Contents
Multiple Choice Questions (MCQs)
1. Which dataset is used in this project?
a. CIFAR-10 Dataset
b. Iris Dataset
c. HuffPost News Category Dataset
d. Titanic Dataset
Answer: c. HuffPost News Category Dataset
The project uses the HuffPost News Category Dataset to recommend similar news articles.
2. Which feature extraction technique is used to convert text into numerical values?
a. Linear Regression
b. K-Means Clustering
c. TF-IDF Vectorization
d. Decision Tree
Answer: c. TF-IDF Vectorization
TF-IDF converts textual information into numerical feature vectors for similarity analysis.
3. Which technique is used to calculate the similarity between news articles?
a. Euclidean Distance
b. Cosine Similarity
c. Manhattan Distance
d. Logistic Regression
Answer: b. Cosine Similarity
Cosine Similarity measures how closely two news articles are related based on their textual content.
4. What type of recommendation system is implemented in this project?
a. Collaborative Filtering
b. Hybrid Recommendation
c. Content-Based Recommendation
d. Knowledge-Based Recommendation
Answer: c. Content-Based Recommendation
The recommendation system suggests news articles by comparing the content of each article.
5. Which Python library provides the TF-IDF Vectorizer used in this project?
a. TensorFlow
b. OpenCV
c. Scikit-learn
d. Keras
Answer: c. Scikit-learn
Scikit-learn provides the TF-IDF Vectorizer and Cosine Similarity functions used in the recommendation system.










