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The "Salary Prediction Dataset" is a synthetic
dataset generated for the purpose of
exploring salary prediction tasks. It contains
simulated data reflecting various factors
influencing salary levels such as education,
experience, location, job title, age, and
gender. This dataset can be utilized for
predictive modeling tasks to estimate salaries
based on these factors.
Data Information
Education: Categorical
variable indicating
the highest level of
education attained by
an individual. Options
include 'High
Experience: Integer
variable representing
the number of years of
professional
experience.
Location: Categorical
variable denoting the
geographical location
of employment,
categorized as
'Urban', 'Suburban',
or 'Rural'.
Job_Title: Categorical
variable indicating
the job title or
position held by an
individual, with
options such as
'Engineer
Age: Integer variable
representing the age
of the individua
Gender: Categorical
variable representing
the gender of the
individual,
categorized as 'Male'
or 'Female’
Salary : Continuous
variable representing
the annual salary of
the individual,
calculated based on a
combination of
education,
Data Insights
EDA
Title distribution Withing Manager,
Director, Analyst, Engineer. Which
shows Count of Manager and Director
“Gender Relationship” that shows the
distribution of people by gender. The
pie chart is divided into two slices,
colored blue and orange. The blue
slice, labeled "Male", represents
51.60% of the population and the orange
slice, labeled "Female", represents
The salary distribution for all
four job titles is positively
skewed. This means that there are
more people towards the lower end
of the salary range.
• Managers have the lowest
median salary among the four
job titles.
• Directors have a higher median
salary than managers.
• Analysts have a median salary
that is lower than directors
but higher than managers.
• Engineers have the highest
median salary among the four
job titles..
• Education vs. Salary: There is
a slight tendency for people
with higher education levels
to have higher salaries.
• Experience vs. Salary: there
is a clearer tendency for
people with more experience to
have higher salaries.
• Age vs. Salary: there is a
slight tendency for people
with higher age to have higher
salaries.
• Location vs. Salary: For some
locations, there is a weak
positive correlation, while
for others there is no
correlation.
Label Encoding
Dropping
Unnecessa
ry
Columns
Splitting
Data into
Dependent
and
Independen
t Variable
Applying Linear
Regression Algorithm
Dividing data into Train
and Test
Applying
Random
Forest
Regressor
Algorithm &
Decision
Tree
Regressor
Applying
Random
Forest
Regresso
r
Algorith
m
• Since the score
given by Random
forest regressor
is on higher
side. We will go
with Random
forest regressor

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Demystifying Salaries: A Data Science Approach to Predicting Salary Ranges

  • 1. The "Salary Prediction Dataset" is a synthetic dataset generated for the purpose of exploring salary prediction tasks. It contains simulated data reflecting various factors influencing salary levels such as education, experience, location, job title, age, and gender. This dataset can be utilized for predictive modeling tasks to estimate salaries based on these factors.
  • 2.
  • 3. Data Information Education: Categorical variable indicating the highest level of education attained by an individual. Options include 'High Experience: Integer variable representing the number of years of professional experience. Location: Categorical variable denoting the geographical location of employment, categorized as 'Urban', 'Suburban', or 'Rural'. Job_Title: Categorical variable indicating the job title or position held by an individual, with options such as 'Engineer Age: Integer variable representing the age of the individua Gender: Categorical variable representing the gender of the individual, categorized as 'Male' or 'Female’ Salary : Continuous variable representing the annual salary of the individual, calculated based on a combination of education,
  • 5. EDA Title distribution Withing Manager, Director, Analyst, Engineer. Which shows Count of Manager and Director “Gender Relationship” that shows the distribution of people by gender. The pie chart is divided into two slices, colored blue and orange. The blue slice, labeled "Male", represents 51.60% of the population and the orange slice, labeled "Female", represents
  • 6. The salary distribution for all four job titles is positively skewed. This means that there are more people towards the lower end of the salary range. • Managers have the lowest median salary among the four job titles. • Directors have a higher median salary than managers. • Analysts have a median salary that is lower than directors but higher than managers. • Engineers have the highest median salary among the four job titles..
  • 7. • Education vs. Salary: There is a slight tendency for people with higher education levels to have higher salaries. • Experience vs. Salary: there is a clearer tendency for people with more experience to have higher salaries. • Age vs. Salary: there is a slight tendency for people with higher age to have higher salaries. • Location vs. Salary: For some locations, there is a weak positive correlation, while for others there is no correlation.
  • 14. • Since the score given by Random forest regressor is on higher side. We will go with Random forest regressor