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15. Which data is used to evaluate the performance of the AI model?
a. Training data b. Testing Data
c. Raw data d. Unstructured data
B. Fill in the blanks.
1. The library needs to be imported in Python in order to plot graphs.
2. Depending on the kind of data being collected, there are two main categories of data collection methods:
and .
3. is collecting data on the Earth's surface and atmosphere through satellite.
4. involves collecting, exploring, and presenting large datasets to identify patterns and trends.
5. High variance suggests data points are widely dispersed from the mean and to .
6. Training data is in size than testing data.
7. A is a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with
labelled axis (rows and columns).
8. scales the data to a common range.
C. State whether the following statement is true or false.
1. It is a well-known fact that Artificial Intelligence (AI) is fundamentally driven by data.
2. Collecting large amounts of data can be the hardest part of a machine learning project.
3. Primary data sources might save time and resources.
4. Ordinal data consists of categories arranged in a random manner.
5. Central tendency helps in predicting future trends and making informed decisions based on
historical patterns.
6. We can have multiple mode values.
7. Non-graphical strategies are ineffective when we want to make decisions based on a set of data.
8. xlabel( ) gives the value limit for Y-axis.
SECTION B (Subjective Type Questions)
A. Short answer type questions.
1. Define a primary data source.
Ans. A primary data source refers to the original source from which data is collected firsthand. This data is obtained
directly from its origin, without any intermediary sources or interpretations. Primary data sources include surveys,
interviews, observations, experiments, and any other method where data is collected directly by the researcher or
organisation for a specific purpose. This type of data is considered valuable because it is tailored to the specific
research or business needs and is often more accurate.
2. How does statistical methods and visualisation tools help to explore data?
Ans. Data exploration use statistical methods and visualisation tools to:
• Evaluate the size and quality of the data.
• Detect outliers or anomalies.
• Identify possible links between data components, files, and tables.
• Look for similarity, patterns, and outliers.
• Determine the relationships between different variables.
310 Touchpad Artificial Intelligence (Ver. 3.0)-XI

