Exploring Data Preprocessing Techniques

What is data preprocessing in data mining?

• Data preprocessing involves only data visualization techniques.

• Data preprocessing is the process of cleaning and transforming raw data into a suitable format for analysis in data mining.

• Data preprocessing is the process of storing data in a database.

• Data preprocessing is the final step in data mining.

Why is data cleaning important in preprocessing?

• Data cleaning only affects the visual presentation of data.

• Data cleaning is unnecessary for small datasets.

• Data cleaning is primarily for data storage optimization.

• Data cleaning is important because it improves data quality and ensures accurate analysis.

Name three common techniques used for data cleaning.

• Removing duplicates, handling missing values, correcting inconsistencies

• Data encryption methods

• Machine learning algorithms

• Data visualization techniques

What is normalization and why is it used?

• Normalization is a method to increase data redundancy.

• Normalization is a technique for data encryption.

• Normalization is used to enhance data retrieval speed.

• Normalization is used to reduce data redundancy and improve data integrity in database design.

Explain the difference between standardization and normalization.

• Standardization and normalization are the same process with different names.

• Standardization is used for categorical data; normalization is for numerical data.

• Standardization scales data to a range of [-1, 1]; normalization adjusts to a mean of 0.

• Standardization adjusts data to a mean of 0 and standard deviation of 1; normalization rescales data to a range of [0, 1].

What role does data transformation play in preprocessing?

• Data transformation ensures that data is in the right format and scale for effective analysis and modeling.

• Data transformation only changes the data's visual representation.

• Data transformation is irrelevant to data analysis.

• Data transformation is only necessary for large datasets.

What is the purpose of feature selection?

• The purpose of feature selection is to improve model performance and reduce complexity by selecting relevant features.

• To increase the number of features in the model

• To eliminate all features from the dataset

• To ensure all features are equally weighted in the model

How can missing values be handled during preprocessing?

• Replace missing values with the mean of the dataset

• Remove, impute, or use algorithms that handle missing values.

• Assume all missing values are zero

• Ignore missing values completely

What is the impact of outliers on data analysis?

• Outliers can distort statistical results and lead to inaccurate conclusions.

• Outliers are always beneficial for drawing conclusions.

• Outliers have no effect on data analysis.

• Outliers improve the accuracy of statistical results.

Describe the process of data discretization and its benefits.

• Data discretization simplifies data analysis, improves model performance, enhances interpretability, and reduces noise by focusing on significant patterns.

• Data discretization increases data complexity and reduces model accuracy.

• Data discretization eliminates all noise and guarantees perfect model performance.

• Data discretization is only useful for categorical data and has no impact on numerical data.