Data Exploration and Variable Identification

The video tutorial covers data exploration and variable preparation for predictive analytics. It focuses on identifying predictor and target variables based on problem definitions, using a Kaggle competition dataset as an example. The tutorial demonstrates importing necessary libraries and datasets, identifying data types, and separating categorical and numerical columns.

What is the primary focus of data exploration in this session?

• Variable identification

• Data visualization

• Model training

• Data cleaning

How can you determine if a variable is a predictor or target variable?

• By using a specific library

• By analyzing the problem definition

• By checking the data type

• By looking at the variable name

Which library is NOT mentioned as being imported for data exploration?

• numpy

• scikit-learn

• pandas

• matplotlib

In the Kaggle competition example, what is the target variable?

• House size

• Number of rooms

• Sales price

• Location

How many predictor variables are mentioned in the Kaggle competition example?

• 38

• 43

• 81

• 79

What method is used to select categorical columns in the dataset?

• get_categorical

• filter_columns

• categorical_select

• select_dtypes

What data type is associated with numerical variables?

• boolean

• int64 and float

• string

• object

How many categorical columns are identified in the dataset?

• 79

• 43

• 38

• 81

What is the purpose of bifurcating categorical and numerical columns?

• To improve data visualization

• To simplify data analysis

• To prepare for model training

• To enhance data cleaning

What is the next step after variable identification in data exploration?

• Data cleaning

• Univariate analysis

• Model evaluation

• Bivariate analysis