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Homework [DRAFT]

In this homework, we will use Bank credit scoring dataset from here.

Note: sometimes your answer doesn't match one of the options exactly. That's fine. Select the option that's closest to your solution.

Note: we recommend using python 3.10 in this homework.

Question 1

  • Install Pipenv
  • What's the version of pipenv you installed?
  • Use --version to find out

Question 2

  • Use Pipenv to install Scikit-Learn version 1.3.1
  • What's the first hash for scikit-learn you get in Pipfile.lock?

Note: you should create an empty folder for homework and do it there.

Models

We've prepared a dictionary vectorizer and a model.

They were trained (roughly) using this code:

features = ['job','duration', 'poutcome']
dicts = df[features].to_dict(orient='records')

dv = DictVectorizer(sparse=False)
X = dv.fit_transform(dicts)

model = LogisticRegression().fit(X, y)

Note: You don't need to train the model. This code is just for your reference.

And then saved with Pickle. Download them:

With wget:

PREFIX=https://raw.githubusercontent.com/DataTalksClub/machine-learning-zoomcamp/master/cohorts/2023/05-deployment/homework
wget $PREFIX/model1.bin
wget $PREFIX/dv.bin

Question 3

Let's use these models!

  • Write a script for loading these models with pickle
  • Score this client:
{"job": "retired", "duration": 445, "poutcome": "success"}

What's the probability that this client will get a credit?

  • 0.162
  • 0.392
  • 0.652
  • 0.902

If you're getting errors when unpickling the files, check their checksum:

$ md5sum model1.bin dv.bin
8ebfdf20010cfc7f545c43e3b52fc8a1  model1.bin
924b496a89148b422c74a62dbc92a4fb  dv.bin

Question 4

Now let's serve this model as a web service

  • Install Flask and gunicorn (or waitress, if you're on Windows)
  • Write Flask code for serving the model
  • Now score this client using requests:
url = "YOUR_URL"
client = {"job": "unknown", "duration": 270, "poutcome": "failure"}
requests.post(url, json=client).json()

What's the probability that this client will get a credit?

  • 0.140
  • 0.440
  • 0.645
  • 0.845

Docker

Install Docker. We will use it for the next two questions.

For these questions, we prepared a base image: svizor/zoomcamp-model:3.10.12-slim. You'll need to use it (see Question 5 for an example).

This image is based on python:3.10.12-slim and has a logistic regression model (a different one) as well a dictionary vectorizer inside.

This is how the Dockerfile for this image looks like:

FROM python:3.10.12-slim
WORKDIR /app
COPY ["model2.bin", "dv.bin", "./"]

We already built it and then pushed it to svizor/zoomcamp-model:3.10.12-slim.

Note: You don't need to build this docker image, it's just for your reference.

Question 5

Download the base image svizor/zoomcamp-model:3.10.12-slim. You can easily make it by using docker pull command.

So what's the size of this base image?

  • 47 MB
  • 147 MB
  • 374 MB
  • 574 MB

You can get this information when running docker images - it'll be in the "SIZE" column.

Dockerfile

Now create your own Dockerfile based on the image we prepared.

It should start like that:

FROM svizor/zoomcamp-model:3.10.12-slim
# add your stuff here

Now complete it:

  • Install all the dependencies form the Pipenv file
  • Copy your Flask script
  • Run it with Gunicorn

After that, you can build your docker image.

Question 6

Let's run your docker container!

After running it, score this client once again:

url = "YOUR_URL"
client = {"job": "retired", "duration": 445, "poutcome": "success"}
requests.post(url, json=client).json()

What's the probability that this client will get a credit now?

  • 0.168
  • 0.530
  • 0.730
  • 0.968

Submit the results

  • Submit your results here: TBA
  • If your answer doesn't match options exactly, select the closest one