Data Preprocessing

What is the main purpose of data preprocessing in machine learning?

• To reduce the size of the dataset

• To improve the accuracy of the model

• To make raw data suitable for analysis

• To generate new features

Which of the following is NOT a common data preprocessing step?

• Data Cleaning
  

• Data Encoding
  

• Data Compilation
  

• Feature Scaling

What technique is used to handle missing data by replacing it with the average of the column?

• Normalization
  

• Binarization
  

• Imputation
  

• Feature Scaling

Why is handling missing data important?

• Missing data does not affect the model's performance
  

• It improves the interpretability of the dataset
  

• Models cannot be trained if missing data exists
  

• It ensures that the model learns effectively from the data

What is the impact of outliers on a dataset?

• They have no effect on the dataset
  

• They can skew the results of the analysis
  

• They improve the model's accuracy
  

• They are always removed from the dataset

Which technique is used to convert categorical data into numerical format?

• Data Imputation

• Feature Scaling
  

• Label Encoding
  

• Data Cleaning
  

Which method is commonly used to detect and remove outliers in a dataset?

• Standardization
  

• Z-Score or IQR (Interquartile Range) method
  

• One-Hot Encoding
  

• Label Encoding

Which of these techniques can be used for data cleaning?

• Removing duplicates
  

• Handling missing values
  

• Removing outliers
  

• All of the above

Which encoding technique is most suitable for ordinal categorical data?

• One-Hot Encoding
  

• Label Encoding
  

• Min-Max Scaling
  

• Robust Scaling

What is the purpose of feature selection in data preprocessing?

• To remove irrelevant or redundant features.

• To create new features from existing ones.

• • To transform data into a different format.

• • To handle missing values.