https://github.com/gui-sitton/y.music

In this project I compared the musical preferences of the citizens of Springfild and Shelbyville. I examined real Y.Music data to test hypotheses and compare the behavior of users in these two cities.
https://github.com/gui-sitton/y.music

data data-analysis data-analysis-python data-science data-visualization python

Last synced: 12 months ago
JSON representation

In this project I compared the musical preferences of the citizens of Springfild and Shelbyville. I examined real Y.Music data to test hypotheses and compare the behavior of users in these two cities.

• Host: GitHub
• URL: https://github.com/gui-sitton/y.music
• Owner: Gui-Sitton
• Created: 2023-05-02T19:25:38.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-08-29T18:34:18.000Z (over 2 years ago)
• Last Synced: 2025-01-24T09:12:30.088Z (about 1 year ago)
• Topics: data, data-analysis, data-analysis-python, data-science, data-visualization, python
• Language: Jupyter Notebook
• Homepage:
• Size: 74.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Y.music

In this project I compared the musical preferences of the citizens of Springfild and Shelbyville. I examined real data from Y.Music to test hypotheses and compare the behavior of users in these two cities.

**Hypotheses**

1. User activity is different depending on the day of the week and the city.

2. During Monday mornings, residents of Springfield and Shelbyville listen to different genres. This is also true for Friday nights.

3. Listeners in Springfield and Shelbyville have different preferences. In Springfield, people prefer pop, while Shelbyville has more rap fans.

**Column Descriptions**

* 'userID' - user identifier

* 'Track' - track title

* 'artist' - artist name

* 'genre' - genre of music

* 'city' - user's city

* 'time' - the exact time the track was played

* 'Day' - day of the week

**Conclusions**

After analyzing the data, I concluded that:

1. User activity in Springfield and Shelbyville depends on the day of the week, although the cities vary in different ways. 

The first hypothesis is fully accepted.

2. Music preferences do not vary significantly over the course of the week in either Springfield or Shelbyville. We can see small differences in the order on Mondays, but in Springfield and Shelbyville, people listen to more pop music.

So we can accept this hypothesis. We should also bear in mind that the result might have been different if it hadn't been for the missing values.

3. It turns out that the musical preferences of users in Springfield and Shelbyville are quite similar.

The third hypothesis was rejected. If there is a difference in preferences, it can't be seen in this data.