Spam detection pipeline in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the necessary library for text vectorization.

NLP

from sklearn.feature_extraction.text import [1]

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ATfidfVectorizer

BStandardScaler

CLabelEncoder

DCountVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing CountVectorizer which counts words but doesn't weigh them.

Using LabelEncoder which is for labels, not text features.

The TfidfVectorizer converts text data into numerical features based on term frequency-inverse document frequency, which is commonly used in spam detection.

2fill in blank

medium

Complete the code to split the dataset into training and testing sets.

NLP

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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A1.0

B0.5

C0.1

D0.2

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1.0 which means all data is test set, leaving no training data.

Using 0.5 which splits data evenly but reduces training data too much.

Using test_size=0.2 splits 20% of data for testing, which is a common practice to evaluate model performance.

3fill in blank

hard

Fix the error in the model training code by completing the missing classifier name.

NLP

from sklearn.naive_bayes import [1]
model = [1]()
model.fit(X_train, y_train)

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AGaussianNB

BBernoulliNB

CMultinomialNB

DComplementNB

Attempts:

3 left

💡 Hint

Common Mistakes

Using GaussianNB which assumes continuous features, not suitable for text counts.

Using BernoulliNB which is for binary features, less common for tf-idf.

MultinomialNB is the best choice for text classification tasks like spam detection because it works well with discrete features such as word counts or tf-idf.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length if the length is greater than 3.

NLP

word_lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using less than '<' instead of greater than '>' in the condition.

The dictionary comprehension maps each word to its length using len(word) and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary from data where keys are uppercase and values are positive.

NLP

filtered = [1]: [2] for k, v in data.items() if v [3] 0}

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Using '<' instead of '>' in the condition.

The dictionary comprehension uses k.upper() to convert keys to uppercase, keeps values v, and filters for values greater than zero using >.