DW Simpson actuarial salary data

Description

The data actuary_salaries contains the salaries of actuaries collected from the DW Simpson survey.

Usage

actuary_salaries

Format

data.frame, 138 observations of 6 variables:

industry

The industry of the actuary, having values of Casualty, Health, Penson, and Life

exams

The number of exams passed. Values of ASA, FSA, 5,4,3,2,1

experience

Years of experience, in the range 1 - 20

salary

Annual salary range, in $1,000

salary_low

Lower end of the annual salary range

salary_high

Higher end of the annual salary range

Apartment Apps

Description

Apartment applications as used in ExamPA.net’s Practice Exam

Usage

apartment_apps

Format

data.frame, 1430 observations of 41 variables:

applicants

The total number of people who apply for a lease at that apartment building, including all apartment units.

sale_price

The sale price of each apartment unit.

num_units

The number of units in the apartment building.

year_sold

Year that apartment building was sold or remodeled.

month_sold

Month that apartment building was sold or remodeled.

overall_qual

Rates the overall material and finish of the building on a scale from 1 to 10 with 10 being the best and 1 being the worst.

total_sq_feet

Total square feet.

gr_liv_area

Above ground living area in square feet.

tot_bathrooms

The number of bathroom of each unit.

lot_area

Lot size in square feet.

exter_qual

Rates the external quality of the building on a scale from 1 to 10 with 10 being the best and 1 being the worst.

full_bath

The number of full-size bathroom in each unit.

central_air

Whether or not each unit has a central air conditioning system (1 = yes, 0 = no).

garage_type_attchd

1 = Attached garage.

garage_type_basment

1 = Basement garage.

garage_type_builtIn

1 = Build in garage.

garage_type_detchd

1 = Detached garage.

garage_type_no_garage

1 = No garage.

NeighborhoodBrDale

1 = Dale

neighborhood_brk_side

1 = Brookside.

neighborhood_clear_cr

1 = Clear Circle.

neighborhood_collg_cr

1 = College Circle.

neighborhood_crawfor

1 = Crawford.

neighborhood_edwards

1 = Edwards.

neighborhood_gilbert

1 = Gilbert.

neighborhood_idottrr

1 = DOTRR.

neighborhood_meadowv

1 = Meadow.

neighborhood_mitchel

1 = Mitchel.

neighborhood_n_ames

1 = North Ames

neighborhood_n_ridge

1 = North Ridge.

neighborhood_n_ridge_hghts

1 = North Ridge Heights.

neighborhood_n_w_ames

1 = North West Ames.

neighborhood_old_town

1 = Old Town.

neighborhood_sawyer

1 = Sawyer.

neighborhood_sawyer_w

1 = Sawyer West.

neighborhood_somerst

1 = Somer St.

neighborhood_stone_br

1 = Stone Bridge.

neighborhood_swisu

1 = SWISU.

neighborhood_timber

1 = Timber.

neighborhood_veenker

1 = Veenker.

neighborhood_saleprice

The mean sale price for all units in that neighborhood.

Automotive claims

Description

Automotive claims

Usage

auto_claim

Format

data.frame, 10296 observations of 29 variables:

POLICYNO

Policy number.

PLCYDATE

Date that policy was signed.

CLM_FREQ5

Number of claims.

CLM_AMT5

Aggregate claim loss of policy (in thousands).

CLM_AMT

KIDSDRIV

Number of child passengers.

TRAVTIME

Time to commute.

CAR_USE

(1) Private or (2) commercial use.

BLUEBOOK

(log) car value.

RETAINED

Whether the policy was retained or not.

NPOLICY

Number of policies.

CAR_TYPE

(0-1 dummy variables) Type of car : (base) Panel Truck, (2) Pickup,(3) Sedan, (4) Sports Car, (5) SUV, (6) Van

RED_CAR

Whether the color of the car is (2) car or (1) not.

REVOLKED

Whether the policyholder's license was (2) revoked in the past or (1) not.

MVR_PTS

Number of motor vehicle record points.

CLM_FLAG

Whether there was a claim or not.

AGE

Age.

HOMEKIDS

Number of children at home.

YOJ

Year of job.

INCOME

Annual income.

GENDER

Gender of policyholder : (1) female or (2) male.

MARRIED

Whether the policyholder is (2) married or (1) not.

PARENT1

Whether (2) the policyholder grew up in a single-parent family or (1) not.

JOBCLASS

(0-1 dummy variables) Job class of policyholder: (base) Unknown, (2) Blue Collar, (3) Clerical, (4) Doctor, (5) Home Maker, (6) Lawyer, (7) Manager, (8) Professional, (9) Student

MAX_EDUC

(0-1 dummy variables) Maximal level of education of policyholder: (base) less than High School, (2) Bachelors, (3) High School, (4) Masters, (5) PhD.

HOME_VAL

Value of home.

SAMEHOME

Whether they grew up in the same home as their current home.

AREA

(1) Rural or (2) urban area.

IN_YY

Year.

Bank Loans

Description

Credit data from UCI Machine Learning Repository.

Usage

bank_loans

Format

data.frame, 41188 observations of 21 variables:

age

age (numeric).

job

type of job (categorical.

marital

marital status (categorical).

education

'basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'illiterate', 'professional.course', 'university.degree', 'unknown')

default

has credit in default? (categorical).

housing

has housing loan? (categorical).

loan

has personal loan? (categorical).

contact

contact communication type (categorical).

month

last contact month of year (categorical).

day_of_week

last contact day of the week (categorical).

duration

last contact duration, in seconds (numeric). Important note - this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

campaign

number of contacts performed during this campaign and for this client (numeric, includes last contact)

pdays

number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted).

previous

number of contacts performed before this campaign and for this client (numeric).

poutcome

outcome of the previous marketing campaign (categorical).

emp.var.rate

employment variation rate.

cons.price.idx

consumer price index.

cons.conf.idx

consumer confidence index.

euribor3m

euribor 3 month rate.

nr.employed

number of employees.

y

has the client subscribed a term deposit?

Bike sharing demand

Description

bike sharing demand dataset

Usage

bike_sharing_demand

Format

data.frame, 17376 observations of 10 variables:

season

Season. 1 - winter, 2 - spring, 3 - summer, 4 - fall.

year

Year. 0 - 2011, 1 - 2012

hour

Hour.

holiday

Whether the day is a holiday.

weekday

Day of the week.

weathersit

Weather situation. 1 - clear of partly cloudy, 2 - mist, 3 - rain or snow.

temp

Normalized temperature in Celsius. The values are derived via (t - t_min)/(t_max - t_min), t_min = -9, t_max = +39.

humidity

Normalized humidity. The values are divided by 100 (max).

windspeed

Normalized windspeed. The values are divided by 67 (max).

bikes_per_hour

Count of rental bikes in each hour.

Boston

Description

Boston housing data set

Usage

boston

Format

data.frame, 506 observations of 14 variables:

crim

per capita crime rate by town.

zn

proportion of residential land zoned for lots over 25,000 sq.ft.

indus

proportion of non-retail business acres per town.

chas

Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).

nox

nitrogen oxides concentration (parts per 10 million).

rm

average number of rooms per dwelling.

age

proportion of owner-occupied units built prior to 1940.

dis

weighted mean of distances to five Boston employment centers.

rad

index of accessibility to radial highways.

tax

full-value property-tax rate per $10,000.

ptratio

pupil-teacher ratio by town.

black

1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.

lstat

lower status of the population (percent).

medv

median value of owner-occupied homes in $1000s.

Customer Phone Calls

Description

Data used on June 18, 2020 Exam PA

Usage

customer_phone_calls

Format

data.frame, 10000 observations of 14 variables:

age

Age of the prospective customer. Integer from 17 to 98.

job

Occupation category. Factor with 11 levels.

marital

Marital status. Factor with levels divorced, married, and single

housing

Indicates whether the prospect has a housing loan. Factor with levels yes and no.

loan

Indicates whether the prospect has a consumer loan. Factor with levels yes and no.

phone

The type of phone the prospect uses. Factors with levels cellular and landline.

month

The month of the marketing call. Factor with 12 levels.

weekday

The day of the week of the marketing call. Factor with five levels.

CPI

Consumer price index at the time of the call. Numeric from 92.20 to 94.77.

CCI

Consumer confidence index at the time of the call. Numeric from -50.8 to -26.9.

irate

Short term interest rate at the tie of the call. Numeric from 0.634 to 5.045.

employment

Number of employees of ABC Insurance at the time of the call. Numeric from 4964 to 5228.

purchase

Indicator of purchase. Integer (1 for purchase, or 0 for no purchase.)

edu_years

Years of education. Integer from 1 to 16.

Customer Value

Description

Customer value data set from December 2019 PA

Usage

customer_value

Format

data.frame, 48842 observations of 8 variables:

age

Age of the prospective policyholder. Integer from 17 - 90

education_num

Indicator of the amount of education - it is not the number of years of education, but a higher number does mean more years. Integer from 1 to 16.

marital_status

For married, AF means alternative form while civ means civil. Factor with seven levels.

occupation

Occupations have been grouped into five categories. There is no indication regarding what they mean. A sixth group represents cases where the occupation is unknown. Factor with six levels.

cap_gain

Capital gains recorded on investments. Integer from 0 to 99,999.

hours_per_week

Number of hours worked per week. Integer from 1 to 99

score

A proprietary "insurance score" developed by MEB. Real number with two decimal places.

value_flag

Indicator a policy holder being High or Low value. Factor with two levels.

Exam PA Titanic

Description

Titanic passengers as used in ExamPA.net’s practice exam

Usage

exam_pa_titanic

Format

data.frame, 906 observations of 11 variables:

passengerid

Passenger id

survived

Survived Y/N

pclass

Ticket class

name

Name

sex

male, female

age

Age

sibsp

# of siblings

parch

# of parents or children aboard the Titanic.

ticket

Number fare

fare

Cost of ticket.

embarked

Port of Embarkation. C = Cherbourg, Q = Queenstown, S = Southampton.

Health insurance

Description

Health insurance claims as used in ExamPA.net’s Practice Exam. The data set consists of prior year’s health insurance claims, along with patient demographic information, from Freedom Health.

Usage

health_insurance

Format

data.frame, 1338 observations of 7 variables:

age

Age of policy holder.

sex

M or F.

bmi

Body Mass Index: weight divided by height.

children

Number of children.

smoker

Smoker status. Yes or No.

region

Geographic region.

charges

Annual medical claims for this policy.

June_pa

Description

Auto crash data set from SOA June 2019 PA

Usage

june_pa

Format

data.frame, 23137 observations of 14 variables:

Crash_Score

Measure the extent of the crash using factors such as number of injuries and fatalities, the number of vehicles involved, and other factors. A positive number with two decimal places.

year

Calendar year of the crash. Integer 2014 - 2019.

Month

Calendar month of the crash. Integer 1 - 12 (1 = January, 12 = December.)

Time_of_Day

Time of day, on 4-hour blocks. Integer 1 - 6 (1 = midnight to 4am, 6 = 8pm to midnight.)

Rd_Feature

Special features of the road where the crash occurred. NONE = no special feature, INTERSECTION = the meeting of at least two roads, RAMP = exit or entrance ramp to a controlled access road, DRIVEWAY = entrance to home of business, OTHER.

Rd_Character

Description of the road where the crash occurred. STRAIGHT-LEVEL = no curves or hills, STRAIGHT-GRADE = no curves, but on a hill (up or down), STRAIGHT-OTHER, CURVE-LEVEL = on a curve but no hill, CURVE-GRADE = on a curve and on a hill, CURVE-OTHER, OTHER.

Rd_Class

Classification of the road type. STATE HWY = maintained by the state government, US HWY = maintained by the federal government.

Rd_Configuration

Design of the road. TWO-WAY-PROTECTED-MEDIAN = traffic in both directions, separated with a barrier, TWO-WAY-UNPROTECTED-MEDIAN = separated but with no barrier, TWO-WAY-NO-MEDIAN = no separation, ONE-WAY, UNKNOWN.

Rd_Surface

Material used for the road surface. SMOOTH ASPHALT, COARSE ASPHALT, CONCRETE, GROOVED CONCRETE, OTHER.

Rd_Conditions

Condition of the road. DRY, WET, ICE-SNOW-SLUSH, OTHER.

Light

Lighting. DAYLIGHT, DARK-NOT-LIT = no street lamps in area, DARK-LIT, DUSK, DAWN, OTHER.

Weather

Weather conditions. CLEAR, RAIN, CLOUDY, SNOW, OTHER.

Traffic_Control

Any items that control traffic flow. SIGNAL = lighted stop/go signal, STOP-SIGN, YIELD, NONE, OTHER.

Work_Area

Whether the crash in a work area? YES/NO

Patient Length of Stay

Description

Data used on June 16, 2020 Exam PA

Usage

patient_length_of_stay

Format

data.frame, 10000 observations of 13 variables:

days

Number of days between admission into and discharge from hospital. Integer 1 - 14.

gender

Patient gender. Male or Female.

age

Patient age (in 10-year age bands). [0, 10), [10, 20), ..., [90, 100)

race

patient race. AfricanAmerican, Asian, Caucasian, Hispanic, Other.

weight

Patient weight (in 25-pound weight bands). [0, 25), [25, 50), [175, 200)

admit_type_id

Identifier corresponding to the type of hospital admission. 1 = Emergency, 2 = Urgent, 3 = Elective, 4 = Not Available.

metformin

Indicates whether upon admission, metformin was prescribed or there was a change in the dosage. Up = dosage was increased, Down = dosage was decreased, Steady = dosage did not change, No = drug was not prescribed.

insulin

Indicates whether upon admission, insulin was prescribed or there was a change in the dosage. Up = dosage was increased, Down = dosage was decreased, Steady = dosage did not change, No = drug was not prescribed.

readmitted

Indicates whether patient had been readmitted after an inpatient stay in the twelve months preceding the encounter. <30 = patient was readmitted in less than 30 days, >30 = patient was readmitted in more than 30 days, No = no record of readmission.

num_procs

Number of procedures performed in the twelve months preceding the encounter. Integer 0 - 6.

num_meds

Number of distinct medications administered in the twelve months preceding the encounter. Integer 1 - 67.

num_ip

Number of the inpatient visits of the patient in the twelve months preceding the encounter. Integer 0 -21.

num_diags

Number of diagnoses entered to the system in the twelve months preceding the encounter. Integer 1 - 16.

Patient Number of Labs

Description

Data used on June 19, 2020 Exam PA

Usage

patient_num_labs

Format

data.frame, 10000 observations of 14 variables:

age

Age of prospective customer. Integer from 17 to 98.

job

Occupation category. Factor with 11 levels.

marital

Marital status. Factor with levels divorced, married, and single

housing

Indicates whether the prospect has a housing loan. Factor with levels no, yes.

loan

Indicates whether the prospect has a consumer loan. Factor with levels no, yes.

phone

The type of phone the prospect uses. Factor with levels cellular, landline.

month

The month of the marketing call. Factor with 12 levels.

weekday

The day of the week of the marketing call. Factor with five levels.

CPI

Consumer price index at the time of the call. Numeric from 92.20 to 94.77.

CCI

Consumer confidence index at the time of the call. Numeric from -50.8 to -26.9.

irate

Short term interest rate at the tie of the call. Numeric from 0.634 to 5.045.

employment

Number of employees of ABC Insurance at the time of the call. Numeric from 4964 to 5228.

purchase

Indicator of purchase. Integer (1 for purchase, or 0 for no purchase.)

edu_years

Years of education. Integer from 1 to 16.

Pedestrian activity

Description

pedestrian activity dataset

Usage

pedestrian_activity

Format

data.frame, 11373 observations of 7 variables:

pedestrians

The count of pedestrians during one hour starting at the indicated time.

weather

Hourly weather condition, eleven categories.

temperature

Hourly temperature in degrees Fahrenheit.

precipitation

Hourly precipitation in inches.

hour

Time at beginning of the measuring hour.

weekday

Day of the week.

temp_forecast

Predicted daily average temperature in degrees Fahrenheit.

Readmission

Description

SOA Hospital Readmissions Sample Exam, 2019.

Usage

readmission

Format

data.frame, 66782 observations of 9 variables:

Readmission.Status

The target variable, it is 1 for patients that were readmitted, 0 otherwise.

Gender

M indicates male, F indicates female.

Race

There are four categories: Black, Hispanic, Others, White.

ER

The number of emergency room visits prior to the hospital stay associated with the readmission, an integer.

DRG.Class

Diagnostic Related Group classification. There are three categories: MED (for medical), SURG (for surgical), UNGROUP.

LOS

Length of hospital stay in days, an integer.

Age

The patient's age in years, an integer. (Note that while most Medicare recipients are age 65 or older there are circumstances in which whose under 65 can receive benefits.)

HCC.Riskscore

Hierarchical Condition Category risk score. It is designed to be an estimate of a patient's condition and prospective costs. It is a continuous variable, rounded to three decimal places. Higher numbers indicate greater risk.

DRG.Complication

Complications, with five levels: MedicalMCC.CC, MecialNoc, Other, SurgMCC.CC, SurgNoC, MCC.CC complications or comorbidities that may be major. NoC means no complications or comorbidities.

Student Success

Description

SOA Student Success PA Sample Project, 2019.

Usage

student_success

Format

data.frame, 585 observations of 33 variables:

school

student's school (binary: GP (Grand Pines) or MHS (Marble Hill School)).

sex

student's sex (binary: female or male).

age

student's age (numeric: from 15 to 22).

address

student's home address type (binary: U (Urban) or R (Rural)).

famsize

family size (binary: GT3 (>3) or LE3 (<3)).

Pstatus

parent's status (binary: A (Apart) or T (Together)).

Medu

mother's education (numeric from 0 - 4. 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education (high school), or 4 - higher education (college)).

Fedu

father's education. (numeric from 0 - 4. 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education (high school), or 4 - higher education (college)).

Mjob

mother's job (nominal, teacher, health (health care related), services (civil services, administrative or police), at_home, or other)

Fjob

father's job (nominal, teacher, health (health care related), services (civil services, administrative or police), at_home, or other)

reason

reason to choose school (nominal: home (close to home), reputation (school reputation), course (course preference), other).

guardian

student's guardian (nominal: mother, father, or other).

traveltime

home to school travel time (numeric: 1 - < 15 min, 2 - 15 to 30 min, 3 - 30 min to 1 hour, or 4 - > 1 hour).

studytime

weekly study time (numeric: 1 - < 2 hour, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - > 10 hours).

failures

number of past class failures (numeric: n if 0 <= n < 3, else 3).

schoolsup

extra educational support (binary: yes or no).

famsup

extra family supplement (binary: yes or no).

paid

extra paid classes (binary: yes or no).

activities

extra-curricular activities (binary: yes or no).

nursery

attended nursery school (binary: yes or no).

higher

wants to take higher education (binary: yes or no).

internet

internet access at home (binary: yes or no).

romantic

has a romantic relationship (binary: yes or no).

famrel

quality of family relationships (numeric: from 1 - very bad to 5 - very excellent).

freetime

free time after school (numeric: from 1 - very low to 5 - very high).

goout

going out with friends (numeric: from 1 - very low to 5 - very high)

Dalc

weekday alcohol consumption (numeric: from 1 - very low to 5 - very high).

Walc

weekend alcohol consumption (numeric: from 1 - very low to 5 - very high).

health

current health status (numeric: from 1 - very bad to 5 - very good).

absences

number of school absences (numeric: from 0 to 75).

G1

first trimester grade (numeric: from 0 to 20).

G2

second trimester grade (numeric: from 0 to 20).

G3

third trimester grade (numeric: from 0 to 20).

Travel insurance data

Description

The travel insurance dataset.

Usage

travel_insurance

Format

data.frame, 10000 observations of 7 variables:

Distance

Distance traveled in trip, in km

Duration

Number of nights spent on trip

Reason

Main reason for the trip. Vacation includes holiday, leisure, or recration. Visit includes visiting friends or relatives.

Age

Age of adult survey respondent in six age bins. 1: 19-24, 2: 25-34, 3: 35:44, 4: 45:54, 5: 55-64, 6: 65+

Others

Number of other persons that accompanied the respondent on the trip

Mode

Main mode of transportation, car or plane

Cost

Total spending on trip, in Canadian $

Travel spending data

Description

The travel spending dataset.

Usage

travel_spending

Format

data.frame, 4884 observations of 11 variables:

Q

Calender quarter of trip

ProvO

Trip province of origin

Distance

Distance traveled in trip, in km

Duration

Number of nights spent on trip

Reason

Main reason for the trip. Vacation includes holiday, leisure, or recration. Visit includes visiting friends or relatives.

Age

Age of adult survey respondent in six age bins

Gender

Gender of adult survey respondent

HHI

Household income, in Canadian $

Others

Number of other persons that accompanied the respondent on the trip

Mode

Main mode of transportation, car or plane

Cost

Total spending on trip, in Canadian $