This project is a beginner-level Python analysis of Spotify data, utilising data visualisation techniques to explore and analyse trends, popularity, and other key insights from albums on the platform. The data used in this project was sourced from Kaggle, and various Python libraries were employed to clean, process, and visualise the data.
Gain an understanding of the trends in Spotify music albums.
Explore the relationship between different variables like popularity, album release year and more.
Use data visualisation to highlight significant patterns and insights.
Data collection and cleaning from the Kaggle Spotify dataset.
Exploration of key attributes such as album popularity, release year, genre, and track details.
Visual representation of data trends using charts like bar plots, histograms, scatter plots, and line charts.
Insights into how various factors impact the popularity of albums.
Python: The primary language for data analysis and visualization.
Pandas: For data manipulation and cleaning.
Matplotlib: For creating static, animated, and interactive visualizations.
Seaborn: For enhanced data visualizations and statistical plotting.
Kaggle Dataset: The dataset used to analyze Spotify’s music catalog.