This project is an Exploratory Data Analysis (EDA) on the Spotify dataset. The dataset contains information about various songs, including their features such as danceability, energy, loudness, and more. Through this analysis, we aim to gain insights into the characteristics of the songs and explore any patterns or trends.
Before running the code, make sure you have the following dependencies installed:
- Python (3.x)
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
To get started, follow the steps below:
- Clone the repository:
git clone https://github.com/shaadclt/Spotify-Dataset-EDA.git- Change into the project directory:
cd Spotify-Dataset-EDA-
Install the required dependencies:
-
Run Jupyter Notebook:
jupyter notebook-
Open the
Spotify Dataset Analysis.ipynbnotebook in Jupyter. -
Run the notebook cells to load the dataset, perform exploratory data analysis, and visualize the insights.
The notebook provides a step-by-step guide to explore the Spotify dataset. The EDA includes the following tasks:
- Loading and understanding the dataset
- Data cleaning and preprocessing
- Statistical analysis of the song features
- Visualization of song characteristics
- Correlation analysis to identify relationships between features
- Exploring trends and patterns within the dataset
Throughout the analysis, various visualizations and statistical summaries are provided to showcase the findings. These insights may include relationships between song features, distributions of specific characteristics, or any other interesting observations. Feel free to refer to the notebook for detailed results and interpretations.
This EDA serves as a starting point, and you can customize the analysis based on your specific requirements. You can explore additional features, apply different visualization techniques, or extend the analysis to answer specific research questions related to the Spotify dataset.
This project is licensed under the MIT License. See the LICENSE file for more information.
- The analysis is inspired by the desire to understand the characteristics and patterns within the Spotify music collection.
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.