{
  "format": "dataset-manifest/1",
  "slug": "peer-to-peer-lending-platform",
  "version": 1,
  "seed": "peer-to-peer-lending-platform:1",
  "spec": {
    "format": "dataset/1",
    "slug": "peer-to-peer-lending-platform",
    "name": "Peer Lending Transactions Dataset",
    "tagline": "Transactions of loans and investments on a peer-to-peer lending platform, including repayment schedules.",
    "description": "Borrowers request loans while investors provide funds on a peer-to-peer lending platform, creating a dynamic ecosystem where money flows directly between individuals. This platform not only facilitates the transactions but also tracks the lifecycle of each loan, from initial requests to repayments. Users engage based on varying loan amounts, some opting for smaller amounts to meet immediate needs, while others make significant investments for potentially higher returns, adding diversity to the lending activity.\n\nThe transactions table serves as the critical hub of data, documenting every lending and borrowing event. Each row represents a unique transaction, detailing essential elements such as the borrower ID, the investor ID, the loan amount, and the repayment schedule. This single table highlights how individual users interact with the platform, and the relationships between borrowers and lenders can be explored through the unique identifiers assigned to each user, allowing for insights into their activity patterns. Developers can utilize this data to analyze trends in borrowing and investment behavior, as well as the impact of different promotions on user engagement and platform revenue.\n\nQuestions that can be answered using the provided data include: What is the average loan amount requested? How do repayment patterns differ based on loan size? What trends can be identified in investor behavior over time? Practicing on this dataset offers a chance to explore not only individual transaction dynamics but also broader patterns that emerge from the collective borrowing and investment activities. Keep in mind that the data is synthetic, designed to mimic real-world scenarios without using actual user information.",
    "domain": "banking",
    "shape": "ledger",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "peer lending",
      "transactions",
      "loans",
      "investments",
      "repayments",
      "promotions",
      "finance",
      "banking"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "transactions": {
        "description": "Each row in this table represents a transaction on the peer-to-peer lending platform, linking borrowers and lenders. The table captures detailed information about each loan request and the corresponding investment, making it easy to track loan performance and repayment schedules.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "borrower_id": {
            "kind": "uuid",
            "description": "Unique identifier for the borrower involved in the transaction."
          },
          "lender_id": {
            "kind": "uuid",
            "description": "Unique identifier for the lender involved in the transaction."
          },
          "loan_amount": {
            "kind": "decimal",
            "min": 100,
            "max": 10000,
            "scale": 2,
            "description": "The total amount of the loan requested by the borrower."
          },
          "investment_amount": {
            "kind": "decimal",
            "min": 50,
            "max": 10000,
            "scale": 2,
            "description": "The amount invested by the lender in the loan."
          },
          "interest_rate": {
            "kind": "decimal",
            "min": 0.01,
            "max": 0.2,
            "scale": 4,
            "description": "The interest rate applied to the loan."
          },
          "repayment_term": {
            "kind": "int",
            "min": 1,
            "max": 36,
            "description": "The duration of the loan repayment in months.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "completed",
                "defaulted"
              ]
            }
          },
          "status": {
            "kind": "enum",
            "values": [
              "pending",
              "active",
              "completed",
              "defaulted"
            ],
            "description": "The current status of the loan."
          },
          "promotion_applied": {
            "kind": "bool",
            "trueRate": 0.3,
            "description": "Indicates if a promotional offer was applied to the loan.",
            "nullable": true
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-01-01",
            "description": "The date and time when the transaction was created.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "monetary",
      "nullable",
      "categorical"
    ],
    "analysisQuestions": [
      "What is the average loan amount requested by borrowers?",
      "How do interest rates vary based on loan status?",
      "What percentage of loans applied for are completed successfully?",
      "Are there trends in loan requests based on promotional offers?",
      "What is the average investment amount per lender?"
    ],
    "suggestedQueries": [
      {
        "title": "Basic Transaction Overview",
        "sql": "SELECT * FROM transactions LIMIT 10;",
        "dialect": "postgres"
      },
      {
        "title": "Average Loan Amount",
        "sql": "SELECT AVG(loan_amount) FROM transactions;",
        "dialect": "postgres"
      },
      {
        "title": "Loan Status Distribution",
        "sql": "SELECT status, COUNT(*) FROM transactions GROUP BY status;",
        "dialect": "postgres"
      },
      {
        "title": "Total Investment Amount by Status",
        "sql": "SELECT status, SUM(investment_amount) FROM transactions GROUP BY status;",
        "dialect": "postgres"
      },
      {
        "title": "Loans with Promotions Analyzed by Status",
        "sql": "SELECT status, COUNT(*) FROM transactions WHERE promotion_applied = TRUE GROUP BY status;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Financial Analysis Course",
        "body": "In a financial analysis course, instructors can utilize the Peer Lending Transactions Dataset to teach students about risk assessment and investment strategies. By analyzing the dataset, students can learn how to evaluate loan performance and understand the impact of interest rates on profitability."
      },
      {
        "title": "SQL Practice for Beginners",
        "body": "Beginners in SQL can use this dataset to practice writing queries that retrieve and manipulate data. The variety of transaction types and statuses provides ample opportunities to explore different SQL functions and aggregate techniques, enhancing their learning experience."
      },
      {
        "title": "Peer-to-Peer Lending Research",
        "body": "Researchers studying peer-to-peer lending can leverage this dataset to analyze trends in borrower behavior and investment patterns. By examining the data, they can identify key factors that influence loan approvals and repayment rates, contributing valuable insights to the financial industry."
      }
    ]
  },
  "counts": {
    "transactions": 500
  },
  "stats": {
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        "investment_amount": {
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        "repayment_term": {
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        "status": {
          "nulls": 0,
          "distinct": 4,
          "min": "active",
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        "promotion_applied": {
          "nulls": 57,
          "distinct": 2,
          "min": "false",
          "max": "true"
        },
        "created_at": {
          "nulls": 0,
          "distinct": 500,
          "min": "2020-01-03 20:25:11",
          "max": "2022-12-30 11:12:34"
        }
      }
    }
  },
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