# HELOC
The [HELOC dataset](https://community.fico.com/s/explainable-machine-learning-challenge?tabset-158d9=d157e) from FICO.
Each entry in the dataset is a line of credit, typically offered by a bank as a percentage of home equity (the difference between the current market value of a home and its purchase price).
The customers in this dataset have requested a credit line in the range of $5,000 - $150,000.
The fundamental task is to use the information about the applicant in their credit report to predict whether they will repay their HELOC account within 2 years.

# Configurations and tasks
| **Configuration** | **Task**                  | **Description**                                                 |
|-------------------|---------------------------|-----------------------------------------------------------------|
| risk              | Binary classification     | Will the customer default?                                      |

# Usage
```python
from datasets import load_dataset

dataset = load_dataset("mstz/heloc")["train"]
```

# Features
|**Feature**                                |**Type**|
|-------------------------------------------|--------|
|`estimate_of_risk`                         |`int8`  |
|`months_since_first_trade`                 |`int32` |
|`months_since_last_trade`                  |`int32` |
|`average_duration_of_resolution`           |`int32` |
|`number_of_satisfactory_trades`            |`int16` |
|`nr_trades_insolvent_for_over_60_days`     |`int16` |
|`nr_trades_insolvent_for_over_90_days`     |`int16` |
|`percentage_of_legal_trades`               |`int16` |
|`months_since_last_illegal_trade`          |`int32` |
|`maximum_illegal_trades_over_last_year`    |`int8`  |
|`maximum_illegal_trades`                   |`int16` |
|`nr_total_trades`                          |`int16` |
|`nr_trades_initiated_in_last_year`         |`int16` |
|`percentage_of_installment_trades`         |`int16` |
|`months_since_last_inquiry_not_recent`     |`int16` |
|`nr_inquiries_in_last_6_months`            |`int16` |
|`nr_inquiries_in_last_6_months_not_recent` |`int16` |
|`net_fraction_of_revolving_burden`         |`int32` |
|`net_fraction_of_installment_burden`       |`int32` |
|`nr_revolving_trades_with_balance`         |`int16` |
|`nr_installment_trades_with_balance`       |`int16` |
|`nr_banks_with_high_ratio`                 |`int16` |
|`percentage_trades_with_balance`           |`int16` |
task_categories:tabular-classification size_categories:10K<n<100K language:en license:cc heloc fico tabular_classification binary_classification region:us
{"language": ["en"], "license": "cc", "size_categories": ["10K<n<100K"], "task_categories": ["tabular-classification"], "pretty_name": "Heloc", "tags": ["heloc", "fico", "tabular_classification", "binary_classification"], "configs": ["risk"]}
TAGS
#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #heloc #fico #tabular_classification #binary_classification #region-us 

HELOC
=====


The HELOC dataset from FICO.
Each entry in the dataset is a line of credit, typically offered by a bank as a percentage of home equity (the difference between the current market value of a home and its purchase price).
The customers in this dataset have requested a credit line in the range of $5,000 - $150,000.
The fundamental task is to use the information about the applicant in their credit report to predict whether they will repay their HELOC account within 2 years.


Configurations and tasks
========================


Configuration: risk, Task: Binary classification, Description: Will the customer default?


Usage
=====


Features
========




TAGS
#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #heloc #fico #tabular_classification #binary_classification #region-us 

passage: TAGS
#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #heloc #fico #tabular_classification #binary_classification #region-us 
