This project is a Spotify Data Analysis Dashboard built using Power BI, designed to provide valuable insights into Spotify's data metrics. The project demonstrates a structured approach to data analysis, from extracting raw data to creating an interactive and visually appealing dashboard.
Project Overview Data Source: The dataset was sourced from Kaggle and contains various attributes about Spotify songs, such as song name, artist, album, genre, duration, and popularity metrics.
Data Preprocessing: The raw Spotify data was cleaned and prepped using data transformation techniques. This process involved handling missing values, normalizing data formats, and filtering unnecessary columns to streamline analysis. A MySQL query was used to organize and manage data effectively.
Power BI Dashboard Creation: The cleaned data was imported into Power BI, where an interactive dashboard was created. The dashboard includes visualizations such as:
Top Tracks & Artists: Visualizing the most popular tracks and artists based on metrics like play count and popularity score. Genre Distribution: Pie charts and bar graphs to analyze the distribution of songs across different genres. Popularity Trends Over Time: A time series analysis to showcase the rise or fall in popularity for tracks or albums over specific time periods. Feature Analysis: Visual representation of song attributes like tempo, energy, danceability, and mood, helping users explore the sonic characteristics of popular tracks. Key Features Data Exploration: The dashboard allows real-time exploration of Spotify data, giving users the ability to filter by genre, artist, or year, and drill down into detailed statistics. Actionable Insights: Data analytics techniques were employed to derive insights such as the correlation between song attributes and popularity, trends in user preferences, and evolving genres. User-Friendly Interface: The dashboard's design focuses on a seamless user experience, ensuring clarity and ease of data interpretation through visually engaging charts and graphs. This project highlights a comprehensive workflow from data extraction to actionable insights, offering a strong use case for utilizing Power BI in data-driven projects.