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Answer: To visualize and quantify the linear relationships between pairs of numerical variables in the dataset.
A correlation matrix is a fundamental tool in EDA that helps in understanding the linear relationships between numerical variables. It is particularly useful in the early stages of data analysis for identifying potential predictors that have a strong linear relationship with the target variable, in this case, housing prices. This aids in feature selection and understanding the data's structure before moving on to model building. The other options do not accurately describe the primary purpose of a correlation matrix: deploying the model (A) is a later stage activity, feature engineering (B) involves creating or modifying variables, and removing missing values (C) is part of data cleaning, not the primary role of a correlation matrix.
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In the context of Exploratory Data Analysis (EDA), a data scientist is working on a project to predict housing prices based on various features such as location, size, and age of the property. The dataset includes numerical and categorical variables. The team is in the initial stages of understanding the data's structure and relationships. Which of the following best describes the primary purpose of generating a correlation matrix in this scenario? Choose one correct option.
A
To directly deploy the predictive model into production.
B
To perform feature engineering by creating new variables based on existing ones.
C
To identify and remove all missing values from the dataset.
D
To visualize and quantify the linear relationships between pairs of numerical variables in the dataset.