*About the Data *Target Variable Submission Format Performance Metric *Features Example Row *References
My goal is to predict whether a mortgage application was accepted (meaning the loan was originated) or denied according to the given dataset, which is adapted from the Federal Financial Institutions Examination Council's (FFIEC).
I am trying to predict the variable accepted (a binary variable) for each row of the test data set.
My job is to:
*Train a model using the inputs in train_values.csv and the labels train_labels.csv
*Predict value for each row in test_values.csv for which i don't know the true value of accepted.
*Output My predictions in a format that matches submission_format.csv exactly.
The format for the submission file is two columns with row_id and accepted. The data type of accepted is an integer, only valid values are 0 and 1.
If you predicted 1 accepted for each respondent, the .csv file that you submit would look like:
row_id,accepted
0,1
1,1
2,1
3,1
4,1
⁝
We're predicting a binary variable, so this is a classification problem. To measure classification, we'll use a metric known as accuracy (also known as "classification rate"). Accuracy is one metric for evaluating classification models. Informally, accuracy is the fraction of predictions our model got right. For binary classification, accuracy can be calculated in terms of positives and negatives as follows:
where TP means True Positives, TN means True Negatives, FP means False Positives, and FN means False Negatives.
There are 21 variables in this dataset. Each row in the dataset represents a HMDA-reported loan application, and the dataset we are working with covers one particular year.
We provide a unique identifier called lender for each individual loan-making institution.
The variables are as follows:
*msa_md (categorical) - A categorical with no ordering indicating Metropolitan Statistical Area/Metropolitan Division where a value of -1 indicates a missing value
state_code(categorical) - A categorical with no ordering indicating the U.S. state where a value of-1indicates a missing valuecounty_code(categorical) - A categorical with no ordering indicating the county where a value of-1indicates a missing value
-
lender(categorical) - A categorical with no ordering indicating which of the lenders was the authority in approving or denying this loan -
loan_amount(int) - Size of the requested loan in thousands of dollars -
loan_type(categorical) - Indicates whether the loan granted, applied for, or purchased was conventional, government-guaranteed, or government-insured; available values are:
1 -- Conventional (any loan other than FHA, VA, FSA, or RHS loans)
2 -- FHA-insured (Federal Housing Administration)
3 -- VA-guaranteed (Veterans Administration)
4 -- FSA/RHS (Farm Service Agency or Rural Housing Service)
property_type(categorical) - Indicates whether the loan or application was for a one-to-four-family dwelling (other than manufactured housing), manufactured housing, or multifamily dwelling; available values are:
1 -- One to four-family (other than manufactured housing)
2 -- Manufactured housing
3 -- Multifamily
loan_purpose(categorical) - Indicates whether the purpose of the loan or application was for home purchase, home improvement, or refinancing; available values are:
1 -- Home purchase
2 -- Home improvement
3 -- Refinancing
occupancy(categorical) - Indicates whether the property to which the loan application relates will be the owner's principal dwelling; available values are:
1 -- Owner-occupied as a principal dwelling
2 -- Not owner-occupied
3 -- Not applicable
preapproval(categorical) - Indicate whether the application or loan involved a request for a pre-approval of a home purchase loan; available values are:
1 -- Preapproval was requested
2 -- Preapproval was not requested
3 -- Not applicable
-
applicant_income(int) - In thousands of dollars -
applicant_ethnicity(categorical) - Ethnicity of the applicant; available values are:
1 -- Hispanic or Latino
2 -- Not Hispanic or Latino
3 -- Information not provided by applicant in mail, Internet, or telephone pplication
4 -- Not applicable
5 -- No co-applicant
applicant_race(categorical) - Race of the applicant; available values are:
1 -- American Indian or Alaska Native
2 -- Asian
3 -- Black or African American
4 -- Native Hawaiian or Other Pacific Islander
5 -- White
6 -- Information not provided by applicant in mail, Internet, or telephone application
7 -- Not applicable
8 -- No co-applicant
applicant_sex(categorical) - Sex of the applicant; available values are:
1 -- Male
2 -- Female
3 -- Information not provided by applicant in mail, Internet, or telephone application
4 or 5 -- Not applicable
co_applicant(bool) - Indicates whether there is a co-applicant (often a spouse) or not
population- Total population in tractminority_population_pct- Percentage of minority population to total population for tractffiecmedian_family_income- FFIEC Median family income in dollars for the MSA/MD in which the tract is located (adjusted annually by FFIEC)tract_to_msa_md_income_pct- % of tract median family income compared to MSA/MD median family income *number_of_owner-occupied_units- Number of dwellings, including individual condominiums, that are lived in by the ownernumber_of_1_to_4_family_units- Dwellings that are built to house fewer than 5 families
row_id- A unique identifier with no intrinsic meaning, but the IDs in your submission must match the submission format exactlyaccepted- Indicates whether the mortgage application was accepted (successfully originated) with a value of1or denied with a value of0
Here's an example of one of the rows in the dataset so that you can see the kinds of values you might expect in the dataset. Most are categorical, a few are numerical, and there can be missing values.
| row_id | 0 |
| loan_type | 1 |
| property_type | 1 |
| loan_purpose | 3 |
| occupancy | 1 |
| loan_amount | 116 |
| preapproval | 3 |
| msa_md| 24 |
| state_code | 4 |
| county_code | 106 |
| applicant_ethnicity | 2 |
| applicant_race | 3 |
| applicant_sex | 2 |
| applicant_income | 66 |
| population | 3263 |
| minority_population_pct | 52.815 |
| ffiecmedian_family_income | 71852 |
| tract_to_msa_md_income_pct | 81.198 |
| number_of_owner-occupied_units | 786 |
| number_of_1_to_4_family_units | 1067 |
| number_of_1_to_4_family_units | 1067 |
| lender | 494 |
| co_applicant | False |
FFIEC. Home Mortgage Disclosure Act
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