This project conducts an exploratory data analysis of Spotify Technology S.A., a global leader in audio streaming and media services. The analysis delves into Spotify's stock performance, utilizing daily stock prices data from January 1, 2018, to the present date.

The dataset includes comprehensive daily stock metrics such as opening, closing, high, low, and adjusted closing prices, along with the trading volume. The data is sourced from Yahoo Finance using Python's pandas_datareader library.

The analysis is aimed at unraveling various aspects of Spotify's stock performance through different lenses:

Examination of the evolution of Spotify's stock prices over the years. Identification of patterns or trends using rolling average impact analysis.

Analysis of periods with high volatility in stock prices. Investigation of the potential impact of Spotify's announcements or global events on stock volatility.

Comparison of Spotify's stock performance with major players in the Technology sector. Exploration of industry-wide trends versus Spotify-specific influences.

Impact of global events on Spotify's stock prices. Correlation between stock performance and music industry trends or shifts in consumer behavior.

Developing a predictive model for Spotify's future stock prices. Analysis of key factors influencing stock price movements.

Exploration of the correlation between Spotify's user base percentage and living standards. Impact of regional music trends on Spotify's global performance.

Gathering additional insights from news articles and financial reports. Analysis of how major announcements or controversies are reflected in stock price movements.

• Python
• Pandas and Pandas_DataReader
• Matplotlib and Seaborn for visualization
• Additional libraries for data manipulation and analysis