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single_point_prediction/finance/Bitcoin_Historical_Data_B1/Bitcoin_Historical_Data_B1_001.json

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+{
+  "id": "001",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Bitcoin Historical Data",
+  "table_path": "kaggle/Bitcoin Historical Data",
+  "query": "Looking at this snapshot from the past, the data shows a price of $115.90 that didn't budge—it was the open, high, low, and close all at once, captured at time 1365873960. Seeing such a flat line, I can't help but ask, how many bitcoins were people actually buying and selling in that single minute?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Timestamp": 1365873960.0,
+          "Open": 115.9,
+          "High": 115.9,
+          "Low": 115.9,
+          "Close": 115.9,
+          "Volume": 0.49751
+        }
+      }
+    ],
+    "target_column": "Volume",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/Bitcoin_Historical_Data_B1_002.json

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+{
+  "id": "002",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Bitcoin Historical Data",
+  "table_path": "kaggle/Bitcoin Historical Data",
+  "query": "Wow, looking at this old bitcoin snapshot from back in 2012, it's fascinating. The moment captured was exactly at timestamp 1325412060. The price was completely flat that minute, opening at $4.58, hitting a high of $4.58, and a low of $4.58 before closing at that same $4.58. With such a stable price for that entire minute, how many bitcoins do you think were actually traded during that window?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Timestamp": 1325412060.0,
+          "Open": 4.58,
+          "High": 4.58,
+          "Low": 4.58,
+          "Close": 4.58,
+          "Volume": 0.0
+        }
+      }
+    ],
+    "target_column": "Volume",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/Bitcoin_Historical_Data_B1_003.json

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+{
+  "id": "003",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Bitcoin Historical Data",
+  "table_path": "kaggle/Bitcoin Historical Data",
+  "query": "Just pulled up an old trading record from back in May 2017. The timestamp shows it was a quiet moment, with the price frozen at an opening value of $1566.1, which also happened to be the high and low for that minute before closing at that exact same number. For a minute where nothing seemed to move, I'm wondering how many bitcoins actually changed hands?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Timestamp": 1493974680.0,
+          "Open": 1566.1,
+          "High": 1566.1,
+          "Low": 1566.1,
+          "Close": 1566.1,
+          "Volume": 0.49750944
+        }
+      }
+    ],
+    "target_column": "Volume",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/Bitcoin_Historical_Data_B1_004.json

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+{
+  "id": "004",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Bitcoin Historical Data",
+  "table_path": "kaggle/Bitcoin Historical Data",
+  "query": "The screen flickered, showing a timestamp of 1497676080, and a market that was incredibly still. The price opened at $2,454.80, hit a peak of $2,454.82, and found a floor at $2,454.80, finally settling at $2,454.82. It was a quiet minute, but I'm curious, what was the total trading activity in terms of bitcoin volume?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Timestamp": 1497676080.0,
+          "Open": 2454.8,
+          "High": 2454.82,
+          "Low": 2454.8,
+          "Close": 2454.82,
+          "Volume": 0.49750949
+        }
+      }
+    ],
+    "target_column": "Volume",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/Bitcoin_Historical_Data_B1_005.json

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+{
+  "id": "005",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Bitcoin Historical Data",
+  "table_path": "kaggle/Bitcoin Historical Data",
+  "query": "The record for timestamp 1658288340 shows a really stable minute of trading. It started at $23,272.90, dipped only slightly to that same figure for its low, and then finished strong at $23,296.32. With the high also being $23,296.32, it seems like it was a decisive move upwards without much pushback. Since I've given you all our past logs, what's your take on the amount of Bitcoin that changed hands during that particular sixty-second period?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Timestamp": 1658288340.0,
+          "Open": 23272.9,
+          "High": 23296.32,
+          "Low": 23272.9,
+          "Close": 23296.32,
+          "Volume": 0.49750982
+        }
+      }
+    ],
+    "target_column": "Volume",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/Bitcoin_Historical_Data_B1_006.json

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+{
+  "id": "006",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Bitcoin Historical Data",
+  "table_path": "kaggle/Bitcoin Historical Data",
+  "query": "Looking at this specific timestamp in the log, the market seems to have frozen solid, with the price pinned at $675.02 from start to finish. My records show this happened at the exact moment marked 1468861560. With the complete history I've sent over, can you tell me how many Bitcoins were actually traded when the price was completely locked like that?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Timestamp": 1468861560.0,
+          "Open": 675.02,
+          "High": 675.02,
+          "Low": 675.02,
+          "Close": 675.02,
+          "Volume": 0.0
+        }
+      }
+    ],
+    "target_column": "Volume",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/Bitcoin_Historical_Data_B1_007.json

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+{
+  "id": "007",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Bitcoin Historical Data",
+  "table_path": "kaggle/Bitcoin Historical Data",
+  "query": "This morning's trading data just came in showing the price opened at exactly $300, hit a high of $300, dipped ever so slightly to $299.98, and then closed right back at $300 at 1412593620. I've shared our complete Bitcoin archive with you, which tracks all our past activity. Given this exact price movement in the recent window, how much Bitcoin volume do you think was actually traded?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Timestamp": 1412593620.0,
+          "Open": 300.0,
+          "High": 300.0,
+          "Low": 299.98,
+          "Close": 300.0,
+          "Volume": 4545.89384565
+        }
+      }
+    ],
+    "target_column": "Volume",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/Bitcoin_Historical_Data_B1_008.json

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+{
+  "id": "008",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Bitcoin Historical Data",
+  "table_path": "kaggle/Bitcoin Historical Data",
+  "query": "Okay, pulling up the old logs for you. There's a moment here where things got interesting: the price opened strong at $306.86, hit a high of the same, then saw a significant drop to a low of $294, closing out at $299.38. This was all captured at that precise 1412538360 timestamp. Looking at the patterns in our shared history, what would you estimate the total trading volume was for that period?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Timestamp": 1412538360.0,
+          "Open": 306.86,
+          "High": 306.86,
+          "Low": 294.0,
+          "Close": 299.38,
+          "Volume": 5853.85216588
+        }
+      }
+    ],
+    "target_column": "Volume",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/info.json

@@ -0,0 +1,52 @@
+{
+    "name": "Bitcoin Historical Data",
+    "source": "https://www.kaggle.com/datasets/mczielinski/bitcoin-historical-data/data",
+    "data_intro": "CSV files for select bitcoin exchanges for the time period of Jan 2012 to Present (Measured by UTC day), with minute to minute updates of OHLC (Open, High, Low, Close) and Volume in BTC",
+    "is_splited": false,
+    "overall_size": 6944800,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 0,
+    "n_classes": 7,
+    "task_type": "regression",
+    "target": "Volume",
+    "cat_feature_intro": {},
+    "num_feature_intro": {
+        "Timestamp": "- Timestamp: Start time of time window (60s window), in Unix time. If a timestamp is missing, or if there are jumps, this may be because the exchange (or its API) was down, the exchange (or its API) did not exist, or some other unforeseen technical error in data reporting or gathering.",
+        "Open": "- Open: Open price at start time window",
+        "High": "- High: High price at start time window",
+        "Low": "- Low: Low price at start time window",
+        "Close": "- Close: Close price at start time window",
+        "Volume": "- Volume: Volume of BTC transacted in this window",
+        "datatime": "- datatime: The time that this infometion recorded"
+    },
+    "evaluation_metric": null,
+    "num_feature_value": {
+        "Close": [
+            3.8,
+            108960.0
+        ],
+        "High": [
+            3.8,
+            109030.0
+        ],
+        "Low": [
+            3.8,
+            108776.0
+        ],
+        "Open": [
+            3.8,
+            108946.0
+        ],
+        "Timestamp": [
+            1325412060.0,
+            1741305600.0
+        ],
+        "Volume": [
+            0.0,
+            5853.85216588
+        ],
+        "datatime": []
+    },
+    "cat_feature_value": {}
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/info_mod.json

@@ -0,0 +1,70 @@
+{
+  "name": "Bitcoin Historical Data",
+  "source": "https://www.kaggle.com/datasets/mczielinski/bitcoin-historical-data/data",
+  "data_intro": "CSV files for select bitcoin exchanges for the time period of Jan 2012 to Present (Measured by UTC day), with minute to minute updates of OHLC (Open, High, Low, Close) and Volume in BTC",
+  "is_splited": false,
+  "overall_size": 6944800,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 0,
+  "n_classes": 7,
+  "task_type": "regression",
+  "target": "Volume",
+  "cat_feature_intro": {},
+  "num_feature_intro": {
+    "Timestamp": "- Timestamp: Start time of time window (60s window), in Unix time. If a timestamp is missing, or if there are jumps, this may be because the exchange (or its API) was down, the exchange (or its API) did not exist, or some other unforeseen technical error in data reporting or gathering.",
+    "Open": "- Open: Open price at start time window",
+    "High": "- High: High price at start time window",
+    "Low": "- Low: Low price at start time window",
+    "Close": "- Close: Close price at start time window"
+  },
+  "evaluation_metric": null,
+  "num_feature_value": {
+    "Timestamp": [
+      1325412060.0,
+      1741305600.0
+    ],
+    "Open": [
+      3.8,
+      108946.0
+    ],
+    "High": [
+      3.8,
+      109030.0
+    ],
+    "Low": [
+      3.8,
+      108776.0
+    ],
+    "Close": [
+      3.8,
+      108960.0
+    ]
+  },
+  "cat_feature_value": {},
+  "columns": [
+    "Timestamp",
+    "Open",
+    "High",
+    "Low",
+    "Close",
+    "Volume"
+  ],
+  "feature_columns": [
+    "Timestamp",
+    "Open",
+    "High",
+    "Low",
+    "Close"
+  ],
+  "feature_types": {
+    "Timestamp": "numeric",
+    "Open": "numeric",
+    "High": "numeric",
+    "Low": "numeric",
+    "Close": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

single_point_prediction/finance/Bitcoin_Historical_Data_B1/test.csv

@@ -0,0 +1,9 @@
+Timestamp,Open,High,Low,Close,Volume
+1365873960.0,115.9,115.9,115.9,115.9,0.49751
+1325412060.0,4.58,4.58,4.58,4.58,0.0
+1493974680.0,1566.1,1566.1,1566.1,1566.1,0.49750944
+1497676080.0,2454.8,2454.82,2454.8,2454.82,0.49750949
+1658288340.0,23272.9,23296.32,23272.9,23296.32,0.49750982
+1468861560.0,675.02,675.02,675.02,675.02,0.0
+1412593620.0,300.0,300.0,299.98,300.0,4545.89384565
+1412538360.0,306.86,306.86,294.0,299.38,5853.85216588

single_point_prediction/finance/Campus_Recruitment_B1/Campus_Recruitment_B1_001.json

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+{
+  "id": "001",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Campus Recruitment",
+  "table_path": "kaggle/Campus Recruitment",
+  "query": "The admissions office just sent over my academic profile for the campus recruitment drive. It shows a male student who completed both secondary and higher secondary school under the Central board, scoring 63% and 66.2% respectively with a Commerce focus in higher secondary. He then pursued a Comm&Mgmt degree, graduating with 65.6%, and scored 60% on the employability test. Now, he's finishing his MBA in Mkt&Fin with a 62.54% average. With this entire academic journey laid out, what kind of salary package can he realistically expect from a corporate recruiter?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "sl_no": "NULL",
+          "gender": "M",
+          "ssc_p": 63.0,
+          "ssc_b": "Central",
+          "hsc_p": 66.2,
+          "hsc_b": "Central",
+          "hsc_s": "Commerce",
+          "degree_p": 65.6,
+          "degree_t": "Comm&Mgmt",
+          "workex": "NULL",
+          "etest_p": 60.0,
+          "specialisation": "Mkt&Fin",
+          "mba_p": 62.54,
+          "status": "NULL",
+          "salary": 300000.0
+        }
+      }
+    ],
+    "target_column": "salary",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Campus_Recruitment_B1/Campus_Recruitment_B1_002.json

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+{
+  "id": "002",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Campus Recruitment",
+  "table_path": "kaggle/Campus Recruitment",
+  "query": "The report for this placed male graduate shows a consistent academic record: 79.33% in 10th (Central), 78.33% in 12th Science (Others board), and a 77.48% in his Sci&Tech degree. His profile is bolstered by prior work experience and a high employability score of 86.5%. Having specialized in Mkt&Fin for his MBA, where he scored 66.28%, he’s all set for his career. I'm trying to help him plan his finances; any idea what his starting pay might look like?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "sl_no": 2,
+          "gender": "M",
+          "ssc_p": 79.33,
+          "ssc_b": "Central",
+          "hsc_p": 78.33,
+          "hsc_b": "Others",
+          "hsc_s": "Science",
+          "degree_p": 77.48,
+          "degree_t": "Sci&Tech",
+          "workex": "Yes",
+          "etest_p": 86.5,
+          "specialisation": "Mkt&Fin",
+          "mba_p": 66.28,
+          "status": "Placed",
+          "salary": 200000.0
+        }
+      }
+    ],
+    "target_column": "salary",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Campus_Recruitment_B1/Campus_Recruitment_B1_004.json

@@ -0,0 +1,45 @@
+{
+  "id": "004",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Campus Recruitment",
+  "table_path": "kaggle/Campus Recruitment",
+  "query": "The latest placement report highlights candidate #161 - a male Science student from Central boards throughout school, achieving 87% in 10th and 74% in 12th grade. His academic journey continued with a 65% Science & Technology degree, valuable work experience, and solid scores of 75% on the employability test and 72.29% in his Marketing & HR MBA program. Given he's been successfully placed, what compensation figure are we looking at here?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "sl_no": 161,
+          "gender": "M",
+          "ssc_p": 87.0,
+          "ssc_b": "Central",
+          "hsc_p": 74.0,
+          "hsc_b": "Central",
+          "hsc_s": "Science",
+          "degree_p": 65.0,
+          "degree_t": "Sci&Tech",
+          "workex": "Yes",
+          "etest_p": 75.0,
+          "specialisation": "Mkt&HR",
+          "mba_p": 72.29,
+          "status": "Placed",
+          "salary": 300000.0
+        }
+      }
+    ],
+    "target_column": "salary",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Campus_Recruitment_B1/Campus_Recruitment_B1_005.json

@@ -0,0 +1,45 @@
+{
+  "id": "005",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Campus Recruitment",
+  "table_path": "kaggle/Campus Recruitment",
+  "query": "Flipping through our placement records, which I've provided to you, record number 62 really stands out. A score of 84.2% in secondary school from a Central board, followed by 73.4% in the Commerce stream for higher secondary. Their degree percentage sits at 66.89%, and they passed the employability test with 61.6% without any prior work history. They later specialized in Mkt&Fin for their MBA, earning a 62.48%, and their status is marked as placed. From our past data, what should we expect the offered salary to be?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "sl_no": 62,
+          "gender": "NULL",
+          "ssc_p": 84.2,
+          "ssc_b": "Central",
+          "hsc_p": 73.4,
+          "hsc_b": "NULL",
+          "hsc_s": "Commerce",
+          "degree_p": 66.89,
+          "degree_t": "NULL",
+          "workex": "No",
+          "etest_p": 61.6,
+          "specialisation": "Mkt&Fin",
+          "mba_p": 62.48,
+          "status": "Placed",
+          "salary": 300000.0
+        }
+      }
+    ],
+    "target_column": "salary",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Campus_Recruitment_B1/Campus_Recruitment_B1_007.json

@@ -0,0 +1,45 @@
+{
+  "id": "007",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Campus Recruitment",
+  "table_path": "kaggle/Campus Recruitment",
+  "query": "This report from our campus placement office details a female graduate who completed both her secondary and higher secondary education through the Central board. She scored 63% in her 12th grade, focusing on Arts, and then achieved a 56% in her degree. With a 'Yes' to work experience and an impressive 80% on the employability test, she specialized in Mkt&HR for her MBA, graduating with 56.63%. Her status is confirmed as Placed. I'm providing our entire placement history for context. Given all these specifics from her profile, what salary offer should she expect?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "sl_no": "NULL",
+          "gender": "F",
+          "ssc_p": "NULL",
+          "ssc_b": "Central",
+          "hsc_p": 63.0,
+          "hsc_b": "Central",
+          "hsc_s": "Arts",
+          "degree_p": 56.0,
+          "degree_t": "NULL",
+          "workex": "Yes",
+          "etest_p": 80.0,
+          "specialisation": "Mkt&HR",
+          "mba_p": 56.63,
+          "status": "Placed",
+          "salary": 300000.0
+        }
+      }
+    ],
+    "target_column": "salary",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Campus_Recruitment_B1/Campus_Recruitment_B1_008.json

@@ -0,0 +1,45 @@
+{
+  "id": "008",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Campus Recruitment",
+  "table_path": "kaggle/Campus Recruitment",
+  "query": "Flipping through the placement records, entry number 108 immediately stands out. It details a male student from the Commerce stream who scored a solid 82% in his secondary education under the 'Others' board. His academic performance is consistently strong, with 90% in higher secondary commerce, also from the 'Others' board, and an 83% in his Comm&Mgmt degree. Despite having no prior work experience, he aced the employability test with an 80% and secured a 73.52% in his Mkt&HR MBA, which ultimately led to him being placed. I'm sharing our historical placement archives with you. Given this candidate's specific educational journey and final status, what kind of salary package should he expect?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "sl_no": 108,
+          "gender": "M",
+          "ssc_p": 82.0,
+          "ssc_b": "Others",
+          "hsc_p": 90.0,
+          "hsc_b": "Others",
+          "hsc_s": "Commerce",
+          "degree_p": 83.0,
+          "degree_t": "Comm&Mgmt",
+          "workex": "No",
+          "etest_p": 80.0,
+          "specialisation": "Mkt&HR",
+          "mba_p": 73.52,
+          "status": "Placed",
+          "salary": 200000.0
+        }
+      }
+    ],
+    "target_column": "salary",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Campus_Recruitment_B1/history.csv

@@ -0,0 +1,201 @@
+sl_no,gender,ssc_p,ssc_b,hsc_p,hsc_b,hsc_s,degree_p,degree_t,workex,etest_p,specialisation,mba_p,status,salary
+9,M,73.0,Central,79.0,Central,Commerce,72.0,Comm&Mgmt,No,91.34,Mkt&Fin,61.29,Placed,231000.0
+10,M,58.0,Central,70.0,Central,Commerce,61.0,Comm&Mgmt,No,54.0,Mkt&Fin,52.21,Not Placed,
+11,M,58.0,Central,61.0,Central,Commerce,60.0,Comm&Mgmt,Yes,62.0,Mkt&HR,60.85,Placed,260000.0
+12,M,69.6,Central,68.4,Central,Commerce,78.3,Comm&Mgmt,Yes,60.0,Mkt&Fin,63.7,Placed,250000.0
+13,F,47.0,Central,55.0,Others,Science,65.0,Comm&Mgmt,No,62.0,Mkt&HR,65.04,Not Placed,
+14,F,77.0,Central,87.0,Central,Commerce,59.0,Comm&Mgmt,No,68.0,Mkt&Fin,68.63,Placed,218000.0
+15,M,62.0,Central,47.0,Central,Commerce,50.0,Comm&Mgmt,No,76.0,Mkt&HR,54.96,Not Placed,
+16,F,65.0,Central,75.0,Central,Commerce,69.0,Comm&Mgmt,Yes,72.0,Mkt&Fin,64.66,Placed,200000.0
+18,F,55.0,Central,67.0,Central,Commerce,64.0,Comm&Mgmt,No,60.0,Mkt&Fin,67.28,Not Placed,
+19,F,63.0,Central,66.0,Central,Commerce,64.0,Comm&Mgmt,No,68.0,Mkt&HR,64.08,Not Placed,
+20,M,60.0,Others,67.0,Others,Arts,70.0,Comm&Mgmt,Yes,50.48,Mkt&Fin,77.89,Placed,236000.0
+21,M,62.0,Others,65.0,Others,Commerce,66.0,Comm&Mgmt,No,50.0,Mkt&HR,56.7,Placed,265000.0
+22,F,79.0,Others,76.0,Others,Commerce,85.0,Comm&Mgmt,No,95.0,Mkt&Fin,69.06,Placed,393000.0
+23,F,69.8,Others,60.8,Others,Science,72.23,Sci&Tech,No,55.53,Mkt&HR,68.81,Placed,360000.0
+24,F,77.4,Others,60.0,Others,Science,64.74,Sci&Tech,Yes,92.0,Mkt&Fin,63.62,Placed,300000.0
+25,M,76.5,Others,97.7,Others,Science,78.86,Sci&Tech,No,97.4,Mkt&Fin,74.01,Placed,360000.0
+26,F,52.58,Others,54.6,Central,Commerce,50.2,Comm&Mgmt,Yes,76.0,Mkt&Fin,65.33,Not Placed,
+27,M,71.0,Others,79.0,Others,Commerce,66.0,Comm&Mgmt,Yes,94.0,Mkt&Fin,57.55,Placed,240000.0
+28,M,63.0,Others,67.0,Others,Commerce,66.0,Comm&Mgmt,No,68.0,Mkt&HR,57.69,Placed,265000.0
+29,M,76.76,Others,76.5,Others,Commerce,67.5,Comm&Mgmt,Yes,73.35,Mkt&Fin,64.15,Placed,350000.0
+30,M,62.0,Central,67.0,Central,Commerce,58.0,Comm&Mgmt,No,77.0,Mkt&Fin,51.29,Not Placed,
+31,F,64.0,Central,73.5,Central,Commerce,73.0,Comm&Mgmt,No,52.0,Mkt&HR,56.7,Placed,250000.0
+32,F,67.0,Central,53.0,Central,Science,65.0,Sci&Tech,No,64.0,Mkt&HR,58.32,Not Placed,
+33,F,61.0,Central,81.0,Central,Commerce,66.4,Comm&Mgmt,No,50.89,Mkt&HR,62.21,Placed,278000.0
+34,F,87.0,Others,65.0,Others,Science,81.0,Comm&Mgmt,Yes,88.0,Mkt&Fin,72.78,Placed,260000.0
+35,M,62.0,Others,51.0,Others,Science,52.0,Others,No,68.44,Mkt&HR,62.77,Not Placed,
+36,F,69.0,Central,78.0,Central,Commerce,72.0,Comm&Mgmt,No,71.0,Mkt&HR,62.74,Placed,300000.0
+37,M,51.0,Central,44.0,Central,Commerce,57.0,Comm&Mgmt,No,64.0,Mkt&Fin,51.45,Not Placed,
+38,F,79.0,Central,76.0,Central,Science,65.6,Sci&Tech,No,58.0,Mkt&HR,55.47,Placed,320000.0
+39,F,73.0,Others,58.0,Others,Science,66.0,Comm&Mgmt,No,53.7,Mkt&HR,56.86,Placed,240000.0
+40,M,81.0,Others,68.0,Others,Science,64.0,Sci&Tech,No,93.0,Mkt&Fin,62.56,Placed,411000.0
+41,F,78.0,Central,77.0,Others,Commerce,80.0,Comm&Mgmt,No,60.0,Mkt&Fin,66.72,Placed,287000.0
+42,F,74.0,Others,63.16,Others,Commerce,65.0,Comm&Mgmt,Yes,65.0,Mkt&HR,69.76,Not Placed,
+43,M,49.0,Others,39.0,Central,Science,65.0,Others,No,63.0,Mkt&Fin,51.21,Not Placed,
+44,M,87.0,Others,87.0,Others,Commerce,68.0,Comm&Mgmt,No,95.0,Mkt&HR,62.9,Placed,300000.0
+45,F,77.0,Others,73.0,Others,Commerce,81.0,Comm&Mgmt,Yes,89.0,Mkt&Fin,69.7,Placed,200000.0
+46,F,76.0,Central,64.0,Central,Science,72.0,Sci&Tech,No,58.0,Mkt&HR,66.53,Not Placed,
+47,F,70.89,Others,71.98,Others,Science,65.6,Comm&Mgmt,No,68.0,Mkt&HR,71.63,Not Placed,
+48,M,63.0,Central,60.0,Central,Commerce,57.0,Comm&Mgmt,Yes,78.0,Mkt&Fin,54.55,Placed,204000.0
+49,M,63.0,Others,62.0,Others,Commerce,68.0,Comm&Mgmt,No,64.0,Mkt&Fin,62.46,Placed,250000.0
+50,F,50.0,Others,37.0,Others,Arts,52.0,Others,No,65.0,Mkt&HR,56.11,Not Placed,
+51,F,75.2,Central,73.2,Central,Science,68.4,Comm&Mgmt,No,65.0,Mkt&HR,62.98,Placed,200000.0
+52,M,54.4,Central,61.12,Central,Commerce,56.2,Comm&Mgmt,No,67.0,Mkt&HR,62.65,Not Placed,
+53,F,40.89,Others,45.83,Others,Commerce,53.0,Comm&Mgmt,No,71.2,Mkt&HR,65.49,Not Placed,
+54,M,80.0,Others,70.0,Others,Science,72.0,Sci&Tech,No,87.0,Mkt&HR,71.04,Placed,450000.0
+55,F,74.0,Central,60.0,Others,Science,69.0,Comm&Mgmt,No,78.0,Mkt&HR,65.56,Placed,216000.0
+56,M,60.4,Central,66.6,Others,Science,65.0,Comm&Mgmt,No,71.0,Mkt&HR,52.71,Placed,220000.0
+57,M,63.0,Others,71.4,Others,Commerce,61.4,Comm&Mgmt,No,68.0,Mkt&Fin,66.88,Placed,240000.0
+58,M,68.0,Central,76.0,Central,Commerce,74.0,Comm&Mgmt,No,80.0,Mkt&Fin,63.59,Placed,360000.0
+59,M,74.0,Central,62.0,Others,Science,68.0,Comm&Mgmt,No,74.0,Mkt&Fin,57.99,Placed,268000.0
+60,M,52.6,Central,65.58,Others,Science,72.11,Sci&Tech,No,57.6,Mkt&Fin,56.66,Placed,265000.0
+61,M,74.0,Central,70.0,Central,Science,72.0,Comm&Mgmt,Yes,60.0,Mkt&Fin,57.24,Placed,260000.0
+63,F,86.5,Others,64.2,Others,Science,67.4,Sci&Tech,No,59.0,Mkt&Fin,59.69,Placed,240000.0
+64,M,61.0,Others,70.0,Others,Commerce,64.0,Comm&Mgmt,No,68.5,Mkt&HR,59.5,Not Placed,
+65,M,80.0,Others,73.0,Others,Commerce,75.0,Comm&Mgmt,No,61.0,Mkt&Fin,58.78,Placed,240000.0
+66,M,54.0,Others,47.0,Others,Science,57.0,Comm&Mgmt,No,89.69,Mkt&HR,57.1,Not Placed,
+67,M,83.0,Others,74.0,Others,Science,66.0,Comm&Mgmt,No,68.92,Mkt&HR,58.46,Placed,275000.0
+68,M,80.92,Others,78.5,Others,Commerce,67.0,Comm&Mgmt,No,68.71,Mkt&Fin,60.99,Placed,275000.0
+69,F,69.7,Central,47.0,Central,Commerce,72.7,Sci&Tech,No,79.0,Mkt&HR,59.24,Not Placed,
+70,M,73.0,Central,73.0,Central,Science,66.0,Sci&Tech,Yes,70.0,Mkt&Fin,68.07,Placed,275000.0
+71,M,82.0,Others,61.0,Others,Science,62.0,Sci&Tech,No,89.0,Mkt&Fin,65.45,Placed,360000.0
+72,M,75.0,Others,70.29,Others,Commerce,71.0,Comm&Mgmt,No,95.0,Mkt&Fin,66.94,Placed,240000.0
+73,M,84.86,Others,67.0,Others,Science,78.0,Comm&Mgmt,No,95.5,Mkt&Fin,68.53,Placed,240000.0
+74,M,64.6,Central,83.83,Others,Commerce,71.72,Comm&Mgmt,No,86.0,Mkt&Fin,59.75,Placed,218000.0
+75,M,56.6,Central,64.8,Central,Commerce,70.2,Comm&Mgmt,No,84.27,Mkt&Fin,67.2,Placed,336000.0
+76,F,59.0,Central,62.0,Others,Commerce,77.5,Comm&Mgmt,No,74.0,Mkt&HR,67.0,Not Placed,
+77,F,66.5,Others,70.4,Central,Arts,71.93,Comm&Mgmt,No,61.0,Mkt&Fin,64.27,Placed,230000.0
+78,M,64.0,Others,80.0,Others,Science,65.0,Sci&Tech,Yes,69.0,Mkt&Fin,57.65,Placed,500000.0
+79,M,84.0,Others,90.9,Others,Science,64.5,Sci&Tech,No,86.04,Mkt&Fin,59.42,Placed,270000.0
+80,F,69.0,Central,62.0,Central,Science,66.0,Sci&Tech,No,75.0,Mkt&HR,67.99,Not Placed,
+81,F,69.0,Others,62.0,Others,Commerce,69.0,Comm&Mgmt,Yes,67.0,Mkt&HR,62.35,Placed,240000.0
+82,M,81.7,Others,63.0,Others,Science,67.0,Comm&Mgmt,Yes,86.0,Mkt&Fin,70.2,Placed,300000.0
+83,M,63.0,Central,67.0,Central,Commerce,74.0,Comm&Mgmt,No,82.0,Mkt&Fin,60.44,Not Placed,
+84,M,84.0,Others,79.0,Others,Science,68.0,Sci&Tech,Yes,84.0,Mkt&Fin,66.69,Placed,300000.0
+85,M,70.0,Central,63.0,Others,Science,70.0,Sci&Tech,Yes,55.0,Mkt&Fin,62.0,Placed,300000.0
+86,F,83.84,Others,89.83,Others,Commerce,77.2,Comm&Mgmt,Yes,78.74,Mkt&Fin,76.18,Placed,400000.0
+87,M,62.0,Others,63.0,Others,Commerce,64.0,Comm&Mgmt,No,67.0,Mkt&Fin,57.03,Placed,220000.0
+88,M,59.6,Central,51.0,Central,Science,60.0,Others,No,75.0,Mkt&HR,59.08,Not Placed,
+89,F,66.0,Central,62.0,Central,Commerce,73.0,Comm&Mgmt,No,58.0,Mkt&HR,64.36,Placed,210000.0
+90,F,84.0,Others,75.0,Others,Science,69.0,Sci&Tech,Yes,62.0,Mkt&HR,62.36,Placed,210000.0
+91,F,85.0,Others,90.0,Others,Commerce,82.0,Comm&Mgmt,No,92.0,Mkt&Fin,68.03,Placed,300000.0
+92,M,52.0,Central,57.0,Central,Commerce,50.8,Comm&Mgmt,No,67.0,Mkt&HR,62.79,Not Placed,
+93,F,60.23,Central,69.0,Central,Science,66.0,Comm&Mgmt,No,72.0,Mkt&Fin,59.47,Placed,230000.0
+94,M,52.0,Central,62.0,Central,Commerce,54.0,Comm&Mgmt,No,72.0,Mkt&HR,55.41,Not Placed,
+95,M,58.0,Central,62.0,Central,Commerce,64.0,Comm&Mgmt,No,53.88,Mkt&Fin,54.97,Placed,260000.0
+96,M,73.0,Central,78.0,Others,Commerce,65.0,Comm&Mgmt,Yes,95.46,Mkt&Fin,62.16,Placed,420000.0
+97,F,76.0,Central,70.0,Central,Science,76.0,Comm&Mgmt,Yes,66.0,Mkt&Fin,64.44,Placed,300000.0
+98,F,70.5,Central,62.5,Others,Commerce,61.0,Comm&Mgmt,No,93.91,Mkt&Fin,69.03,Not Placed,
+99,F,69.0,Central,73.0,Central,Commerce,65.0,Comm&Mgmt,No,70.0,Mkt&Fin,57.31,Placed,220000.0
+100,M,54.0,Central,82.0,Others,Commerce,63.0,Sci&Tech,No,50.0,Mkt&Fin,59.47,Not Placed,
+101,F,45.0,Others,57.0,Others,Commerce,58.0,Comm&Mgmt,Yes,56.39,Mkt&HR,64.95,Not Placed,
+102,M,63.0,Central,72.0,Central,Commerce,68.0,Comm&Mgmt,No,78.0,Mkt&HR,60.44,Placed,380000.0
+103,F,77.0,Others,61.0,Others,Commerce,68.0,Comm&Mgmt,Yes,57.5,Mkt&Fin,61.31,Placed,300000.0
+104,M,73.0,Central,78.0,Central,Science,73.0,Sci&Tech,Yes,85.0,Mkt&HR,65.83,Placed,240000.0
+105,M,69.0,Central,63.0,Others,Science,65.0,Comm&Mgmt,Yes,55.0,Mkt&HR,58.23,Placed,360000.0
+106,M,59.0,Central,64.0,Others,Science,58.0,Sci&Tech,No,85.0,Mkt&HR,55.3,Not Placed,
+107,M,61.08,Others,50.0,Others,Science,54.0,Sci&Tech,No,71.0,Mkt&Fin,65.69,Not Placed,
+109,M,61.0,Central,82.0,Central,Commerce,69.0,Comm&Mgmt,No,84.0,Mkt&Fin,58.31,Placed,300000.0
+110,M,52.0,Central,63.0,Others,Science,65.0,Sci&Tech,Yes,86.0,Mkt&HR,56.09,Not Placed,
+111,F,69.5,Central,70.0,Central,Science,72.0,Sci&Tech,No,57.2,Mkt&HR,54.8,Placed,250000.0
+112,M,51.0,Others,54.0,Others,Science,61.0,Sci&Tech,No,60.0,Mkt&HR,60.64,Not Placed,
+113,M,58.0,Others,61.0,Others,Commerce,61.0,Comm&Mgmt,No,58.0,Mkt&HR,53.94,Placed,250000.0
+114,F,73.96,Others,79.0,Others,Commerce,67.0,Comm&Mgmt,No,72.15,Mkt&Fin,63.08,Placed,280000.0
+115,M,65.0,Central,68.0,Others,Science,69.0,Comm&Mgmt,No,53.7,Mkt&HR,55.01,Placed,250000.0
+116,F,73.0,Others,63.0,Others,Science,66.0,Comm&Mgmt,No,89.0,Mkt&Fin,60.5,Placed,216000.0
+117,M,68.2,Central,72.8,Central,Commerce,66.6,Comm&Mgmt,Yes,96.0,Mkt&Fin,70.85,Placed,300000.0
+118,M,77.0,Others,75.0,Others,Science,73.0,Sci&Tech,No,80.0,Mkt&Fin,67.05,Placed,240000.0
+119,M,76.0,Central,80.0,Central,Science,78.0,Sci&Tech,Yes,97.0,Mkt&HR,70.48,Placed,276000.0
+120,M,60.8,Central,68.4,Central,Commerce,64.6,Comm&Mgmt,Yes,82.66,Mkt&Fin,64.34,Placed,940000.0
+121,M,58.0,Others,40.0,Others,Science,59.0,Comm&Mgmt,No,73.0,Mkt&HR,58.81,Not Placed,
+122,F,64.0,Central,67.0,Others,Science,69.6,Sci&Tech,Yes,55.67,Mkt&HR,71.49,Placed,250000.0
+123,F,66.5,Central,66.8,Central,Arts,69.3,Comm&Mgmt,Yes,80.4,Mkt&Fin,71.0,Placed,236000.0
+124,M,74.0,Others,59.0,Others,Commerce,73.0,Comm&Mgmt,Yes,60.0,Mkt&HR,56.7,Placed,240000.0
+125,M,67.0,Central,71.0,Central,Science,64.33,Others,Yes,64.0,Mkt&HR,61.26,Placed,250000.0
+126,F,84.0,Central,73.0,Central,Commerce,73.0,Comm&Mgmt,No,75.0,Mkt&Fin,73.33,Placed,350000.0
+127,F,79.0,Others,61.0,Others,Science,75.5,Sci&Tech,Yes,70.0,Mkt&Fin,68.2,Placed,210000.0
+128,F,72.0,Others,60.0,Others,Science,69.0,Comm&Mgmt,No,55.5,Mkt&HR,58.4,Placed,250000.0
+129,M,80.4,Central,73.4,Central,Science,77.72,Sci&Tech,Yes,81.2,Mkt&HR,76.26,Placed,400000.0
+130,M,76.7,Central,89.7,Others,Commerce,66.0,Comm&Mgmt,Yes,90.0,Mkt&Fin,68.55,Placed,250000.0
+131,M,62.0,Central,65.0,Others,Commerce,60.0,Comm&Mgmt,No,84.0,Mkt&Fin,64.15,Not Placed,
+132,F,74.9,Others,57.0,Others,Science,62.0,Others,Yes,80.0,Mkt&Fin,60.78,Placed,360000.0
+133,M,67.0,Others,68.0,Others,Commerce,64.0,Comm&Mgmt,Yes,74.4,Mkt&HR,53.49,Placed,300000.0
+134,M,73.0,Central,64.0,Others,Commerce,77.0,Comm&Mgmt,Yes,65.0,Mkt&HR,60.98,Placed,250000.0
+135,F,77.44,Central,92.0,Others,Commerce,72.0,Comm&Mgmt,Yes,94.0,Mkt&Fin,67.13,Placed,250000.0
+136,F,72.0,Central,56.0,Others,Science,69.0,Comm&Mgmt,No,55.6,Mkt&HR,65.63,Placed,200000.0
+137,F,47.0,Central,59.0,Central,Arts,64.0,Comm&Mgmt,No,78.0,Mkt&Fin,61.58,Not Placed,
+138,M,67.0,Others,63.0,Central,Commerce,72.0,Comm&Mgmt,No,56.0,Mkt&HR,60.41,Placed,225000.0
+139,F,82.0,Others,64.0,Others,Science,73.0,Sci&Tech,Yes,96.0,Mkt&Fin,71.77,Placed,250000.0
+140,M,77.0,Central,70.0,Central,Commerce,59.0,Comm&Mgmt,Yes,58.0,Mkt&Fin,54.43,Placed,220000.0
+141,M,65.0,Central,64.8,Others,Commerce,69.5,Comm&Mgmt,Yes,56.0,Mkt&Fin,56.94,Placed,265000.0
+142,M,66.0,Central,64.0,Central,Science,60.0,Comm&Mgmt,No,60.0,Mkt&HR,61.9,Not Placed,
+143,M,85.0,Central,60.0,Others,Science,73.43,Sci&Tech,Yes,60.0,Mkt&Fin,61.29,Placed,260000.0
+144,M,77.67,Others,64.89,Others,Commerce,70.67,Comm&Mgmt,No,89.0,Mkt&Fin,60.39,Placed,300000.0
+145,M,52.0,Others,50.0,Others,Arts,61.0,Comm&Mgmt,No,60.0,Mkt&Fin,58.52,Not Placed,
+146,M,89.4,Others,65.66,Others,Science,71.25,Sci&Tech,No,72.0,Mkt&HR,63.23,Placed,400000.0
+147,M,62.0,Central,63.0,Others,Science,66.0,Comm&Mgmt,No,85.0,Mkt&HR,55.14,Placed,233000.0
+148,M,70.0,Central,74.0,Central,Commerce,65.0,Comm&Mgmt,No,83.0,Mkt&Fin,62.28,Placed,300000.0
+149,F,77.0,Central,86.0,Central,Arts,56.0,Others,No,57.0,Mkt&Fin,64.08,Placed,240000.0
+150,M,44.0,Central,58.0,Central,Arts,55.0,Comm&Mgmt,Yes,64.25,Mkt&HR,58.54,Not Placed,
+151,M,71.0,Central,58.66,Central,Science,58.0,Sci&Tech,Yes,56.0,Mkt&Fin,61.3,Placed,690000.0
+152,M,65.0,Central,65.0,Central,Commerce,75.0,Comm&Mgmt,No,83.0,Mkt&Fin,58.87,Placed,270000.0
+153,F,75.4,Others,60.5,Central,Science,84.0,Sci&Tech,No,98.0,Mkt&Fin,65.25,Placed,240000.0
+154,M,49.0,Others,59.0,Others,Science,65.0,Sci&Tech,Yes,86.0,Mkt&Fin,62.48,Placed,340000.0
+155,M,53.0,Central,63.0,Others,Science,60.0,Comm&Mgmt,Yes,70.0,Mkt&Fin,53.2,Placed,250000.0
+156,M,51.57,Others,74.66,Others,Commerce,59.9,Comm&Mgmt,Yes,56.15,Mkt&HR,65.99,Not Placed,
+157,M,84.2,Central,69.4,Central,Science,65.0,Sci&Tech,Yes,80.0,Mkt&HR,52.72,Placed,255000.0
+158,M,66.5,Central,62.5,Central,Commerce,60.9,Comm&Mgmt,No,93.4,Mkt&Fin,55.03,Placed,300000.0
+159,M,67.0,Others,63.0,Others,Science,64.0,Sci&Tech,No,60.0,Mkt&Fin,61.87,Not Placed,
+160,M,52.0,Central,49.0,Others,Commerce,58.0,Comm&Mgmt,No,62.0,Mkt&HR,60.59,Not Placed,
+162,M,55.6,Others,51.0,Others,Commerce,57.5,Comm&Mgmt,No,57.63,Mkt&HR,62.72,Not Placed,
+163,M,74.2,Central,87.6,Others,Commerce,77.25,Comm&Mgmt,Yes,75.2,Mkt&Fin,66.06,Placed,285000.0
+164,M,63.0,Others,67.0,Others,Science,64.0,Sci&Tech,No,75.0,Mkt&Fin,66.46,Placed,500000.0
+165,F,67.16,Central,72.5,Central,Commerce,63.35,Comm&Mgmt,No,53.04,Mkt&Fin,65.52,Placed,250000.0
+166,F,63.3,Central,78.33,Others,Commerce,74.0,Comm&Mgmt,No,80.0,Mkt&Fin,74.56,Not Placed,
+167,M,62.0,Others,62.0,Others,Commerce,60.0,Comm&Mgmt,Yes,63.0,Mkt&HR,52.38,Placed,240000.0
+168,M,67.9,Others,62.0,Others,Science,67.0,Sci&Tech,Yes,58.1,Mkt&Fin,75.71,Not Placed,
+169,F,48.0,Central,51.0,Central,Commerce,58.0,Comm&Mgmt,Yes,60.0,Mkt&HR,58.79,Not Placed,
+170,M,59.96,Others,42.16,Others,Science,61.26,Sci&Tech,No,54.48,Mkt&HR,65.48,Not Placed,
+171,F,63.4,Others,67.2,Others,Commerce,60.0,Comm&Mgmt,No,58.06,Mkt&HR,69.28,Not Placed,
+172,M,80.0,Others,80.0,Others,Commerce,72.0,Comm&Mgmt,Yes,63.79,Mkt&Fin,66.04,Placed,290000.0
+173,M,73.0,Others,58.0,Others,Commerce,56.0,Comm&Mgmt,No,84.0,Mkt&HR,52.64,Placed,300000.0
+174,F,52.0,Others,52.0,Others,Science,55.0,Sci&Tech,No,67.0,Mkt&HR,59.32,Not Placed,
+175,M,73.24,Others,50.83,Others,Science,64.27,Sci&Tech,Yes,64.0,Mkt&Fin,66.23,Placed,500000.0
+176,M,63.0,Others,62.0,Others,Science,65.0,Sci&Tech,No,87.5,Mkt&HR,60.69,Not Placed,
+177,F,59.0,Central,60.0,Others,Commerce,56.0,Comm&Mgmt,No,55.0,Mkt&HR,57.9,Placed,220000.0
+178,F,73.0,Central,97.0,Others,Commerce,79.0,Comm&Mgmt,Yes,89.0,Mkt&Fin,70.81,Placed,650000.0
+179,M,68.0,Others,56.0,Others,Science,68.0,Sci&Tech,No,73.0,Mkt&HR,68.07,Placed,350000.0
+180,F,77.8,Central,64.0,Central,Science,64.2,Sci&Tech,No,75.5,Mkt&HR,72.14,Not Placed,
+181,M,65.0,Central,71.5,Others,Commerce,62.8,Comm&Mgmt,Yes,57.0,Mkt&Fin,56.6,Placed,265000.0
+182,M,62.0,Central,60.33,Others,Science,64.21,Sci&Tech,No,63.0,Mkt&HR,60.02,Not Placed,
+183,M,52.0,Others,65.0,Others,Arts,57.0,Others,Yes,75.0,Mkt&Fin,59.81,Not Placed,
+184,M,65.0,Central,77.0,Central,Commerce,69.0,Comm&Mgmt,No,60.0,Mkt&HR,61.82,Placed,276000.0
+185,F,56.28,Others,62.83,Others,Commerce,59.79,Comm&Mgmt,No,60.0,Mkt&HR,57.29,Not Placed,
+186,F,88.0,Central,72.0,Central,Science,78.0,Others,No,82.0,Mkt&HR,71.43,Placed,252000.0
+187,F,52.0,Central,64.0,Central,Commerce,61.0,Comm&Mgmt,No,55.0,Mkt&Fin,62.93,Not Placed,
+188,M,78.5,Central,65.5,Central,Science,67.0,Sci&Tech,Yes,95.0,Mkt&Fin,64.86,Placed,280000.0
+189,M,61.8,Others,47.0,Others,Commerce,54.38,Comm&Mgmt,No,57.0,Mkt&Fin,56.13,Not Placed,
+190,F,54.0,Central,77.6,Others,Commerce,69.2,Comm&Mgmt,No,95.65,Mkt&Fin,66.94,Not Placed,
+191,F,64.0,Others,70.2,Central,Commerce,61.0,Comm&Mgmt,No,50.0,Mkt&Fin,62.5,Not Placed,
+192,M,67.0,Others,61.0,Central,Science,72.0,Comm&Mgmt,No,72.0,Mkt&Fin,61.01,Placed,264000.0
+193,M,65.2,Central,61.4,Central,Commerce,64.8,Comm&Mgmt,Yes,93.4,Mkt&Fin,57.34,Placed,270000.0
+195,M,52.0,Others,55.0,Others,Commerce,56.3,Comm&Mgmt,No,59.0,Mkt&Fin,64.74,Not Placed,
+196,M,66.0,Central,76.0,Central,Commerce,72.0,Comm&Mgmt,Yes,84.0,Mkt&HR,58.95,Placed,275000.0
+197,M,72.0,Others,63.0,Others,Science,77.5,Sci&Tech,Yes,78.0,Mkt&Fin,54.48,Placed,250000.0
+198,F,83.96,Others,53.0,Others,Science,91.0,Sci&Tech,No,59.32,Mkt&HR,69.71,Placed,260000.0
+199,F,67.0,Central,70.0,Central,Commerce,65.0,Others,No,88.0,Mkt&HR,71.96,Not Placed,
+200,M,69.0,Others,65.0,Others,Commerce,57.0,Comm&Mgmt,No,73.0,Mkt&HR,55.8,Placed,265000.0
+201,M,69.0,Others,60.0,Others,Commerce,65.0,Comm&Mgmt,No,87.55,Mkt&Fin,52.81,Placed,300000.0
+202,M,54.2,Central,63.0,Others,Science,58.0,Comm&Mgmt,No,79.0,Mkt&HR,58.44,Not Placed,
+203,M,70.0,Central,63.0,Central,Science,66.0,Sci&Tech,No,61.28,Mkt&HR,60.11,Placed,240000.0
+204,M,55.68,Others,61.33,Others,Commerce,56.87,Comm&Mgmt,No,66.0,Mkt&HR,58.3,Placed,260000.0
+205,F,74.0,Others,73.0,Others,Commerce,73.0,Comm&Mgmt,Yes,80.0,Mkt&Fin,67.69,Placed,210000.0
+206,M,61.0,Others,62.0,Others,Commerce,65.0,Comm&Mgmt,No,62.0,Mkt&Fin,56.81,Placed,250000.0
+207,M,41.0,Central,42.0,Central,Science,60.0,Comm&Mgmt,No,97.0,Mkt&Fin,53.39,Not Placed,
+208,M,83.33,Central,78.0,Others,Commerce,61.0,Comm&Mgmt,Yes,88.56,Mkt&Fin,71.55,Placed,300000.0
+210,M,62.0,Central,72.0,Central,Commerce,65.0,Comm&Mgmt,No,67.0,Mkt&Fin,56.49,Placed,216000.0
+211,M,80.6,Others,82.0,Others,Commerce,77.6,Comm&Mgmt,No,91.0,Mkt&Fin,74.49,Placed,400000.0
+212,M,58.0,Others,60.0,Others,Science,72.0,Sci&Tech,No,74.0,Mkt&Fin,53.62,Placed,275000.0
+213,M,67.0,Others,67.0,Others,Commerce,73.0,Comm&Mgmt,Yes,59.0,Mkt&Fin,69.72,Placed,295000.0
+214,F,74.0,Others,66.0,Others,Commerce,58.0,Comm&Mgmt,No,70.0,Mkt&HR,60.23,Placed,204000.0

single_point_prediction/finance/Campus_Recruitment_B1/info.json

@@ -0,0 +1,99 @@
+{
+    "name": "Campus Recruitment",
+    "source": "https://www.kaggle.com/datasets/benroshan/factors-affecting-campus-placement/data",
+    "data_intro": "This data set consists of Placement data of students in a XYZ campus. It includes secondary and higher secondary school percentage and specialization. It also includes degree specialization, type and Work experience and salary offers to the placed students",
+    "is_splited": false,
+    "overall_size": 215,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 8,
+    "n_classes": 7,
+    "task_type": "regression",
+    "target": "salary",
+    "cat_feature_intro": {
+        "gender": "- gender:Gender of this people, Male='M',Female='F'",
+        "ssc_b": "- ssc_b: Board of Education of secondary school people, Central, Others",
+        "hsc_b": "- hsc_b: Board of Education of higher secondary school, Central, Others",
+        "hsc_s": "- hsc_s: Specialization in Higher Secondary Education, Commerce, Science, Others",
+        "degree_t": "- degree_t: Under Graduation(Degree type) or field of degree education, Comm&Mgmt, Sci&Tech, Others",
+        "workex": "- workex: Work Experience, true, false",
+        "specialisation": "- specialisation:Post Graduation(MBA)-Specialization, Mkt&Fin, Mkt&HR",
+        "status": "- status: Status of placement, Placed, Not placed"
+    },
+    "num_feature_intro": {
+        "sl_no": "- sl_no: Serial Numbe",
+        "ssc_p": "- ssc_p: Secondary Education(10 Grade) percentage,For example:Student A scores 80/100 in his 10th grade. The percentage would be 80% ",
+        "hsc_p": "- hsc_p: Higher Secondary Education(12th Grade) percentage,For example:Student A scores 120/200 in his 12th grade. The percentage would be 60% ",
+        "degree_p": "- degree_p: Degree Percentage",
+        "etest_p": "- etest_p: Employability test percentage ( conducted by college)",
+        "mba_p": "- mba_p: MBA percentage",
+        "salary": "Salary offered by corporate to candidates"
+    },
+    "evaluation_metric": null,
+    "num_feature_value": {
+        "degree_p": [
+            50.0,
+            91.0
+        ],
+        "etest_p": [
+            50.0,
+            98.0
+        ],
+        "hsc_p": [
+            37.0,
+            97.7
+        ],
+        "mba_p": [
+            51.21,
+            77.89
+        ],
+        "salary": [
+            200000.0,
+            940000.0
+        ],
+        "sl_no": [
+            1.0,
+            215.0
+        ],
+        "ssc_p": [
+            40.89,
+            89.4
+        ]
+    },
+    "cat_feature_value": {
+        "degree_t": [
+            "Comm&Mgmt",
+            "Others",
+            "Sci&Tech"
+        ],
+        "gender": [
+            "F",
+            "M"
+        ],
+        "hsc_b": [
+            "Central",
+            "Others"
+        ],
+        "hsc_s": [
+            "Arts",
+            "Commerce",
+            "Science"
+        ],
+        "specialisation": [
+            "Mkt&Fin",
+            "Mkt&HR"
+        ],
+        "ssc_b": [
+            "Central",
+            "Others"
+        ],
+        "status": [
+            "Not Placed",
+            "Placed"
+        ],
+        "workex": [
+            "No",
+            "Yes"
+        ]
+    }
+}

single_point_prediction/finance/Campus_Recruitment_B1/info_mod.json

@@ -0,0 +1,146 @@
+{
+  "name": "Campus Recruitment",
+  "source": "https://www.kaggle.com/datasets/benroshan/factors-affecting-campus-placement/data",
+  "data_intro": "This data set consists of Placement data of students in a XYZ campus. It includes secondary and higher secondary school percentage and specialization. It also includes degree specialization, type and Work experience and salary offers to the placed students",
+  "is_splited": false,
+  "overall_size": 215,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 8,
+  "n_classes": 7,
+  "task_type": "regression",
+  "target": "salary",
+  "cat_feature_intro": {
+    "gender": "- gender:Gender of this people, Male='M',Female='F'",
+    "ssc_b": "- ssc_b: Board of Education of secondary school people, Central, Others",
+    "hsc_b": "- hsc_b: Board of Education of higher secondary school, Central, Others",
+    "hsc_s": "- hsc_s: Specialization in Higher Secondary Education, Commerce, Science, Others",
+    "degree_t": "- degree_t: Under Graduation(Degree type) or field of degree education, Comm&Mgmt, Sci&Tech, Others",
+    "workex": "- workex: Work Experience, true, false",
+    "specialisation": "- specialisation:Post Graduation(MBA)-Specialization, Mkt&Fin, Mkt&HR",
+    "status": "- status: Status of placement, Placed, Not placed"
+  },
+  "num_feature_intro": {
+    "sl_no": "- sl_no: Serial Numbe",
+    "ssc_p": "- ssc_p: Secondary Education(10 Grade) percentage,For example:Student A scores 80/100 in his 10th grade. The percentage would be 80% ",
+    "hsc_p": "- hsc_p: Higher Secondary Education(12th Grade) percentage,For example:Student A scores 120/200 in his 12th grade. The percentage would be 60% ",
+    "degree_p": "- degree_p: Degree Percentage",
+    "etest_p": "- etest_p: Employability test percentage ( conducted by college)",
+    "mba_p": "- mba_p: MBA percentage"
+  },
+  "evaluation_metric": null,
+  "num_feature_value": {
+    "sl_no": [
+      1.0,
+      215.0
+    ],
+    "ssc_p": [
+      40.89,
+      89.4
+    ],
+    "hsc_p": [
+      37.0,
+      97.7
+    ],
+    "degree_p": [
+      50.0,
+      91.0
+    ],
+    "etest_p": [
+      50.0,
+      98.0
+    ],
+    "mba_p": [
+      51.21,
+      77.89
+    ]
+  },
+  "cat_feature_value": {
+    "gender": [
+      "F",
+      "M"
+    ],
+    "ssc_b": [
+      "Central",
+      "Others"
+    ],
+    "hsc_b": [
+      "Central",
+      "Others"
+    ],
+    "hsc_s": [
+      "Arts",
+      "Commerce",
+      "Science"
+    ],
+    "degree_t": [
+      "Comm&Mgmt",
+      "Others",
+      "Sci&Tech"
+    ],
+    "workex": [
+      "No",
+      "Yes"
+    ],
+    "specialisation": [
+      "Mkt&Fin",
+      "Mkt&HR"
+    ],
+    "status": [
+      "Not Placed",
+      "Placed"
+    ]
+  },
+  "columns": [
+    "sl_no",
+    "gender",
+    "ssc_p",
+    "ssc_b",
+    "hsc_p",
+    "hsc_b",
+    "hsc_s",
+    "degree_p",
+    "degree_t",
+    "workex",
+    "etest_p",
+    "specialisation",
+    "mba_p",
+    "status",
+    "salary"
+  ],
+  "feature_columns": [
+    "sl_no",
+    "gender",
+    "ssc_p",
+    "ssc_b",
+    "hsc_p",
+    "hsc_b",
+    "hsc_s",
+    "degree_p",
+    "degree_t",
+    "workex",
+    "etest_p",
+    "specialisation",
+    "mba_p",
+    "status"
+  ],
+  "feature_types": {
+    "sl_no": "numeric",
+    "gender": "categorical",
+    "ssc_p": "numeric",
+    "ssc_b": "categorical",
+    "hsc_p": "numeric",
+    "hsc_b": "categorical",
+    "hsc_s": "categorical",
+    "degree_p": "numeric",
+    "degree_t": "categorical",
+    "workex": "categorical",
+    "etest_p": "numeric",
+    "specialisation": "categorical",
+    "mba_p": "numeric",
+    "status": "categorical"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": []
+}

single_point_prediction/finance/Campus_Recruitment_B1/test.csv

@@ -0,0 +1,9 @@
+sl_no,gender,ssc_p,ssc_b,hsc_p,hsc_b,hsc_s,degree_p,degree_t,workex,etest_p,specialisation,mba_p,status,salary
+17,M,63.0,Central,66.2,Central,Commerce,65.6,Comm&Mgmt,Yes,60.0,Mkt&Fin,62.54,Placed,300000.0
+2,M,79.33,Central,78.33,Others,Science,77.48,Sci&Tech,Yes,86.5,Mkt&Fin,66.28,Placed,200000.0
+209,F,43.0,Central,60.0,Others,Science,65.0,Comm&Mgmt,No,92.66,Mkt&HR,62.92,Not Placed,
+161,M,87.0,Central,74.0,Central,Science,65.0,Sci&Tech,Yes,75.0,Mkt&HR,72.29,Placed,300000.0
+62,M,84.2,Central,73.4,Central,Commerce,66.89,Comm&Mgmt,No,61.6,Mkt&Fin,62.48,Placed,300000.0
+215,M,62.0,Central,58.0,Others,Science,53.0,Comm&Mgmt,No,89.0,Mkt&HR,60.22,Not Placed,
+194,F,60.0,Central,63.0,Central,Arts,56.0,Others,Yes,80.0,Mkt&HR,56.63,Placed,300000.0
+108,M,82.0,Others,90.0,Others,Commerce,83.0,Comm&Mgmt,No,80.0,Mkt&HR,73.52,Placed,200000.0

single_point_prediction/finance/Campus_Recruitment_B1/train.csv

@@ -0,0 +1,201 @@
+sl_no,gender,ssc_p,ssc_b,hsc_p,hsc_b,hsc_s,degree_p,degree_t,workex,etest_p,specialisation,mba_p,status,salary
+9,M,73.0,Central,79.0,Central,Commerce,72.0,Comm&Mgmt,No,91.34,Mkt&Fin,61.29,Placed,231000.0
+10,M,58.0,Central,70.0,Central,Commerce,61.0,Comm&Mgmt,No,54.0,Mkt&Fin,52.21,Not Placed,
+11,M,58.0,Central,61.0,Central,Commerce,60.0,Comm&Mgmt,Yes,62.0,Mkt&HR,60.85,Placed,260000.0
+12,M,69.6,Central,68.4,Central,Commerce,78.3,Comm&Mgmt,Yes,60.0,Mkt&Fin,63.7,Placed,250000.0
+13,F,47.0,Central,55.0,Others,Science,65.0,Comm&Mgmt,No,62.0,Mkt&HR,65.04,Not Placed,
+14,F,77.0,Central,87.0,Central,Commerce,59.0,Comm&Mgmt,No,68.0,Mkt&Fin,68.63,Placed,218000.0
+15,M,62.0,Central,47.0,Central,Commerce,50.0,Comm&Mgmt,No,76.0,Mkt&HR,54.96,Not Placed,
+16,F,65.0,Central,75.0,Central,Commerce,69.0,Comm&Mgmt,Yes,72.0,Mkt&Fin,64.66,Placed,200000.0
+18,F,55.0,Central,67.0,Central,Commerce,64.0,Comm&Mgmt,No,60.0,Mkt&Fin,67.28,Not Placed,
+19,F,63.0,Central,66.0,Central,Commerce,64.0,Comm&Mgmt,No,68.0,Mkt&HR,64.08,Not Placed,
+20,M,60.0,Others,67.0,Others,Arts,70.0,Comm&Mgmt,Yes,50.48,Mkt&Fin,77.89,Placed,236000.0
+21,M,62.0,Others,65.0,Others,Commerce,66.0,Comm&Mgmt,No,50.0,Mkt&HR,56.7,Placed,265000.0
+22,F,79.0,Others,76.0,Others,Commerce,85.0,Comm&Mgmt,No,95.0,Mkt&Fin,69.06,Placed,393000.0
+23,F,69.8,Others,60.8,Others,Science,72.23,Sci&Tech,No,55.53,Mkt&HR,68.81,Placed,360000.0
+24,F,77.4,Others,60.0,Others,Science,64.74,Sci&Tech,Yes,92.0,Mkt&Fin,63.62,Placed,300000.0
+25,M,76.5,Others,97.7,Others,Science,78.86,Sci&Tech,No,97.4,Mkt&Fin,74.01,Placed,360000.0
+26,F,52.58,Others,54.6,Central,Commerce,50.2,Comm&Mgmt,Yes,76.0,Mkt&Fin,65.33,Not Placed,
+27,M,71.0,Others,79.0,Others,Commerce,66.0,Comm&Mgmt,Yes,94.0,Mkt&Fin,57.55,Placed,240000.0
+28,M,63.0,Others,67.0,Others,Commerce,66.0,Comm&Mgmt,No,68.0,Mkt&HR,57.69,Placed,265000.0
+29,M,76.76,Others,76.5,Others,Commerce,67.5,Comm&Mgmt,Yes,73.35,Mkt&Fin,64.15,Placed,350000.0
+30,M,62.0,Central,67.0,Central,Commerce,58.0,Comm&Mgmt,No,77.0,Mkt&Fin,51.29,Not Placed,
+31,F,64.0,Central,73.5,Central,Commerce,73.0,Comm&Mgmt,No,52.0,Mkt&HR,56.7,Placed,250000.0
+32,F,67.0,Central,53.0,Central,Science,65.0,Sci&Tech,No,64.0,Mkt&HR,58.32,Not Placed,
+33,F,61.0,Central,81.0,Central,Commerce,66.4,Comm&Mgmt,No,50.89,Mkt&HR,62.21,Placed,278000.0
+34,F,87.0,Others,65.0,Others,Science,81.0,Comm&Mgmt,Yes,88.0,Mkt&Fin,72.78,Placed,260000.0
+35,M,62.0,Others,51.0,Others,Science,52.0,Others,No,68.44,Mkt&HR,62.77,Not Placed,
+36,F,69.0,Central,78.0,Central,Commerce,72.0,Comm&Mgmt,No,71.0,Mkt&HR,62.74,Placed,300000.0
+37,M,51.0,Central,44.0,Central,Commerce,57.0,Comm&Mgmt,No,64.0,Mkt&Fin,51.45,Not Placed,
+38,F,79.0,Central,76.0,Central,Science,65.6,Sci&Tech,No,58.0,Mkt&HR,55.47,Placed,320000.0
+39,F,73.0,Others,58.0,Others,Science,66.0,Comm&Mgmt,No,53.7,Mkt&HR,56.86,Placed,240000.0
+40,M,81.0,Others,68.0,Others,Science,64.0,Sci&Tech,No,93.0,Mkt&Fin,62.56,Placed,411000.0
+41,F,78.0,Central,77.0,Others,Commerce,80.0,Comm&Mgmt,No,60.0,Mkt&Fin,66.72,Placed,287000.0
+42,F,74.0,Others,63.16,Others,Commerce,65.0,Comm&Mgmt,Yes,65.0,Mkt&HR,69.76,Not Placed,
+43,M,49.0,Others,39.0,Central,Science,65.0,Others,No,63.0,Mkt&Fin,51.21,Not Placed,
+44,M,87.0,Others,87.0,Others,Commerce,68.0,Comm&Mgmt,No,95.0,Mkt&HR,62.9,Placed,300000.0
+45,F,77.0,Others,73.0,Others,Commerce,81.0,Comm&Mgmt,Yes,89.0,Mkt&Fin,69.7,Placed,200000.0
+46,F,76.0,Central,64.0,Central,Science,72.0,Sci&Tech,No,58.0,Mkt&HR,66.53,Not Placed,
+47,F,70.89,Others,71.98,Others,Science,65.6,Comm&Mgmt,No,68.0,Mkt&HR,71.63,Not Placed,
+48,M,63.0,Central,60.0,Central,Commerce,57.0,Comm&Mgmt,Yes,78.0,Mkt&Fin,54.55,Placed,204000.0
+49,M,63.0,Others,62.0,Others,Commerce,68.0,Comm&Mgmt,No,64.0,Mkt&Fin,62.46,Placed,250000.0
+50,F,50.0,Others,37.0,Others,Arts,52.0,Others,No,65.0,Mkt&HR,56.11,Not Placed,
+51,F,75.2,Central,73.2,Central,Science,68.4,Comm&Mgmt,No,65.0,Mkt&HR,62.98,Placed,200000.0
+52,M,54.4,Central,61.12,Central,Commerce,56.2,Comm&Mgmt,No,67.0,Mkt&HR,62.65,Not Placed,
+53,F,40.89,Others,45.83,Others,Commerce,53.0,Comm&Mgmt,No,71.2,Mkt&HR,65.49,Not Placed,
+54,M,80.0,Others,70.0,Others,Science,72.0,Sci&Tech,No,87.0,Mkt&HR,71.04,Placed,450000.0
+55,F,74.0,Central,60.0,Others,Science,69.0,Comm&Mgmt,No,78.0,Mkt&HR,65.56,Placed,216000.0
+56,M,60.4,Central,66.6,Others,Science,65.0,Comm&Mgmt,No,71.0,Mkt&HR,52.71,Placed,220000.0
+57,M,63.0,Others,71.4,Others,Commerce,61.4,Comm&Mgmt,No,68.0,Mkt&Fin,66.88,Placed,240000.0
+58,M,68.0,Central,76.0,Central,Commerce,74.0,Comm&Mgmt,No,80.0,Mkt&Fin,63.59,Placed,360000.0
+59,M,74.0,Central,62.0,Others,Science,68.0,Comm&Mgmt,No,74.0,Mkt&Fin,57.99,Placed,268000.0
+60,M,52.6,Central,65.58,Others,Science,72.11,Sci&Tech,No,57.6,Mkt&Fin,56.66,Placed,265000.0
+61,M,74.0,Central,70.0,Central,Science,72.0,Comm&Mgmt,Yes,60.0,Mkt&Fin,57.24,Placed,260000.0
+63,F,86.5,Others,64.2,Others,Science,67.4,Sci&Tech,No,59.0,Mkt&Fin,59.69,Placed,240000.0
+64,M,61.0,Others,70.0,Others,Commerce,64.0,Comm&Mgmt,No,68.5,Mkt&HR,59.5,Not Placed,
+65,M,80.0,Others,73.0,Others,Commerce,75.0,Comm&Mgmt,No,61.0,Mkt&Fin,58.78,Placed,240000.0
+66,M,54.0,Others,47.0,Others,Science,57.0,Comm&Mgmt,No,89.69,Mkt&HR,57.1,Not Placed,
+67,M,83.0,Others,74.0,Others,Science,66.0,Comm&Mgmt,No,68.92,Mkt&HR,58.46,Placed,275000.0
+68,M,80.92,Others,78.5,Others,Commerce,67.0,Comm&Mgmt,No,68.71,Mkt&Fin,60.99,Placed,275000.0
+69,F,69.7,Central,47.0,Central,Commerce,72.7,Sci&Tech,No,79.0,Mkt&HR,59.24,Not Placed,
+70,M,73.0,Central,73.0,Central,Science,66.0,Sci&Tech,Yes,70.0,Mkt&Fin,68.07,Placed,275000.0
+71,M,82.0,Others,61.0,Others,Science,62.0,Sci&Tech,No,89.0,Mkt&Fin,65.45,Placed,360000.0
+72,M,75.0,Others,70.29,Others,Commerce,71.0,Comm&Mgmt,No,95.0,Mkt&Fin,66.94,Placed,240000.0
+73,M,84.86,Others,67.0,Others,Science,78.0,Comm&Mgmt,No,95.5,Mkt&Fin,68.53,Placed,240000.0
+74,M,64.6,Central,83.83,Others,Commerce,71.72,Comm&Mgmt,No,86.0,Mkt&Fin,59.75,Placed,218000.0
+75,M,56.6,Central,64.8,Central,Commerce,70.2,Comm&Mgmt,No,84.27,Mkt&Fin,67.2,Placed,336000.0
+76,F,59.0,Central,62.0,Others,Commerce,77.5,Comm&Mgmt,No,74.0,Mkt&HR,67.0,Not Placed,
+77,F,66.5,Others,70.4,Central,Arts,71.93,Comm&Mgmt,No,61.0,Mkt&Fin,64.27,Placed,230000.0
+78,M,64.0,Others,80.0,Others,Science,65.0,Sci&Tech,Yes,69.0,Mkt&Fin,57.65,Placed,500000.0
+79,M,84.0,Others,90.9,Others,Science,64.5,Sci&Tech,No,86.04,Mkt&Fin,59.42,Placed,270000.0
+80,F,69.0,Central,62.0,Central,Science,66.0,Sci&Tech,No,75.0,Mkt&HR,67.99,Not Placed,
+81,F,69.0,Others,62.0,Others,Commerce,69.0,Comm&Mgmt,Yes,67.0,Mkt&HR,62.35,Placed,240000.0
+82,M,81.7,Others,63.0,Others,Science,67.0,Comm&Mgmt,Yes,86.0,Mkt&Fin,70.2,Placed,300000.0
+83,M,63.0,Central,67.0,Central,Commerce,74.0,Comm&Mgmt,No,82.0,Mkt&Fin,60.44,Not Placed,
+84,M,84.0,Others,79.0,Others,Science,68.0,Sci&Tech,Yes,84.0,Mkt&Fin,66.69,Placed,300000.0
+85,M,70.0,Central,63.0,Others,Science,70.0,Sci&Tech,Yes,55.0,Mkt&Fin,62.0,Placed,300000.0
+86,F,83.84,Others,89.83,Others,Commerce,77.2,Comm&Mgmt,Yes,78.74,Mkt&Fin,76.18,Placed,400000.0
+87,M,62.0,Others,63.0,Others,Commerce,64.0,Comm&Mgmt,No,67.0,Mkt&Fin,57.03,Placed,220000.0
+88,M,59.6,Central,51.0,Central,Science,60.0,Others,No,75.0,Mkt&HR,59.08,Not Placed,
+89,F,66.0,Central,62.0,Central,Commerce,73.0,Comm&Mgmt,No,58.0,Mkt&HR,64.36,Placed,210000.0
+90,F,84.0,Others,75.0,Others,Science,69.0,Sci&Tech,Yes,62.0,Mkt&HR,62.36,Placed,210000.0
+91,F,85.0,Others,90.0,Others,Commerce,82.0,Comm&Mgmt,No,92.0,Mkt&Fin,68.03,Placed,300000.0
+92,M,52.0,Central,57.0,Central,Commerce,50.8,Comm&Mgmt,No,67.0,Mkt&HR,62.79,Not Placed,
+93,F,60.23,Central,69.0,Central,Science,66.0,Comm&Mgmt,No,72.0,Mkt&Fin,59.47,Placed,230000.0
+94,M,52.0,Central,62.0,Central,Commerce,54.0,Comm&Mgmt,No,72.0,Mkt&HR,55.41,Not Placed,
+95,M,58.0,Central,62.0,Central,Commerce,64.0,Comm&Mgmt,No,53.88,Mkt&Fin,54.97,Placed,260000.0
+96,M,73.0,Central,78.0,Others,Commerce,65.0,Comm&Mgmt,Yes,95.46,Mkt&Fin,62.16,Placed,420000.0
+97,F,76.0,Central,70.0,Central,Science,76.0,Comm&Mgmt,Yes,66.0,Mkt&Fin,64.44,Placed,300000.0
+98,F,70.5,Central,62.5,Others,Commerce,61.0,Comm&Mgmt,No,93.91,Mkt&Fin,69.03,Not Placed,
+99,F,69.0,Central,73.0,Central,Commerce,65.0,Comm&Mgmt,No,70.0,Mkt&Fin,57.31,Placed,220000.0
+100,M,54.0,Central,82.0,Others,Commerce,63.0,Sci&Tech,No,50.0,Mkt&Fin,59.47,Not Placed,
+101,F,45.0,Others,57.0,Others,Commerce,58.0,Comm&Mgmt,Yes,56.39,Mkt&HR,64.95,Not Placed,
+102,M,63.0,Central,72.0,Central,Commerce,68.0,Comm&Mgmt,No,78.0,Mkt&HR,60.44,Placed,380000.0
+103,F,77.0,Others,61.0,Others,Commerce,68.0,Comm&Mgmt,Yes,57.5,Mkt&Fin,61.31,Placed,300000.0
+104,M,73.0,Central,78.0,Central,Science,73.0,Sci&Tech,Yes,85.0,Mkt&HR,65.83,Placed,240000.0
+105,M,69.0,Central,63.0,Others,Science,65.0,Comm&Mgmt,Yes,55.0,Mkt&HR,58.23,Placed,360000.0
+106,M,59.0,Central,64.0,Others,Science,58.0,Sci&Tech,No,85.0,Mkt&HR,55.3,Not Placed,
+107,M,61.08,Others,50.0,Others,Science,54.0,Sci&Tech,No,71.0,Mkt&Fin,65.69,Not Placed,
+109,M,61.0,Central,82.0,Central,Commerce,69.0,Comm&Mgmt,No,84.0,Mkt&Fin,58.31,Placed,300000.0
+110,M,52.0,Central,63.0,Others,Science,65.0,Sci&Tech,Yes,86.0,Mkt&HR,56.09,Not Placed,
+111,F,69.5,Central,70.0,Central,Science,72.0,Sci&Tech,No,57.2,Mkt&HR,54.8,Placed,250000.0
+112,M,51.0,Others,54.0,Others,Science,61.0,Sci&Tech,No,60.0,Mkt&HR,60.64,Not Placed,
+113,M,58.0,Others,61.0,Others,Commerce,61.0,Comm&Mgmt,No,58.0,Mkt&HR,53.94,Placed,250000.0
+114,F,73.96,Others,79.0,Others,Commerce,67.0,Comm&Mgmt,No,72.15,Mkt&Fin,63.08,Placed,280000.0
+115,M,65.0,Central,68.0,Others,Science,69.0,Comm&Mgmt,No,53.7,Mkt&HR,55.01,Placed,250000.0
+116,F,73.0,Others,63.0,Others,Science,66.0,Comm&Mgmt,No,89.0,Mkt&Fin,60.5,Placed,216000.0
+117,M,68.2,Central,72.8,Central,Commerce,66.6,Comm&Mgmt,Yes,96.0,Mkt&Fin,70.85,Placed,300000.0
+118,M,77.0,Others,75.0,Others,Science,73.0,Sci&Tech,No,80.0,Mkt&Fin,67.05,Placed,240000.0
+119,M,76.0,Central,80.0,Central,Science,78.0,Sci&Tech,Yes,97.0,Mkt&HR,70.48,Placed,276000.0
+120,M,60.8,Central,68.4,Central,Commerce,64.6,Comm&Mgmt,Yes,82.66,Mkt&Fin,64.34,Placed,940000.0
+121,M,58.0,Others,40.0,Others,Science,59.0,Comm&Mgmt,No,73.0,Mkt&HR,58.81,Not Placed,
+122,F,64.0,Central,67.0,Others,Science,69.6,Sci&Tech,Yes,55.67,Mkt&HR,71.49,Placed,250000.0
+123,F,66.5,Central,66.8,Central,Arts,69.3,Comm&Mgmt,Yes,80.4,Mkt&Fin,71.0,Placed,236000.0
+124,M,74.0,Others,59.0,Others,Commerce,73.0,Comm&Mgmt,Yes,60.0,Mkt&HR,56.7,Placed,240000.0
+125,M,67.0,Central,71.0,Central,Science,64.33,Others,Yes,64.0,Mkt&HR,61.26,Placed,250000.0
+126,F,84.0,Central,73.0,Central,Commerce,73.0,Comm&Mgmt,No,75.0,Mkt&Fin,73.33,Placed,350000.0
+127,F,79.0,Others,61.0,Others,Science,75.5,Sci&Tech,Yes,70.0,Mkt&Fin,68.2,Placed,210000.0
+128,F,72.0,Others,60.0,Others,Science,69.0,Comm&Mgmt,No,55.5,Mkt&HR,58.4,Placed,250000.0
+129,M,80.4,Central,73.4,Central,Science,77.72,Sci&Tech,Yes,81.2,Mkt&HR,76.26,Placed,400000.0
+130,M,76.7,Central,89.7,Others,Commerce,66.0,Comm&Mgmt,Yes,90.0,Mkt&Fin,68.55,Placed,250000.0
+131,M,62.0,Central,65.0,Others,Commerce,60.0,Comm&Mgmt,No,84.0,Mkt&Fin,64.15,Not Placed,
+132,F,74.9,Others,57.0,Others,Science,62.0,Others,Yes,80.0,Mkt&Fin,60.78,Placed,360000.0
+133,M,67.0,Others,68.0,Others,Commerce,64.0,Comm&Mgmt,Yes,74.4,Mkt&HR,53.49,Placed,300000.0
+134,M,73.0,Central,64.0,Others,Commerce,77.0,Comm&Mgmt,Yes,65.0,Mkt&HR,60.98,Placed,250000.0
+135,F,77.44,Central,92.0,Others,Commerce,72.0,Comm&Mgmt,Yes,94.0,Mkt&Fin,67.13,Placed,250000.0
+136,F,72.0,Central,56.0,Others,Science,69.0,Comm&Mgmt,No,55.6,Mkt&HR,65.63,Placed,200000.0
+137,F,47.0,Central,59.0,Central,Arts,64.0,Comm&Mgmt,No,78.0,Mkt&Fin,61.58,Not Placed,
+138,M,67.0,Others,63.0,Central,Commerce,72.0,Comm&Mgmt,No,56.0,Mkt&HR,60.41,Placed,225000.0
+139,F,82.0,Others,64.0,Others,Science,73.0,Sci&Tech,Yes,96.0,Mkt&Fin,71.77,Placed,250000.0
+140,M,77.0,Central,70.0,Central,Commerce,59.0,Comm&Mgmt,Yes,58.0,Mkt&Fin,54.43,Placed,220000.0
+141,M,65.0,Central,64.8,Others,Commerce,69.5,Comm&Mgmt,Yes,56.0,Mkt&Fin,56.94,Placed,265000.0
+142,M,66.0,Central,64.0,Central,Science,60.0,Comm&Mgmt,No,60.0,Mkt&HR,61.9,Not Placed,
+143,M,85.0,Central,60.0,Others,Science,73.43,Sci&Tech,Yes,60.0,Mkt&Fin,61.29,Placed,260000.0
+144,M,77.67,Others,64.89,Others,Commerce,70.67,Comm&Mgmt,No,89.0,Mkt&Fin,60.39,Placed,300000.0
+145,M,52.0,Others,50.0,Others,Arts,61.0,Comm&Mgmt,No,60.0,Mkt&Fin,58.52,Not Placed,
+146,M,89.4,Others,65.66,Others,Science,71.25,Sci&Tech,No,72.0,Mkt&HR,63.23,Placed,400000.0
+147,M,62.0,Central,63.0,Others,Science,66.0,Comm&Mgmt,No,85.0,Mkt&HR,55.14,Placed,233000.0
+148,M,70.0,Central,74.0,Central,Commerce,65.0,Comm&Mgmt,No,83.0,Mkt&Fin,62.28,Placed,300000.0
+149,F,77.0,Central,86.0,Central,Arts,56.0,Others,No,57.0,Mkt&Fin,64.08,Placed,240000.0
+150,M,44.0,Central,58.0,Central,Arts,55.0,Comm&Mgmt,Yes,64.25,Mkt&HR,58.54,Not Placed,
+151,M,71.0,Central,58.66,Central,Science,58.0,Sci&Tech,Yes,56.0,Mkt&Fin,61.3,Placed,690000.0
+152,M,65.0,Central,65.0,Central,Commerce,75.0,Comm&Mgmt,No,83.0,Mkt&Fin,58.87,Placed,270000.0
+153,F,75.4,Others,60.5,Central,Science,84.0,Sci&Tech,No,98.0,Mkt&Fin,65.25,Placed,240000.0
+154,M,49.0,Others,59.0,Others,Science,65.0,Sci&Tech,Yes,86.0,Mkt&Fin,62.48,Placed,340000.0
+155,M,53.0,Central,63.0,Others,Science,60.0,Comm&Mgmt,Yes,70.0,Mkt&Fin,53.2,Placed,250000.0
+156,M,51.57,Others,74.66,Others,Commerce,59.9,Comm&Mgmt,Yes,56.15,Mkt&HR,65.99,Not Placed,
+157,M,84.2,Central,69.4,Central,Science,65.0,Sci&Tech,Yes,80.0,Mkt&HR,52.72,Placed,255000.0
+158,M,66.5,Central,62.5,Central,Commerce,60.9,Comm&Mgmt,No,93.4,Mkt&Fin,55.03,Placed,300000.0
+159,M,67.0,Others,63.0,Others,Science,64.0,Sci&Tech,No,60.0,Mkt&Fin,61.87,Not Placed,
+160,M,52.0,Central,49.0,Others,Commerce,58.0,Comm&Mgmt,No,62.0,Mkt&HR,60.59,Not Placed,
+162,M,55.6,Others,51.0,Others,Commerce,57.5,Comm&Mgmt,No,57.63,Mkt&HR,62.72,Not Placed,
+163,M,74.2,Central,87.6,Others,Commerce,77.25,Comm&Mgmt,Yes,75.2,Mkt&Fin,66.06,Placed,285000.0
+164,M,63.0,Others,67.0,Others,Science,64.0,Sci&Tech,No,75.0,Mkt&Fin,66.46,Placed,500000.0
+165,F,67.16,Central,72.5,Central,Commerce,63.35,Comm&Mgmt,No,53.04,Mkt&Fin,65.52,Placed,250000.0
+166,F,63.3,Central,78.33,Others,Commerce,74.0,Comm&Mgmt,No,80.0,Mkt&Fin,74.56,Not Placed,
+167,M,62.0,Others,62.0,Others,Commerce,60.0,Comm&Mgmt,Yes,63.0,Mkt&HR,52.38,Placed,240000.0
+168,M,67.9,Others,62.0,Others,Science,67.0,Sci&Tech,Yes,58.1,Mkt&Fin,75.71,Not Placed,
+169,F,48.0,Central,51.0,Central,Commerce,58.0,Comm&Mgmt,Yes,60.0,Mkt&HR,58.79,Not Placed,
+170,M,59.96,Others,42.16,Others,Science,61.26,Sci&Tech,No,54.48,Mkt&HR,65.48,Not Placed,
+171,F,63.4,Others,67.2,Others,Commerce,60.0,Comm&Mgmt,No,58.06,Mkt&HR,69.28,Not Placed,
+172,M,80.0,Others,80.0,Others,Commerce,72.0,Comm&Mgmt,Yes,63.79,Mkt&Fin,66.04,Placed,290000.0
+173,M,73.0,Others,58.0,Others,Commerce,56.0,Comm&Mgmt,No,84.0,Mkt&HR,52.64,Placed,300000.0
+174,F,52.0,Others,52.0,Others,Science,55.0,Sci&Tech,No,67.0,Mkt&HR,59.32,Not Placed,
+175,M,73.24,Others,50.83,Others,Science,64.27,Sci&Tech,Yes,64.0,Mkt&Fin,66.23,Placed,500000.0
+176,M,63.0,Others,62.0,Others,Science,65.0,Sci&Tech,No,87.5,Mkt&HR,60.69,Not Placed,
+177,F,59.0,Central,60.0,Others,Commerce,56.0,Comm&Mgmt,No,55.0,Mkt&HR,57.9,Placed,220000.0
+178,F,73.0,Central,97.0,Others,Commerce,79.0,Comm&Mgmt,Yes,89.0,Mkt&Fin,70.81,Placed,650000.0
+179,M,68.0,Others,56.0,Others,Science,68.0,Sci&Tech,No,73.0,Mkt&HR,68.07,Placed,350000.0
+180,F,77.8,Central,64.0,Central,Science,64.2,Sci&Tech,No,75.5,Mkt&HR,72.14,Not Placed,
+181,M,65.0,Central,71.5,Others,Commerce,62.8,Comm&Mgmt,Yes,57.0,Mkt&Fin,56.6,Placed,265000.0
+182,M,62.0,Central,60.33,Others,Science,64.21,Sci&Tech,No,63.0,Mkt&HR,60.02,Not Placed,
+183,M,52.0,Others,65.0,Others,Arts,57.0,Others,Yes,75.0,Mkt&Fin,59.81,Not Placed,
+184,M,65.0,Central,77.0,Central,Commerce,69.0,Comm&Mgmt,No,60.0,Mkt&HR,61.82,Placed,276000.0
+185,F,56.28,Others,62.83,Others,Commerce,59.79,Comm&Mgmt,No,60.0,Mkt&HR,57.29,Not Placed,
+186,F,88.0,Central,72.0,Central,Science,78.0,Others,No,82.0,Mkt&HR,71.43,Placed,252000.0
+187,F,52.0,Central,64.0,Central,Commerce,61.0,Comm&Mgmt,No,55.0,Mkt&Fin,62.93,Not Placed,
+188,M,78.5,Central,65.5,Central,Science,67.0,Sci&Tech,Yes,95.0,Mkt&Fin,64.86,Placed,280000.0
+189,M,61.8,Others,47.0,Others,Commerce,54.38,Comm&Mgmt,No,57.0,Mkt&Fin,56.13,Not Placed,
+190,F,54.0,Central,77.6,Others,Commerce,69.2,Comm&Mgmt,No,95.65,Mkt&Fin,66.94,Not Placed,
+191,F,64.0,Others,70.2,Central,Commerce,61.0,Comm&Mgmt,No,50.0,Mkt&Fin,62.5,Not Placed,
+192,M,67.0,Others,61.0,Central,Science,72.0,Comm&Mgmt,No,72.0,Mkt&Fin,61.01,Placed,264000.0
+193,M,65.2,Central,61.4,Central,Commerce,64.8,Comm&Mgmt,Yes,93.4,Mkt&Fin,57.34,Placed,270000.0
+195,M,52.0,Others,55.0,Others,Commerce,56.3,Comm&Mgmt,No,59.0,Mkt&Fin,64.74,Not Placed,
+196,M,66.0,Central,76.0,Central,Commerce,72.0,Comm&Mgmt,Yes,84.0,Mkt&HR,58.95,Placed,275000.0
+197,M,72.0,Others,63.0,Others,Science,77.5,Sci&Tech,Yes,78.0,Mkt&Fin,54.48,Placed,250000.0
+198,F,83.96,Others,53.0,Others,Science,91.0,Sci&Tech,No,59.32,Mkt&HR,69.71,Placed,260000.0
+199,F,67.0,Central,70.0,Central,Commerce,65.0,Others,No,88.0,Mkt&HR,71.96,Not Placed,
+200,M,69.0,Others,65.0,Others,Commerce,57.0,Comm&Mgmt,No,73.0,Mkt&HR,55.8,Placed,265000.0
+201,M,69.0,Others,60.0,Others,Commerce,65.0,Comm&Mgmt,No,87.55,Mkt&Fin,52.81,Placed,300000.0
+202,M,54.2,Central,63.0,Others,Science,58.0,Comm&Mgmt,No,79.0,Mkt&HR,58.44,Not Placed,
+203,M,70.0,Central,63.0,Central,Science,66.0,Sci&Tech,No,61.28,Mkt&HR,60.11,Placed,240000.0
+204,M,55.68,Others,61.33,Others,Commerce,56.87,Comm&Mgmt,No,66.0,Mkt&HR,58.3,Placed,260000.0
+205,F,74.0,Others,73.0,Others,Commerce,73.0,Comm&Mgmt,Yes,80.0,Mkt&Fin,67.69,Placed,210000.0
+206,M,61.0,Others,62.0,Others,Commerce,65.0,Comm&Mgmt,No,62.0,Mkt&Fin,56.81,Placed,250000.0
+207,M,41.0,Central,42.0,Central,Science,60.0,Comm&Mgmt,No,97.0,Mkt&Fin,53.39,Not Placed,
+208,M,83.33,Central,78.0,Others,Commerce,61.0,Comm&Mgmt,Yes,88.56,Mkt&Fin,71.55,Placed,300000.0
+210,M,62.0,Central,72.0,Central,Commerce,65.0,Comm&Mgmt,No,67.0,Mkt&Fin,56.49,Placed,216000.0
+211,M,80.6,Others,82.0,Others,Commerce,77.6,Comm&Mgmt,No,91.0,Mkt&Fin,74.49,Placed,400000.0
+212,M,58.0,Others,60.0,Others,Science,72.0,Sci&Tech,No,74.0,Mkt&Fin,53.62,Placed,275000.0
+213,M,67.0,Others,67.0,Others,Commerce,73.0,Comm&Mgmt,Yes,59.0,Mkt&Fin,69.72,Placed,295000.0
+214,F,74.0,Others,66.0,Others,Commerce,58.0,Comm&Mgmt,No,70.0,Mkt&HR,60.23,Placed,204000.0

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_001.json

@@ -0,0 +1,55 @@
+{
+  "id": "001",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Default of Credit Card Clients Dataset",
+  "table_path": "kaggle/Default of Credit Card Clients Dataset",
+  "query": "This credit account #7228 has a pretty high limit of NT$410,000, and it's for a 34-year-old man. His repayments have been on time for August, June, and May, and he was actually ahead of schedule back in April. The bills have been fluctuating—NT$15,134 in August, down to NT$5,063 in July, then a low of NT$1,019 in June before jumping back up. He's been making decent payments too, like NT$15,000 last month and NT$4,867 in August. With this mix of high credit and varying spending, do you think he's likely to miss his next payment?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": 7228.0,
+          "LIMIT_BAL": 410000.0,
+          "SEX": 1.0,
+          "EDUCATION": "NULL",
+          "MARRIAGE": "NULL",
+          "AGE": 34.0,
+          "PAY_0": "NULL",
+          "PAY_2": 0.0,
+          "PAY_3": "NULL",
+          "PAY_4": 0.0,
+          "PAY_5": 0.0,
+          "PAY_6": -1.0,
+          "BILL_AMT1": "NULL",
+          "BILL_AMT2": 15134.0,
+          "BILL_AMT3": 5063.0,
+          "BILL_AMT4": 1019.0,
+          "BILL_AMT5": 14585.0,
+          "BILL_AMT6": 5370.0,
+          "PAY_AMT1": 15000.0,
+          "PAY_AMT2": 4867.0,
+          "PAY_AMT3": 803.0,
+          "PAY_AMT4": 14436.0,
+          "PAY_AMT5": 5396.0,
+          "PAY_AMT6": 875.0,
+          "default.payment.next.month": 1.0
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_002.json

@@ -0,0 +1,55 @@
+{
+  "id": "002",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Default of Credit Card Clients Dataset",
+  "table_path": "kaggle/Default of Credit Card Clients Dataset",
+  "query": "Looking at credit application #17823 for my young cousin who's 24, single with a graduate school education. He has a NT$100,000 credit limit and has been paying duly this September, with no delays at all in the previous five months. His bill statements have been consistently around NT$36,000, but his payments have only been about NT$1,200 to NT$1,800 each month. As his family member trying to advise him, do you think he's heading for a missed payment next month?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": 17823.0,
+          "LIMIT_BAL": 100000.0,
+          "SEX": 1.0,
+          "EDUCATION": 1.0,
+          "MARRIAGE": 2.0,
+          "AGE": 24.0,
+          "PAY_0": -1.0,
+          "PAY_2": 0.0,
+          "PAY_3": 0.0,
+          "PAY_4": 0.0,
+          "PAY_5": 0.0,
+          "PAY_6": 0.0,
+          "BILL_AMT1": 33921.0,
+          "BILL_AMT2": 34920.0,
+          "BILL_AMT3": 36030.0,
+          "BILL_AMT4": 37173.0,
+          "BILL_AMT5": 35875.0,
+          "BILL_AMT6": 36263.0,
+          "PAY_AMT1": 1563.0,
+          "PAY_AMT2": 1673.0,
+          "PAY_AMT3": 1816.0,
+          "PAY_AMT4": 1213.0,
+          "PAY_AMT5": 1248.0,
+          "PAY_AMT6": 1212.0,
+          "default.payment.next.month": 0.0
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_003.json

@@ -0,0 +1,55 @@
+{
+  "id": "003",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Default of Credit Card Clients Dataset",
+  "table_path": "kaggle/Default of Credit Card Clients Dataset",
+  "query": "This credit card account #15527 with a NT$140,000 limit belongs to a 47-year-old male client who completed high school. His recent repayment status shows he paid duly in September and August, but back in June, he was two months delayed. The bill amounts have been fluctuating—NT$292 in September, NT$396 in August, NT$792 in July, NT$884 in June, NT$488 in May, and NT$1,092 in April. His payments have been NT$500 in September, NT$792 in August, NT$488 in July, nothing in June, and NT$1,000 in May. With this mixed payment history, do you think he's heading towards missing his next payment?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": 15527.0,
+          "LIMIT_BAL": 140000.0,
+          "SEX": 1.0,
+          "EDUCATION": 3.0,
+          "MARRIAGE": "NULL",
+          "AGE": 47.0,
+          "PAY_0": -1.0,
+          "PAY_2": -1.0,
+          "PAY_3": "NULL",
+          "PAY_4": 2.0,
+          "PAY_5": "NULL",
+          "PAY_6": "NULL",
+          "BILL_AMT1": 292.0,
+          "BILL_AMT2": 396.0,
+          "BILL_AMT3": 792.0,
+          "BILL_AMT4": 884.0,
+          "BILL_AMT5": 488.0,
+          "BILL_AMT6": 1092.0,
+          "PAY_AMT1": 500.0,
+          "PAY_AMT2": 792.0,
+          "PAY_AMT3": 488.0,
+          "PAY_AMT4": 0.0,
+          "PAY_AMT5": 1000.0,
+          "PAY_AMT6": "NULL",
+          "default.payment.next.month": 1.0
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_004.json

@@ -0,0 +1,55 @@
+{
+  "id": "004",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Default of Credit Card Clients Dataset",
+  "table_path": "kaggle/Default of Credit Card Clients Dataset",
+  "query": "My credit card statement just arrived with ID 6711, and looking at my $80,000 credit limit, I'm a bit concerned. As a 25-year-old single guy with a high school education, I've mostly paid on time; September, August, July, May, and April all show no delay, and I was even ahead in June. But my bill amounts are worrying—$78,230 in September, $76,277 in August, then a big drop to around $36,000 and $30,000 in the previous months. My payments have been erratic, too, from a large $31,063 payment in July down to just over $1,000 in other months. With this pattern, do you think I'm headed for a missed payment next month?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": 6711.0,
+          "LIMIT_BAL": 80000.0,
+          "SEX": 1.0,
+          "EDUCATION": 3.0,
+          "MARRIAGE": 2.0,
+          "AGE": 25.0,
+          "PAY_0": 0.0,
+          "PAY_2": 0.0,
+          "PAY_3": 0.0,
+          "PAY_4": -1.0,
+          "PAY_5": 0.0,
+          "PAY_6": 0.0,
+          "BILL_AMT1": 78230.0,
+          "BILL_AMT2": 76277.0,
+          "BILL_AMT3": 36232.0,
+          "BILL_AMT4": 30200.0,
+          "BILL_AMT5": 30326.0,
+          "BILL_AMT6": 30290.0,
+          "PAY_AMT1": 2289.0,
+          "PAY_AMT2": 1005.0,
+          "PAY_AMT3": 31063.0,
+          "PAY_AMT4": 1094.0,
+          "PAY_AMT5": 1109.0,
+          "PAY_AMT6": 1013.0,
+          "default.payment.next.month": 0.0
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_005.json

@@ -0,0 +1,55 @@
+{
+  "id": "005",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Default of Credit Card Clients Dataset",
+  "table_path": "kaggle/Default of Credit Card Clients Dataset",
+  "query": "The account numbered 29854 belongs to a 39-year-old university graduate with NT$50,000 in available credit. After being two months late in both April and May, this client has maintained perfect payment status from June through September. Their billing amounts have followed an interesting trajectory from NT$31,597 to NT$32,417 then down to NT$25,637, while making payments between NT$2,000 and NT$3,500. With our historical records of payment defaults now in your hands, would you flag this account as likely to miss the next payment?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": 29854.0,
+          "LIMIT_BAL": 50000.0,
+          "SEX": "NULL",
+          "EDUCATION": 2.0,
+          "MARRIAGE": "NULL",
+          "AGE": 39.0,
+          "PAY_0": 0.0,
+          "PAY_2": "NULL",
+          "PAY_3": 0.0,
+          "PAY_4": 0.0,
+          "PAY_5": 2.0,
+          "PAY_6": 2.0,
+          "BILL_AMT1": 25637.0,
+          "BILL_AMT2": 28371.0,
+          "BILL_AMT3": 29913.0,
+          "BILL_AMT4": 32417.0,
+          "BILL_AMT5": 31597.0,
+          "BILL_AMT6": "NULL",
+          "PAY_AMT1": 3500.0,
+          "PAY_AMT2": 2000.0,
+          "PAY_AMT3": 3000.0,
+          "PAY_AMT4": "NULL",
+          "PAY_AMT5": "NULL",
+          "PAY_AMT6": 3000.0,
+          "default.payment.next.month": 1.0
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_006.json

@@ -0,0 +1,55 @@
+{
+  "id": "006",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Default of Credit Card Clients Dataset",
+  "table_path": "kaggle/Default of Credit Card Clients Dataset",
+  "query": "Walking through the credit department, file 24658 just landed on my desk. This gentleman, 43 years old with a university background and married, has a solid credit line of 180,000 NT dollars. His record is spotless—every single repayment status from April right through to September shows he's been paying duly. The bill amounts have been quite manageable, hovering around the 2000 to 3000 mark, and he's been paying them off, even making a hefty payment of 12,553 back in April. I'm looking at our entire history of client payment records that I've shared with you. Given this perfect track record, do you think he's at risk of defaulting next month?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": 24658.0,
+          "LIMIT_BAL": 180000.0,
+          "SEX": 1.0,
+          "EDUCATION": 2.0,
+          "MARRIAGE": 1.0,
+          "AGE": 43.0,
+          "PAY_0": -1.0,
+          "PAY_2": -1.0,
+          "PAY_3": -1.0,
+          "PAY_4": -1.0,
+          "PAY_5": -1.0,
+          "PAY_6": -1.0,
+          "BILL_AMT1": 2745.0,
+          "BILL_AMT2": 3873.0,
+          "BILL_AMT3": 3275.0,
+          "BILL_AMT4": 2347.0,
+          "BILL_AMT5": 3020.0,
+          "BILL_AMT6": 2193.0,
+          "PAY_AMT1": 3884.0,
+          "PAY_AMT2": 3284.0,
+          "PAY_AMT3": 2354.0,
+          "PAY_AMT4": 3029.0,
+          "PAY_AMT5": 2199.0,
+          "PAY_AMT6": 12553.0,
+          "default.payment.next.month": 0.0
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_007.json

@@ -0,0 +1,55 @@
+{
+  "id": "007",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Default of Credit Card Clients Dataset",
+  "table_path": "kaggle/Default of Credit Card Clients Dataset",
+  "query": "The file for client ID 2648 shows a credit line of 210,000. This gentleman is single and 31 years old. His repayment status is flawless across the board, showing no delays from September all the way back. The bill amounts have been a constant 326 dollars for months, and his payments have kept pace, with 326 paid in September and 133 paid back in April. I'm sharing our complete payment history archive with you. With this financial picture, what's the outlook for him defaulting next month?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": 2648.0,
+          "LIMIT_BAL": 210000.0,
+          "SEX": 1.0,
+          "EDUCATION": "NULL",
+          "MARRIAGE": 2.0,
+          "AGE": 31.0,
+          "PAY_0": -1.0,
+          "PAY_2": -1.0,
+          "PAY_3": -1.0,
+          "PAY_4": -1.0,
+          "PAY_5": "NULL",
+          "PAY_6": "NULL",
+          "BILL_AMT1": 326.0,
+          "BILL_AMT2": 326.0,
+          "BILL_AMT3": 326.0,
+          "BILL_AMT4": 326.0,
+          "BILL_AMT5": 326.0,
+          "BILL_AMT6": 326.0,
+          "PAY_AMT1": 326.0,
+          "PAY_AMT2": "NULL",
+          "PAY_AMT3": 326.0,
+          "PAY_AMT4": "NULL",
+          "PAY_AMT5": "NULL",
+          "PAY_AMT6": 133.0,
+          "default.payment.next.month": 1.0
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/Default_of_Credit_Card_Clients_Dataset_B1_008.json

@@ -0,0 +1,55 @@
+{
+  "id": "008",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Default of Credit Card Clients Dataset",
+  "table_path": "kaggle/Default of Credit Card Clients Dataset",
+  "query": "Hey there, I'm reviewing a client file for someone with the ID 5758, a 30-year-old married woman with a university education who has a credit limit of 380,000 NT dollars. Her record for the past six months, from April all the way to September, shows she's paid everything on time, never delayed. Her bill statements have been pretty consistent, hovering around 300,000, with the latest from September at 295,543. She's also been making regular payments, like 11,000 in September and 12,602 in August. I've shared our company's historical client data with you. Given all this consistent history, do you think she's likely to default next month?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "ID": 5758.0,
+          "LIMIT_BAL": 380000.0,
+          "SEX": 2.0,
+          "EDUCATION": 2.0,
+          "MARRIAGE": 1.0,
+          "AGE": 30.0,
+          "PAY_0": 0.0,
+          "PAY_2": 0.0,
+          "PAY_3": 0.0,
+          "PAY_4": 0.0,
+          "PAY_5": 0.0,
+          "PAY_6": 0.0,
+          "BILL_AMT1": 295543.0,
+          "BILL_AMT2": 299922.0,
+          "BILL_AMT3": 307166.0,
+          "BILL_AMT4": 311716.0,
+          "BILL_AMT5": 306830.0,
+          "BILL_AMT6": 303453.0,
+          "PAY_AMT1": 11000.0,
+          "PAY_AMT2": 12602.0,
+          "PAY_AMT3": 11500.0,
+          "PAY_AMT4": 10500.0,
+          "PAY_AMT5": 11000.0,
+          "PAY_AMT6": 10152.0,
+          "default.payment.next.month": 0.0
+        }
+      }
+    ],
+    "target_column": "default.payment.next.month",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/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": [
+            -165580.0,
+            964511.0
+        ],
+        "BILL_AMT2": [
+            -69777.0,
+            983931.0
+        ],
+        "BILL_AMT3": [
+            -157264.0,
+            1664089.0
+        ],
+        "BILL_AMT4": [
+            -170000.0,
+            891586.0
+        ],
+        "BILL_AMT5": [
+            -81334.0,
+            927171.0
+        ],
+        "BILL_AMT6": [
+            -339603.0,
+            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": [
+            -2.0,
+            8.0
+        ],
+        "PAY_4": [
+            -2.0,
+            8.0
+        ],
+        "PAY_5": [
+            -2.0,
+            8.0
+        ],
+        "PAY_6": [
+            -2.0,
+            8.0
+        ],
+        "PAY_AMT1": [
+            0.0,
+            873552.0
+        ],
+        "PAY_AMT2": [
+            0.0,
+            1684259.0
+        ],
+        "PAY_AMT3": [
+            0.0,
+            896040.0
+        ],
+        "PAY_AMT4": [
+            0.0,
+            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": {}
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/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": [
+      1.0,
+      2.0
+    ],
+    "EDUCATION": [
+      0.0,
+      6.0
+    ],
+    "MARRIAGE": [
+      0.0,
+      3.0
+    ],
+    "AGE": [
+      21.0,
+      79.0
+    ],
+    "PAY_0": [
+      -2.0,
+      8.0
+    ],
+    "PAY_2": [
+      -2.0,
+      8.0
+    ],
+    "PAY_3": [
+      -2.0,
+      8.0
+    ],
+    "PAY_4": [
+      -2.0,
+      8.0
+    ],
+    "PAY_5": [
+      -2.0,
+      8.0
+    ],
+    "PAY_6": [
+      -2.0,
+      8.0
+    ],
+    "BILL_AMT1": [
+      -165580.0,
+      964511.0
+    ],
+    "BILL_AMT2": [
+      -69777.0,
+      983931.0
+    ],
+    "BILL_AMT3": [
+      -157264.0,
+      1664089.0
+    ],
+    "BILL_AMT4": [
+      -170000.0,
+      891586.0
+    ],
+    "BILL_AMT5": [
+      -81334.0,
+      927171.0
+    ],
+    "BILL_AMT6": [
+      -339603.0,
+      961664.0
+    ],
+    "PAY_AMT1": [
+      0.0,
+      873552.0
+    ],
+    "PAY_AMT2": [
+      0.0,
+      1684259.0
+    ],
+    "PAY_AMT3": [
+      0.0,
+      896040.0
+    ],
+    "PAY_AMT4": [
+      0.0,
+      621000.0
+    ],
+    "PAY_AMT5": [
+      0.0,
+      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": []
+}

single_point_prediction/finance/Default_of_Credit_Card_Clients_Dataset_B1/test.csv

@@ -0,0 +1,9 @@
+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
+7228,410000.0,1,1,1,34,-1,0,0,0,0,-1,109256.0,15134.0,5063.0,1019.0,14585.0,5370.0,15000.0,4867.0,803.0,14436.0,5396.0,875.0,1
+17823,100000.0,1,1,2,24,-1,0,0,0,0,0,33921.0,34920.0,36030.0,37173.0,35875.0,36263.0,1563.0,1673.0,1816.0,1213.0,1248.0,1212.0,0
+15527,140000.0,1,3,1,47,-1,-1,-1,2,0,0,292.0,396.0,792.0,884.0,488.0,1092.0,500.0,792.0,488.0,0.0,1000.0,0.0,1
+6711,80000.0,1,3,2,25,0,0,0,-1,0,0,78230.0,76277.0,36232.0,30200.0,30326.0,30290.0,2289.0,1005.0,31063.0,1094.0,1109.0,1013.0,0
+29854,50000.0,1,2,2,39,0,0,0,0,2,2,25637.0,28371.0,29913.0,32417.0,31597.0,33575.0,3500.0,2000.0,3000.0,0.0,2500.0,3000.0,1
+24658,180000.0,1,2,1,43,-1,-1,-1,-1,-1,-1,2745.0,3873.0,3275.0,2347.0,3020.0,2193.0,3884.0,3284.0,2354.0,3029.0,2199.0,12553.0,0
+2648,210000.0,1,1,2,31,-1,-1,-1,-1,-1,-1,326.0,326.0,326.0,326.0,326.0,326.0,326.0,326.0,326.0,326.0,326.0,133.0,1
+5758,380000.0,2,2,1,30,0,0,0,0,0,0,295543.0,299922.0,307166.0,311716.0,306830.0,303453.0,11000.0,12602.0,11500.0,10500.0,11000.0,10152.0,0

single_point_prediction/finance/Diamonds_B1/Diamonds_B1_001.json

@@ -0,0 +1,41 @@
+{
+  "id": "001",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Diamonds",
+  "table_path": "kaggle/Diamonds",
+  "query": "\"Take a look at this one,\" the jeweler said, sliding the velvet tray across the counter. The tag on the setting for diamond #51730 notes it's a 0.58 carat stone with an \"Ideal\" cut. He pointed out the high \"F\" color and \"VVS2\" clarity, mentioning the excellent proportions: a 61.9 depth percentage, a 56.0 table, and physical dimensions of 5.38 x 5.35 x 3.32 mm. Hearing all these impressive specs, I have to ask, what kind of price tag do you think comes with it?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Unnamed: 0": 51730,
+          "carat": 0.58,
+          "cut": "Ideal",
+          "color": "F",
+          "clarity": "VVS2",
+          "depth": 61.9,
+          "table": 56.0,
+          "price": 2401,
+          "x": 5.38,
+          "y": 5.35,
+          "z": 3.32
+        }
+      }
+    ],
+    "target_column": "price",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Diamonds_B1/Diamonds_B1_002.json

@@ -0,0 +1,41 @@
+{
+  "id": "002",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Diamonds",
+  "table_path": "kaggle/Diamonds",
+  "query": "This gorgeous diamond has really caught my eye! The certificate says it's a Premium cut with I color and VS2 clarity, which sounds lovely. The tag notes it weighs 2.29 carats and has depth and table percentages of 60.8 and 60.0 respectively. Looking at the measurements, it's 8.5 mm long, 8.47 mm wide, and 5.16 mm deep. For a stone with these exact qualities, what should I realistically expect to pay?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Unnamed: 0": 27750,
+          "carat": 2.29,
+          "cut": "Premium",
+          "color": "I",
+          "clarity": "VS2",
+          "depth": 60.8,
+          "table": 60.0,
+          "price": 18823,
+          "x": 8.5,
+          "y": 8.47,
+          "z": 5.16
+        }
+      }
+    ],
+    "target_column": "price",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Diamonds_B1/Diamonds_B1_003.json

@@ -0,0 +1,41 @@
+{
+  "id": "003",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Diamonds",
+  "table_path": "kaggle/Diamonds",
+  "query": "Browsing through the jeweler's catalog, entry 51723 catches my eye: a brilliant 0.7-carat diamond with an Ideal cut. The description highlights its F color and SI1 clarity, while the specifications list a depth of 61.8%, table of 55.0, and measurements of 5.71 mm in length, 5.75 mm in width, and 3.54 mm in depth. Looking at all these quality markers, how much should I expect to pay for this particular stone?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Unnamed: 0": 51723,
+          "carat": 0.7,
+          "cut": "Ideal",
+          "color": "F",
+          "clarity": "SI1",
+          "depth": 61.8,
+          "table": 55.0,
+          "price": 2401,
+          "x": 5.71,
+          "y": 5.75,
+          "z": 3.54
+        }
+      }
+    ],
+    "target_column": "price",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Diamonds_B1/Diamonds_B1_004.json

@@ -0,0 +1,41 @@
+{
+  "id": "004",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Diamonds",
+  "table_path": "kaggle/Diamonds",
+  "query": "\"Let's see here... number 51734,\" the jeweler said, placing the diamond under the light. \"It's a 0.58-carat Ideal cut with an F color and VVS2 clarity.\" He then pointed to the spec sheet showing a depth of 62.5%, a table of 56.0, and dimensions of 5.34 x 5.29 x 3.32 mm. After hearing all that, the big question on my mind is, what's the price tag for a stone like this?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Unnamed: 0": 51734,
+          "carat": 0.58,
+          "cut": "Ideal",
+          "color": "F",
+          "clarity": "VVS2",
+          "depth": 62.5,
+          "table": 56.0,
+          "price": 2401,
+          "x": 5.34,
+          "y": 5.29,
+          "z": 3.32
+        }
+      }
+    ],
+    "target_column": "price",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Diamonds_B1/Diamonds_B1_005.json

@@ -0,0 +1,41 @@
+{
+  "id": "005",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Diamonds",
+  "table_path": "kaggle/Diamonds",
+  "query": "This little sparkler is catching my eye! The tag says it's a Premium cut with an E color grade, and the clarity is marked as SI1. It's a smaller stone at 0.21 carats, with a depth of 59.8% and a table of 61. The measurements are 3.89 mm by 3.84 mm by 2.31 mm. I've shared our whole archive of past diamond sales with you. Given all these details from the listing and the history you can see, what do you think its fair price should be?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Unnamed: 0": 2,
+          "carat": 0.21,
+          "cut": "Premium",
+          "color": "E",
+          "clarity": "SI1",
+          "depth": 59.8,
+          "table": 61.0,
+          "price": 326,
+          "x": 3.89,
+          "y": 3.84,
+          "z": 2.31
+        }
+      }
+    ],
+    "target_column": "price",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Diamonds_B1/Diamonds_B1_006.json

@@ -0,0 +1,41 @@
+{
+  "id": "006",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Diamonds",
+  "table_path": "kaggle/Diamonds",
+  "query": "The jeweler just handed me a certification for a diamond, and I've got your database of past sales pulled up for comparison. It describes a 0.7-carat stone with a very good cut. The color is F, and the clarity is rated SI1. The paperwork notes a depth of 59.8, a table of 59, and dimensions of 5.74 by 5.8 by 3.45 millimeters. With this full description and my records, how much should I expect to pay for it?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Unnamed: 0": 51724,
+          "carat": 0.7,
+          "cut": "Very Good",
+          "color": "F",
+          "clarity": "SI1",
+          "depth": 59.8,
+          "table": 59.0,
+          "price": 2401,
+          "x": 5.74,
+          "y": 5.8,
+          "z": 3.45
+        }
+      }
+    ],
+    "target_column": "price",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Diamonds_B1/Diamonds_B1_007.json

@@ -0,0 +1,41 @@
+{
+  "id": "007",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Diamonds",
+  "table_path": "kaggle/Diamonds",
+  "query": "Flipping through the old appraisal records I sent over, one entry stands out: a 0.23 carat diamond. Its proportions are fascinating—a depth percentage of 61.5 and a table of 55, giving it a very balanced look with a length of 3.95 mm, width of 3.98 mm, and depth of 2.43 mm. The gemologist noted it as an Ideal cut with top-tier E color, though the clarity was graded SI2. With our full sales history in your hands, what would you estimate its price to be?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Unnamed: 0": 1,
+          "carat": 0.23,
+          "cut": "Ideal",
+          "color": "E",
+          "clarity": "SI2",
+          "depth": 61.5,
+          "table": 55.0,
+          "price": 326,
+          "x": 3.95,
+          "y": 3.98,
+          "z": 2.43
+        }
+      }
+    ],
+    "target_column": "price",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Diamonds_B1/Diamonds_B1_008.json

@@ -0,0 +1,41 @@
+{
+  "id": "008",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "data_holder",
+  "dataset_name": "Diamonds",
+  "table_path": "kaggle/Diamonds",
+  "query": "The appraisal document for item number 27749 describes a 2-carat diamond. Its 'Very Good' cut and G color are noted right next to the SI1 clarity grade. Flipping through the pages, the physical measurements are 7.9 mm in length, 7.97 mm in width, and 5.04 mm in depth, with a depth percentage of 63.5 and a table of 56.0. I'm hoping the historical pricing data I've provided can give us a solid estimate; based on that, what do you think this is worth?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "Unnamed: 0": 27749,
+          "carat": 2.0,
+          "cut": "Very Good",
+          "color": "G",
+          "clarity": "SI1",
+          "depth": 63.5,
+          "table": 56.0,
+          "price": 18818,
+          "x": 7.9,
+          "y": 7.97,
+          "z": 5.04
+        }
+      }
+    ],
+    "target_column": "price",
+    "task_sub_type": "regression",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Diamonds_B1/info.json

@@ -0,0 +1,90 @@
+{
+    "name": "Diamonds",
+    "source": "https://www.kaggle.com/datasets/shivam2503/diamonds/data",
+    "data_intro": "This classic dataset contains the prices and other attributes of almost 54,000 diamonds. Try to analyze diamonds by their cut, color, clarity, price, ",
+    "is_splited": false,
+    "overall_size": 53940,
+    "train_size": 0,
+    "test_size": 0,
+    "c_classes": 3,
+    "n_classes": 8,
+    "task_type": "regression",
+    "target": {
+        "price": "Given a diamond's cut,color,clarity and other attributes, predict its price in US dollars"
+    },
+    "cat_feature_intro": {
+        "cut": "- cut: Describe cut quality of the diamond. Fair(worst), Good, Very Good, Premium, Ideal(best)",
+        "color": "- color: Color of the diamond, J(worst), I, H, G, F, E, D(best)",
+        "clarity": "- clarity: a measurement of how clear the diamond is ,I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))"
+    },
+    "num_feature_intro": {
+        "#": "- #: index counter",
+        "carat": "- carat: Carat weight of the diamond (0.2--5.01)",
+        "depth": "- depth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) ",
+        "table": "- table: width of top of diamond relative to widest point",
+        "price": "- price: the price of the diamond in US dollars",
+        "x": "- x: length in mm ",
+        "y": "- y: width in mm ",
+        "z": "- z: depth in mm "
+    },
+    "evaluation_metric": null,
+    "num_feature_value": {
+        "#": [],
+        "carat": [
+            0.2,
+            5.01
+        ],
+        "depth": [
+            43.0,
+            79.0
+        ],
+        "price": [
+            326.0,
+            18823.0
+        ],
+        "table": [
+            43.0,
+            95.0
+        ],
+        "x": [
+            0.0,
+            10.74
+        ],
+        "y": [
+            0.0,
+            58.9
+        ],
+        "z": [
+            0.0,
+            31.8
+        ]
+    },
+    "cat_feature_value": {
+        "clarity": [
+            "I1",
+            "IF",
+            "SI1",
+            "SI2",
+            "VS1",
+            "VS2",
+            "VVS1",
+            "VVS2"
+        ],
+        "color": [
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+            "I",
+            "J"
+        ],
+        "cut": [
+            "Fair",
+            "Good",
+            "Ideal",
+            "Premium",
+            "Very Good"
+        ]
+    }
+}

single_point_prediction/finance/Diamonds_B1/info_mod.json

@@ -0,0 +1,128 @@
+{
+  "name": "Diamonds",
+  "source": "https://www.kaggle.com/datasets/shivam2503/diamonds/data",
+  "data_intro": "This classic dataset contains the prices and other attributes of almost 54,000 diamonds. Try to analyze diamonds by their cut, color, clarity, price, ",
+  "is_splited": false,
+  "overall_size": 53940,
+  "train_size": 0,
+  "test_size": 0,
+  "c_classes": 3,
+  "n_classes": 8,
+  "task_type": "regression",
+  "target": "price",
+  "cat_feature_intro": {
+    "cut": "- cut: Describe cut quality of the diamond. Fair(worst), Good, Very Good, Premium, Ideal(best)",
+    "color": "- color: Color of the diamond, J(worst), I, H, G, F, E, D(best)",
+    "clarity": "- clarity: a measurement of how clear the diamond is ,I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))"
+  },
+  "num_feature_intro": {
+    "Unnamed: 0": "Unnamed: 0: numeric feature inferred from dataset columns",
+    "carat": "- carat: Carat weight of the diamond (0.2--5.01)",
+    "depth": "- depth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) ",
+    "table": "- table: width of top of diamond relative to widest point",
+    "x": "- x: length in mm ",
+    "y": "- y: width in mm ",
+    "z": "- z: depth in mm "
+  },
+  "evaluation_metric": null,
+  "num_feature_value": {
+    "Unnamed: 0": [
+      1.0,
+      51734.0
+    ],
+    "carat": [
+      0.2,
+      5.01
+    ],
+    "depth": [
+      43.0,
+      79.0
+    ],
+    "table": [
+      43.0,
+      95.0
+    ],
+    "x": [
+      0.0,
+      10.74
+    ],
+    "y": [
+      0.0,
+      58.9
+    ],
+    "z": [
+      0.0,
+      31.8
+    ]
+  },
+  "cat_feature_value": {
+    "cut": [
+      "Fair",
+      "Good",
+      "Ideal",
+      "Premium",
+      "Very Good"
+    ],
+    "color": [
+      "D",
+      "E",
+      "F",
+      "G",
+      "H",
+      "I",
+      "J"
+    ],
+    "clarity": [
+      "I1",
+      "IF",
+      "SI1",
+      "SI2",
+      "VS1",
+      "VS2",
+      "VVS1",
+      "VVS2"
+    ]
+  },
+  "columns": [
+    "Unnamed: 0",
+    "carat",
+    "cut",
+    "color",
+    "clarity",
+    "depth",
+    "table",
+    "price",
+    "x",
+    "y",
+    "z"
+  ],
+  "feature_columns": [
+    "Unnamed: 0",
+    "carat",
+    "cut",
+    "color",
+    "clarity",
+    "depth",
+    "table",
+    "x",
+    "y",
+    "z"
+  ],
+  "feature_types": {
+    "Unnamed: 0": "numeric",
+    "carat": "numeric",
+    "cut": "categorical",
+    "color": "categorical",
+    "clarity": "categorical",
+    "depth": "numeric",
+    "table": "numeric",
+    "x": "numeric",
+    "y": "numeric",
+    "z": "numeric"
+  },
+  "open_text_feature_intro": {},
+  "open_text_features": [],
+  "missing_from_original_info": [
+    "Unnamed: 0"
+  ]
+}

single_point_prediction/finance/Diamonds_B1/test.csv

@@ -0,0 +1,9 @@
+Unnamed: 0,carat,cut,color,clarity,depth,table,price,x,y,z
+51730,0.58,Ideal,F,VVS2,61.9,56.0,2401,5.38,5.35,3.32
+27750,2.29,Premium,I,VS2,60.8,60.0,18823,8.5,8.47,5.16
+51723,0.7,Ideal,F,SI1,61.8,55.0,2401,5.71,5.75,3.54
+51734,0.58,Ideal,F,VVS2,62.5,56.0,2401,5.34,5.29,3.32
+2,0.21,Premium,E,SI1,59.8,61.0,326,3.89,3.84,2.31
+51724,0.7,Very Good,F,SI1,59.8,59.0,2401,5.74,5.8,3.45
+1,0.23,Ideal,E,SI2,61.5,55.0,326,3.95,3.98,2.43
+27749,2.0,Very Good,G,SI1,63.5,56.0,18818,7.9,7.97,5.04

single_point_prediction/finance/Health_Insurance_Cross_Sell_Prediction_B1/Health_Insurance_Cross_Sell_Prediction_B1_001.json

@@ -0,0 +1,42 @@
+{
+  "id": "001",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Health Insurance Cross Sell Prediction",
+  "table_path": "kaggle/Health Insurance Cross Sell Prediction",
+  "query": "Pulling up the file for customer #139285 shows a 69-year-old gentleman living in region 28. His current vehicle is between one and two years old and has a history of damage. The annual premium he pays is $58,014, his policy was sold through channel 26, and he's been with the company for 170 days. With all this in mind, is he likely to go for the new vehicle insurance we're offering?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "id": 139285,
+          "Gender": "Male",
+          "Age": 69,
+          "Driving_License": "NULL",
+          "Region_Code": 28.0,
+          "Previously_Insured": "NULL",
+          "Vehicle_Age": "1-2 Year",
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": 58014.0,
+          "Policy_Sales_Channel": 26.0,
+          "Vintage": 170,
+          "Response": 1
+        }
+      }
+    ],
+    "target_column": "Response",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}

single_point_prediction/finance/Health_Insurance_Cross_Sell_Prediction_B1/Health_Insurance_Cross_Sell_Prediction_B1_002.json

@@ -0,0 +1,42 @@
+{
+  "id": "002",
+  "task_type": "B1",
+  "subtask_type": "single",
+  "perspective": "user",
+  "dataset_name": "Health Insurance Cross Sell Prediction",
+  "table_path": "kaggle/Health Insurance Cross Sell Prediction",
+  "query": "Looking at customer #250913, a 33-year-old woman from region 15 who just got her first vehicle less than a year ago. She has a valid driving license but surprisingly, has never had vehicle insurance before, even though her car has already been damaged. With an annual premium of ₹44,742 and being contacted through channel 152, she's been with us for 135 days. Given she's currently uninsured but her car has existing damage, is she likely to want our new vehicle insurance policy?",
+  "meta_info": {
+    "domain": "finance"
+  },
+  "ground_truth": {
+    "extracted_features": [
+      {
+        "scenario_id": "001",
+        "features": {
+          "id": 250913,
+          "Gender": "Female",
+          "Age": 33,
+          "Driving_License": 1,
+          "Region_Code": 15.0,
+          "Previously_Insured": 0,
+          "Vehicle_Age": "< 1 Year",
+          "Vehicle_Damage": "Yes",
+          "Annual_Premium": 44742.0,
+          "Policy_Sales_Channel": 152.0,
+          "Vintage": 135,
+          "Response": 0
+        }
+      }
+    ],
+    "target_column": "Response",
+    "task_sub_type": "classification",
+    "final_decision": "",
+    "what_if": "",
+    "ranking_ground_truth": {
+      "top_k_ids": []
+    }
+  },
+  "response": "",
+  "evaluation_score": {}
+}