Answer-first summary for fast verification
Answer: Implement a time series forecasting model, preprocess data by handling missing values and creating lag features.
For forecasting future demand, a time series forecasting model is appropriate. Preprocessing steps include handling missing values, which is crucial for maintaining data integrity, and creating lag features to capture temporal dependencies in the data.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
As part of your role, you need to integrate predictive analytics into a report for a manufacturing company. The goal is to forecast future demand for their products based on historical sales data and external factors like economic indicators and seasonal trends. Describe how you would set up a predictive model using Azure Machine Learning in Microsoft Fabric, including the type of model you would choose and the data preprocessing steps necessary.
A
Use a linear regression model, preprocess data by normalizing and splitting into training and test sets.
B
Choose a decision tree model, preprocess data by encoding categorical variables and scaling numerical features.
C
Implement a time series forecasting model, preprocess data by handling missing values and creating lag features.
D
Select a neural network model, preprocess data by binning and reducing dimensionality.