Sentiment analysis with scikit-learn in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the class used to convert text data into numbers.

ML Python

from sklearn.feature_extraction.text import [1]

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

ALinearRegression

BCountVectorizer

CKMeans

DDecisionTreeClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a regression or clustering class instead of a text vectorizer.

Confusing the vectorizer with a classifier.

The CountVectorizer class converts text into a matrix of token counts, which is essential for text-based machine learning tasks like sentiment analysis.

2fill in blank

medium

Complete the code to create a logistic regression model for sentiment classification.

ML Python

from sklearn.linear_model import [1]

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

ALinearRegression

BSVC

CLogisticRegression

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using linear regression which is for continuous values, not classification.

Choosing clustering or tree-based models by mistake.

LogisticRegression is commonly used for binary classification tasks like sentiment analysis.

3fill in blank

hard

Fix the error in the code to fit the vectorizer on the training text data.

ML Python

vectorizer = CountVectorizer()
vectorizer.[1](train_texts)

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

Afit

Btransform

Cpredict

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform before fitting causes errors.

Using predict on the vectorizer which is not a model.

The fit method learns the vocabulary from the training texts. Using fit_transform would both fit and transform, but here only fitting is needed before separate transformation.

4fill in blank

hard

Fill both blanks to transform training texts and train the logistic regression model.

ML Python

X_train = vectorizer.[1](train_texts)
model.[2](X_train, train_labels)

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

Afit_transform

Bfit

Ctransform

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without fitting vectorizer first.

Trying to predict before training the model.

First, fit_transform converts texts to numbers and learns vocabulary. Then, fit trains the model on these features and labels.

5fill in blank

hard

Fill all three blanks to predict sentiment on test data and calculate accuracy.

ML Python

X_test = vectorizer.[1](test_texts)
predictions = model.[2](X_test)
accuracy = accuracy_score(test_labels, [3])

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

Atransform

Bpredict

Cpredictions

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit on test data which leaks training info.

Passing wrong variable to accuracy function.

We transform test texts with transform, predict labels with predict, and then compare predictions to true labels to get accuracy.