MLProject_01

This project impelemented for midterm of the Machine Learning #Zoomcamp #Alexey Grigorev

Context

Dataset

English question data set file

Feature Description

question answering

English data set data:

check answer

Create a Virtual Environment

Clone the repo:

git clone 
   
    
cd MLProject_01 

   

For the project, virtualenv is used. To install virtualenv:

pip install virtualenv

To create a virtual environment:

virtualenv venv

If it doesn't work then try:

python -m virtualenv venv

Activate the Virtual Environment:

For Windows:

.\venv\Scripts\activate

For Linux and MacOS:

source venv/bin/activate

Install Dependencies

Install the dependencies:

pip install -r requirements.txt

Build Docker Image

To build a Docker image:

docker build -t  .

TO run the image as a container:

docker run --rm -it -p 9696:9696 :latest

To test the prediction API running in docker, run _test.py locally.

Run the Jupyter Notebook

Run Jupiter notebook using the following command assuming we are inside the project directory:

jupyter notebook

Run the Model Locally

The final model training codes are exported in this file. To train the model:

python train.py

For local deployment, start up the Flask server for prediction API:

python predict.py

Or use a WSGI server, Waitress to run:

waitress-serve --listen=0.0.0.0:9696 predict:app

It will run the server on localhost using port 9696.

Finally, send a request to the prediction API http://localhost:9696/predict and get the response:

python predict_test.py

Run the Model in Cloud

The model is deployed on **Heroku ** and can be accessed using:

https://bank-marketing-system.herokuapp.com/predict

The API takes a JSON array of records as input and returns a response JSON array.

How to deploy a basic Flask application to Pythonanywhere can be found here. Only upload the .csv, train.py, and .py files inside the app directory. Then open a terminal and run train.py and predict.py files. Finally, reload the application. If everything is okay, then the API should be up and running.

To test the cloud API, again run _test.py from locally using the cloud API URL.