The Data Analysis Portfolio Project revolves around dissecting the Spotify dataset using Python and leveraging Spotify's WEB APIs to gather comprehensive insights. The project delves into the realms of song attributes, user preferences, and trends within the my musical landscape.
Exploration of Spotify Developer Tools and APIs:
- Explored and harnessed Spotify's Developer Tools and APIs to access music-related data.
- Delved into the functionalities provided by Spotify's APIs.
- Accessed varied details crucial for comprehensive data analysis.
Data Extraction and Preprocessing:
- Retrieved personal all-time data to discern top tracks and favored artists.
- Collected an extensive Spotify dataset encompassing song attributes, artists, genres, popularity metrics, and user interactions.
- Ensured data consistency and integrity through thorough preprocessing and cleaning.
Data Analysis and Visualization:
- Employed Python libraries (Pandas, NumPy, Matplotlib, Seaborn) to perform Exploratory Data Analysis (EDA) and visualize Spotify's dataset.
- Explored relationships between audio features and lyrical sentiments, utilizing sentiment analysis techniques.
- Unveiled insights into music trends, user preferences, and potential music recommendations.
- Leveraged secondary data from Spotify to uncover patterns and correlations among diverse music characteristics.
- Strengthened the ability to interpret and review intricate datasets.
The project encompasses a journey through Spotify's rich dataset, starting from data acquisition via APIs to in-depth exploration using Python-based tools. By unraveling relationships between audio features, lyrical sentiments, and user interactions, this analysis yields valuable insights into music trends and user preferences. The project's culmination lies in the capacity to interpret and draw meaningful conclusions from intricate datasets, supporting the development of analytical skills within the realm of data analysis and interpretation.