This project focuses on exploring and analyzing music data from Spotify to gain valuable insights. The data used in this analysis is from Kaggle.
The analysis involved cleaning the dataset and performing exploratory data analysis (EDA) using Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn. By examining relationships between audio features and conducting sentiment analysis on song lyrics, the project uncovers patterns and trends in music preferences. This project highlights the use of secondary data from Spotify, helping to identify correlations between different musical characteristics while developing strong skills in dataset interpretation and analysis.