/ML-zomcamp-Homework-5

Primary LanguagePython

ML-zomcamp-Homework-5

Homework

In this homework, we will use Credit Card Data from the previous homework.

Question 1

  • Install Pipenv
  • What's the version of pipenv you installed?

Answer : 2022.10.10

Question 2

  • Use Pipenv to install Scikit-Learn version 1.0.2
  • What's the first hash for scikit-learn you get in Pipfile.lock?

Answer : 62db916eaa3ba201789358b59e73eb7630266ef79c9c3f4d67236779aaf5f04a

Models

We've prepared a dictionary vectorizer and a model.

They were trained (roughly) using this code:

features = ['reports', 'share', 'expenditure', 'owner']
dicts = df[features].to_dict(orient='records')

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

model = LogisticRegression(solver='liblinear').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/alexeygrigorev/mlbookcamp-code/master/course-zoomcamp/cohorts/2022/05-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:
{"reports": 0, "share": 0.001694, "expenditure": 0.12, "owner": "yes"}

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

  • 0.162
  • 0.391
  • 0.601
  • 0.993

Answer : 0.162

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 = {"reports": 0, "share": 0.245, "expenditure": 3.438, "owner": "yes"}
requests.post(url, json=client).json()

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

  • 0.274
  • 0.484
  • 0.698
  • 0.928

Answer : 0.928

Docker

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

For these questions, we prepared a base image: svizor/zoomcamp-model:3.9.12-slim. You'll need to use it (see Question 5 for an example).

This image is based on python:3.9.12-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:3.9.12-slim
WORKDIR /app
COPY ["model2.bin", "dv.bin", "./"]

We already built it and then pushed it to svizor/zoomcamp-model:3.9.12-slim.

Question 5

Download the base image svizor/zoomcamp-model:3.9.12-slim. You can easily make it by using docker pull command.

So what's the size of this base image?

  • 15 Mb
  • 125 Mb
  • 275 Mb
  • 415 Mb

Answer: 125 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:3.9.12-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 = {"reports": 0, "share": 0.245, "expenditure": 3.438, "owner": "yes"}
requests.post(url, json=client).json()

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

  • 0.289
  • 0.502
  • 0.769
  • 0.972

Answer : 0.769