This project aims to analyze Spotify song data to uncover insights on song popularity, artist trends, and track characteristics. The workflow involves data cleaning using Python, storing structured data in an SQL database, and creating an interactive Power BI dashboard for visualization.
✔ Defined project objectives and approach.
✔ Acquired and assessed the Spotify dataset.
✔ Outlined the workflow for data processing, SQL integration, and dashboard creation.
✔ Loaded the dataset using pandas.
✔ Performed Exploratory Data Analysis (EDA) to identify missing values and outliers.
✔ Cleaned the data by handling missing values and standardizing formats.
✔ Created new derived columns for better analysis.
✔ Saved the cleaned dataset for SQL integration.
✔ Designed a relational database schema to store the cleaned dataset.
✔ Used SQLAlchemy/pandas to load data into SQL tables.
✔ Optimized queries for efficient data retrieval.
✔ Connected Power BI to the SQL database.
✔ Built an interactive dashboard to visualize song popularity, artist trends, and track characteristics.
✔ Implemented filters and visualizations to analyze different aspects of the dataset.
🔹 Python (pandas, matplotlib, seaborn, SQLAlchemy)
🔹 SQL (database setup and queries)
🔹 Power BI (dashboard creation and visualization)
1️⃣ Clone the repository:
git clone https://github.com/yourusername/spotify-data-analytics.git2️⃣ Install dependencies:
pip install pandas sqlalchemy matplotlib seaborn3️⃣ Run the Python script for data cleaning.
4️⃣ connect the cleaned dataset to an SQL database via python.
5️⃣ Connect Power BI to SQL and open the dashboard file.
🎵 Identified top artists and songs by popularity.
📊 Analyzed trends in song features (duration, tempo, energy).
🎼 Explored popular genres and their characteristics.
This project provides a data-driven approach to understanding music trends on Spotify. The integration of Python, SQL, and Power BI allows for efficient data processing and insightful visualizations. Future enhancements may include real-time data updates or predictive modeling.
📢 Feel free to explore the repository and contribute! 🚀