Personal Spotify Music Preferences Analysis

Overview

The objective of this project is to conduct a comprehensive analysis of the user's music preferences and tastes using data from Spotify's API and the user's streaming history. By leveraging Python programming language and various data analysis libraries, the project aims to provide insights into the user's listening habits, preferred artists, tracks, and other relevant metrics. The ultimate goal is to gain a deeper understanding of the user's musical preferences and behavior.

Key Objectives:

1. Data Collection:

   • Utilize the publicly available API of Spotify to gather data on music genres, artists, and tracks.
   • Obtain my streaming history data from Spotify's Account Privacy page.

2. Data Preprocessing:

   • Cleanse and preprocess the data obtained from both sources to ensure consistency and accuracy.
   • Handle missing values, duplicates, and other anomalies in the data.

3. Exploratory Data Analysis (EDA):

   • Perform exploratory data analysis to uncover patterns and trends in my music listening habits.
   • Visualize the distribution of genres, artists, and tracks in my listening history.
   • Identify correlations between different variables such as time of day, day of week, and music preferences.

4. Feature Engineering:

   • Extract relevant features from the data to facilitate further analysis.
   • Create new variables or metrics to capture specific aspects of my music preferences.

5. Modeling and Analysis:

   • Conduct cluster analysis to group similar artists, or tracks based on my behavior.
   • Explore sentiment analysis to understand the emotional response associated with different types of music.

6. Insights and Recommendations:

   • Summarize findings from the analysis and derive actionable insights about my music preferences.
   • Provide personalized recommendations for new music, playlists, or artists based on the analysis results.
   • Offer suggestions for improving my music listening experience on Spotify.

Tools and Technologies:

• Python programming language
• Pandas, NumPy for data manipulation
• Matplotlib, Seaborn for data visualization
• Scikit-learn for machine learning algorithms
• Spotify API for data retrieval
• Jupyter Notebook for interactive development and documentation

Overview of the Data

• Streaming History Music
• Streaming History Podcast
• Spotify API Data
• Spotify Data