Basic ML and Preprocessing

How do you handle missing or corrupted data in a dataset?

•  Drop missing rows or columns

• Replace missing values with mean/median/mode

• Assign a unique category to missing values

• All of the above

Data cleaning is

• Large collection of data mostly stored in a computer system

• The removal of noise errors and incorrect input from a database

• The systematic description of the syntactic structure of a specific database. It describes the structure of the attributes the tables and foreign key relationships.

• None of these

What are the functions of Data Mining?

• Association and correctional analysis classification

• Prediction and characterization

• Cluster analysis and Evolution analysis

• All of the above

Which one of the following statements is not correct about the data cleaning?

• It refers to the process of data cleaning

• It refers to the transformation of wrong data into correct data

• It refers to correcting inconsistent data

• All of the above

Which one of the following can be considered as the correct application of the data mining?

• Fraud detection

• Corporate Analysis & Risk management

• Management and market analysis

• All of the above

In certain cases, it is not clear what kind of pattern need to find, data mining should_________:

• Try to perform all possible tasks

• Perform both predictive and descriptive task

• It may allow interaction with the user so that he can guide the mining process

• All of the above

To remove noise and inconsistent data ____ is needed.

• Data Cleaning

• Data Transformation

• Data Reduction

• Data Integration

Which Method is used for encoding the categorical variables?

• LabelEncoder

• OneHotEncoder

• CategoryEncoder

• All of the Above

What are some examples of data quality problems:

• Noise and outliers

• Duplicate data

• Missing values

• All of the Above

Examples of Nominal can be:

• ID Numbers, eye colour, zip codes

• Rankings, taste of potato chips, grades, height

• Calendar dates, temperatures in celsius or Fahrenheit, phone numbers

• The temperature in Kelvin, length, time, counts