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Ans. a.

Modelling Widgets Evaluation Widgets
Modelling widgets facilitate a wide range of Evaluation widgets are used to assess the performance
modelling techniques, allowing users to apply and effectiveness of machine learning models. These
various algorithms, and interpret results widgets allow users to validate predictions, compare
without needing extensive programming models, and visualise results based on various
knowledge. evaluation metrics.
b.
Feature Selection widgets Visualization Widgets

Feature Selection widgets are used for Visualization widgets provide various tools to
exploring the data and help perform different understand and interpret the data effectively. These
operations on data. widgets help to visualise data in different ways.

F. Long answer type questions.
1. Explain the significance of Python packages like NumPy, Pandas, and Matplotlib in the context of Data
Science. How do these packages contribute to data analysis and visualization?
Ans. Python packages such as NumPy handle numerical operations, Pandas facilitate data manipulation and
analysis with flexible structures, and Matplotlib aids in data visualization. Together, they make data
analysis and interpretation easier for data scientists.
2. Describe the role of statistical measures like Mean, Median, and Variance in data analysis for AI projects?
How do these measures contribute to understand and interpret data patterns?

Ans. Mean gives the average, Median represents the middle value, and Variance measures data spread. These
statistics help analyze data patterns, understand central tendencies, and gauge data variability, forming
the basis for making informed decisions in AI projects.

3. How does the Evaluation phase in the AI project life cycle play a critical role? Explain the steps involved
and why it is necessary to assess the model’s accuracy before deployment.
Ans. The Evaluation phase involves feeding data into the trained model, predicting outcomes, comparing
predictions with actual values, and checking accuracy. This step ensures the model produces desired
results and meets project objectives before real-time deployment.
4. Discuss the importance of data types in Python for Data Science. How do data formats like Spreadsheet,
CSV, SQL, and ZIP contribute to handling and storing data efficiently in Python?
Ans. Data types in Python are crucial for effective data manipulation. Formats like Spreadsheet, CSV, SQL,
and ZIP provide diverse options for storing and handling data. For instance, CSV simplifies tabular data
storage, while SQL facilitates efficient database management, contributing to streamlined data processes
in Python.
G. Competency-based questions.

1. Sameer was making a project on finding the relation between height and weight of children in the age
group 8-13 years. Which of the following visualisations would be most appropriate for his purpose?
a. Histogram b. Scatt er plot

c. Box plot d. Pie chart
2. Megha was doing some problems related to various statistical measures. In a particular problem, she
had to take some numbers of her own choice and calculate their mean, median, and variance. When she

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