticness — A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic
- Danceability — A description of how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
- Energy — Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy.
- Instrumentalness — Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content.
- Liveness — Detects the presence of an audience in the recording.
- Loudness — The overall loudness of a track in decibels (dB). Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
- Speechiness — Speechiness detects the presence of spoken words in a track.
- Valence — A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track.
- Tempo — The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration
- Mode — Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
- Key — The estimated overall key of the track.
The dataset was a little messy, so I used Pandas to clean it up according to my need for each section. The entire code can be found on the GitHub link at the end of this article.
For Part B, I used this dataset from Kaggle.
Before we begin, I just want to say something… Don’t come at me for my music choice!
#python #analysis #data-science #spotify
1597452410
Spark Structured Streaming – Stateful Streaming
Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog “Internals of Structured Streaming“. And this blog pertains to Stateful Streaming in Spark Structured Streaming. So let’s get started.
Let’s start from the very basic understanding of what is Stateful Stream Processing. But to understand that, let’s first understand what Stateless Stream Processing is.
In my previous blogs of this series, I’ve discussed Stateless Stream Processing.
You can check them before moving ahead – Introduction to Structured Streaming and Internals of Structured Streaming
#analytics #apache spark #big data and fast data #ml #ai and data engineering #scala #spark #streaming #streaming solutions #tech blogs #stateful streaming #structured streaming