Alteryx Advanced - Exercise 5

The video tutorial guides viewers through creating a regression model to predict house prices using an assisted modeling tool. It covers importing a CSV dataset, preparing data types, creating estimation and validation samples, and setting up the machine learning pipeline. The tutorial emphasizes selecting the target variable, choosing the right features, and comparing model results to identify the best model. Finally, it demonstrates validating the model and analyzing prediction accuracy using Random Forest.

What is the first step in preparing the data for a regression model?

• Importing the dataset and checking data types

• Running the machine learning pipeline

• Selecting the target variable

• Validating the model's predictions

How are the data samples divided for training and validation?

• 80% for training and 20% for validation

• 50% for training and 50% for validation

• 60% for training and 40% for validation

• 70% for training and 30% for validation

Which field should be selected as the target variable for predicting house prices?

• Grade

• Price

• Bathrooms

• Square feet

What should be done with columns that have unique values like ID and date?

• Use them as target variables

• Include them in the model

• Convert them to numeric

• Drop them from the model

Which features should be removed during the feature selection step?

• Features with numeric data types

• Features with boolean data types

• Features highly or weakly associated with the target

• Features with missing values

Which model was selected as the best based on performance metrics?

• Random Forest

• Linear Regression

• Decision Tree

• Support Vector Machine

What is the purpose of the validation sample in the model workflow?

• To test the model's predictions

• To convert data types

• To train the model

• To select the target variable