# Bake Off 2: This Time it's for Classification

![Baking](https://media.giphy.com/media/xT0xePLIUyxnXso8co/giphy.gif)

## Challenge 

The goal for this challenge is to predict whether a person will default on their loan. In doing so, we want to utilize all of the different tools we have learned over the course: data cleaning, EDA, feature engineering/transformation, feature selection, hyperparameter tuning, and model evaluation.

The dataset comes from customers default payments.

## Data Files

In this repo, just as before, you will find two csv files.

1. `train_data.csv`
This includes the features and target variables that you will use to train a predictive model

2. `test_features.csv`
This includes a testing set of data with the `default# Bake Off 2: This Time it's for Classification

![Baking](https://media.giphy.com/media/xT0xePLIUyxnXso8co/giphy.gif)

## Challenge 

The goal for this challenge is to predict whether a person will default on their loan. In doing so, we want to utilize all of the different tools we have learned over the course: data cleaning, EDA, feature engineering/transformation, feature selection, hyperparameter tuning, and model evaluation.

The dataset comes from customers default payments.

## Data Files

In this repo, just as before, you will find two csv files.

1. `train_data.csv`
This includes the features and target variables that you will use to train a predictive model

2. `test_features.csv`
This includes a testing set of data with the `default.payment.next.month` column removed. You will make your final predictions on these observations and submit the predictions as a csv file.

## Feature Descriptions

* ID: ID of each client
* LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit
* SEX: Gender (1=male, 2=female)
* EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)
* MARRIAGE: Marital status (1=married, 2=single, 3=others)
* AGE: Age in years
* PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above)
* PAY_2: Repayment status in August, 2005 (scale same as above)
* PAY_3: Repayment status in July, 2005 (scale same as above)
* PAY_4: Repayment status in June, 2005 (scale same as above)
* PAY_5: Repayment status in May, 2005 (scale same as above)
* PAY_6: Repayment status in April, 2005 (scale same as above)
* BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)
* BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)
* BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)
* BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)
* BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)
* BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)
* PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)
* PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)
* PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)
* PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)
* PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)
* PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)
* default.payment.next.month: Default payment (1=yes, 0=no)


- You will be given **1 hour and 45 minutes** to build your best model using the training data. There is a hard cut-off at 6pm. Any late submissions will not be considered. 

## Metrics
Test results will be scored by [ROC-AUC](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html).