Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: Python Packages for Data Scien

The video tutorial introduces support vector machines (SVM) and demonstrates their implementation using the Titanic dataset. It covers the dataset's features, data preparation, and encoding techniques. The tutorial also introduces Python packages like Numpy and Pandas for data handling and visualization, emphasizing the importance of converting non-numeric data to numeric form for machine learning algorithms.

What is the primary goal when using the Titanic data set in this context?

• To predict the survival of passengers

• To find the most expensive ticket

• To classify passengers by their ticket number

• To determine the age of passengers

How many attributes, excluding the class label, are present in the Titanic data set?

• 8

• 9

• 10

• 11

Why is it important to convert non-numeric data to numeric in machine learning?

• Numeric data is faster to process

• Non-numeric data is less accurate

• Machine learning algorithms require numeric input

• Numeric data is easier to visualize

What should be done if a large number of attribute values are missing?

• Replace them with the mean

• Fill them with zeros

• Ignore them

• Drop the attribute

Which Python package is mentioned for handling numeric data efficiently?

• TensorFlow

• Numpy

• Scikit-learn

• Matplotlib

What is the role of data visualization in this context?

• To create beautiful graphs

• To replace data cleaning

• To identify important attributes and their relationships

• To encode non-numeric data

Which package is used for structured data querying and file handling?

• Matplotlib

• Pandas

• Numpy

• Seaborn