> For the complete documentation index, see [llms.txt](https://docs.akkio.com/akkio-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.akkio.com/akkio-docs/rest-api/api-introduction/quickstart.md).
# Quickstart
Get up and running with the Akkio API using one of our convenient libraries.
### API Keys
As noted in the code samples below, you must get your API keys and copy them into your API code. Those can be found under the [team settings page](https://app.akkio.com/team-settings) at the bottom of the Akkio app.
### Test Code
{% tabs %}
{% tab title="Node.js" %}
#### Installation
`npm install akkio --save`
#### Usage
```bash
const akkio = require('akkio')('your API key');
#get your API key at https://app.akkio.com/team-settings
(async () => {
#create a new dataset
let newDataset = await akkio.createDataset('my new dataset');
#populate it with some toy data
let rows = [];
for (var i = 0; i < 1000; i++) {
okay let x = Math.random();
rows.push({
'x': x,
'value larger than 0.5': x > 0.5,
});
}
await akkio.addRowsToDataset(newDataset.dataset_id, rows);
# train a model
let model = await akkio.createModel(newDataset.dataset_id, ['value larger than 0.5'], [], {
duration: 1
});
## field importance
for (let field in model.field_importance) {
console.log('field:', field, 'importance:', model.field_importance[field]);
}
# model stats
for (let field of model.stats) {
for (let outcome of field) {
console.log(outcome);
}
}
# use the trained model to make predictions
let predictions = await akkio.makePrediction(model.model_id, [{
'x': 0.25
}, {
'x': 0.75
}], {
explain: true
});
console.log(predictions);
})();
```
{% endtab %}
{% tab title="Python" %}
**Installation**
The akkio library is available on Python 3 and can be installed easily on your python instance. Run the following command or search your available packages for akkio (image also below.
`pip install akkio`
#### Usage
```python
import akkio
akkio.api_key = 'YOUR-API-KEY-HERE'
# get your API key at https://app.akkio.com/team-settings
models = akkio.get_models()['models']
for model in models:
print(model)
datasets = akkio.get_datasets()['datasets']
for dataset in datasets:
print(dataset)
new_dataset = akkio.create_dataset('python api test')
print(new_dataset)
# create a toy dataset
import random
rows = []
for i in range(1000):
rows.append({
'x': random.random()
})
rows[-1]['y'] = rows[-1]['x'] > 0.5
akkio.add_rows_to_dataset(new_dataset['dataset_id'], rows)
new_model = akkio.create_model(new_dataset['dataset_id'], ['y'], [], {'duration': 1})
print(new_model)
prediction = akkio.make_prediction(new_model['model_id'], [{'x': 0.1}, {'x':0.7}], explain=True)
print(prediction)
```
{% endtab %}
{% endtabs %}
Once you have installed the Akkio custom package and run the above Usage code you should be able to go to app.akkio.com and see an api test project as shown here.
---
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