This repository contains my analysis of a dataset, which then resulted in a blog post.
The dataset https://www.kaggle.com/nadintamer/top-spotify-tracks-of-2018 was analysed. The dataset used contains a list of tracks with their features. These tracks were all member of the Spotify 2018 Top 100 Charts.
In this analysis we want to achieve the following goals:
- Get an understanding of the dataset by learning about the characteristcs of the tracks
- Learn what kind of tracks make up the Top 100 playlist
- Can we distill a pattern of features which would nearly guarantee a spot in the Top 100?
These goals will be achieved by answering the following questions:
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Which artists and what kind of tracks are in the Top 100? Here we will take a closer look at the tracks on a more music theoretical level and also one a dataset level. We will also take a look at what kind of artists are in the Top 100.
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What does it take to get into the Top 100? To answer this question, we will try to understand the importance of the features of each track. We will try to distill a pattern of the tracks in the Top 100.
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Where is the sweet spot of perceivable features to be in the Top 100? We will try to recommend a characteristic or quality of a track in order to be more likely a member of the Top 100 charts.
It is recommended to have Anaconda installed. Tje following python packages are required:
numpy
pandas
seaborn
matplotlib
math
- Download the dataset
- Run the jupyter notebook using
jupyter notebook
- [top2018.csv] - The dataset used for analysis (Source: https://www.kaggle.com/nadintamer/top-spotify-tracks-of-2018)
- [Write A Data Science Blog Post.ipynb] - The jupyter notebook containing the analysis
- [Write A Data Science Blog Post.html] - A compiled version of the jupyter notebook in HTML format.
The results of the analysis can be found on Medium: https://medium.com/@nsiicm0/how-to-get-into-spotifys-annual-top-100-list-f81df826cb9f
The dataset has been taken from Kaggle: https://www.kaggle.com/nadintamer/top-spotify-tracks-of-2018