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README.md

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+---
+license: mit
+pretty_name: TopBench
+task_categories:
+  - tabular-classification
+  - tabular-regression
+  - question-answering
+language:
+  - en
+tags:
+  - tabular
+  - benchmark
+  - predictive-reasoning
+  - llm
+---
+
+# TopBench Dataset
+
+TopBench is a benchmark for predictive reasoning over tabular data. Each example asks a model to infer an unobserved outcome, decision, treatment effect, or ranked/filtering result from historical tables and a natural-language query.
+
+## Layout
+
+```text
+single_point_prediction/
+decision_making/
+treatment_effect_analysis/
+ranking_and_filtering/
+```
+
+Each task directory contains dataset folders with `history.csv`, query JSON files, and metadata. The `ranking_and_filtering` task also includes `current.csv` and expects structured CSV outputs.
+
+Use the code release at https://github.com/LAMDA-Tabular/TopBench for validation, inference, evaluation, and the predict-only baseline.

decision_making/finance/classification/Credit_Card_customers_B2/Credit_Card_customers_B2_001.json

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+{
+  "id": "001",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Credit_Card_customers",
+  "table_path": "kaggle/Credit_Card_customers",
+  "query": "My manager asked me to review these three accounts and flag the one most at risk of attrition. Looking at candidate one, she's a 56-year-old married graduate with one child. Her household income is below $40,000. She's been a customer for just over four years (50 months) and has four of our products. She holds our Blue card with a $1,584 limit, but she's using $1,311 of it, so her available credit is down to $273. That's an 82.8% utilization rate. In the last year, she didn't use the card for three months, and we reached out to her twice. She still made 73 purchases totaling $4,203, though her spending growth from Q1 to Q4 was 0.867, and her transaction growth was 0.78. The account number is 801497508.  Moving on to the second profile, this is a married 54-year-old man supporting two dependents on a $40K-$60K income; his education is unknown. He's been with us for 44 months, has four products, and also uses a Blue card. His limit is $2,902, but he's revolving $2,517 of it, with only $385 left to spend—a 86.7% utilization. He was inactive for three months, we contacted him twice, and his annual spending was quite low at $996 over just 26 transactions. His spending and transaction growth figures are 0.821 and 0.444. His client ID is 709278258.  The third file is for a single 58-year-old female graduate with no dependents and an income under $40K. She's been a client for 36 months and surprisingly has five of our products. Her Blue card has a $3,008 limit with a $2,517 balance, leaving $491 available (83.7% utilized). She had three inactive months but only one contact from us. She spent $4,627 across 68 transactions. Her spending growth was 0.674, and her transaction growth was 0.659. Her number is 717121758.  I'm torn. Based on their behavior and situation, which customer should I mark as most likely to become an \"Attrited Customer\"?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": "801497508",
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": "56",
+          "Gender": "F",
+          "Dependent_count": "1",
+          "Education_Level": "Graduate",
+          "Marital_Status": "Married",
+          "Income_Category": "Less than $40K",
+          "Card_Category": "Blue",
+          "Months_on_book": "50",
+          "Total_Relationship_Count": "4",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": "2",
+          "Credit_Limit": "1584.0",
+          "Total_Revolving_Bal": "1311",
+          "Avg_Open_To_Buy": "273.0",
+          "Total_Amt_Chng_Q4_Q1": "0.867",
+          "Total_Trans_Amt": "4203",
+          "Total_Trans_Ct": "73",
+          "Total_Ct_Chng_Q4_Q1": "0.78",
+          "Avg_Utilization_Ratio": "0.828"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "CLIENTNUM": "709278258",
+          "Attrition_Flag": "Attrited Customer",
+          "Customer_Age": "54",
+          "Gender": "M",
+          "Dependent_count": "2",
+          "Education_Level": "Unknown",
+          "Marital_Status": "Married",
+          "Income_Category": "$40K - $60K",
+          "Card_Category": "Blue",
+          "Months_on_book": "44",
+          "Total_Relationship_Count": "4",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": "2",
+          "Credit_Limit": "2902.0",
+          "Total_Revolving_Bal": "2517",
+          "Avg_Open_To_Buy": "385.0",
+          "Total_Amt_Chng_Q4_Q1": "0.821",
+          "Total_Trans_Amt": "996",
+          "Total_Trans_Ct": "26",
+          "Total_Ct_Chng_Q4_Q1": "0.444",
+          "Avg_Utilization_Ratio": "0.867"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "CLIENTNUM": "717121758",
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": "58",
+          "Gender": "F",
+          "Dependent_count": "0",
+          "Education_Level": "Graduate",
+          "Marital_Status": "Single",
+          "Income_Category": "Less than $40K",
+          "Card_Category": "Blue",
+          "Months_on_book": "36",
+          "Total_Relationship_Count": "5",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": "1",
+          "Credit_Limit": "3008.0",
+          "Total_Revolving_Bal": "2517",
+          "Avg_Open_To_Buy": "491.0",
+          "Total_Amt_Chng_Q4_Q1": "0.674",
+          "Total_Trans_Amt": "4627",
+          "Total_Trans_Ct": "68",
+          "Total_Ct_Chng_Q4_Q1": "0.659",
+          "Avg_Utilization_Ratio": "0.837"
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Credit_Card_customers_B2/Credit_Card_customers_B2_002.json

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+{
+  "id": "002",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Credit_Card_customers",
+  "table_path": "kaggle/Credit_Card_customers",
+  "query": "Our retention team is reviewing a few customer files flagged for review. The first record is for client number 720906033. He's a 35-year-old married man with one dependent, earning between $40K and $60K annually. He's been with the bank for 25 months, but has been inactive for 4 of the last 12 months and we've contacted him 4 times in that period. His credit limit is $5801, with a revolving balance of $1176. His transaction amount change from Q1 to Q4 was 1.12, with a total spend of $2987 last year, and his transaction count change was 0.857. His average card utilization is 20.3%.  Now, the second file is for client 778910883. This customer is 53, married with two dependents, though his education level isn't recorded. He holds a Blue card and has been a client for 34 months, using 4 of our products. In the past year, he was inactive for 3 months and we contacted him twice. His credit limit is $1837, with a $875 revolving balance, leaving an average open-to-buy of $962. His transaction amount change is 0.58, he made 43 transactions last year, and his transaction count change is 0.654.  Finally, looking at client 778439883, we have a 50-year-old with one dependent and a High School education, holding a Blue card. His relationship length is 31 months, but he only uses 1 of our products. He was inactive for 2 months last year and was contacted twice. Interestingly, he has a very high credit limit of $26840, but only a $769 revolving balance, meaning his average available credit is $26071. His transaction amount change is 0.609, with a total spend of $7969 across 93 transactions last year.  Given our goal to identify who is most likely to leave, which of these three individuals should we prioritize for a retention call?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": "720906033",
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": "35",
+          "Gender": "M",
+          "Dependent_count": "1",
+          "Education_Level": null,
+          "Marital_Status": "Married",
+          "Income_Category": "$40K - $60K",
+          "Card_Category": null,
+          "Months_on_book": "25",
+          "Total_Relationship_Count": null,
+          "Months_Inactive_12_mon": "4",
+          "Contacts_Count_12_mon": "4",
+          "Credit_Limit": "5801.0",
+          "Total_Revolving_Bal": "1176",
+          "Avg_Open_To_Buy": null,
+          "Total_Amt_Chng_Q4_Q1": "1.12",
+          "Total_Trans_Amt": "2987",
+          "Total_Trans_Ct": null,
+          "Total_Ct_Chng_Q4_Q1": "0.857",
+          "Avg_Utilization_Ratio": "0.203"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "CLIENTNUM": "778910883",
+          "Attrition_Flag": "Attrited Customer",
+          "Customer_Age": "53",
+          "Gender": null,
+          "Dependent_count": "2",
+          "Education_Level": "Unknown",
+          "Marital_Status": "Married",
+          "Income_Category": null,
+          "Card_Category": "Blue",
+          "Months_on_book": "34",
+          "Total_Relationship_Count": "4",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": "2",
+          "Credit_Limit": "1837.0",
+          "Total_Revolving_Bal": "875",
+          "Avg_Open_To_Buy": "962.0",
+          "Total_Amt_Chng_Q4_Q1": "0.58",
+          "Total_Trans_Amt": null,
+          "Total_Trans_Ct": "43",
+          "Total_Ct_Chng_Q4_Q1": "0.654",
+          "Avg_Utilization_Ratio": null
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "CLIENTNUM": "778439883",
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": "50",
+          "Gender": null,
+          "Dependent_count": "1",
+          "Education_Level": "High School",
+          "Marital_Status": null,
+          "Income_Category": null,
+          "Card_Category": "Blue",
+          "Months_on_book": "31",
+          "Total_Relationship_Count": "1",
+          "Months_Inactive_12_mon": "2",
+          "Contacts_Count_12_mon": "2",
+          "Credit_Limit": "26840.0",
+          "Total_Revolving_Bal": "769",
+          "Avg_Open_To_Buy": "26071.0",
+          "Total_Amt_Chng_Q4_Q1": "0.609",
+          "Total_Trans_Amt": "7969",
+          "Total_Trans_Ct": "93",
+          "Total_Ct_Chng_Q4_Q1": null,
+          "Avg_Utilization_Ratio": null
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Credit_Card_customers_B2/Credit_Card_customers_B2_003.json

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+{
+  "id": "003",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Credit_Card_customers",
+  "table_path": "kaggle/Credit_Card_customers",
+  "query": "Client 779808333 is a single female graduate holding a Blue card and supporting two dependents. She has two of our bank's products, was inactive for only one month, and possesses a credit limit of 8881.0. Her spending increased by a factor of 0.808, culminating in 15530 across 110 transactions, with the transaction count itself rising by 0.692. Her average card usage sits at 0.214.  Shifting focus, the record for 712164333 describes a 40-year-old male with four dependents, a high school education, and an income between $60K and $80K. He uses three products but was inactive for three months, resulting in two contacts. He carries a revolving balance of 1636. His transaction amount change is 0.503, with total spending of 1758 over 51 transactions. The change in his number of transactions is 0.457, and he utilizes 0.13 of his limit.  Lastly, we have client 716717733, a 55-year-old single woman earning under $40K with a Blue card. She holds two products. Her profile shows five months of inactivity and four contacts. Her credit limit is 4841.0 with a revolving balance of 859. She spent 8918 in 87 transactions, using 0.177 of her available credit. With retention as the goal, which of these three individuals should I be most confident about?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": "779808333",
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": null,
+          "Gender": "F",
+          "Dependent_count": "2",
+          "Education_Level": "Graduate",
+          "Marital_Status": "Single",
+          "Income_Category": null,
+          "Card_Category": "Blue",
+          "Months_on_book": null,
+          "Total_Relationship_Count": "2",
+          "Months_Inactive_12_mon": "1",
+          "Contacts_Count_12_mon": null,
+          "Credit_Limit": "8881.0",
+          "Total_Revolving_Bal": null,
+          "Avg_Open_To_Buy": null,
+          "Total_Amt_Chng_Q4_Q1": "0.808",
+          "Total_Trans_Amt": "15530",
+          "Total_Trans_Ct": "110",
+          "Total_Ct_Chng_Q4_Q1": "0.692",
+          "Avg_Utilization_Ratio": "0.214"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "CLIENTNUM": "712164333",
+          "Attrition_Flag": "Attrited Customer",
+          "Customer_Age": "40",
+          "Gender": "M",
+          "Dependent_count": "4",
+          "Education_Level": "High School",
+          "Marital_Status": null,
+          "Income_Category": "$60K - $80K",
+          "Card_Category": null,
+          "Months_on_book": null,
+          "Total_Relationship_Count": "3",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": "2",
+          "Credit_Limit": null,
+          "Total_Revolving_Bal": "1636",
+          "Avg_Open_To_Buy": null,
+          "Total_Amt_Chng_Q4_Q1": "0.503",
+          "Total_Trans_Amt": "1758",
+          "Total_Trans_Ct": "51",
+          "Total_Ct_Chng_Q4_Q1": "0.457",
+          "Avg_Utilization_Ratio": "0.13"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "CLIENTNUM": "716717733",
+          "Attrition_Flag": "Attrited Customer",
+          "Customer_Age": "55",
+          "Gender": "F",
+          "Dependent_count": null,
+          "Education_Level": null,
+          "Marital_Status": "Single",
+          "Income_Category": "Less than $40K",
+          "Card_Category": "Blue",
+          "Months_on_book": null,
+          "Total_Relationship_Count": "2",
+          "Months_Inactive_12_mon": "5",
+          "Contacts_Count_12_mon": "4",
+          "Credit_Limit": "4841.0",
+          "Total_Revolving_Bal": "859",
+          "Avg_Open_To_Buy": null,
+          "Total_Amt_Chng_Q4_Q1": null,
+          "Total_Trans_Amt": "8918",
+          "Total_Trans_Ct": "87",
+          "Total_Ct_Chng_Q4_Q1": null,
+          "Avg_Utilization_Ratio": "0.177"
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Credit_Card_customers_B2/Credit_Card_customers_B2_004.json

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+{
+  "id": "004",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Credit_Card_customers",
+  "table_path": "kaggle/Credit_Card_customers",
+  "query": "The first client, ID number 794560833, is a 47-year-old woman. She has three dependents and her education level is listed as Graduate. Her annual income falls in the $40K to $60K bracket and she holds a Blue category card. She's been with the bank for 31 months and holds a total of 6 different products with us. However, in the last year, she's been inactive for 3 months and we've contacted her 6 times. Her total revolving balance on the card is 0, and the change in her transaction amount from the first to the fourth quarter was 0.548. She made 34 transactions in the last year, with a change in transaction count of 0.308 between those quarters. Her average card utilization ratio is 0.0.  The second client is a 39-year-old man with four dependents. His income is higher, between $80K and $120K. He's been a customer for 32 months and has 5 products. He also had 3 inactive months in the past year. His credit limit is 26710, with a revolving balance of 1681, leaving an average open-to-buy credit line of 25029. His total transaction amount over twelve months was 14043 across 117 transactions. The change in his transaction count was 0.746, and his average utilization ratio is 0.063. I've got my whole archive of past customer logs here for you to check patterns against.  Given the history you can see, which of these two new profiles has me more worried about potential attrition?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": "794560833",
+          "Attrition_Flag": "Attrited Customer",
+          "Customer_Age": "47",
+          "Gender": "F",
+          "Dependent_count": "3",
+          "Education_Level": "Graduate",
+          "Marital_Status": null,
+          "Income_Category": "$40K - $60K",
+          "Card_Category": "Blue",
+          "Months_on_book": "31",
+          "Total_Relationship_Count": "6",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": "6",
+          "Credit_Limit": null,
+          "Total_Revolving_Bal": "0",
+          "Avg_Open_To_Buy": null,
+          "Total_Amt_Chng_Q4_Q1": "0.548",
+          "Total_Trans_Amt": null,
+          "Total_Trans_Ct": "34",
+          "Total_Ct_Chng_Q4_Q1": "0.308",
+          "Avg_Utilization_Ratio": "0.0"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "CLIENTNUM": null,
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": "39",
+          "Gender": "M",
+          "Dependent_count": "4",
+          "Education_Level": null,
+          "Marital_Status": null,
+          "Income_Category": "$80K - $120K",
+          "Card_Category": null,
+          "Months_on_book": "32",
+          "Total_Relationship_Count": "5",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": null,
+          "Credit_Limit": "26710.0",
+          "Total_Revolving_Bal": "1681",
+          "Avg_Open_To_Buy": "25029.0",
+          "Total_Amt_Chng_Q4_Q1": null,
+          "Total_Trans_Amt": "14043",
+          "Total_Trans_Ct": "117",
+          "Total_Ct_Chng_Q4_Q1": "0.746",
+          "Avg_Utilization_Ratio": "0.063"
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Credit_Card_customers_B2/Credit_Card_customers_B2_005.json

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+{
+  "id": "005",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Credit_Card_customers",
+  "table_path": "kaggle/Credit_Card_customers",
+  "query": "My historical data on who stays and who leaves makes these three new cases really interesting to look at. Take client 714058308: 43 years old, single, and has five people depending on them. They're uneducated and make under $40K a year. A Blue card for 36 months, but inactive for three of the last twelve, leading to four contacts. Their credit limit is $2,689 with just $305 often left to spend. The change in their transaction amount is 0.365, with $1,941 spent in 53 transactions, and the count change is 0.656.  Then, a 47-year-old male graduate. His relationship status is unknown, but he earns $40K-$60K. Another Blue card user, with 34 months on the books and two total relationships. He was inactive for three months. He's working with a $9,080 limit and has a $2,174 revolving balance. His transaction amount jumped by 0.653 (total $2,098), and his transaction count change is 0.586.  The last one, 772353408, is a woman with five dependents in that same mid-income band. She's been around for 33 months and has four products. Three months inactive, four contacts. Credit limit of $5,330, a balance of $1,161. Her spending change is 0.558 ($1,441 total, 43 transactions), and she utilizes about 22% of her credit.  I can share the past logs for patterns, but from these details, who do you think fits the 'Existing Customer' mold best?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": "714058308",
+          "Attrition_Flag": "Attrited Customer",
+          "Customer_Age": "43",
+          "Gender": null,
+          "Dependent_count": "5",
+          "Education_Level": "Uneducated",
+          "Marital_Status": "Single",
+          "Income_Category": "Less than $40K",
+          "Card_Category": "Blue",
+          "Months_on_book": "36",
+          "Total_Relationship_Count": null,
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": "4",
+          "Credit_Limit": "2689.0",
+          "Total_Revolving_Bal": null,
+          "Avg_Open_To_Buy": "305.0",
+          "Total_Amt_Chng_Q4_Q1": "0.365",
+          "Total_Trans_Amt": "1941",
+          "Total_Trans_Ct": "53",
+          "Total_Ct_Chng_Q4_Q1": "0.656",
+          "Avg_Utilization_Ratio": null
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "CLIENTNUM": null,
+          "Attrition_Flag": "Attrited Customer",
+          "Customer_Age": "47",
+          "Gender": "M",
+          "Dependent_count": null,
+          "Education_Level": "Graduate",
+          "Marital_Status": "Unknown",
+          "Income_Category": "$40K - $60K",
+          "Card_Category": "Blue",
+          "Months_on_book": "34",
+          "Total_Relationship_Count": "2",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": null,
+          "Credit_Limit": "9080.0",
+          "Total_Revolving_Bal": "2174",
+          "Avg_Open_To_Buy": null,
+          "Total_Amt_Chng_Q4_Q1": "0.653",
+          "Total_Trans_Amt": "2098",
+          "Total_Trans_Ct": null,
+          "Total_Ct_Chng_Q4_Q1": "0.586",
+          "Avg_Utilization_Ratio": null
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "CLIENTNUM": "772353408",
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": null,
+          "Gender": "F",
+          "Dependent_count": "5",
+          "Education_Level": null,
+          "Marital_Status": null,
+          "Income_Category": "$40K - $60K",
+          "Card_Category": null,
+          "Months_on_book": "33",
+          "Total_Relationship_Count": "4",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": "4",
+          "Credit_Limit": "5330.0",
+          "Total_Revolving_Bal": "1161",
+          "Avg_Open_To_Buy": null,
+          "Total_Amt_Chng_Q4_Q1": "0.558",
+          "Total_Trans_Amt": "1441",
+          "Total_Trans_Ct": "43",
+          "Total_Ct_Chng_Q4_Q1": null,
+          "Avg_Utilization_Ratio": "0.218"
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "003",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Credit_Card_customers_B2/Credit_Card_customers_B2_006.json

@@ -0,0 +1,77 @@
+{
+  "id": "006",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Credit_Card_customers",
+  "table_path": "kaggle/Credit_Card_customers",
+  "query": "These two new client profiles have me unsure, so I'm pulling up our past records for context. Client 823615983 is a single male, age 54, with two dependents. He is uneducated and has an annual income between forty and sixty thousand dollars, using a Blue category card. His account has been open for 50 months, and he holds two of our financial products. He was inactive for 1 of the last 12 months, during which we contacted him 3 times. The credit limit on his card is 3682 dollars, with a revolving balance of 2517, so his average available credit is 1165. His total transaction amount over the year is 1642 from 35 transactions, with the amount change at 0.379 and the count change at 0.25. His card utilization ratio averages 0.684. The other client, 713894358, is a 51-year-old male post-graduate, also with two dependents (marital status unknown). He earns eighty to one hundred twenty thousand and has a Gold card. His tenure is 46 months with two products. He was inactive for 3 months last year, resulting in 2 contacts. He has a substantial credit limit of 34516 but a low balance of 814, leaving 33702 typically open to buy. His yearly spend is 7889 over 98 transactions, with an amount change of 0.735 and a count change of 0.508. His utilization is a minimal 0.024. I have the historical archive for comparison—does the first or the second individual better match the profile of someone likely to attrite?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "CLIENTNUM": "823615983",
+          "Attrition_Flag": "Attrited Customer",
+          "Customer_Age": "54",
+          "Gender": "M",
+          "Dependent_count": "2",
+          "Education_Level": "Uneducated",
+          "Marital_Status": "Single",
+          "Income_Category": "$40K - $60K",
+          "Card_Category": "Blue",
+          "Months_on_book": "50",
+          "Total_Relationship_Count": "2",
+          "Months_Inactive_12_mon": "1",
+          "Contacts_Count_12_mon": "3",
+          "Credit_Limit": "3682.0",
+          "Total_Revolving_Bal": "2517",
+          "Avg_Open_To_Buy": "1165.0",
+          "Total_Amt_Chng_Q4_Q1": "0.379",
+          "Total_Trans_Amt": "1642",
+          "Total_Trans_Ct": "35",
+          "Total_Ct_Chng_Q4_Q1": "0.25",
+          "Avg_Utilization_Ratio": "0.684"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "CLIENTNUM": "713894358",
+          "Attrition_Flag": "Existing Customer",
+          "Customer_Age": "51",
+          "Gender": "M",
+          "Dependent_count": "2",
+          "Education_Level": "Post-Graduate",
+          "Marital_Status": "Unknown",
+          "Income_Category": "$80K - $120K",
+          "Card_Category": "Gold",
+          "Months_on_book": "46",
+          "Total_Relationship_Count": "2",
+          "Months_Inactive_12_mon": "3",
+          "Contacts_Count_12_mon": "2",
+          "Credit_Limit": "34516.0",
+          "Total_Revolving_Bal": "814",
+          "Avg_Open_To_Buy": "33702.0",
+          "Total_Amt_Chng_Q4_Q1": "0.735",
+          "Total_Trans_Amt": "7889",
+          "Total_Trans_Ct": "98",
+          "Total_Ct_Chng_Q4_Q1": "0.508",
+          "Avg_Utilization_Ratio": "0.024"
+        }
+      }
+    ],
+    "target_column": "Attrition_Flag",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Credit_Card_customers_B2/current.csv

@@ -0,0 +1,17 @@
+CLIENTNUM,Attrition_Flag,Customer_Age,Gender,Dependent_count,Education_Level,Marital_Status,Income_Category,Card_Category,Months_on_book,Total_Relationship_Count,Months_Inactive_12_mon,Contacts_Count_12_mon,Credit_Limit,Total_Revolving_Bal,Avg_Open_To_Buy,Total_Amt_Chng_Q4_Q1,Total_Trans_Amt,Total_Trans_Ct,Total_Ct_Chng_Q4_Q1,Avg_Utilization_Ratio
+801497508,Existing Customer,56,F,1,Graduate,Married,Less than $40K,Blue,50,4,3,2,1584.0,1311,273.0,0.867,4203,73,0.78,0.828
+709278258,Attrited Customer,54,M,2,Unknown,Married,$40K - $60K,Blue,44,4,3,2,2902.0,2517,385.0,0.821,996,26,0.444,0.867
+717121758,Existing Customer,58,F,0,Graduate,Single,Less than $40K,Blue,36,5,3,1,3008.0,2517,491.0,0.674,4627,68,0.659,0.837
+720906033,Existing Customer,35,M,1,Graduate,Married,$40K - $60K,Blue,25,5,4,4,5801.0,1176,4625.0,1.12,2987,52,0.857,0.203
+778910883,Attrited Customer,53,M,2,Unknown,Married,$80K - $120K,Blue,34,4,3,2,1837.0,875,962.0,0.58,2169,43,0.654,0.476
+778439883,Existing Customer,50,M,1,High School,Unknown,$80K - $120K,Blue,31,1,2,2,26840.0,769,26071.0,0.609,7969,93,0.661,0.029
+779808333,Existing Customer,48,F,2,Graduate,Single,Less than $40K,Blue,38,2,1,2,8881.0,1901,6980.0,0.808,15530,110,0.692,0.214
+712164333,Attrited Customer,40,M,4,High School,Married,$60K - $80K,Blue,29,3,3,2,12544.0,1636,10908.0,0.503,1758,51,0.457,0.13
+716717733,Attrited Customer,55,F,3,Uneducated,Single,Less than $40K,Blue,47,2,5,4,4841.0,859,3982.0,1.003,8918,87,0.977,0.177
+794560833,Attrited Customer,47,F,3,Graduate,Married,$40K - $60K,Blue,31,6,3,6,5496.0,0,5496.0,0.548,1913,34,0.308,0.0
+711289533,Existing Customer,39,M,4,Post-Graduate,Single,$80K - $120K,Blue,32,5,3,2,26710.0,1681,25029.0,0.741,14043,117,0.746,0.063
+714058308,Attrited Customer,43,F,5,Uneducated,Single,Less than $40K,Blue,36,5,3,4,2689.0,2384,305.0,0.365,1941,53,0.656,0.887
+710658483,Attrited Customer,47,M,4,Graduate,Unknown,$40K - $60K,Blue,34,2,3,6,9080.0,2174,6906.0,0.653,2098,46,0.586,0.239
+772353408,Existing Customer,43,F,5,Unknown,Single,$40K - $60K,Blue,33,4,3,4,5330.0,1161,4169.0,0.558,1441,43,0.387,0.218
+823615983,Attrited Customer,54,M,2,Uneducated,Single,$40K - $60K,Blue,50,2,1,3,3682.0,2517,1165.0,0.379,1642,35,0.25,0.684
+713894358,Existing Customer,51,M,2,Post-Graduate,Unknown,$80K - $120K,Gold,46,2,3,2,34516.0,814,33702.0,0.735,7889,98,0.508,0.024

decision_making/finance/classification/Credit_Card_customers_B2/info.json

@@ -0,0 +1,152 @@
+{
+    "name": "Credit Card customers",
+    "source": "https://www.kaggle.com/datasets/sakshigoyal7/credit-card-customers/data",
+    "data_intro": "this dataset consists of 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc,try to predict what kind of client is likely to leave",
+    "is_splited": false,
+    "overall_size": 10127,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 6,
+    "n_classes": 17,
+    "task_type": "classification",
+    "target": {
+        "Attrition_Flag": "Given a customer's all other data, predict whether the customer will be Existing Customer or Attrited Customer"
+    },
+    "cat_feature_intro": {
+        "Attrition_Flag": "- Attrition_Flag:Internal event (customer activity) variable - if the account is closed then 1 else 0,Existing Customer,Attrited Customer",
+        "Gender": "- Gender: Demographic variable - M=Male, F=Female",
+        "Education_Level": "- Education_Level:Demographic variable - Educational Qualification of the account holder (example: high school, college graduate, etc.)",
+        "Marital_Status": "- Marital_Status:Demographic variable - Married, Single, Divorced, Unknown",
+        "Income_Category": "- Income_Category: Demographic variable - Annual Income Category of the account holder (< $40K, $40K - 60K, $60K - $80K, $80K-$120K, > $120K, Unknown)",
+        "Card_Category": "- Card_Category: Product Variable - Type of Card (Blue, Silver, Gold, Platinum)"
+    },
+    "num_feature_intro": {
+        "CLIENTNUM": "- CLIENTNUM:Client number. Unique identifier for the customer holding the account",
+        "Customer_Age": "- Customer_Age:Demographic variable - Customer's Age in Years",
+        "Dependent_count": "- Dependent_count:Demographic variable - Number of dependents",
+        "Months_on_book": "- Months_on_book:Period of relationship with bank",
+        "Total_Relationship_Count": "- Total_Relationship_Count:Total no. of products held by the customer",
+        "Months_Inactive_12_mon": "- Months_Inactive_12_mon:No. of months inactive in the last 12 months",
+        "Contacts_Count_12_mon": "- Contacts_Count_12_mon:No. of Contacts in the last 12 months",
+        "Credit_Limit": "- Credit_Limit:Credit Limit on the Credit Card",
+        "Total_Revolving_Bal": "- Total_Revolving_Bal:Total Revolving Balance on the Credit Card",
+        "Avg_Open_To_Buy": "- Avg_Open_To_Buy:Open to Buy Credit Line (Average of last 12 months)",
+        "Total_Amt_Chng_Q4_Q1": "- Total_Amt_Chng_Q4_Q1:Change in Transaction Amount (Q4 over Q1)",
+        "Total_Trans_Amt": "- Total_Trans_Amt:Total Transaction Amount (Last 12 months)",
+        "Total_Trans_Ct": "- Total_Trans_Ct:Total Transaction Count (Last 12 months)",
+        "Total_Ct_Chng_Q4_Q1": "- Total_Ct_Chng_Q4_Q1:Change in Transaction Count (Q4 over Q1)",
+        "Avg_Utilization_Ratio": "- Avg_Utilization_Ratio:Average Card Utilization Ratio",
+        "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1": "- Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1:Naive Bayes",
+        "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2": "- Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2:Naive Bayes"
+    },
+    "evaluation_metric": null,
+    "num_feature_value": {
+        "Avg_Open_To_Buy": [
+            3.0,
+            34516.0
+        ],
+        "Avg_Utilization_Ratio": [
+            0.0,
+            0.999
+        ],
+        "CLIENTNUM": [
+            708082083.0,
+            828343083.0
+        ],
+        "Contacts_Count_12_mon": [
+            0.0,
+            6.0
+        ],
+        "Credit_Limit": [
+            1438.3,
+            34516.0
+        ],
+        "Customer_Age": [
+            26.0,
+            73.0
+        ],
+        "Dependent_count": [
+            0.0,
+            5.0
+        ],
+        "Months_Inactive_12_mon": [
+            0.0,
+            6.0
+        ],
+        "Months_on_book": [
+            13.0,
+            56.0
+        ],
+        "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1": [
+            7.6642e-06,
+            0.99958
+        ],
+        "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2": [
+            0.00041998,
+            0.99999
+        ],
+        "Total_Amt_Chng_Q4_Q1": [
+            0.0,
+            3.397
+        ],
+        "Total_Ct_Chng_Q4_Q1": [
+            0.0,
+            3.714
+        ],
+        "Total_Relationship_Count": [
+            1.0,
+            6.0
+        ],
+        "Total_Revolving_Bal": [
+            0.0,
+            2517.0
+        ],
+        "Total_Trans_Amt": [
+            510.0,
+            18484.0
+        ],
+        "Total_Trans_Ct": [
+            10.0,
+            139.0
+        ]
+    },
+    "cat_feature_value": {
+        "Attrition_Flag": [
+            "Attrited Customer",
+            "Existing Customer"
+        ],
+        "Card_Category": [
+            "Blue",
+            "Gold",
+            "Platinum",
+            "Silver"
+        ],
+        "Education_Level": [
+            "College",
+            "Doctorate",
+            "Graduate",
+            "High School",
+            "Post-Graduate",
+            "Uneducated",
+            "Unknown"
+        ],
+        "Gender": [
+            "F",
+            "M"
+        ],
+        "Income_Category": [
+            "$120K +",
+            "$40K - $60K",
+            "$60K - $80K",
+            "$80K - $120K",
+            "Less than $40K",
+            "Unknown"
+        ],
+        "Marital_Status": [
+            "Divorced",
+            "Married",
+            "Single",
+            "Unknown"
+        ]
+    }
+}

decision_making/finance/classification/Credit_Card_customers_B2/info_mod.json

@@ -0,0 +1,202 @@
+{
+  "name": "Credit Card customers",
+  "source": "https://www.kaggle.com/datasets/sakshigoyal7/credit-card-customers/data",
+  "data_intro": "this dataset consists of 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc,try to predict what kind of client is likely to leave",
+  "is_splited": false,
+  "overall_size": 10127,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 6,
+  "n_classes": 17,
+  "task_type": "classification",
+  "target": "Attrition_Flag",
+  "cat_feature_intro": {
+    "Gender": "- Gender: Demographic variable - M=Male, F=Female",
+    "Education_Level": "- Education_Level:Demographic variable - Educational Qualification of the account holder (example: high school, college graduate, etc.)",
+    "Marital_Status": "- Marital_Status:Demographic variable - Married, Single, Divorced, Unknown",
+    "Income_Category": "- Income_Category: Demographic variable - Annual Income Category of the account holder (< $40K, $40K - 60K, $60K - $80K, $80K-$120K, > $120K, Unknown)",
+    "Card_Category": "- Card_Category: Product Variable - Type of Card (Blue, Silver, Gold, Platinum)"
+  },
+  "num_feature_intro": {
+    "CLIENTNUM": "- CLIENTNUM:Client number. Unique identifier for the customer holding the account",
+    "Customer_Age": "- Customer_Age:Demographic variable - Customer's Age in Years",
+    "Dependent_count": "- Dependent_count:Demographic variable - Number of dependents",
+    "Months_on_book": "- Months_on_book:Period of relationship with bank",
+    "Total_Relationship_Count": "- Total_Relationship_Count:Total no. of products held by the customer",
+    "Months_Inactive_12_mon": "- Months_Inactive_12_mon:No. of months inactive in the last 12 months",
+    "Contacts_Count_12_mon": "- Contacts_Count_12_mon:No. of Contacts in the last 12 months",
+    "Credit_Limit": "- Credit_Limit:Credit Limit on the Credit Card",
+    "Total_Revolving_Bal": "- Total_Revolving_Bal:Total Revolving Balance on the Credit Card",
+    "Avg_Open_To_Buy": "- Avg_Open_To_Buy:Open to Buy Credit Line (Average of last 12 months)",
+    "Total_Amt_Chng_Q4_Q1": "- Total_Amt_Chng_Q4_Q1:Change in Transaction Amount (Q4 over Q1)",
+    "Total_Trans_Amt": "- Total_Trans_Amt:Total Transaction Amount (Last 12 months)",
+    "Total_Trans_Ct": "- Total_Trans_Ct:Total Transaction Count (Last 12 months)",
+    "Total_Ct_Chng_Q4_Q1": "- Total_Ct_Chng_Q4_Q1:Change in Transaction Count (Q4 over Q1)",
+    "Avg_Utilization_Ratio": "- Avg_Utilization_Ratio:Average Card Utilization Ratio"
+  },
+  "evaluation_metric": null,
+  "num_feature_value": {
+    "CLIENTNUM": [
+      708082083.0,
+      828343083.0
+    ],
+    "Customer_Age": [
+      26.0,
+      73.0
+    ],
+    "Dependent_count": [
+      0.0,
+      5.0
+    ],
+    "Months_on_book": [
+      13.0,
+      56.0
+    ],
+    "Total_Relationship_Count": [
+      1.0,
+      6.0
+    ],
+    "Months_Inactive_12_mon": [
+      0.0,
+      6.0
+    ],
+    "Contacts_Count_12_mon": [
+      0.0,
+      6.0
+    ],
+    "Credit_Limit": [
+      1438.3,
+      34516.0
+    ],
+    "Total_Revolving_Bal": [
+      0.0,
+      2517.0
+    ],
+    "Avg_Open_To_Buy": [
+      3.0,
+      34516.0
+    ],
+    "Total_Amt_Chng_Q4_Q1": [
+      0.0,
+      3.397
+    ],
+    "Total_Trans_Amt": [
+      510.0,
+      18484.0
+    ],
+    "Total_Trans_Ct": [
+      10.0,
+      139.0
+    ],
+    "Total_Ct_Chng_Q4_Q1": [
+      0.0,
+      3.714
+    ],
+    "Avg_Utilization_Ratio": [
+      0.0,
+      0.999
+    ]
+  },
+  "cat_feature_value": {
+    "Gender": [
+      "F",
+      "M"
+    ],
+    "Education_Level": [
+      "College",
+      "Doctorate",
+      "Graduate",
+      "High School",
+      "Post-Graduate",
+      "Uneducated"
+    ],
+    "Marital_Status": [
+      "Divorced",
+      "Married",
+      "Single"
+    ],
+    "Income_Category": [
+      "$120K +",
+      "$40K - $60K",
+      "$60K - $80K",
+      "$80K - $120K",
+      "Less than $40K"
+    ],
+    "Card_Category": [
+      "Blue",
+      "Gold",
+      "Platinum",
+      "Silver"
+    ]
+  },
+  "columns": [
+    "CLIENTNUM",
+    "Attrition_Flag",
+    "Customer_Age",
+    "Gender",
+    "Dependent_count",
+    "Education_Level",
+    "Marital_Status",
+    "Income_Category",
+    "Card_Category",
+    "Months_on_book",
+    "Total_Relationship_Count",
+    "Months_Inactive_12_mon",
+    "Contacts_Count_12_mon",
+    "Credit_Limit",
+    "Total_Revolving_Bal",
+    "Avg_Open_To_Buy",
+    "Total_Amt_Chng_Q4_Q1",
+    "Total_Trans_Amt",
+    "Total_Trans_Ct",
+    "Total_Ct_Chng_Q4_Q1",
+    "Avg_Utilization_Ratio"
+  ],
+  "feature_columns": [
+    "CLIENTNUM",
+    "Customer_Age",
+    "Gender",
+    "Dependent_count",
+    "Education_Level",
+    "Marital_Status",
+    "Income_Category",
+    "Card_Category",
+    "Months_on_book",
+    "Total_Relationship_Count",
+    "Months_Inactive_12_mon",
+    "Contacts_Count_12_mon",
+    "Credit_Limit",
+    "Total_Revolving_Bal",
+    "Avg_Open_To_Buy",
+    "Total_Amt_Chng_Q4_Q1",
+    "Total_Trans_Amt",
+    "Total_Trans_Ct",
+    "Total_Ct_Chng_Q4_Q1",
+    "Avg_Utilization_Ratio"
+  ],
+  "feature_types": {
+    "CLIENTNUM": "numeric",
+    "Customer_Age": "numeric",
+    "Gender": "categorical",
+    "Dependent_count": "numeric",
+    "Education_Level": "categorical",
+    "Marital_Status": "categorical",
+    "Income_Category": "categorical",
+    "Card_Category": "categorical",
+    "Months_on_book": "numeric",
+    "Total_Relationship_Count": "numeric",
+    "Months_Inactive_12_mon": "numeric",
+    "Contacts_Count_12_mon": "numeric",
+    "Credit_Limit": "numeric",
+    "Total_Revolving_Bal": "numeric",
+    "Avg_Open_To_Buy": "numeric",
+    "Total_Amt_Chng_Q4_Q1": "numeric",
+    "Total_Trans_Amt": "numeric",
+    "Total_Trans_Ct": "numeric",
+    "Total_Ct_Chng_Q4_Q1": "numeric",
+    "Avg_Utilization_Ratio": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

decision_making/finance/classification/Credit_Card_customers_B2/test.csv

@@ -0,0 +1,17 @@
+CLIENTNUM,Attrition_Flag,Customer_Age,Gender,Dependent_count,Education_Level,Marital_Status,Income_Category,Card_Category,Months_on_book,Total_Relationship_Count,Months_Inactive_12_mon,Contacts_Count_12_mon,Credit_Limit,Total_Revolving_Bal,Avg_Open_To_Buy,Total_Amt_Chng_Q4_Q1,Total_Trans_Amt,Total_Trans_Ct,Total_Ct_Chng_Q4_Q1,Avg_Utilization_Ratio
+801497508,Existing Customer,56,F,1,Graduate,Married,Less than $40K,Blue,50,4,3,2,1584.0,1311,273.0,0.867,4203,73,0.78,0.828
+709278258,Attrited Customer,54,M,2,Unknown,Married,$40K - $60K,Blue,44,4,3,2,2902.0,2517,385.0,0.821,996,26,0.444,0.867
+717121758,Existing Customer,58,F,0,Graduate,Single,Less than $40K,Blue,36,5,3,1,3008.0,2517,491.0,0.674,4627,68,0.659,0.837
+720906033,Existing Customer,35,M,1,Graduate,Married,$40K - $60K,Blue,25,5,4,4,5801.0,1176,4625.0,1.12,2987,52,0.857,0.203
+778910883,Attrited Customer,53,M,2,Unknown,Married,$80K - $120K,Blue,34,4,3,2,1837.0,875,962.0,0.58,2169,43,0.654,0.476
+778439883,Existing Customer,50,M,1,High School,Unknown,$80K - $120K,Blue,31,1,2,2,26840.0,769,26071.0,0.609,7969,93,0.661,0.029
+779808333,Existing Customer,48,F,2,Graduate,Single,Less than $40K,Blue,38,2,1,2,8881.0,1901,6980.0,0.808,15530,110,0.692,0.214
+712164333,Attrited Customer,40,M,4,High School,Married,$60K - $80K,Blue,29,3,3,2,12544.0,1636,10908.0,0.503,1758,51,0.457,0.13
+716717733,Attrited Customer,55,F,3,Uneducated,Single,Less than $40K,Blue,47,2,5,4,4841.0,859,3982.0,1.003,8918,87,0.977,0.177
+794560833,Attrited Customer,47,F,3,Graduate,Married,$40K - $60K,Blue,31,6,3,6,5496.0,0,5496.0,0.548,1913,34,0.308,0.0
+711289533,Existing Customer,39,M,4,Post-Graduate,Single,$80K - $120K,Blue,32,5,3,2,26710.0,1681,25029.0,0.741,14043,117,0.746,0.063
+714058308,Attrited Customer,43,F,5,Uneducated,Single,Less than $40K,Blue,36,5,3,4,2689.0,2384,305.0,0.365,1941,53,0.656,0.887
+710658483,Attrited Customer,47,M,4,Graduate,Unknown,$40K - $60K,Blue,34,2,3,6,9080.0,2174,6906.0,0.653,2098,46,0.586,0.239
+772353408,Existing Customer,43,F,5,Unknown,Single,$40K - $60K,Blue,33,4,3,4,5330.0,1161,4169.0,0.558,1441,43,0.387,0.218
+823615983,Attrited Customer,54,M,2,Uneducated,Single,$40K - $60K,Blue,50,2,1,3,3682.0,2517,1165.0,0.379,1642,35,0.25,0.684
+713894358,Existing Customer,51,M,2,Post-Graduate,Unknown,$80K - $120K,Gold,46,2,3,2,34516.0,814,33702.0,0.735,7889,98,0.508,0.024

decision_making/finance/classification/Credit_Card_customers_B2/test_001.csv

@@ -0,0 +1,4 @@
+CLIENTNUM,Attrition_Flag,Customer_Age,Gender,Dependent_count,Education_Level,Marital_Status,Income_Category,Card_Category,Months_on_book,Total_Relationship_Count,Months_Inactive_12_mon,Contacts_Count_12_mon,Credit_Limit,Total_Revolving_Bal,Avg_Open_To_Buy,Total_Amt_Chng_Q4_Q1,Total_Trans_Amt,Total_Trans_Ct,Total_Ct_Chng_Q4_Q1,Avg_Utilization_Ratio
+801497508,Existing Customer,56,F,1,Graduate,Married,Less than $40K,Blue,50,4,3,2,1584.0,1311,273.0,0.867,4203,73,0.78,0.828
+709278258,Attrited Customer,54,M,2,Unknown,Married,$40K - $60K,Blue,44,4,3,2,2902.0,2517,385.0,0.821,996,26,0.444,0.867
+717121758,Existing Customer,58,F,0,Graduate,Single,Less than $40K,Blue,36,5,3,1,3008.0,2517,491.0,0.674,4627,68,0.659,0.837

decision_making/finance/classification/Credit_Card_customers_B2/test_002.csv

@@ -0,0 +1,4 @@
+CLIENTNUM,Attrition_Flag,Customer_Age,Gender,Dependent_count,Education_Level,Marital_Status,Income_Category,Card_Category,Months_on_book,Total_Relationship_Count,Months_Inactive_12_mon,Contacts_Count_12_mon,Credit_Limit,Total_Revolving_Bal,Avg_Open_To_Buy,Total_Amt_Chng_Q4_Q1,Total_Trans_Amt,Total_Trans_Ct,Total_Ct_Chng_Q4_Q1,Avg_Utilization_Ratio
+720906033,Existing Customer,35,M,1,Graduate,Married,$40K - $60K,Blue,25,5,4,4,5801.0,1176,4625.0,1.12,2987,52,0.857,0.203
+778910883,Attrited Customer,53,M,2,Unknown,Married,$80K - $120K,Blue,34,4,3,2,1837.0,875,962.0,0.58,2169,43,0.654,0.476
+778439883,Existing Customer,50,M,1,High School,Unknown,$80K - $120K,Blue,31,1,2,2,26840.0,769,26071.0,0.609,7969,93,0.661,0.029

decision_making/finance/classification/Credit_Card_customers_B2/test_003.csv

@@ -0,0 +1,4 @@
+CLIENTNUM,Attrition_Flag,Customer_Age,Gender,Dependent_count,Education_Level,Marital_Status,Income_Category,Card_Category,Months_on_book,Total_Relationship_Count,Months_Inactive_12_mon,Contacts_Count_12_mon,Credit_Limit,Total_Revolving_Bal,Avg_Open_To_Buy,Total_Amt_Chng_Q4_Q1,Total_Trans_Amt,Total_Trans_Ct,Total_Ct_Chng_Q4_Q1,Avg_Utilization_Ratio
+779808333,Existing Customer,48,F,2,Graduate,Single,Less than $40K,Blue,38,2,1,2,8881.0,1901,6980.0,0.808,15530,110,0.692,0.214
+712164333,Attrited Customer,40,M,4,High School,Married,$60K - $80K,Blue,29,3,3,2,12544.0,1636,10908.0,0.503,1758,51,0.457,0.13
+716717733,Attrited Customer,55,F,3,Uneducated,Single,Less than $40K,Blue,47,2,5,4,4841.0,859,3982.0,1.003,8918,87,0.977,0.177

decision_making/finance/classification/Credit_Card_customers_B2/test_004.csv

@@ -0,0 +1,3 @@
+CLIENTNUM,Attrition_Flag,Customer_Age,Gender,Dependent_count,Education_Level,Marital_Status,Income_Category,Card_Category,Months_on_book,Total_Relationship_Count,Months_Inactive_12_mon,Contacts_Count_12_mon,Credit_Limit,Total_Revolving_Bal,Avg_Open_To_Buy,Total_Amt_Chng_Q4_Q1,Total_Trans_Amt,Total_Trans_Ct,Total_Ct_Chng_Q4_Q1,Avg_Utilization_Ratio
+794560833,Attrited Customer,47,F,3,Graduate,Married,$40K - $60K,Blue,31,6,3,6,5496.0,0,5496.0,0.548,1913,34,0.308,0.0
+711289533,Existing Customer,39,M,4,Post-Graduate,Single,$80K - $120K,Blue,32,5,3,2,26710.0,1681,25029.0,0.741,14043,117,0.746,0.063

decision_making/finance/classification/Credit_Card_customers_B2/test_005.csv

@@ -0,0 +1,4 @@
+CLIENTNUM,Attrition_Flag,Customer_Age,Gender,Dependent_count,Education_Level,Marital_Status,Income_Category,Card_Category,Months_on_book,Total_Relationship_Count,Months_Inactive_12_mon,Contacts_Count_12_mon,Credit_Limit,Total_Revolving_Bal,Avg_Open_To_Buy,Total_Amt_Chng_Q4_Q1,Total_Trans_Amt,Total_Trans_Ct,Total_Ct_Chng_Q4_Q1,Avg_Utilization_Ratio
+714058308,Attrited Customer,43,F,5,Uneducated,Single,Less than $40K,Blue,36,5,3,4,2689.0,2384,305.0,0.365,1941,53,0.656,0.887
+710658483,Attrited Customer,47,M,4,Graduate,Unknown,$40K - $60K,Blue,34,2,3,6,9080.0,2174,6906.0,0.653,2098,46,0.586,0.239
+772353408,Existing Customer,43,F,5,Unknown,Single,$40K - $60K,Blue,33,4,3,4,5330.0,1161,4169.0,0.558,1441,43,0.387,0.218

decision_making/finance/classification/Credit_Card_customers_B2/test_006.csv

@@ -0,0 +1,3 @@
+CLIENTNUM,Attrition_Flag,Customer_Age,Gender,Dependent_count,Education_Level,Marital_Status,Income_Category,Card_Category,Months_on_book,Total_Relationship_Count,Months_Inactive_12_mon,Contacts_Count_12_mon,Credit_Limit,Total_Revolving_Bal,Avg_Open_To_Buy,Total_Amt_Chng_Q4_Q1,Total_Trans_Amt,Total_Trans_Ct,Total_Ct_Chng_Q4_Q1,Avg_Utilization_Ratio
+823615983,Attrited Customer,54,M,2,Uneducated,Single,$40K - $60K,Blue,50,2,1,3,3682.0,2517,1165.0,0.379,1642,35,0.25,0.684
+713894358,Existing Customer,51,M,2,Post-Graduate,Unknown,$80K - $120K,Gold,46,2,3,2,34516.0,814,33702.0,0.735,7889,98,0.508,0.024

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_001.json

@@ -0,0 +1,115 @@
+{
+  "id": "001",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Default_of_Credit_Card_Clients_Dataset",
+  "table_path": "kaggle/Default_of_Credit_Card_Clients_Dataset",
+  "query": "My task is to identify the riskiest client from these three files. File one describes a married man, 50 years old, who went to university. His client ID is 27473, and he has a credit limit of 60,000. He paid his bills fully on time in September, August, July, and June (status -1). In May and April, his status shows he used revolving credit (status 0). The amounts he was billed from April to September were 12,710, 14,458, 18,041, 9,588, 13,989, and 7,888. During those months, he paid 2,828, 5,029, 4,016, 18,064, 9,700, and 14,026. On the other hand, file two is for a single woman, 32, with a high school education. ID 28331 has a 300,000 limit. She was one month late in September but paid fully in August. The records show she used revolving credit in July and June (status 0), and had a status of -2 in May and April. Her statement amounts are quite variable: 0, 0, 0, 10,000, 72,044, and -27. She paid 1,188, 0, 0, 0, 2,050, and 72,071. Lastly, file three is for a 51-year-old married man with a graduate degree, ID 13579, limit 120,000. He was also one month late in September. His history shows no consumption in August and July (-2), paid fully in June (-1), revolving credit in May (0), and paid fully in April (-1). His bills are 1,398, 416, 832, -1,248, -416, and 0, with payments of 0, 1,398, 0, 2,080, 0, and 0. I'm hesitating because each has different red flags. Can you help me pick out the one most probable to default?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": "27473",
+          "LIMIT_BAL": "60000.0",
+          "SEX": "1",
+          "EDUCATION": "2",
+          "MARRIAGE": "1",
+          "AGE": "50",
+          "PAY_0": "-1",
+          "PAY_2": "-1",
+          "PAY_3": "-1",
+          "PAY_4": "-1",
+          "PAY_5": "0",
+          "PAY_6": "0",
+          "BILL_AMT1": "7888.0",
+          "BILL_AMT2": "13989.0",
+          "BILL_AMT3": "9588.0",
+          "BILL_AMT4": "18041.0",
+          "BILL_AMT5": "14458.0",
+          "BILL_AMT6": "12710.0",
+          "PAY_AMT1": "14026.0",
+          "PAY_AMT2": "9700.0",
+          "PAY_AMT3": "18064.0",
+          "PAY_AMT4": "4016.0",
+          "PAY_AMT5": "5029.0",
+          "PAY_AMT6": "2828.0",
+          "default.payment.next.month": "0"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "ID": "28331",
+          "LIMIT_BAL": "300000.0",
+          "SEX": "2",
+          "EDUCATION": "3",
+          "MARRIAGE": "2",
+          "AGE": "32",
+          "PAY_0": "1",
+          "PAY_2": "-1",
+          "PAY_3": "0",
+          "PAY_4": "0",
+          "PAY_5": "-2",
+          "PAY_6": "-2",
+          "BILL_AMT1": "-27.0",
+          "BILL_AMT2": "72044.0",
+          "BILL_AMT3": "10000.0",
+          "BILL_AMT4": "0.0",
+          "BILL_AMT5": "0.0",
+          "BILL_AMT6": "0.0",
+          "PAY_AMT1": "72071.0",
+          "PAY_AMT2": "2050.0",
+          "PAY_AMT3": "0.0",
+          "PAY_AMT4": "0.0",
+          "PAY_AMT5": "0.0",
+          "PAY_AMT6": "1188.0",
+          "default.payment.next.month": "0"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "ID": "13579",
+          "LIMIT_BAL": "120000.0",
+          "SEX": "1",
+          "EDUCATION": "1",
+          "MARRIAGE": "1",
+          "AGE": "51",
+          "PAY_0": "1",
+          "PAY_2": "-2",
+          "PAY_3": "-2",
+          "PAY_4": "-1",
+          "PAY_5": "0",
+          "PAY_6": "-1",
+          "BILL_AMT1": "0.0",
+          "BILL_AMT2": "-416.0",
+          "BILL_AMT3": "-1248.0",
+          "BILL_AMT4": "832.0",
+          "BILL_AMT5": "416.0",
+          "BILL_AMT6": "1398.0",
+          "PAY_AMT1": "0.0",
+          "PAY_AMT2": "0.0",
+          "PAY_AMT3": "2080.0",
+          "PAY_AMT4": "0.0",
+          "PAY_AMT5": "1398.0",
+          "PAY_AMT6": "0.0",
+          "default.payment.next.month": "1"
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "003",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_002.json

@@ -0,0 +1,85 @@
+{
+  "id": "002",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Default_of_Credit_Card_Clients_Dataset",
+  "table_path": "kaggle/Default_of_Credit_Card_Clients_Dataset",
+  "query": "The manager needs a second opinion on these two accounts. Candidate A has ID number 11645. This client is a male, aged 40, married, and a university graduate. He has an 80,000 NT dollar credit line. A review of his last six months shows he has never been late on a payment—every repayment status is recorded as 0. His billed amounts have consistently decreased from 45,747 down to 19,766, and he has made payments each month ranging from 627 to 1,618. Switching to candidate B, ID 17189, we have a 31-year-old married female with a high school education and a 20,000 limit. Her payment history tells a different story: while current in September, she had severe delays of five, four, three, and two months in the preceding months. Her bills have been relatively stable between 11,500 and 14,320, but her actual payments have been inconsistent, with three months showing zero payment. Given our goal of identifying potential defaulters, which of these two individuals should raise a bigger red flag?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": "11645",
+          "LIMIT_BAL": "80000.0",
+          "SEX": "1",
+          "EDUCATION": "2",
+          "MARRIAGE": "1",
+          "AGE": "40",
+          "PAY_0": "0",
+          "PAY_2": "0",
+          "PAY_3": "0",
+          "PAY_4": "0",
+          "PAY_5": "0",
+          "PAY_6": "0",
+          "BILL_AMT1": "45747.0",
+          "BILL_AMT2": "40851.0",
+          "BILL_AMT3": "35933.0",
+          "BILL_AMT4": "30605.0",
+          "BILL_AMT5": "25189.0",
+          "BILL_AMT6": "19766.0",
+          "PAY_AMT1": "1618.0",
+          "PAY_AMT2": "1533.0",
+          "PAY_AMT3": "1038.0",
+          "PAY_AMT4": "852.0",
+          "PAY_AMT5": "765.0",
+          "PAY_AMT6": "627.0",
+          "default.payment.next.month": "0"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "ID": "17189",
+          "LIMIT_BAL": "20000.0",
+          "SEX": "2",
+          "EDUCATION": "3",
+          "MARRIAGE": "1",
+          "AGE": "31",
+          "PAY_0": "-1",
+          "PAY_2": "5",
+          "PAY_3": "4",
+          "PAY_4": "3",
+          "PAY_5": "2",
+          "PAY_6": "0",
+          "BILL_AMT1": "14320.0",
+          "BILL_AMT2": "13815.0",
+          "BILL_AMT3": "13403.0",
+          "BILL_AMT4": "12888.0",
+          "BILL_AMT5": "12019.0",
+          "BILL_AMT6": "11500.0",
+          "PAY_AMT1": "0.0",
+          "PAY_AMT2": "86.0",
+          "PAY_AMT3": "0.0",
+          "PAY_AMT4": "0.0",
+          "PAY_AMT5": "435.0",
+          "PAY_AMT6": "480.0",
+          "default.payment.next.month": "1"
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_003.json

@@ -0,0 +1,115 @@
+{
+  "id": "003",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Default_of_Credit_Card_Clients_Dataset",
+  "table_path": "kaggle/Default_of_Credit_Card_Clients_Dataset",
+  "query": "We're reviewing three client files for a special credit program, and I need to pick the one most likely to default next month. The first client, ID 13336, is a 28-year-old single man with a high school education. His credit limit is 100,000 NT dollars. His repayment history shows he paid duly in September, had no delay in August and July, and paid duly in June, May, and April. His recent bill amounts are quite high: 103,728 in September, 104,908 in August, 65,800 in July, 18,300 in June, 33,336 in May, and 31,558 in April. However, his recent payments were 3,184 in September, 3,000 in August, a large 18,300 in July, 33,336 in June, 984 in May, and a significant 63,000 in April. It's a mixed picture.  The second record, ID 15572, is a 50-year-old married man with a graduate school education and a much higher credit limit of 500,000. His repayment status is excellent: he paid duly in September, August, July, and June, with no delay in May and April. His bills are surprisingly low: 2,112, 6,000, 2,079, 2,710, 2,040, and even a negative -55 from April. Correspondingly, his payments are 6,031, 2,509, 2,713, 2,010, 0, and 4,642. He seems very stable.  Finally, client ID 1118 is a 24-year-old single man, also a graduate. His limit is only 20,000. His repayment status is concerning: a one-month delay in September, a two-month delay in August, no delay in July, no delay in June, no delay in May, and he was two months ahead in April. His bills are 18,738, 18,016, 7,190, 7,190, 0, and 0. His payments, however, are minimal: just 20 in September, 1,500 in August, and 0 for the four months prior. This one makes me nervous. Given the goal of identifying a default risk, which of these three should I flag?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": "13336",
+          "LIMIT_BAL": "100000.0",
+          "SEX": "1",
+          "EDUCATION": "3",
+          "MARRIAGE": "2",
+          "AGE": "28",
+          "PAY_0": "-1",
+          "PAY_2": "0",
+          "PAY_3": "0",
+          "PAY_4": "-1",
+          "PAY_5": "-1",
+          "PAY_6": "-1",
+          "BILL_AMT1": "103728.0",
+          "BILL_AMT2": "104908.0",
+          "BILL_AMT3": "65800.0",
+          "BILL_AMT4": "18300.0",
+          "BILL_AMT5": "33336.0",
+          "BILL_AMT6": "31558.0",
+          "PAY_AMT1": "3184.0",
+          "PAY_AMT2": "3000.0",
+          "PAY_AMT3": "18300.0",
+          "PAY_AMT4": "33336.0",
+          "PAY_AMT5": "984.0",
+          "PAY_AMT6": "63000.0",
+          "default.payment.next.month": "1"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "ID": "15572",
+          "LIMIT_BAL": "500000.0",
+          "SEX": "1",
+          "EDUCATION": "1",
+          "MARRIAGE": "1",
+          "AGE": "50",
+          "PAY_0": "-1",
+          "PAY_2": "-1",
+          "PAY_3": "-1",
+          "PAY_4": "-1",
+          "PAY_5": "0",
+          "PAY_6": "0",
+          "BILL_AMT1": "2112.0",
+          "BILL_AMT2": "6000.0",
+          "BILL_AMT3": "2079.0",
+          "BILL_AMT4": "2710.0",
+          "BILL_AMT5": "2040.0",
+          "BILL_AMT6": "-55.0",
+          "PAY_AMT1": "6031.0",
+          "PAY_AMT2": "2509.0",
+          "PAY_AMT3": "2713.0",
+          "PAY_AMT4": "2010.0",
+          "PAY_AMT5": "0.0",
+          "PAY_AMT6": "4642.0",
+          "default.payment.next.month": "0"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "ID": "1118",
+          "LIMIT_BAL": "20000.0",
+          "SEX": "1",
+          "EDUCATION": "1",
+          "MARRIAGE": "2",
+          "AGE": "24",
+          "PAY_0": "1",
+          "PAY_2": "2",
+          "PAY_3": "0",
+          "PAY_4": "0",
+          "PAY_5": "0",
+          "PAY_6": "-2",
+          "BILL_AMT1": "18738.0",
+          "BILL_AMT2": "18016.0",
+          "BILL_AMT3": "7190.0",
+          "BILL_AMT4": "7190.0",
+          "BILL_AMT5": "0.0",
+          "BILL_AMT6": "0.0",
+          "PAY_AMT1": "20.0",
+          "PAY_AMT2": "1500.0",
+          "PAY_AMT3": "0.0",
+          "PAY_AMT4": "0.0",
+          "PAY_AMT5": "0.0",
+          "PAY_AMT6": "0.0",
+          "default.payment.next.month": "0"
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_004.json

@@ -0,0 +1,115 @@
+{
+  "id": "004",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Default_of_Credit_Card_Clients_Dataset",
+  "table_path": "kaggle/Default_of_Credit_Card_Clients_Dataset",
+  "query": "I'm looking at three new credit applications on my screen right now, and I need to figure out which one is the safest bet. I have our historical client archive from 2005 open for comparison, but let me walk you through these new cases. The first applicant, ID 23359, is a 40-year-old single woman with a university education. Her credit limit is 110,000 NT dollars. Her repayment history for the last six months, from April to September, shows she has paid duly every single month—all those status codes are zero. Her bill statements have been consistently high, around the 77,000 to 96,950 range over those months, and she's been making payments between 2,800 and 4,000 each month. The second record, ID 23340, is a 44-year-old married woman with a high school education. She has a higher limit of 160,000. Her recent repayment status is concerning: for September and August, she's shown a two-month payment delay, but for July back to April, her status is listed as \"-2,\" which I believe from our old logs means she had a credit balance so no payment was due. Strangely, her bill amount for September is only 1,500 and zero for all previous months, and she has made no payments at all in the last six months. The third candidate, ID 21501, is a 39-year-old single woman, also university-educated, with a 160,000 limit. Her repayment is mixed: she paid duly in September and August, but had a two-month delay in July, June, and May, and was back to duly in April. Her bill amounts have fluctuated between about 30,000 and 49,619, and her payments have been irregular—7,000 in September, nothing in August, then 7,847, 1,026, 7,000, and 7,968 in the earlier months. Based on these profiles, which one of these clients is most likely to have no default next month?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": "23359",
+          "LIMIT_BAL": "110000.0",
+          "SEX": "2",
+          "EDUCATION": "2",
+          "MARRIAGE": "2",
+          "AGE": "40",
+          "PAY_0": "0",
+          "PAY_2": "0",
+          "PAY_3": "0",
+          "PAY_4": "0",
+          "PAY_5": "0",
+          "PAY_6": "0",
+          "BILL_AMT1": "96950.0",
+          "BILL_AMT2": "93571.0",
+          "BILL_AMT3": "86595.0",
+          "BILL_AMT4": "81077.0",
+          "BILL_AMT5": "77747.0",
+          "BILL_AMT6": "78913.0",
+          "PAY_AMT1": "3255.0",
+          "PAY_AMT2": "3056.0",
+          "PAY_AMT3": "2802.0",
+          "PAY_AMT4": "2813.0",
+          "PAY_AMT5": "4000.0",
+          "PAY_AMT6": "3500.0",
+          "default.payment.next.month": "0"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "ID": "23340",
+          "LIMIT_BAL": "160000.0",
+          "SEX": "2",
+          "EDUCATION": "3",
+          "MARRIAGE": "1",
+          "AGE": "44",
+          "PAY_0": "2",
+          "PAY_2": "2",
+          "PAY_3": "-2",
+          "PAY_4": "-2",
+          "PAY_5": "-2",
+          "PAY_6": "-2",
+          "BILL_AMT1": "1500.0",
+          "BILL_AMT2": "0.0",
+          "BILL_AMT3": "0.0",
+          "BILL_AMT4": "0.0",
+          "BILL_AMT5": "0.0",
+          "BILL_AMT6": "0.0",
+          "PAY_AMT1": "0.0",
+          "PAY_AMT2": "0.0",
+          "PAY_AMT3": "0.0",
+          "PAY_AMT4": "0.0",
+          "PAY_AMT5": "0.0",
+          "PAY_AMT6": "0.0",
+          "default.payment.next.month": "1"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "ID": "21501",
+          "LIMIT_BAL": "160000.0",
+          "SEX": "2",
+          "EDUCATION": "2",
+          "MARRIAGE": "2",
+          "AGE": "39",
+          "PAY_0": "0",
+          "PAY_2": "0",
+          "PAY_3": "2",
+          "PAY_4": "2",
+          "PAY_5": "2",
+          "PAY_6": "0",
+          "BILL_AMT1": "30633.0",
+          "BILL_AMT2": "37119.0",
+          "BILL_AMT3": "36263.0",
+          "BILL_AMT4": "43481.0",
+          "BILL_AMT5": "43515.0",
+          "BILL_AMT6": "49619.0",
+          "PAY_AMT1": "7000.0",
+          "PAY_AMT2": "0.0",
+          "PAY_AMT3": "7847.0",
+          "PAY_AMT4": "1026.0",
+          "PAY_AMT5": "7000.0",
+          "PAY_AMT6": "7968.0",
+          "default.payment.next.month": "1"
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_005.json

@@ -0,0 +1,85 @@
+{
+  "id": "005",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Default_of_Credit_Card_Clients_Dataset",
+  "table_path": "kaggle/Default_of_Credit_Card_Clients_Dataset",
+  "query": "I've pulled two new client files from this month's batch, and I want to see if either looks like they might be heading for a default next month. I have all the old payment history logs to check patterns against, but let me walk you through these two new cases. First, client ID 26813. This person has a credit limit of 290,000 NT dollars. He's a 30-year-old single man with a university education. Looking at his recent repayment status, he paid duly in September (that's a 0), duly in August (0), duly in July (0), had a two-month advance in June (that's -2), another two-month advance in May (-2), and was one month ahead in April (-1). His bill statements have been quite volatile: 10,823 in September, 13,226 in August, then a sharp drop to 652 in July, a zero bill in June, back to 652 in May, and a huge spike to 92,775 in April. His corresponding payments were 3,000 in September, 978 in August, nothing in July, 652 in June, a massive 92,775 in May (which cleared that huge April bill), and 4,000 in April. It's a very uneven pattern.  The second file is for client ID 3021. This is a 44-year-old married man with a high school education and a much lower credit limit of 30,000. His repayment status shows a two-month delay for September, August, and July (all marked as 2), but then he paid duly for June, May, and April (all 0). His bill amounts are more consistent but high relative to his limit: 18,659, 22,544, 21,889, 22,721, 23,401, and 25,000 going back from September to April. His payments have been 4,500 in September, nothing in August, 1,500 in July, 1,200 in June, 2,000 in May, and 3,000 in April. Given the historical data I have on what leads to defaults, which of these two new clients would you flag as the more likely candidate to miss their payment next month?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": "26813",
+          "LIMIT_BAL": "290000.0",
+          "SEX": "1",
+          "EDUCATION": "2",
+          "MARRIAGE": "2",
+          "AGE": "30",
+          "PAY_0": "0",
+          "PAY_2": "0",
+          "PAY_3": "0",
+          "PAY_4": "-2",
+          "PAY_5": "-2",
+          "PAY_6": "-1",
+          "BILL_AMT1": "10823.0",
+          "BILL_AMT2": "13226.0",
+          "BILL_AMT3": "652.0",
+          "BILL_AMT4": "0.0",
+          "BILL_AMT5": "652.0",
+          "BILL_AMT6": "92775.0",
+          "PAY_AMT1": "3000.0",
+          "PAY_AMT2": "978.0",
+          "PAY_AMT3": "0.0",
+          "PAY_AMT4": "652.0",
+          "PAY_AMT5": "92775.0",
+          "PAY_AMT6": "4000.0",
+          "default.payment.next.month": "0"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "ID": "3021",
+          "LIMIT_BAL": "30000.0",
+          "SEX": "1",
+          "EDUCATION": "3",
+          "MARRIAGE": "1",
+          "AGE": "44",
+          "PAY_0": "2",
+          "PAY_2": "2",
+          "PAY_3": "2",
+          "PAY_4": "0",
+          "PAY_5": "0",
+          "PAY_6": "0",
+          "BILL_AMT1": "18659.0",
+          "BILL_AMT2": "22544.0",
+          "BILL_AMT3": "21889.0",
+          "BILL_AMT4": "22721.0",
+          "BILL_AMT5": "23401.0",
+          "BILL_AMT6": "25000.0",
+          "PAY_AMT1": "4500.0",
+          "PAY_AMT2": "0.0",
+          "PAY_AMT3": "1500.0",
+          "PAY_AMT4": "1200.0",
+          "PAY_AMT5": "2000.0",
+          "PAY_AMT6": "3000.0",
+          "default.payment.next.month": "1"
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/Default_of_Credit_Card_Clients_Dataset_B2_006.json

@@ -0,0 +1,115 @@
+{
+  "id": "006",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Default_of_Credit_Card_Clients_Dataset",
+  "table_path": "kaggle/Default_of_Credit_Card_Clients_Dataset",
+  "query": "I need to figure out which one seems safest. Let me walk you through these new cases. First, client ID 14567. This is a 50-year-old male, a graduate school graduate, with his marital status listed as 'others'. He has a credit limit of 200,000 NT dollars. His recent repayment history is concerning: he was 3 months delayed in September, 2 months delayed in August, July, and June, though he was current in May and April. His bill statements have been consistently high, around 176,000 to 191,000 NT dollars for the last six months. His payments have been variable: 8,000 in September, 3,000 in August, 3,400 in July, 6,800 in June, 13,600 in May, and 5,900 in April. The second record is for ID 29365. This is a 22-year-old single male, also a graduate school grad. His credit limit is much lower at 20,000. His repayment status is interesting—it shows as '-2' for every single month from April to September, which I'd need to check my historical logs to confirm the exact meaning, but it suggests a very different pattern. His bill amounts are strange: negative 7 NT dollars in September and August, then 730 in July, 1,000 in June, 300 in May, and 0 in April. His payments are 0 in September, 737 in August, 1,060 in July, 300 in June, and 0 in both May and April. The third applicant, ID 10316, is a 32-year-old single female with a graduate education and a 160,000 credit limit. Her repayment history is perfect: she paid duly (status '-1') every single month from April through September. Her bill statements have fluctuated: 1,179 in September, 4,042 in August, 270 in July, 52 in June, 0 in May, and 394 in April. Her payments seem to closely match previous bills: she paid 4,042 in September, 270 in August, 0 in July, 0 in June, 394 in May, and 646 in April. Given these profiles, which of these three new cases seems most likely to have no default next month?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": "14567",
+          "LIMIT_BAL": "200000.0",
+          "SEX": "1",
+          "EDUCATION": "1",
+          "MARRIAGE": "3",
+          "AGE": "50",
+          "PAY_0": "3",
+          "PAY_2": "2",
+          "PAY_3": "2",
+          "PAY_4": "2",
+          "PAY_5": "0",
+          "PAY_6": "0",
+          "BILL_AMT1": "176077.0",
+          "BILL_AMT2": "179769.0",
+          "BILL_AMT3": "178358.0",
+          "BILL_AMT4": "177340.0",
+          "BILL_AMT5": "181281.0",
+          "BILL_AMT6": "191644.0",
+          "PAY_AMT1": "8000.0",
+          "PAY_AMT2": "3000.0",
+          "PAY_AMT3": "3400.0",
+          "PAY_AMT4": "6800.0",
+          "PAY_AMT5": "13600.0",
+          "PAY_AMT6": "5900.0",
+          "default.payment.next.month": "1"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "ID": "29365",
+          "LIMIT_BAL": "20000.0",
+          "SEX": "1",
+          "EDUCATION": "1",
+          "MARRIAGE": "2",
+          "AGE": "22",
+          "PAY_0": "-2",
+          "PAY_2": "-2",
+          "PAY_3": "-2",
+          "PAY_4": "-2",
+          "PAY_5": "-2",
+          "PAY_6": "-2",
+          "BILL_AMT1": "-7.0",
+          "BILL_AMT2": "-7.0",
+          "BILL_AMT3": "730.0",
+          "BILL_AMT4": "1000.0",
+          "BILL_AMT5": "300.0",
+          "BILL_AMT6": "0.0",
+          "PAY_AMT1": "0.0",
+          "PAY_AMT2": "737.0",
+          "PAY_AMT3": "1060.0",
+          "PAY_AMT4": "300.0",
+          "PAY_AMT5": "0.0",
+          "PAY_AMT6": "0.0",
+          "default.payment.next.month": "0"
+        }
+      },
+      {
+        "scenario_id": "003",
+        "features": {
+          "ID": "10316",
+          "LIMIT_BAL": "160000.0",
+          "SEX": "2",
+          "EDUCATION": "1",
+          "MARRIAGE": "2",
+          "AGE": "32",
+          "PAY_0": "-1",
+          "PAY_2": "-1",
+          "PAY_3": "-1",
+          "PAY_4": "-1",
+          "PAY_5": "-1",
+          "PAY_6": "-1",
+          "BILL_AMT1": "1179.0",
+          "BILL_AMT2": "4042.0",
+          "BILL_AMT3": "270.0",
+          "BILL_AMT4": "52.0",
+          "BILL_AMT5": "0.0",
+          "BILL_AMT6": "394.0",
+          "PAY_AMT1": "4042.0",
+          "PAY_AMT2": "270.0",
+          "PAY_AMT3": "0.0",
+          "PAY_AMT4": "0.0",
+          "PAY_AMT5": "394.0",
+          "PAY_AMT6": "646.0",
+          "default.payment.next.month": "1"
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/current.csv

@@ -0,0 +1,17 @@
+ID,LIMIT_BAL,SEX,EDUCATION,MARRIAGE,AGE,PAY_0,PAY_2,PAY_3,PAY_4,PAY_5,PAY_6,BILL_AMT1,BILL_AMT2,BILL_AMT3,BILL_AMT4,BILL_AMT5,BILL_AMT6,PAY_AMT1,PAY_AMT2,PAY_AMT3,PAY_AMT4,PAY_AMT5,PAY_AMT6,default.payment.next.month
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+28331,300000.0,2,3,2,32,1,-1,0,0,-2,-2,-27.0,72044.0,10000.0,0.0,0.0,0.0,72071.0,2050.0,0.0,0.0,0.0,1188.0,0
+13579,120000.0,1,1,1,51,1,-2,-2,-1,0,-1,0.0,-416.0,-1248.0,832.0,416.0,1398.0,0.0,0.0,2080.0,0.0,1398.0,0.0,1
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+17189,20000.0,2,3,1,31,-1,5,4,3,2,0,14320.0,13815.0,13403.0,12888.0,12019.0,11500.0,0.0,86.0,0.0,0.0,435.0,480.0,1
+13336,100000.0,1,3,2,28,-1,0,0,-1,-1,-1,103728.0,104908.0,65800.0,18300.0,33336.0,31558.0,3184.0,3000.0,18300.0,33336.0,984.0,63000.0,1
+15572,500000.0,1,1,1,50,-1,-1,-1,-1,0,0,2112.0,6000.0,2079.0,2710.0,2040.0,-55.0,6031.0,2509.0,2713.0,2010.0,0.0,4642.0,0
+1118,20000.0,1,1,2,24,1,2,0,0,0,-2,18738.0,18016.0,7190.0,7190.0,0.0,0.0,20.0,1500.0,0.0,0.0,0.0,0.0,0
+23359,110000.0,2,2,2,40,0,0,0,0,0,0,96950.0,93571.0,86595.0,81077.0,77747.0,78913.0,3255.0,3056.0,2802.0,2813.0,4000.0,3500.0,0
+23340,160000.0,2,3,1,44,2,2,-2,-2,-2,-2,1500.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1
+21501,160000.0,2,2,2,39,0,0,2,2,2,0,30633.0,37119.0,36263.0,43481.0,43515.0,49619.0,7000.0,0.0,7847.0,1026.0,7000.0,7968.0,1
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+14567,200000.0,1,1,3,50,3,2,2,2,0,0,176077.0,179769.0,178358.0,177340.0,181281.0,191644.0,8000.0,3000.0,3400.0,6800.0,13600.0,5900.0,1
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decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/info.json

@@ -0,0 +1,147 @@
+{
+    "name": "Default of Credit Card Clients Dataset",
+    "source": "https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset/data",
+    "data_intro": "This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.",
+    "is_splited": false,
+    "overall_size": 30000,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 0,
+    "n_classes": 25,
+    "task_type": "classification",
+    "target": {
+        "default.payment.next.month": "Given other attributes, predict whether the client will default on the next payment or not."
+    },
+    "cat_feature_intro": {},
+    "num_feature_intro": {
+        "ID": "- ID: ID of each client",
+        "LIMIT_BAL": "- LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit",
+        "SEX": "- SEX: Gender ,1=male, 2=female",
+        "EDUCATION": "- EDUCATION: 1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown",
+        "MARRIAGE": "- MARRIAGE: Marital status, 1=married, 2=single, 3=others",
+        "AGE": "- AGE: Age in years",
+        "PAY_0": "- 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": "- PAY_2: Repayment status in August, 2005 (scale same as above)",
+        "PAY_3": "- PAY_3: Repayment status in July, 2005 (scale same as above)",
+        "PAY_4": "- PAY_4 Repayment status in June, 2005 (scale same as above)",
+        "PAY_5": "- PAY_5- Repayment status in May, 2005 (scale same as above)",
+        "PAY_6": "- PAY_6: Repayment status in April, 2005 (scale same as above)",
+        "BILL_AMT1": "- BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)",
+        "BILL_AMT2": "- BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)",
+        "BILL_AMT3": "- BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)",
+        "BILL_AMT4": "- BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)",
+        "BILL_AMT5": "- BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)",
+        "BILL_AMT6": "- BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)",
+        "PAY_AMT1": "- PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)",
+        "PAY_AMT2": "- PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)",
+        "PAY_AMT3": "- PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)",
+        "PAY_AMT4": "- PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)",
+        "PAY_AMT5": "- PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)",
+        "PAY_AMT6": "- PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)",
+        "default.payment.next.month": "- default.payment.next.month: Default payment (1=yes, 0=no)"
+    },
+    "evaluation_metric": null,
+    "num_feature_value": {
+        "AGE": [
+            21.0,
+            79.0
+        ],
+        "BILL_AMT1": [
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+            964511.0
+        ],
+        "BILL_AMT2": [
+            -69777.0,
+            983931.0
+        ],
+        "BILL_AMT3": [
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+            1664089.0
+        ],
+        "BILL_AMT4": [
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+            891586.0
+        ],
+        "BILL_AMT5": [
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+            927171.0
+        ],
+        "BILL_AMT6": [
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+            961664.0
+        ],
+        "EDUCATION": [
+            0.0,
+            6.0
+        ],
+        "ID": [
+            1.0,
+            30000.0
+        ],
+        "LIMIT_BAL": [
+            10000.0,
+            1000000.0
+        ],
+        "MARRIAGE": [
+            0.0,
+            3.0
+        ],
+        "PAY_0": [
+            -2.0,
+            8.0
+        ],
+        "PAY_2": [
+            -2.0,
+            8.0
+        ],
+        "PAY_3": [
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+            8.0
+        ],
+        "PAY_4": [
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+            8.0
+        ],
+        "PAY_5": [
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+            8.0
+        ],
+        "PAY_6": [
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+            8.0
+        ],
+        "PAY_AMT1": [
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+            873552.0
+        ],
+        "PAY_AMT2": [
+            0.0,
+            1684259.0
+        ],
+        "PAY_AMT3": [
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+            896040.0
+        ],
+        "PAY_AMT4": [
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+            621000.0
+        ],
+        "PAY_AMT5": [
+            0.0,
+            426529.0
+        ],
+        "PAY_AMT6": [
+            0.0,
+            528666.0
+        ],
+        "SEX": [
+            1.0,
+            2.0
+        ],
+        "default.payment.next.month": [
+            0.0,
+            1.0
+        ]
+    },
+    "cat_feature_value": {}
+}

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/info_mod.json

@@ -0,0 +1,222 @@
+{
+  "name": "Default of Credit Card Clients Dataset",
+  "source": "https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset/data",
+  "data_intro": "This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.",
+  "is_splited": false,
+  "overall_size": 30000,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 0,
+  "n_classes": 25,
+  "task_type": "classification",
+  "target": "default.payment.next.month",
+  "cat_feature_intro": {},
+  "num_feature_intro": {
+    "ID": "- ID: ID of each client",
+    "LIMIT_BAL": "- LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit",
+    "SEX": "- SEX: Gender ,1=male, 2=female",
+    "EDUCATION": "- EDUCATION: 1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown",
+    "MARRIAGE": "- MARRIAGE: Marital status, 1=married, 2=single, 3=others",
+    "AGE": "- AGE: Age in years",
+    "PAY_0": "- 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": "- PAY_2: Repayment status in August, 2005 (scale same as above)",
+    "PAY_3": "- PAY_3: Repayment status in July, 2005 (scale same as above)",
+    "PAY_4": "- PAY_4 Repayment status in June, 2005 (scale same as above)",
+    "PAY_5": "- PAY_5- Repayment status in May, 2005 (scale same as above)",
+    "PAY_6": "- PAY_6: Repayment status in April, 2005 (scale same as above)",
+    "BILL_AMT1": "- BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)",
+    "BILL_AMT2": "- BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)",
+    "BILL_AMT3": "- BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)",
+    "BILL_AMT4": "- BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)",
+    "BILL_AMT5": "- BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)",
+    "BILL_AMT6": "- BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)",
+    "PAY_AMT1": "- PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)",
+    "PAY_AMT2": "- PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)",
+    "PAY_AMT3": "- PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)",
+    "PAY_AMT4": "- PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)",
+    "PAY_AMT5": "- PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)",
+    "PAY_AMT6": "- PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)"
+  },
+  "evaluation_metric": null,
+  "num_feature_value": {
+    "ID": [
+      1.0,
+      30000.0
+    ],
+    "LIMIT_BAL": [
+      10000.0,
+      1000000.0
+    ],
+    "SEX": [
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+      2.0
+    ],
+    "EDUCATION": [
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+    ],
+    "MARRIAGE": [
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+    ],
+    "AGE": [
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+      79.0
+    ],
+    "PAY_0": [
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+      8.0
+    ],
+    "PAY_2": [
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+      8.0
+    ],
+    "PAY_3": [
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+    ],
+    "PAY_4": [
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+      8.0
+    ],
+    "PAY_5": [
+      -2.0,
+      8.0
+    ],
+    "PAY_6": [
+      -2.0,
+      8.0
+    ],
+    "BILL_AMT1": [
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+      964511.0
+    ],
+    "BILL_AMT2": [
+      -69777.0,
+      983931.0
+    ],
+    "BILL_AMT3": [
+      -157264.0,
+      1664089.0
+    ],
+    "BILL_AMT4": [
+      -170000.0,
+      891586.0
+    ],
+    "BILL_AMT5": [
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+      927171.0
+    ],
+    "BILL_AMT6": [
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+      961664.0
+    ],
+    "PAY_AMT1": [
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+    ],
+    "PAY_AMT2": [
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+      1684259.0
+    ],
+    "PAY_AMT3": [
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+    ],
+    "PAY_AMT4": [
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+      621000.0
+    ],
+    "PAY_AMT5": [
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+      426529.0
+    ],
+    "PAY_AMT6": [
+      0.0,
+      528666.0
+    ]
+  },
+  "cat_feature_value": {},
+  "columns": [
+    "ID",
+    "LIMIT_BAL",
+    "SEX",
+    "EDUCATION",
+    "MARRIAGE",
+    "AGE",
+    "PAY_0",
+    "PAY_2",
+    "PAY_3",
+    "PAY_4",
+    "PAY_5",
+    "PAY_6",
+    "BILL_AMT1",
+    "BILL_AMT2",
+    "BILL_AMT3",
+    "BILL_AMT4",
+    "BILL_AMT5",
+    "BILL_AMT6",
+    "PAY_AMT1",
+    "PAY_AMT2",
+    "PAY_AMT3",
+    "PAY_AMT4",
+    "PAY_AMT5",
+    "PAY_AMT6",
+    "default.payment.next.month"
+  ],
+  "feature_columns": [
+    "ID",
+    "LIMIT_BAL",
+    "SEX",
+    "EDUCATION",
+    "MARRIAGE",
+    "AGE",
+    "PAY_0",
+    "PAY_2",
+    "PAY_3",
+    "PAY_4",
+    "PAY_5",
+    "PAY_6",
+    "BILL_AMT1",
+    "BILL_AMT2",
+    "BILL_AMT3",
+    "BILL_AMT4",
+    "BILL_AMT5",
+    "BILL_AMT6",
+    "PAY_AMT1",
+    "PAY_AMT2",
+    "PAY_AMT3",
+    "PAY_AMT4",
+    "PAY_AMT5",
+    "PAY_AMT6"
+  ],
+  "feature_types": {
+    "ID": "numeric",
+    "LIMIT_BAL": "numeric",
+    "SEX": "numeric",
+    "EDUCATION": "numeric",
+    "MARRIAGE": "numeric",
+    "AGE": "numeric",
+    "PAY_0": "numeric",
+    "PAY_2": "numeric",
+    "PAY_3": "numeric",
+    "PAY_4": "numeric",
+    "PAY_5": "numeric",
+    "PAY_6": "numeric",
+    "BILL_AMT1": "numeric",
+    "BILL_AMT2": "numeric",
+    "BILL_AMT3": "numeric",
+    "BILL_AMT4": "numeric",
+    "BILL_AMT5": "numeric",
+    "BILL_AMT6": "numeric",
+    "PAY_AMT1": "numeric",
+    "PAY_AMT2": "numeric",
+    "PAY_AMT3": "numeric",
+    "PAY_AMT4": "numeric",
+    "PAY_AMT5": "numeric",
+    "PAY_AMT6": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/test.csv

@@ -0,0 +1,17 @@
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decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/test_001.csv

@@ -0,0 +1,4 @@
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decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/test_002.csv

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decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/test_003.csv

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decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/test_004.csv

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decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/test_005.csv

@@ -0,0 +1,3 @@
+ID,LIMIT_BAL,SEX,EDUCATION,MARRIAGE,AGE,PAY_0,PAY_2,PAY_3,PAY_4,PAY_5,PAY_6,BILL_AMT1,BILL_AMT2,BILL_AMT3,BILL_AMT4,BILL_AMT5,BILL_AMT6,PAY_AMT1,PAY_AMT2,PAY_AMT3,PAY_AMT4,PAY_AMT5,PAY_AMT6,default.payment.next.month
+26813,290000.0,1,2,2,30,0,0,0,-2,-2,-1,10823.0,13226.0,652.0,0.0,652.0,92775.0,3000.0,978.0,0.0,652.0,92775.0,4000.0,0
+3021,30000.0,1,3,1,44,2,2,2,0,0,0,18659.0,22544.0,21889.0,22721.0,23401.0,25000.0,4500.0,0.0,1500.0,1200.0,2000.0,3000.0,1

decision_making/finance/classification/Default_of_Credit_Card_Clients_Dataset_B2/test_006.csv

@@ -0,0 +1,4 @@
+ID,LIMIT_BAL,SEX,EDUCATION,MARRIAGE,AGE,PAY_0,PAY_2,PAY_3,PAY_4,PAY_5,PAY_6,BILL_AMT1,BILL_AMT2,BILL_AMT3,BILL_AMT4,BILL_AMT5,BILL_AMT6,PAY_AMT1,PAY_AMT2,PAY_AMT3,PAY_AMT4,PAY_AMT5,PAY_AMT6,default.payment.next.month
+14567,200000.0,1,1,3,50,3,2,2,2,0,0,176077.0,179769.0,178358.0,177340.0,181281.0,191644.0,8000.0,3000.0,3400.0,6800.0,13600.0,5900.0,1
+29365,20000.0,1,1,2,22,-2,-2,-2,-2,-2,-2,-7.0,-7.0,730.0,1000.0,300.0,0.0,0.0,737.0,1060.0,300.0,0.0,0.0,0
+10316,160000.0,2,1,2,32,-1,-1,-1,-1,-1,-1,1179.0,4042.0,270.0,52.0,0.0,394.0,4042.0,270.0,0.0,0.0,394.0,646.0,1

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/Health_Insurance_Cross_Sell_Prediction_B2_001.json

@@ -0,0 +1,59 @@
+{
+  "id": "001",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Health_Insurance_Cross_Sell_Prediction",
+  "table_path": "kaggle/Health_Insurance_Cross_Sell_Prediction",
+  "query": "My task is to evaluate these two profiles. Option one is for a female driver aged 36, with ID 293653. She's in region 2.0, holds a license, and is currently without vehicle insurance. Her car is 1-2 years old and has been damaged. The annual cost for her policy is 21621.0, sold via channel 154.0, and she's been associated with us for just 24 days.  Moving on to the second record, it's for another woman, this time 69 years old (ID 263430, region 28.0). She is licensed and already has a vehicle insurance policy. Her vehicle is of similar age but has no damage history. She pays a premium of 47493.0 through channel 122.0 and has a longer relationship with the company at 112 days.  I need to select the one most probable to decline the offer. What's your take?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "id": "293653",
+          "Gender": "Female",
+          "Age": "36",
+          "Driving_License": "1",
+          "Region_Code": "2.0",
+          "Previously_Insured": "0",
+          "Vehicle_Age": "1-2 Year",
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": "21621.0",
+          "Policy_Sales_Channel": "154.0",
+          "Vintage": "24",
+          "Response": "1"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "id": "263430",
+          "Gender": "Female",
+          "Age": "69",
+          "Driving_License": "1",
+          "Region_Code": "28.0",
+          "Previously_Insured": "1",
+          "Vehicle_Age": "1-2 Year",
+          "Vehicle_Damage": "No",
+          "Annual_Premium": "47493.0",
+          "Policy_Sales_Channel": "122.0",
+          "Vintage": "112",
+          "Response": "0"
+        }
+      }
+    ],
+    "target_column": "Response",
+    "task_sub_type": "classification",
+    "final_decision": "002",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/Health_Insurance_Cross_Sell_Prediction_B2_002.json

@@ -0,0 +1,59 @@
+{
+  "id": "002",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Health_Insurance_Cross_Sell_Prediction",
+  "table_path": "kaggle/Health_Insurance_Cross_Sell_Prediction",
+  "query": "Here are the two cases I'm comparing. The first involves a man aged 37. His ID is 27977, he has a driving license, and his vehicle damage status is 'Yes'. His annual premium is 26917, the policy sales channel is coded as 124, and his vintage is 77 days. For the second candidate, ID 161561, the age is 36, they hold a license, and their region code is 28. Their vehicle is more than two years old and has been damaged. The sales channel is 154, with 66 days of tenure. I'm hesitating because both have factors that could indicate interest. Help me choose the one most likely to respond with a 'yes'.",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "id": "27977",
+          "Gender": "Male",
+          "Age": "37",
+          "Driving_License": "1",
+          "Region_Code": null,
+          "Previously_Insured": null,
+          "Vehicle_Age": null,
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": "26917.0",
+          "Policy_Sales_Channel": "124.0",
+          "Vintage": "77",
+          "Response": "1"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "id": "161561",
+          "Gender": null,
+          "Age": "36",
+          "Driving_License": "1",
+          "Region_Code": "28.0",
+          "Previously_Insured": null,
+          "Vehicle_Age": "> 2 Years",
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": null,
+          "Policy_Sales_Channel": "154.0",
+          "Vintage": "66",
+          "Response": "0"
+        }
+      }
+    ],
+    "target_column": "Response",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/Health_Insurance_Cross_Sell_Prediction_B2_003.json

@@ -0,0 +1,59 @@
+{
+  "id": "003",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "user",
+  "dataset_name": "Health_Insurance_Cross_Sell_Prediction",
+  "table_path": "kaggle/Health_Insurance_Cross_Sell_Prediction",
+  "query": "Looking at option one, a 36-year-old female driver in region 2.0 has been a customer for 81 days. She possesses a driving license and has no existing vehicle insurance for her 1-2 year old car. The sale was handled through channel 31.0, under record ID 327777. On the other hand, option two describes a 63-year-old woman from region 46.0. She is already insured, drives a car of the same age bracket with no prior damage, pays an annual premium of 22578.0, and was acquired via channel 124.0. I'm trying to figure out which profile signals a stronger potential buyer. Can you help me pick the most promising lead?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "id": "327777",
+          "Gender": "Female",
+          "Age": "36",
+          "Driving_License": "1",
+          "Region_Code": "2.0",
+          "Previously_Insured": "0",
+          "Vehicle_Age": "1-2 Year",
+          "Vehicle_Damage": null,
+          "Annual_Premium": null,
+          "Policy_Sales_Channel": "31.0",
+          "Vintage": "81",
+          "Response": "1"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "id": null,
+          "Gender": "Female",
+          "Age": "63",
+          "Driving_License": null,
+          "Region_Code": "46.0",
+          "Previously_Insured": "1",
+          "Vehicle_Age": "1-2 Year",
+          "Vehicle_Damage": "No",
+          "Annual_Premium": "22578.0",
+          "Policy_Sales_Channel": "124.0",
+          "Vintage": null,
+          "Response": "0"
+        }
+      }
+    ],
+    "target_column": "Response",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/Health_Insurance_Cross_Sell_Prediction_B2_004.json

@@ -0,0 +1,59 @@
+{
+  "id": "004",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Health_Insurance_Cross_Sell_Prediction",
+  "table_path": "kaggle/Health_Insurance_Cross_Sell_Prediction",
+  "query": "Let me describe these two new ones to you. The first candidate, ID 379678, is a 44-year-old female. She holds a valid driving license and is in region code 8. She doesn't currently have vehicle insurance. Her vehicle is between 1 and 2 years old and has been damaged in the past. The annual premium for her would be 41720, and she was originally contacted through sales channel 124. She's been a customer with us for 131 days. The second person, ID 139291, is a 39-year-old female with a license in region 16. She also isn't previously insured. Her vehicle is also in that 1-2 year age bracket and has damage. Her quoted annual premium is lower, at 32074, and her policy came via channel 152. She's a much newer customer, with only 70 days of vintage. Given the target is finding interest, who seems like the stronger lead for me to call first? I have my old customer logs to check patterns against.",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "id": "379678",
+          "Gender": "Female",
+          "Age": "44",
+          "Driving_License": "1",
+          "Region_Code": "8.0",
+          "Previously_Insured": "0",
+          "Vehicle_Age": "1-2 Year",
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": "41720.0",
+          "Policy_Sales_Channel": "124.0",
+          "Vintage": "131",
+          "Response": "1"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "id": "139291",
+          "Gender": "Female",
+          "Age": "39",
+          "Driving_License": "1",
+          "Region_Code": "16.0",
+          "Previously_Insured": "0",
+          "Vehicle_Age": "1-2 Year",
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": "32074.0",
+          "Policy_Sales_Channel": "152.0",
+          "Vintage": "70",
+          "Response": "0"
+        }
+      }
+    ],
+    "target_column": "Response",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/Health_Insurance_Cross_Sell_Prediction_B2_005.json

@@ -0,0 +1,59 @@
+{
+  "id": "005",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Health_Insurance_Cross_Sell_Prediction",
+  "table_path": "kaggle/Health_Insurance_Cross_Sell_Prediction",
+  "query": "I've got two new customer records on my screen that I need to evaluate for our vehicle insurance cross-sell campaign. I'm going to send you our historical archives of past customer logs so you can see the patterns we usually work with. For this first new case, the customer ID is 174316. She's a 22-year-old female who holds a valid driving license. Her region code is 30. She already has vehicle insurance, so she's previously insured. Her vehicle is less than a year old and, notably, it has not been damaged in the past. The annual premium for her current policy is 28531, and she was originally sold her policy through sales channel 152. She's been a customer with us for 107 days. The second new record is for customer ID 7934. He is a 54-year-old male, also with a license, from region code 45. He does not currently have vehicle insurance. His vehicle is between 1 and 2 years old and it *has* been damaged before. His quoted annual premium is higher, at 48431, and the policy sales channel associated is 124. He has been with our company for 104 days. Given our goal is to predict who is more likely to be interested in buying this new policy, and historically we know '0' means not interested... I'm really torn on which one of these two seems like the less interested candidate for our outreach?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "id": "174316",
+          "Gender": "Female",
+          "Age": "22",
+          "Driving_License": "1",
+          "Region_Code": "30.0",
+          "Previously_Insured": "1",
+          "Vehicle_Age": "< 1 Year",
+          "Vehicle_Damage": "No",
+          "Annual_Premium": "28531.0",
+          "Policy_Sales_Channel": "152.0",
+          "Vintage": "107",
+          "Response": "0"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "id": "7934",
+          "Gender": "Male",
+          "Age": "54",
+          "Driving_License": "1",
+          "Region_Code": "45.0",
+          "Previously_Insured": "0",
+          "Vehicle_Age": "1-2 Year",
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": "48431.0",
+          "Policy_Sales_Channel": "124.0",
+          "Vintage": "104",
+          "Response": "1"
+        }
+      }
+    ],
+    "target_column": "Response",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/Health_Insurance_Cross_Sell_Prediction_B2_006.json

@@ -0,0 +1,59 @@
+{
+  "id": "006",
+  "task_type": "B2",
+  "subtask_type": "choice",
+  "perspective": "data_holder",
+  "dataset_name": "Health_Insurance_Cross_Sell_Prediction",
+  "table_path": "kaggle/Health_Insurance_Cross_Sell_Prediction",
+  "query": "I'm trying to figure out which one is less likely to be interested in our new vehicle insurance offer. I've got my old historical logs here to check patterns against, but let me tell you about these two. The first candidate has an ID of 178752. He's a male customer from region code 28, and he doesn't currently have vehicle insurance. His vehicle has been damaged in the past. The annual premium amount for his existing policy with us is 43881, and he was originally signed up through sales channel 26. He's been a customer with us for 259 days. The second person is also male, but he's 68 years old, from region code 8. He also doesn't have existing vehicle insurance. His car is older, more than two years, and it has been damaged before. His annual premium is 36754, he came through the same sales channel, 26, and has been with the company slightly longer at 266 days. Which of these two current cases should get the lower priority?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "id": "178752",
+          "Gender": "Male",
+          "Age": null,
+          "Driving_License": null,
+          "Region_Code": "28.0",
+          "Previously_Insured": "0",
+          "Vehicle_Age": null,
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": "43881.0",
+          "Policy_Sales_Channel": "26.0",
+          "Vintage": "259",
+          "Response": "0"
+        }
+      },
+      {
+        "scenario_id": "002",
+        "features": {
+          "id": null,
+          "Gender": "Male",
+          "Age": "68",
+          "Driving_License": null,
+          "Region_Code": "8.0",
+          "Previously_Insured": "0",
+          "Vehicle_Age": "> 2 Years",
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": "36754.0",
+          "Policy_Sales_Channel": "26.0",
+          "Vintage": "266",
+          "Response": "1"
+        }
+      }
+    ],
+    "target_column": "Response",
+    "task_sub_type": "classification",
+    "final_decision": "001",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/current.csv

@@ -0,0 +1,13 @@
+id,Gender,Age,Driving_License,Region_Code,Previously_Insured,Vehicle_Age,Vehicle_Damage,Annual_Premium,Policy_Sales_Channel,Vintage,Response
+293653,Female,36,1,2.0,0,1-2 Year,Yes,21621.0,154.0,24,1
+263430,Female,69,1,28.0,1,1-2 Year,No,47493.0,122.0,112,0
+27977,Male,37,1,14.0,0,1-2 Year,Yes,26917.0,124.0,77,1
+161561,Male,36,1,28.0,0,> 2 Years,Yes,36776.0,154.0,66,0
+327777,Female,36,1,2.0,0,1-2 Year,Yes,24772.0,31.0,81,1
+314715,Female,63,1,46.0,1,1-2 Year,No,22578.0,124.0,68,0
+379678,Female,44,1,8.0,0,1-2 Year,Yes,41720.0,124.0,131,1
+139291,Female,39,1,16.0,0,1-2 Year,Yes,32074.0,152.0,70,0
+174316,Female,22,1,30.0,1,< 1 Year,No,28531.0,152.0,107,0
+7934,Male,54,1,45.0,0,1-2 Year,Yes,48431.0,124.0,104,1
+178752,Male,23,1,28.0,0,< 1 Year,Yes,43881.0,26.0,259,0
+224038,Male,68,1,8.0,0,> 2 Years,Yes,36754.0,26.0,266,1

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/info.json

@@ -0,0 +1,83 @@
+{
+    "name": "Health Insurance Cross Sell Prediction",
+    "source": "https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction/data",
+    "data_intro": "This dataset is used to predict whether an insurance customer will be interested in purchasing a new vehicle insurance policy. The evaluation metric for this competition is the ROC AUC score.",
+    "is_splited": true,
+    "overall_size": 635183,
+    "train_size": 381109,
+    "test_size": 127037,
+    "c_classes": 3,
+    "n_classes": 9,
+    "cat_feature_intro": {
+        "Gender": "- **Gender**: Gender of the customer (Male/Female)",
+        "Vehicle_Age": "- **Vehicle_Age**: Age of the vehicle (e.g., < 1 Year, 1-2 Year, > 2 Years)",
+        "Vehicle_Damage": "- **Vehicle_Damage**: Whether the customer’s vehicle has been damaged in the past (Yes/No)"
+    },
+    "num_feature_intro": {
+        "id": "- **id**: Unique identifier for each customer",
+        "Response": "- **Response**: Target variable (1: Customer is interested, 0: Not interested)",
+        "Age": "- **Age**: Age of the customer in years",
+        "Driving_License": "- **Driving_License**: Whether the customer has a valid driving license (1: Yes, 0: No)",
+        "Region_Code": "- **Region_Code**: Unique code representing the customer’s region",
+        "Previously_Insured": "- **Previously_Insured**: Whether the customer already has vehicle insurance (1: Yes, 0: No)",
+        "Annual_Premium": "- **Annual_Premium**: Annual premium amount the customer has to pay",
+        "Policy_Sales_Channel": "- **Policy_Sales_Channel**: Encoded channel through which the policy was sold (e.g., Agent, Email, Phone, In-person)",
+        "Vintage": "- **Vintage**: Number of days the customer has been associated with the company"
+    },
+    "evaluation_metric": "ROC AUC",
+    "task_type": "classification",
+    "target": "Response",
+    "num_feature_value": {
+        "Age": [
+            20.0,
+            85.0
+        ],
+        "Annual_Premium": [
+            2630.0,
+            540165.0
+        ],
+        "Driving_License": [
+            0.0,
+            1.0
+        ],
+        "Policy_Sales_Channel": [
+            1.0,
+            163.0
+        ],
+        "Previously_Insured": [
+            0.0,
+            1.0
+        ],
+        "Region_Code": [
+            0.0,
+            52.0
+        ],
+        "Response": [
+            0.0,
+            1.0
+        ],
+        "Vintage": [
+            10.0,
+            299.0
+        ],
+        "id": [
+            1.0,
+            508146.0
+        ]
+    },
+    "cat_feature_value": {
+        "Gender": [
+            "Female",
+            "Male"
+        ],
+        "Vehicle_Age": [
+            "1-2 Year",
+            "< 1 Year",
+            "> 2 Years"
+        ],
+        "Vehicle_Damage": [
+            "No",
+            "Yes"
+        ]
+    }
+}

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/info_mod.json

@@ -0,0 +1,121 @@
+{
+  "name": "Health Insurance Cross Sell Prediction",
+  "source": "https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction/data",
+  "data_intro": "This dataset is used to predict whether an insurance customer will be interested in purchasing a new vehicle insurance policy. The evaluation metric for this competition is the ROC AUC score.",
+  "is_splited": true,
+  "overall_size": 635183,
+  "train_size": 381109,
+  "test_size": 127037,
+  "c_classes": 3,
+  "n_classes": 9,
+  "cat_feature_intro": {
+    "Gender": "- **Gender**: Gender of the customer (Male/Female)",
+    "Vehicle_Age": "- **Vehicle_Age**: Age of the vehicle (e.g., < 1 Year, 1-2 Year, > 2 Years)",
+    "Vehicle_Damage": "- **Vehicle_Damage**: Whether the customer’s vehicle has been damaged in the past (Yes/No)"
+  },
+  "num_feature_intro": {
+    "id": "- **id**: Unique identifier for each customer",
+    "Age": "- **Age**: Age of the customer in years",
+    "Driving_License": "- **Driving_License**: Whether the customer has a valid driving license (1: Yes, 0: No)",
+    "Region_Code": "- **Region_Code**: Unique code representing the customer’s region",
+    "Previously_Insured": "- **Previously_Insured**: Whether the customer already has vehicle insurance (1: Yes, 0: No)",
+    "Annual_Premium": "- **Annual_Premium**: Annual premium amount the customer has to pay",
+    "Policy_Sales_Channel": "- **Policy_Sales_Channel**: Encoded channel through which the policy was sold (e.g., Agent, Email, Phone, In-person)",
+    "Vintage": "- **Vintage**: Number of days the customer has been associated with the company"
+  },
+  "evaluation_metric": "ROC AUC",
+  "task_type": "classification",
+  "target": "Response",
+  "num_feature_value": {
+    "id": [
+      1.0,
+      508146.0
+    ],
+    "Age": [
+      20.0,
+      85.0
+    ],
+    "Driving_License": [
+      0.0,
+      1.0
+    ],
+    "Region_Code": [
+      0.0,
+      52.0
+    ],
+    "Previously_Insured": [
+      0.0,
+      1.0
+    ],
+    "Annual_Premium": [
+      2630.0,
+      540165.0
+    ],
+    "Policy_Sales_Channel": [
+      1.0,
+      163.0
+    ],
+    "Vintage": [
+      10.0,
+      299.0
+    ]
+  },
+  "cat_feature_value": {
+    "Gender": [
+      "Female",
+      "Male"
+    ],
+    "Vehicle_Age": [
+      "1-2 Year",
+      "< 1 Year",
+      "> 2 Years"
+    ],
+    "Vehicle_Damage": [
+      "No",
+      "Yes"
+    ]
+  },
+  "columns": [
+    "id",
+    "Gender",
+    "Age",
+    "Driving_License",
+    "Region_Code",
+    "Previously_Insured",
+    "Vehicle_Age",
+    "Vehicle_Damage",
+    "Annual_Premium",
+    "Policy_Sales_Channel",
+    "Vintage",
+    "Response"
+  ],
+  "feature_columns": [
+    "id",
+    "Gender",
+    "Age",
+    "Driving_License",
+    "Region_Code",
+    "Previously_Insured",
+    "Vehicle_Age",
+    "Vehicle_Damage",
+    "Annual_Premium",
+    "Policy_Sales_Channel",
+    "Vintage"
+  ],
+  "feature_types": {
+    "id": "numeric",
+    "Gender": "categorical",
+    "Age": "numeric",
+    "Driving_License": "numeric",
+    "Region_Code": "numeric",
+    "Previously_Insured": "numeric",
+    "Vehicle_Age": "categorical",
+    "Vehicle_Damage": "categorical",
+    "Annual_Premium": "numeric",
+    "Policy_Sales_Channel": "numeric",
+    "Vintage": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/test.csv

@@ -0,0 +1,13 @@
+id,Gender,Age,Driving_License,Region_Code,Previously_Insured,Vehicle_Age,Vehicle_Damage,Annual_Premium,Policy_Sales_Channel,Vintage,Response
+293653,Female,36,1,2.0,0,1-2 Year,Yes,21621.0,154.0,24,1
+263430,Female,69,1,28.0,1,1-2 Year,No,47493.0,122.0,112,0
+27977,Male,37,1,14.0,0,1-2 Year,Yes,26917.0,124.0,77,1
+161561,Male,36,1,28.0,0,> 2 Years,Yes,36776.0,154.0,66,0
+327777,Female,36,1,2.0,0,1-2 Year,Yes,24772.0,31.0,81,1
+314715,Female,63,1,46.0,1,1-2 Year,No,22578.0,124.0,68,0
+379678,Female,44,1,8.0,0,1-2 Year,Yes,41720.0,124.0,131,1
+139291,Female,39,1,16.0,0,1-2 Year,Yes,32074.0,152.0,70,0
+174316,Female,22,1,30.0,1,< 1 Year,No,28531.0,152.0,107,0
+7934,Male,54,1,45.0,0,1-2 Year,Yes,48431.0,124.0,104,1
+178752,Male,23,1,28.0,0,< 1 Year,Yes,43881.0,26.0,259,0
+224038,Male,68,1,8.0,0,> 2 Years,Yes,36754.0,26.0,266,1

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/test_001.csv

@@ -0,0 +1,3 @@
+id,Gender,Age,Driving_License,Region_Code,Previously_Insured,Vehicle_Age,Vehicle_Damage,Annual_Premium,Policy_Sales_Channel,Vintage,Response
+293653,Female,36,1,2.0,0,1-2 Year,Yes,21621.0,154.0,24,1
+263430,Female,69,1,28.0,1,1-2 Year,No,47493.0,122.0,112,0

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/test_002.csv

@@ -0,0 +1,3 @@
+id,Gender,Age,Driving_License,Region_Code,Previously_Insured,Vehicle_Age,Vehicle_Damage,Annual_Premium,Policy_Sales_Channel,Vintage,Response
+27977,Male,37,1,14.0,0,1-2 Year,Yes,26917.0,124.0,77,1
+161561,Male,36,1,28.0,0,> 2 Years,Yes,36776.0,154.0,66,0

decision_making/finance/classification/Health_Insurance_Cross_Sell_Prediction_B2/test_003.csv

@@ -0,0 +1,3 @@
+id,Gender,Age,Driving_License,Region_Code,Previously_Insured,Vehicle_Age,Vehicle_Damage,Annual_Premium,Policy_Sales_Channel,Vintage,Response
+327777,Female,36,1,2.0,0,1-2 Year,Yes,24772.0,31.0,81,1
+314715,Female,63,1,46.0,1,1-2 Year,No,22578.0,124.0,68,0