MCQs

1. What type of machine learning problem is house price prediction?

a. Classification

b. Clustering

c. Regression

d. Reinforcement Learning

Answer: c. Regression

House price prediction involves predicting continuous numerical values, making it a regression problem.

2. What is the purpose of the train-test split?

a. To increase dataset size

b. To evaluate model performance on unseen data

c. To remove missing values

d. To improve visualization

Answer: b. To evaluate model performance on unseen data

The train-test split helps measure how well the model generalizes to new data.

3. Which algorithm is used in this project?

a. Logistic Regression

b. K-Means

c. Random Forest Classifier

d. XGBoost Regressor

Answer: d. XGBoost Regressor

XGBoost is a powerful ensemble learning algorithm commonly used for regression and prediction tasks.

4. What does the R² Score measure?

a. Average prediction error

b. Percentage of missing values

c. How well the model explains variation in the target variable

d. Number of features in the dataset

Answer: c. How well the model explains variation in the target variable

A higher R² Score indicates better predictive performance.

5. Why is Exploratory Data Analysis (EDA) important?

a. To deploy the application

b. To understand data patterns and relationships

c. To replace machine learning algorithms

d. To increase dataset size

Answer: b. To understand data patterns and relationships

EDA helps identify important features, correlations, and potential issues before model training.