Capstone Project Assignment

Preliminary information

Use the following to run scripts and application

• To setup the virtual environment for python (for example using conda) % conda create -n test2 python=3.7 anaconda

• To activate the virtual environment % conda activate test2

• To change theworking directory % cd to_dir_with_assigment

• To install the required python library $ pip install -r requirements.txt

Capstone Project: checklist

Question #1: Are there unit tests for the API?

• to test api (run api server before ) $ python -m unittest tests/api_tests.py

Question #2: Are there unit tests for the model?

• to test models: $ python -m unittest tests/Model_tests.py

Question #3: Are there unit tests for the logging?

• to test logging capabilites run: $ python -m unittest tests/Logger_tests.py

Question #4 Can all of the unit tests be run with a single script and do all of the unit tests pass?

• or run all tests $ python run_all_test.py

Question #5: Is there a mechanism to monitor performance?

$ python part-2.py

Question #6: Was there an attempt to isolate the read/write unit tests from production models and logs?

Question #7: Does the API work as expected? For example, can you get predictions for a specific country as well as for all countries combined?

• start api by running: $ python app.py

• open following url in browser:

http://localhost:8080/predict?country=united_kingdom&date=2019-05-05

http://localhost:8080/predict?country=all&date=2019-05-05

Question #8: Does the data ingestion exists as a function or script to facilitate automation?

helper/fetchlib.py helper/modeltools.py

Question #9: Were multiple models compared?

time-series-notebooks

Question #10: Did the EDA investigation use visualizations?

Capstone Part1.ipynb

Question #11: Is everything containerized within a working Docker image?

Dockerfile

Question #12: Did they use a visualization to compare their model to the baseline model?

time-series-notebooks

Question #13: Build the Docker image and run it

• build docker image $ docker build -t apiapp .

• Check that the image is there.

$ docker image ls

• run docker $ docker run -d -p 8080:8080 apiapp

• check API open below url in a browser:

http://localhost:8080/predict?country=united_kingdom&date=2019-05-05