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Project Name: divorce-prediction

Predicting American Divorce


Overview/Synopsis

The goal of this project is to find a model that accurately predicts if a person is divorced, single or married. We think that there are a series of social, economic, racial, religious and geographic variables that relate to a certain person being divorced.

Use

We are ceating a tool that accurate predicts if your are marriage status. Wether you are divorced, single or married, this tool will predict with an accuracy in the 80s percentage, given certain characteristics.

We use decision trees model to predict the outcomes (divorced, single or married). We create a prediction with CP = 0 (most precise and really high computing power). After it, we create a loop and start "pruning" the tree with it, in order to get the highest mean F1 and CP possible. As it turns out, our mean F1 on the the test sample is around 81% with a CP = 2.176e^(-5).

Data Used

We use the Current Population Survey (http://thedataweb.rm.census.gov/ftp/cps_ftp.html). We downloaded the data using a parsing script written within DOC and given by Prof Jeff Chen for public use. We modified it to get the data for the twelve months of 2016.

We have approximately 1.8 million observations and 389 variables. From those 389 variables available in the dataset we use 35 variables to predict the outcome. These are the variables used with their importance:

varname importance DESCRIPTION
prtage 48027.2043 PERSONS AGE
hrnumhou 19580.93662 TOTAL NUMBER OF PERSONS LIVING
prnmchld 10172.78591 Number of own children < 18 years of age
prchld 10130.38892 PRESENCE OF OWN CHILDREN < 18 YEARS
pemlr 4419.647807 MONTHLY LABOR FORCE RECODE
hefaminc 4143.967869 FAMILY INCOME
puwk 3366.378441 LAST WEEK, DID YOU DO ANY WORK
prtfage 2525.231903 TOP CODE FLAG FOR AGE
peeduca 2101.682639 HIGHEST LEVEL OF SCHOOL
prwksch 1873.531408 LABOR FORCE BY TIME
prcivlf 1733.776621 CIVILIAN LABOR FORCE
ptdtrace 1521.411985 RACE
prinusyr 1411.366186 IMMIGRANT'S YEAR OF ENTRY
prwkstat 1346.103785 FULL/PART-TIME WORK STATUS
prmjind1 762.5797876 MAJOR INDUSTRY RECODE - JOB 1
gediv 726.3161318 DIVISION
gtcbsasz 656.6140444 Metropolitan Area (CBSA) SIZE
gtindvpc 554.5695261 INDIVIDUAL PRINCIPAL CITY
prcitshp 543.2361561 CITIZENSHIP STATUS
prmjocc1 533.5237143 MAJOR OCCUPATION RECODE
pesex 530.7090073 SEX
gereg 389.8060756 REGION
gtcbsast 383.5875216 PRINCIPAL CITY/BALANCE STATUS
hehousut 268.7665871 TYPE OF HOUSING UNIT
hurespli 256.9704462 LINE NUMBER OF THE CURRENT
gtmetsta 183.9798964 METROPOLITAN STATUS
peafever 181.6559453 DID YOU EVER SERVE ON ACTIVE
huspnish 125.2598896 IS SPANISH THE ONLY LANGUAGE SPOKEN
peafnow 109.5453365 ARE YOU NOW IN THE ARMED FORCES
penlfact 106.0165516 WHAT BEST DESCRIBES YOUR SITUATION AT
pehspnon 85.31919456 HISPANIC OR NON-HISPANIC
pedisrem 65.85950161 BECAUSE OF A PHYSICAL, MENTAL, OR
hubus 49.12820317 DOES ANYONE IN THIS HOUSEHOLD
pedisear 17.44051162 IS...DEAF OR DOES...HAVE SERIOUS DIFFICULTY HEARING?
prftlf 9.76262516 FULL TIME LABOR FORCE

The parsing script also creates a dictonary for these variables.

Usage

Our project can be checked in the divorce-prediction.Rmd file.

This file project was broken down into 7 main parts

  • 1 Installing all the libraries needed.
  • 2 Declaring functions that will be used in other parts of the project.
  • 3 Creating a Data Dictionary.
  • 4 Downloading the data.
  • 5 Culling and cleaning the data.
  • 6 Training the model.
  • 7 Optimizing the model.
  • 8 Iterating to find the best mean F1.

NOTE: in order for this script to work working directory and paths in the files must be updated to fit the user

Progress Log

  • April 17, 2017: We started wrinting our read-me file and gathering the data.
  • April 20, 2017: Updates on Read-me file.
  • April 24, 2017: Finishing Read-me file.
  • April 30, 2017: Finishing the code.
  • May 8, 2017: Code Finished and Read-Me Finished.

Credits

Credits of this work go out to Carlos Delgado and Christian Ueland.

License

No license needed.

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