Dataset Overview

The dataset consists of 500 rows and 9 columns, mimicking real-world financial transactions. It includes both income and expense transactions across different companies.

Dataset : https://colorstech.net/wp-content/uploads/2025/03/Generated_Finance_Expenses.xlsx

Column Breakdown

1. Account – The type of account used for the transaction.
   • Possible values: "Checking", "Credit", "Savings"
   • Example: "Checking" (means the transaction was made from a checking account)
2. Date – The date of the transaction.
   • Range: Between January 1, 2023, to December 31, 2023
   • Example: "2023-05-14"
3. Company – The company associated with the transaction.
   • Possible values: "Company A", "Company B", "Company C", "Company D"
   • Example: "Company A" (a transaction was made involving Company A)
4. Debit – The amount deducted from the account (expenses).
   • If the transaction is an expense, this column has a value, and Credit is NaN (empty).
   • Example: 500 (means ₹500 was spent)
   • For income transactions, this remains empty (NaN).
5. Credit – The amount added to the account (income).
   • If the transaction is an income, this column has a value, and Debit is NaN (empty).
   • Example: 4000 (means ₹4000 was received as salary)
6. Amount – The net transaction value.
   • If it’s an income, the value is positive (same as Credit).
   • If it’s an expense, the value is negative (same as -Debit).
   • Example: -500 (₹500 spent) or 4000 (₹4000 received)
7. Sub-category – A more detailed classification of the transaction.
   • Possible values: "Salary", "Coffee", "Rent", "Entertainment", "Groceries", "Utilities", "Transport"
   • Example: "Rent" (means the transaction was a rent payment)
8. Category – A broader classification of the transaction.
   • Maps to the sub-category to group similar types of transactions.
   • Example: "Living Expenses" (since rent falls under living expenses)
9. Category Type – Indicates whether the transaction is an Income or an Expense.
   • "Income" (for credits like Salary)
   • "Expense" (for debits like Rent, Coffee, Transport, etc.)
   • Example: "Expense" (since rent is an expense)

Example Transactions

Key Insights from the Data

• Income transactions have values in the “Credit” column and belong to the “Income” category.
• Expense transactions have values in the “Debit” column and belong to categories like “Dining Out”, “Living Expenses”, etc.
• Transactions are spread across different dates, companies, and categories, making it suitable for financial analysis or expense tracking.