Course Content
Logistic Regression Mastering
Logistic Regression Mastering
Exploratory Data Analysis
In this chapter, we will explore some of our data's features. Specifically, we will see how the target variable is distributed in the following variables: "gender"
, "relevent_experience"
, "enrolled_university"
, "education_level"
, "major_discipline"
, "experience"
, "company_size"
, "company_type"
.
Methods description
import matplotlib.pyplot as plt
: Imports thepyplot
module from thematplotlib
library and assigns it an aliasplt
.pyplot
provides a MATLAB-like interface for creating plots and visualizations in Python;import seaborn as sns
: Imports theseaborn
library and assigns it an aliassns
. Seaborn is a Python visualization library based on matplotlib that provides a high-level interface for drawing attractive statistical graphics;plt.figure(figsize=[15, 18])
: Creates a new figure object with a specified figure size of 15 inches in width and 18 inches in height;features = [...]
: Defines a list of feature names;plt.subplot(5, 2, n)
: Divides the figure into a grid of 5 rows and 2 columns, then selects the subplot at positionn
;sns.countplot(...)
: Generates a count plot for the specified feature (x=f
) with counts separated by thehue
variable (here, "target"), using data from thedata
DataFrame;plt.title(...)
: Sets the title for the subplot with the name of the feature;plt.tight_layout()
: Adjusts the subplot layout to make sure the plot elements fit within the figure area properly;plt.show()
: Displays the plot.
Task
- Import
matplotlib
andseaborn
(assns
) libraries. - Plot the following features:
"gender"
,"relevent_experience"
,"enrolled_university"
,"education_level"
,"major_discipline"
,"experience"
,"company_size"
,"company_type"
.
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In this chapter, we will explore some of our data's features. Specifically, we will see how the target variable is distributed in the following variables: "gender"
, "relevent_experience"
, "enrolled_university"
, "education_level"
, "major_discipline"
, "experience"
, "company_size"
, "company_type"
.
Methods description
import matplotlib.pyplot as plt
: Imports thepyplot
module from thematplotlib
library and assigns it an aliasplt
.pyplot
provides a MATLAB-like interface for creating plots and visualizations in Python;import seaborn as sns
: Imports theseaborn
library and assigns it an aliassns
. Seaborn is a Python visualization library based on matplotlib that provides a high-level interface for drawing attractive statistical graphics;plt.figure(figsize=[15, 18])
: Creates a new figure object with a specified figure size of 15 inches in width and 18 inches in height;features = [...]
: Defines a list of feature names;plt.subplot(5, 2, n)
: Divides the figure into a grid of 5 rows and 2 columns, then selects the subplot at positionn
;sns.countplot(...)
: Generates a count plot for the specified feature (x=f
) with counts separated by thehue
variable (here, "target"), using data from thedata
DataFrame;plt.title(...)
: Sets the title for the subplot with the name of the feature;plt.tight_layout()
: Adjusts the subplot layout to make sure the plot elements fit within the figure area properly;plt.show()
: Displays the plot.
Task
- Import
matplotlib
andseaborn
(assns
) libraries. - Plot the following features:
"gender"
,"relevent_experience"
,"enrolled_university"
,"education_level"
,"major_discipline"
,"experience"
,"company_size"
,"company_type"
.
Mark tasks as Completed
Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 3
AVAILABLE TO ULTIMATE ONLY