Spotify Analysis
Spotify is a leading music streaming service with a huge collection of songs and user-created playlists, making it a great data source for analytics.
The purpose of this project is to gain insight into user behavior and preferences, such as which songs are the most popular, which artists have the most followers, and which genres are most listened to. This information may be used to improve the user's experience, refine marketing strategies, and assist in decision-making regarding music production and distribution.
Various statistical and analytical techniques are used to analyze the Spotify dataset, including: B. Data cleaning, exploratory data analysis, regression analysis, clustering, and data visualization. These techniques allow us to identify patterns and relationships within the data, giving us a deeper understanding of our users' musical tastes and behaviors.
- Shows the Most Popular Song
- Gives Mechanical aspects of the songs
- Acousticness of song
- Makes the platfomr more interesting to use
What did you learn while building this project? What challenges did you face and how did you overcome them?
This project has given me some valuable lessons about data handling of such a large magnitude.
The most important of all is data cleaning and preparation where such a large data set can have missing values and operating on such values . We also need to standardized the values depending on the frequency of such in the data.
Also I learned to visualize the data through patterns and graphs and charts by the help of python libraries (the language I used to analyse the data efficiently)such as matplotlib and seaborn.
I also learned to interpret the results which will ultimately be useful for the marketing strategies of the platform and enhancing user experience and trends.
Coming onto the challenges, the most prominent was refining the dataset and coming on the most accurate result to be achieved through various charts and graphs. Analyising different aspects of sound and relating it with the popularity of the songs.This also depicts the various information collected by the platform and its user experience.