With millions of tracks and diverse listening habits, Spotify provides a massive playground for data enthusiasts. This project focuses on Exploratory Data Analysis (EDA) of Spotify data. It involves transforming raw data formats into structured CSV files and then analyzing audio features, artist trends, and track characteristics to uncover meaningful insights.
- Data Extraction & Transformation: Converting raw/unstructured data formats into clean, structured
.csvfiles for analysis (convert to csv file.ipynb). - Data Cleaning & Preprocessing: Handling missing values, filtering relevant columns, and formatting data types.
- Exploratory Data Analysis (EDA): Deep diving into the dataset using statistical summaries and aggregations (
Spotify task.ipynb). - Data Visualization: Creating insightful charts and graphs to visualize audio features (like danceability, energy, tempo) and track popularity.
Spotify_Data_Analysis/
├── convert to csv file.ipynb # Script for data transformation and CSV extraction
├── Spotify task.ipynb # Main notebook containing Data Cleaning, EDA, and Visualizations
└── README.md # Project documentation
To run this analysis locally, follow these steps:
1. Clone the repository
git clone [https://github.com/MahmoudAhmmed/Spotify_Data_Analysis.git](https://github.com/MahmoudAhmmed/Spotify_Data_Analysis.git)
cd Spotify_Data_Analysis2. Install required libraries Ensure you have Python installed, then run the following command in your terminal:
pip install pandas numpy matplotlib seaborn jupyter3. Run the Jupyter Notebooks Launch Jupyter Notebook in your project directory:
jupyter notebook- Open
convert to csv file.ipynbif you need to generate or transform the initial dataset. - Open
Spotify task.ipynbto view the step-by-step Exploratory Data Analysis and visualizations.
Feel free to reach out if you have any questions or suggestions regarding this project.
