https://github.com/okerx/spotifymoods
A simple ML model to classify Spotify tracks using audio features.
https://github.com/okerx/spotifymoods
machine-learning pandas python scikit-learn
Last synced: 3 months ago
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A simple ML model to classify Spotify tracks using audio features.
- Host: GitHub
- URL: https://github.com/okerx/spotifymoods
- Owner: okerx
- License: mit
- Created: 2022-01-02T13:18:13.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-16T08:36:48.000Z (over 3 years ago)
- Last Synced: 2024-11-15T10:55:10.339Z (7 months ago)
- Topics: machine-learning, pandas, python, scikit-learn
- Language: Python
- Homepage:
- Size: 15.6 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# spotifymoods
A simple ML model to classify Spotify tracks using audio features.
## Installation
```shell
pip install spotifymoods
```
## Usage
### Train the model
```python
import pandas as pd
from spotifymoods import train
# create a Pandas DataFrame from the file 'training_data.csv'
train_data = pd.read_csv('training_data.csv')
# save the trained model to the files 'trained_model.pkl' & 'scaled.pkl'
train(data=train_data, trained_output='trained.pkl', scaled_output='scaled.pkl')
```
### Predict moods
```python
import pandas as pd
from spotifymoods import predict
# create a Pandas DataFrame from the file 'test_data.csv'
test_data = pd.read_csv('test_data.csv')
# use the trained model from the files 'trained.pkl' & 'scaled.pkl', and return the results as DataFrame
result = predict(data=test_data, trained_path='trained.pkl', scaled_path='scaled.pkl')
# output the results to a .csv file
result.to_csv('result.csv', index=False)
```
### CSV data examples
``training_data.csv``
```text
energy,liveness,tempo,speechiness,acousticness,instrumentalness,danceability,duration_ms,loudness,valence,id,mood
0.549,0.22,130.749,0.0698,0.000798,0.00485,0.357,244573,-7.843,0.531,3kdMzXOcrDIdSWLdONHNK5,Energetic
0.975,0.16,129.022,0.0618,5.13e-05,0.713,0.594,205760,-3.21,0.899,3rFEKOClXOdNFO6fQGuQ9j,Energetic
```
``test_data.csv``
```text
energy,liveness,tempo,speechiness,acousticness,instrumentalness,danceability,duration_ms,loudness,valence,id
0.549,0.22,130.749,0.0698,0.000798,0.00485,0.357,244573,-7.843,0.531,3kdMzXOcrDIdSWLdONHNK5
0.975,0.16,129.022,0.0618,5.13e-05,0.713,0.594,205760,-3.21,0.899,3rFEKOClXOdNFO6fQGuQ9j
```