What is the purpose of feature engineering during the Data Preparation stage?1 pointYou’ll use feature engineering to remove duplicate data valuesYou’ll use feature engineering to create meaningful characteristics for machine learningYou’ll use feature engineering to address missing data valuesYou’ll use feature engineering to create models and algorith
Question
What is the purpose of feature engineering during the Data Preparation stage?1 pointYou’ll use feature engineering to remove duplicate data valuesYou’ll use feature engineering to create meaningful characteristics for machine learningYou’ll use feature engineering to address missing data valuesYou’ll use feature engineering to create models and algorith
Solution
The purpose of feature engineering during the Data Preparation stage is to create meaningful characteristics for machine learning. This process involves transforming raw data into a format that is better understandable for both the machine learning algorithms and the data analysts.
Feature engineering can involve a wide range of activities, such as:
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Extracting features from raw data: This could involve extracting numbers from text, or creating new variables from existing ones (for example, creating a "total income" variable from "salary" and "bonus").
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Transforming variables: This could involve scaling variables (so they all have a similar range), or converting a continuous variable into a categorical one.
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Creating interaction features: This involves creating new variables that represent the interaction between two or more other variables. For example, if you have variables for "height" and "weight", you could create a new variable for "body mass index".
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Handling missing values: If some values are missing in your data, you can use feature engineering to fill in those missing values, either by using a simple method (like filling in the mean or median value), or by using a more complex method (like using machine learning to predict the missing values).
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Removing duplicate data values: This is also a part of data cleaning which ensures that the data fed to the machine learning model is accurate and not repetitive.
However, it's important to note that feature engineering does not involve creating models and algorithms. That's a separate step in the machine learning process, which comes after the data has been prepared through feature engineering.
Similar Questions
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Select the correct statements that describe what data scientists do during the Data Preparation stage.1 pointDuring the Data Preparation stage, data scientists determine the timing of eventsDuring the Data Preparation stage, data scientists separate data and data sources.During the Data Preparation stage, data scientists define the variables to be used in the model.During the Data Preparation stage, data scientists ignore missing data
Which of the following stages contributes to the creation of relevant and informative data elements for the model based on statistical analysis from the exploration stage and business domain expertise?Select one:a. Feature Engineeringb. Data Acquisition and Explorationc. Ideation and Problem definitiond. Model Development
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Question 2What is the purpose of data preprocessing in data mining?1 pointTo develop a formal method for storing dataTo identify the right kind of data needed for data miningTo transform variables from one type to anotherTo ensure the integrity of data, deal with missing data, and remove irrelevant attributes
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