Multiple Choice Questions (MCQs)

1. What is the primary objective of this project?

a. Detect fraudulent transactions

b. Predict stock closing prices

c. Classify weather conditions

d. Recommend movies

Answer: b. Predict stock closing prices

The project focuses on forecasting future stock closing prices using historical market data.

2. Which machine learning algorithm is implemented in this project?

a. Random Forest

b. Linear Regression

c. K-Nearest Neighbors (KNN)

d. Decision Tree

Answer: c. K-Nearest Neighbors (KNN)

KNN Regression predicts stock prices by analyzing the nearest historical data points.

3. Which feature engineering technique is used in this project?

a. Customer segmentation

b. Image augmentation

c. Open–Close and High–Low price differences

d. One-Hot Encoding

Answer: c. Open–Close and High–Low price differences

These engineered features summarize daily price movement and market volatility.

4. Why is GridSearchCV used?

a. To clean the dataset

b. To visualize stock prices

c. To identify the best value of K for the KNN model

d. To calculate RMSE

Answer: c. To identify the best value of K for the KNN model

GridSearchCV automatically evaluates multiple values of K and selects the one that provides the best performance.

5. Which metric measures the average prediction error in the same units as the stock price?

a. Accuracy

b. RMSE

c. Precision

d. Recall

Answer: b. RMSE

Root Mean Squared Error (RMSE) measures prediction error using the same units as the target variable, making it easier to interpret.