Data Preparation for Machine Learning

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Questions and Answers

What is one way to handle missing data in a dataset?

Keep the missing values as they are

Ignore the missing values during analysis

Get rid of the corresponding districts (correct)

Fill in the missing values with random numbers

Which method can be used to create a copy of the data without a specific attribute?

fillna()

Imputer()

dropna()

drop() (correct)

What does the Imputer class in Scikit-Learn help with?

Creating new attributes in the dataset

Filling in missing values with the mean value (correct)

Replacing text attributes with numerical ones

Removing entire rows of data with missing values

In machine learning, what is the purpose of separating predictors and labels in a dataset?

To improve the accuracy of the machine learning model Signup and view all the answers

Which method is used to replace missing values with learned medians?

Imputer() Signup and view all the answers

What does the 'prepare the data for machine learning algorithms' step involve?

Handling missing data and separating predictors from labels Signup and view all the answers

What method can be used to quickly get a description of the data, including the total number of rows and each attribute's type and number of non-null values?

info() Signup and view all the answers

Which method is useful for showing a summary of the numerical attributes in a DataFrame?

describe() Signup and view all the answers

What method can be used to find out the categories that exist in a categorical attribute and the number of instances in each category?

value_counts() Signup and view all the answers

What type of plot can provide insight into the distribution of numerical attributes by showing the instances within given value ranges?

Histogram Signup and view all the answers

What is the purpose of setting aside a test set in a machine learning project?

To evaluate the model's performance on unseen data Signup and view all the answers

What does the radius of each circle represent in a scatterplot of districts in the California Housing dataset?

District's population Signup and view all the answers

What should be done first to load data using Pandas?

Create a small function Signup and view all the answers

What does the color in the scatterplot of districts represent?

Price of housing in the district Signup and view all the answers

Why is it important to identify repetitive values in a column like 'ocean_proximity'?

To understand if it is a categorical attribute Signup and view all the answers

What does the correlation coefficient (Pearson's r) range from when computing correlations between attributes?

-1 to 1 Signup and view all the answers

Why is it recommended to create a scatterplot of all districts in a machine learning project?

To visualize the data geographically Signup and view all the answers

What is the purpose of experimenting with attribute combinations in a machine learning project?

To gain insights and improve model performance Signup and view all the answers

Study Notes

Data Preparation and Exploration

The population per household is a potential attribute combination to analyze.
When dealing with missing data, there are three options:
- Get rid of the corresponding districts.
- Get rid of the whole attribute.
- Set the values to some value (e.g., zero, mean, median, etc.).
Imputer class in Scikit-Learn can be used to handle missing values.
Imputer computes the median of each attribute and stores it in its statistics_ instance variable.

Data Loading and Visualization

Data can be loaded using Pandas and a small function can be written to load the data.
The head() method can be used to view the top five rows of the data.
The info() method provides a quick description of the data, including the total number of rows and each attribute's type and number of non-null values.
The value_counts() method can be used to find the categories and number of districts in each category for a categorical attribute.
The describe() method shows a summary of the numerical attributes.
Histograms can be used to visualize the distribution of numerical attributes.
A scatterplot can be used to visualize geographical data, with the radius of each circle representing the district's population and the color representing the price.

Correlation Analysis

The correlation coefficient (Pearson's r) ranges from –1 to 1.
The correlation coefficient can be computed using the corr() method.
The correlation matrix can be used to visualize the relationships between attributes.

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Description

Learn about preparing data for machine learning algorithms by exploring attribute combinations, handling missing data, and separating predictors from labels. Understand the importance of a clean training set and correlation matrices.