https://github.com/simratk05/spotify_power_bi-dashboard

Spotify dashboard Data Analysis dashboard using Power BI • Extracted and cleaned Spotify data from Kaggle, framing it for analysis. • Created an interactive Power BI dashboard to visualize key Spotify data metrics and trends. • Employed data analytics techniques to provide actionable insights and facilitate real-time data exploration
https://github.com/simratk05/spotify_power_bi-dashboard

datanalaysis kaggle ml powerbi

Last synced: 10 days ago
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Spotify dashboard Data Analysis dashboard using Power BI • Extracted and cleaned Spotify data from Kaggle, framing it for analysis. • Created an interactive Power BI dashboard to visualize key Spotify data metrics and trends. • Employed data analytics techniques to provide actionable insights and facilitate real-time data exploration

• Host: GitHub
• URL: https://github.com/simratk05/spotify_power_bi-dashboard
• Owner: simratk05
• Created: 2024-10-15T13:16:59.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-15T13:18:38.000Z (3 months ago)
• Last Synced: 2024-11-22T18:42:16.268Z (about 2 months ago)
• Topics: datanalaysis, kaggle, ml, powerbi
• Homepage:
• Size: 824 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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.