Contents
Multiple Choice Questions (MCQs)
1. Which dataset is used in this project?
a. MNIST Dataset
b. Iris Dataset
c. Fake Reviews Dataset
d. Titanic Dataset
Answer: c. Fake Reviews Dataset
The project uses the Fake Reviews Dataset to classify customer reviews as fake or genuine.
2. Which feature extraction technique is used to convert review text into numerical values?
a. PCA
b. TF-IDF Vectorization
c. K-Means Clustering
d. Linear Regression
Answer: b. TF-IDF Vectorization
TF-IDF converts customer reviews into numerical feature vectors for machine learning.
3. Which machine learning algorithm is used in this project?
a. Decision Tree
b. K-Nearest Neighbors
c. Logistic Regression
d. Random Forest
Answer: c. Logistic Regression
Logistic Regression is used to classify customer reviews into fake and genuine categories.
4. Which evaluation metric summarizes the model's prediction performance using True Positives, False Positives, True Negatives, and False Negatives?
a. Histogram
b. Confusion Matrix
c. Scatter Plot
d. Pie Chart
Answer: b. Confusion Matrix
The Confusion Matrix provides a detailed breakdown of the model's classification results.
5. Which Python library provides the TF-IDF Vectorizer and Logistic Regression used in this project?
a. TensorFlow
b. OpenCV
c. Scikit-learn
d. Keras
Answer: c. Scikit-learn
Scikit-learn provides both the TF-IDF Vectorizer and the Logistic Regression classifier used in this project.









