Update app.py

app.py

@@ -9,28 +9,27 @@ import seaborn as sns
 # App title
 st.title("🛍️ Customer Segmentation Tool")
 
-# About the App
-st.write("""
-This app uses unsupervised learning techniques to segment customers based on their purchasing behavior. 
-The dataset is preloaded and contains online retail data.
-""")
+# About the App section with a toggle button
+st.sidebar.header("About the App")
+with st.sidebar.expander("Learn more about the app"):
+    st.write("""
+    This app uses unsupervised learning techniques to segment customers based on their purchasing behavior. 
+    The dataset is preloaded from an Excel file containing online retail data.
 
-# How it Works section
-st.write("""
-### How It Works:
-1. **Load Customer Transaction Data**:
-   - The app loads online retail data from an Excel file, including key information such as `Quantity`, `UnitPrice`, and `CustomerID`.
-   
-2. **Data Processing**:
-   - It processes the data by calculating the total amount spent by each customer (`TotalSpent`), derived from multiplying `Quantity` and `UnitPrice`.
-   - The data is then aggregated by `CustomerID` to summarize the total amount spent, the number of unique transactions, and the total quantity purchased.
+    ### How It Works:
+    1. **Load customer transaction data**:
+       - The app loads customer transaction data, including key details like `Quantity`, `UnitPrice`, and `CustomerID`.
+    
+    2. **Process the data**:
+       - The data is processed by calculating the total amount spent (`TotalSpent`) for each customer. This is done by multiplying `Quantity` and `UnitPrice`.
+       - The information is then aggregated by `CustomerID` to summarize the total amount spent, the number of unique transactions, and the total quantity purchased by each customer.
 
-3. **Apply K-Means Clustering**:
-   - Using unsupervised learning, the app applies the K-Means clustering algorithm to segment the customers into distinct groups based on their purchasing behavior.
-   
-4. **Visualize the Customer Segments**:
-   - A scatter plot is generated to visually display the customer segments, showing the `TotalSpent` versus the number of transactions (`NumTransactions`), with different clusters represented by different colors.
-""")
+    3. **Apply K-Means Clustering**:
+       - The app applies K-Means clustering to segment the customers into distinct groups based on their purchasing behavior, using the processed data.
+
+    4. **Visualize the customer segments**:
+       - A scatter plot is created to visualize the customer segments, where customers are grouped based on the `TotalSpent` and the number of transactions (`NumTransactions`), with each cluster represented by different colors.
+    """)
 
 # Load dataset
 file_path = "Online Retail.xlsx"  # Updated file path