The project involved conducting a data analysis on my Spotify data. The analysis was performed
using various data
analysis techniques and tools to gain insights into my listening habits and preferences on the
platform.
The data was first collected by extracting my listening history from my Spotify account, which
included details such as
song names, artist names, and time stamps. This data was then cleaned and processed to remove
any duplicates or
irrelevant information. The following image shows my top musics :
The analysis included exploratory data analysis techniques, such as creating visualizations to
understand patterns and
trends in the data. This involved using tools like Python and Tableau to generate charts and
graphs, highlighting my
most played artists, genres, and songs.
Further analysis was conducted using statistical methods to identify patterns and correlations
in the data. For example,
regression analysis was used to investigate the relationship between my listening habits and
external factors like time
of day or day of the week.
Additionally, a recommender system was created using machine learning techniques, which used my
listening history to
recommend new songs and artists based on my preferences. The recommender system provided
personalized recommendations to
me and demonstrated the potential for machine learning in improving my experience on the
platform.
Skills I developed in this project:
Working with APIs to collect data from Spotify
Cleaning and preprocessing data using Python and Pandas
Joining data from different sources to create a comprehensive dataset
Data storytelling using visualizations and charts
Applying machine learning techniques to create a personalized recommender system
The analysis included exploratory data analysis techniques, such as creating visualizations to
understand patterns and
trends in the data. Further analysis was conducted using statistical methods to identify
patterns and correlations in
the data.
Overall, the data analysis project on my Spotify data provided valuable insights into my music
preferences and listening
habits on the platform. It demonstrated the power of data analysis techniques and tools to gain
insights and make
informed decisions based on the findings.
