Update pages/Data Collection.py

pages/Data Collection.py

@@ -58,5 +58,37 @@ data_type = st.radio(
     ("Structured Data", "Unstructured Data", "Semi-Structured Data"))
 
 if data_type == "Structured":
-    st.subheader("Structured Data")
+    st.markdown("""
+        <div style="text-align: left; margin-top: 20px;">
+            <h3 style="color: #e25822;">What is Structured Data?</h3></div>""", unsafe_allow_html=True)
+    
+    st.subheader("Definition")
+    st.write("""
+    Structured data refers to information that is organized and formatted in a predefined manner, making it easy to store, retrieve, and analyze.
+
+    It is typically stored in tabular formats like rows and columns, where each field contains a specific type of information.
+    
+    This type of data is often used in relational databases and spreadsheets, where relationships between data points are explicitly defined.
+    """)
+    
+    st.subheader("Characteristics")
+    st.write("""
+    - Follows a fixed schema.
+    - Can be easily searched using query languages like SQL.
+    - Suitable for quantitative analysis.
+    """)
+    
+    # Examples
+    st.subheader("Examples")
+    st.write("""
+    A database of employees with fields like name, age, and salary:
+    """)
+    
+    # Add a table for the employee database example
+    st.table({
+        "Name": ["John Doe", "Jane Smith"],
+        "Age": [30, 25],
+        "Salary": ["$5000", "$4500"]
+    })
+