Data Science 🐍 Prepare Data

This video tutorial covers the essentials of preparing data for data science projects. It begins with an introduction to data preparation, emphasizing the importance of conditioning raw data. The tutorial then explores techniques for visualizing and cleaning data, including handling outliers using tools like Numpy and Pandas. It further delves into methods for identifying and removing outliers through statistical tests and conditions. Finally, the video discusses the significance of scaling data and splitting it into training and testing sets to ensure effective machine learning model development.

Why is it important to condition raw data before applying data science techniques?

• To increase the size of the dataset

• To ensure the data is in a usable format

• To make the data more colorful

• To make the data more complex

Which library function can be used to drop missing values in a dataset?

• Numpy's dropna

• Pandas' dropna

• Scikit-learn's dropna

• Matplotlib's dropna

What is the purpose of using a box plot in data visualization?

• To make the data more complex

• To increase the data size

• To identify outliers in the data

• To add colors to the data

What is a limitation of the Grubbs test?

• It can only detect a single outlier

• It can only detect positive numbers

• It can only detect missing values

• It can only detect even numbers

Why is scaling data important for machine learning algorithms?

• To make the data more complex

• To increase the data size

• To ensure algorithms perform optimally

• To make the data more colorful

What does the standard scaler do to the data?

• It adds random noise

• It normalizes data to zero mean and unit variance

• It removes all outliers

• It duplicates the data

What is the typical split ratio for training and testing datasets?

• 50% training, 50% testing

• 70% training, 30% testing

• 80% training, 20% testing

• 90% training, 10% testing

Why is it important to shuffle data before splitting into training and testing sets?

• To increase the data size

• To ensure a random distribution

• To make the data more colorful

• To make the data more complex

What is the effect of outliers on machine learning models?

• They negatively affect predictive capability

• They have no effect

• They make models more complex

• They improve model accuracy

What is one method to automatically cleanse data?

• Ignoring all data

• Using conditions to remove outliers and missing values

• Adding random values

• Manually reviewing each data point