Training data pipeline automation in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a CSV file into a DataFrame using pandas.

MLOps

import pandas as pd
data = pd.read_csv([1])

Drag options to blanks, or click blank then click option'

Adata.csv

B'data.csv'

Cread.csv

Dcsv.read

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the filename

Using incorrect function names

To read a CSV file, the filename must be a string in quotes.

2fill in blank

medium

Complete the code to split data into training and testing sets using scikit-learn.

MLOps

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

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A0.2

B0.5

C2

D20

Attempts:

3 left

💡 Hint

Common Mistakes

Using integers instead of decimals

Setting test_size too large

test_size=0.2 means 20% of data is for testing, which is common practice.

3fill in blank

hard

Fix the error in the code to automate data scaling with StandardScaler.

MLOps

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.[1](X_train)

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Ascale

Btransform

Cfit

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without fitting first

Using fit without transforming

fit_transform fits the scaler and transforms the data in one step.

4fill in blank

hard

Fill both blanks to create a pipeline that scales data and fits a logistic regression model.

MLOps

from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
pipeline = Pipeline([('scaler', [1]()), ('model', [2]())])

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AStandardScaler

BMinMaxScaler

CLogisticRegression

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong scaler or model classes

Mixing up order of pipeline steps

The pipeline first scales data with StandardScaler, then fits LogisticRegression.

5fill in blank

hard

Fill all three blanks to automate training, prediction, and accuracy calculation.

MLOps

pipeline.fit([1], [2])
predictions = pipeline.predict([3])
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, predictions)

Drag options to blanks, or click blank then click option'

AX_train

By_train

CX_test

Dy_test

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up train and test sets

Using labels instead of features for prediction

Train with X_train and y_train, predict on X_test, then compare predictions to y_test.