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https://github.com/nurulashraf/linear-regression-spotify
Data Science - Spotify Linear Regression Analysis
https://github.com/nurulashraf/linear-regression-spotify
data-analysis data-preprocessing data-visualization dataset-exploration feature-selection linear-regression machine-learning matplotlib mean-squared-error model-evaluation multiple-regression music-analytics numpy predictive-modeling python regression-analysis root-mean-squared-error scikit-learn seaborn spotify-data
Last synced: 1 day ago
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Data Science - Spotify Linear Regression Analysis
- Host: GitHub
- URL: https://github.com/nurulashraf/linear-regression-spotify
- Owner: nurulashraf
- License: mit
- Created: 2024-12-14T13:01:36.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-02-03T07:35:04.000Z (11 days ago)
- Last Synced: 2025-02-03T08:30:17.104Z (11 days ago)
- Topics: data-analysis, data-preprocessing, data-visualization, dataset-exploration, feature-selection, linear-regression, machine-learning, matplotlib, mean-squared-error, model-evaluation, multiple-regression, music-analytics, numpy, predictive-modeling, python, regression-analysis, root-mean-squared-error, scikit-learn, seaborn, spotify-data
- Language: Jupyter Notebook
- Homepage:
- Size: 12 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Linear Regression Analysis on Spotify Dataset
This repository contains an analysis of Spotify data using linear regression techniques. The analysis is performed using Jupyter Notebooks (`.ipynb`), making it easy to follow along with the steps and reproduce the results.
---
## Repository Structure
```plaintext
linear-regression-spotify/
├── README.md # Main documentation file
├── LICENSE # Licensing information
├── requirements.txt # Dependencies
├── data/ # Directory for datasets
│ └── spotify_track.csv # Dataset file
├── src/ # Source code directory
│ ├── simple_linear_regression.ipynb
│ ├── multiple_linear_regression_2vars.ipynb
│ └── multiple_linear_regression_more_vars.ipynb
```
### File Descriptions
- **`data/spotify_track.csv`**: Contains the Spotify data used in the analysis.
- **`src/simple_linear_regression.ipynb`**: Demonstrates a single-variable linear regression model.
- **`src/multiple_linear_regression_2vars.ipynb`**: Explores a linear regression model with two variables.
- **`src/multiple_linear_regression_more_vars.ipynb`**: Applies a linear regression model with multiple variables.
---
## Requirements
To run the Jupyter Notebooks, you need the following dependencies:
- Python 3.8 or higher
- Jupyter Notebook or JupyterLab
- Libraries listed in `requirements.txt`
Install the dependencies using pip:
```bash
pip install -r requirements.txt
```
---
## Running the Notebooks
1. Clone the repository:
```bash
git clone https://github.com/nurulashraf/linear-regression-spotify.git
cd linear-regression-spotify
```
2. Launch Jupyter Notebook or JupyterLab:
```bash
jupyter notebook
```
or
```bash
jupyter lab
```
3. Open the desired `.ipynb` file in your browser.
4. Run the cells sequentially to reproduce the results.
---
## Analysis Overview
### Simple Linear Regression
This notebook examines the relationship between one independent variable and a dependent variable using a simple linear regression model.
### Multiple Linear Regression (2 Variables)
This notebook expands the analysis by including two independent variables in the model.
### Multiple Linear Regression (More Variables)
This notebook builds a comprehensive model using multiple independent variables to predict the dependent variable.
---
## Dataset
The dataset is stored in the `data/spotify_track.csv` file. It contains the necessary features for performing the analysis. If you use a custom dataset, ensure it follows the same format.
---
## Visualisations
The Jupyter Notebooks include various visualisations to aid understanding:
- Scatter plots
- Regression lines
- Residual plots
- Metrics comparison charts
---
## Contributing
Contributions are welcome! If you find any issues or have suggestions for improvement, feel free to open an issue or submit a pull request.
---
## License
This project is licensed under the terms of the [MIT License](LICENSE).