AI training-Phase 1-Day2

What is data preprocessing in the context of data science?

• Creating data visualizations

• Deploying machine learning models

• Analyzing data patterns.

• Cleaning and transforming raw data into a usable format.

Which of the following techniques is used for handling missing data?

• Scaling

• imputation

• feature selection

• standardization

What is the purpose of data normalization?

• To scale numerical features to a standard range

• To convert categorical variables into numerical ones

• To fill missing values with the mean of the data

• To remove outliers from the dataset.

What technique is used to convert categorical variables into numerical ones?

• One-Hot Encoding
• Feature Scaling

• Feature selection

• Feature engineering

1.     Which of the following is NOT a task in data preprocessing?

• Feature engineering

• Data cleaning

• Data transformation

• Data engineering

What is an outlier?

• An outlier is a data point that is irrelevant to the dataset

• An outlier is a data point that is always correct
• An outlier is a data point that differs significantly from other observations in a dataset.

• An outlier is a data point that is the average of all observations

What is the purpose of feature scaling in data preprocessing?

• To convert categorical variables into numerical ones

• To scale numerical features to a standard range

• To fill missing values with the mean of the data

• To remove outliers from the dataset

What is the importance of feature engineering in machine learning?

• It helps in deploying machine learning models

• It involves creating data visualizations

• It focuses on analyzing data patterns

• It enhances the predictive power of machine learning algorithms

What is the role of data encoding in data preprocessing?

• To convert categorical variables into numerical ones

• To scale numerical features to a standard range

• To fill missing values with the mean of the data

• To remove outliers from the dataset

Why feature selection is important in data preparation?

• Feature selection is only relevant for small datasets
• Feature selection helps in improving model performance

• Feature selection has no impact on model performance
• Feature selection increases data complexity