18 Questions
What is one way to handle missing data in a dataset?
Get rid of the corresponding districts
Which method can be used to create a copy of the data without a specific attribute?
drop()
What does the Imputer class in Scikit-Learn help with?
Filling in missing values with the mean value
In machine learning, what is the purpose of separating predictors and labels in a dataset?
To improve the accuracy of the machine learning model
Which method is used to replace missing values with learned medians?
Imputer()
What does the 'prepare the data for machine learning algorithms' step involve?
Handling missing data and separating predictors from labels
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()
Which method is useful for showing a summary of the numerical attributes in a DataFrame?
describe()
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()
What type of plot can provide insight into the distribution of numerical attributes by showing the instances within given value ranges?
Histogram
What is the purpose of setting aside a test set in a machine learning project?
To evaluate the model's performance on unseen data
What does the radius of each circle represent in a scatterplot of districts in the California Housing dataset?
District's population
What should be done first to load data using Pandas?
Create a small function
What does the color in the scatterplot of districts represent?
Price of housing in the district
Why is it important to identify repetitive values in a column like 'ocean_proximity'?
To understand if it is a categorical attribute
What does the correlation coefficient (Pearson's r) range from when computing correlations between attributes?
-1 to 1
Why is it recommended to create a scatterplot of all districts in a machine learning project?
To visualize the data geographically
What is the purpose of experimenting with attribute combinations in a machine learning project?
To gain insights and improve model performance
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.
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.
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