{
  "fetched_at": "2026-10-19T22:46:24Z",
  "source_url": "https://www.kaggle.com/competitions/bsc-classification-bakeoff/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Data Files\n\nIn this repo, just as before, you will find two csv files.\n\n1. `train_data.csv`\nThis includes the features and target variables that you will use to train a predictive model\n\n2. `test_features.csv`\nThis 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.\n\n## Feature Descriptions\n\n* ID: ID of each client\n* LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit\n* SEX: Gender (1=male, 2=female)\n* EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)\n* MARRIAGE: Marital status (1=married, 2=single, 3=others)\n* AGE: Age in years\n* 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)\n* PAY_2: Repayment status in August, 2005 (scale same as above)\n* PAY_3: Repayment status in July, 2005 (scale same as above)\n* PAY_4: Repayment status in June, 2005 (scale same as above)\n* PAY_5: Repayment status in May, 2005 (scale same as above)\n* PAY_6: Repayment status in April, 2005 (scale same as above)\n* BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)\n* BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)\n* BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)\n* BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)\n* BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)\n* BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)\n* PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)\n* PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)\n* PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)\n* PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)\n* PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)\n* PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)\n* default.payment.next.month: Default payment (1=yes, 0=no)",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "# Bake Off 2: This Time it's for Classification\n\n![Baking](https://media.giphy.com/media/xT0xePLIUyxnXso8co/giphy.gif)\n\n## Challenge \n\nThe 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.\n\nThe dataset comes from customers default payments.\n\n## Data Files\n\nIn this repo, just as before, you will find two csv files.\n\n1. `train_data.csv`\nThis includes the features and target variables that you will use to train a predictive model\n\n2. `test_features.csv`\nThis includes a testing set of data with the `default# Bake Off 2: This Time it's for Classification\n\n![Baking](https://media.giphy.com/media/xT0xePLIUyxnXso8co/giphy.gif)\n\n## Challenge \n\nThe 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.\n\nThe dataset comes from customers default payments.\n\n## Data Files\n\nIn this repo, just as before, you will find two csv files.\n\n1. `train_data.csv`\nThis includes the features and target variables that you will use to train a predictive model\n\n2. `test_features.csv`\nThis 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.\n\n## Feature Descriptions\n\n* ID: ID of each client\n* LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit\n* SEX: Gender (1=male, 2=female)\n* EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)\n* MARRIAGE: Marital status (1=married, 2=single, 3=others)\n* AGE: Age in years\n* 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)\n* PAY_2: Repayment status in August, 2005 (scale same as above)\n* PAY_3: Repayment status in July, 2005 (scale same as above)\n* PAY_4: Repayment status in June, 2005 (scale same as above)\n* PAY_5: Repayment status in May, 2005 (scale same as above)\n* PAY_6: Repayment status in April, 2005 (scale same as above)\n* BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)\n* BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)\n* BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)\n* BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)\n* BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)\n* BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)\n* PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)\n* PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)\n* PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)\n* PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)\n* PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)\n* PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)\n* default.payment.next.month: Default payment (1=yes, 0=no)\n\n\n- 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. \n\n## Metrics\nTest results will be scored by [ROC-AUC](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html). ",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "## Metric\n\nThis competition will evaluate your predictions using [ROC-AUC](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html). The predictions with the highest ROC-AUC score will earn first place on the Leaderboard.\n\n## Submission Format\n\nYour submission should be in the form of a csv file with two columns. \n1. `ID`\n2. `prediction`\n\nThe `ID` column should be the `ID` column found in the predictors dataset.\n\n**For example,** if I were to submit a csv of predictions where I predict the most common class for every observation, the first three rows of the submission would look like this:\n\n| ID    | prediction  |\n|-------|---------|\n| 4359 |  0 |\n| 7542  | 0 |\n|  4777 | 0 |\n\n\n***It is recommended that you save your predictions to csv using `pd.to_csv` (with  `index=False`) and that you import the saved file into a notebook, to make sure the data is structured as intended.***\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}