# sklearnflask

**Repository Path**: noskystar/sklearnflask

## Basic Information

- **Project Name**: sklearnflask
- **Description**: Flask API for training and predicting using scikit learn models
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-07-31
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Flask API for scikit learn
A simple Flask application that can serve predictions from a scikit-learn model. Reads a pickled sklearn model into memory when the Flask app is started and returns predictions through the /predict endpoint. You can also use the /train endpoint to train/retrain the model. Any sklearn model can be used for prediction.

Read more in [this blog post](https://medium.com/@amirziai/a-flask-api-for-serving-scikit-learn-models-c8bcdaa41daa).

### Dependencies
- scikit-learn
- Flask
- pandas
- numpy

```
pip install -r requirements.txt
```

### Running API
```
python main.py <port>
```

# Endpoints
### /predict (POST)
Returns an array of predictions given a JSON object representing independent variables. Here's a sample input:
```
[
    {'Age': 85, 'Sex': 'male', 'Embarked': 'S'},
    {'Age': 24, 'Sex': 'female', 'Embarked': 'C'},
    {'Age': 3, 'Sex': 'male', 'Embarked': 'C'},
    {'Age': 21, 'Sex': 'male', 'Embarked': 'S'}
]
```

and sample output:
```
{'prediction': [0, 1, 1, 0]}
```


### /train (GET)
Trains the model. This is currently hard-coded to be a random forest model that is run on a subset of columns of the titanic dataset.

### /wipe (GET)
Removes the trained model.