Update eda.py

eda.py

@@ -8,6 +8,41 @@ def app():
     
     # Load Data
     df = pd.read_csv('../Transactions Data.csv')
+    
+    # Creating the table with column names and descriptions
+    data = {
+    "Column Names": [
+        "step",
+        "type",
+        "amount",
+        "nameOrig",
+        "oldbalanceOrg",
+        "newbalanceOrig",
+        "nameDest",
+        "oldbalanceDest",
+        "newbalanceDest",
+        "isFraud",
+        "isFlaggedFraud"
+    ],
+    "Description": [
+        "Represents a unit of time in the transaction process, though the specific time unit is not specified in the dataset. It could denote hours, days, or another unit, depending on the context.",
+        "Describes the type of transaction, such as transfer, payment, etc. This categorical variable allows for the classification of different transaction behaviors.",
+        "Indicates the monetary value of the transaction, providing insight into the financial magnitude of each transaction.",
+        "Serves as the identifier for the origin account or entity initiating the transaction. This helps trace the source of funds in each transaction.",
+        "Represents the balance in the origin account before the transaction occurred, offering a reference point for understanding changes in account balances.",
+        "Reflects the balance in the origin account after the transaction has been processed, providing insight into how the transaction affects the account balance.",
+        "Functions as the identifier for the destination account or entity receiving the funds in each transaction. It helps track where the money is being transferred to.",
+        "Indicates the balance in the destination account before the transaction, offering a baseline for assessing changes in account balances due to incoming funds.",
+        "Represents the balance in the destination account after the transaction has been completed, providing insight into the impact of incoming funds on the account balance.",
+        "A binary indicator (0 or 1) denoting whether the transaction is fraudulent (1) or legitimate (0). This is the target variable for fraud detection modeling.",
+        "Another binary indicator (0 or 1) which may signal whether a transaction has been flagged as potentially fraudulent. This could serve as an additional feature for fraud detection algorithms."
+    ]}
+
+    # Displaying the table using Streamlit
+    st.subheader('Transaction Dataset Column Descriptions')
+    st.table(data)
+    
+    st.divider()
 
     # Data Summary
     st.header('Data Summary')