Update app.py

app.py

@@ -57,35 +57,38 @@ tab1, tab2, tab3 = st.tabs(["🏠 About", "📊 Data Visualization", "🔎 Model
 with tab1:
     st.header("About This App")
     st.markdown("""
-    ## **Overview**
-    This application demonstrates **unsupervised machine learning** using the Iris dataset.
-    The app clusters data points based on the features of iris flowers using the **K-Means clustering algorithm**.
-    After clustering, meaningful labels are assigned based on the cluster’s statistical properties.
-
-    ## **How It Works**
-    1. **Data Preprocessing:**
-        - The dataset is standardized using `StandardScaler` to ensure uniform feature scaling.
-
-    2. **Clustering:**
-        - K-Means clustering is applied to group the data into **three clusters**.
-        - The number of clusters is based on the natural grouping of the Iris dataset.
-
-    3. **Cluster Labeling:**
-        - After clustering, each cluster is assigned a meaningful label based on its centroid properties and domain knowledge.
-
-    4. **Model Testing:**
-        - The app allows the user to enter custom feature values.
-        - The model predicts the cluster and assigns a meaningful label to the input data.
-
-    ## **Dataset Information**
-    The Iris dataset contains **150 samples** of iris flowers.
-    Each sample includes the following features:
-    - 🌸 Sepal Length (cm)
-    - 🌸 Sepal Width (cm)
-    - 🌸 Petal Length (cm)
-    - 🌸 Petal Width (cm)
-
-    The goal of clustering is to find natural patterns among these measurements.
+        ## **Overview**
+        This application demonstrates **unsupervised machine learning** using the Iris dataset.
+        The app clusters data points based on the features of iris flowers using the **K-Means clustering algorithm**.
+        After clustering, meaningful labels are assigned based on the cluster’s statistical properties.
+    
+        ## **How It Works**
+        1. **Data Preprocessing:**
+            - The dataset is standardized using `StandardScaler` to ensure uniform feature scaling.
+    
+        2. **Clustering:**
+            - K-Means clustering is applied to group the data into **three clusters**.
+            - The number of clusters is based on the natural grouping of the Iris dataset.
+    
+        3. **Cluster Labeling:**
+            - After clustering, each cluster is assigned a meaningful label based on its centroid properties and domain knowledge.
+    
+        4. **Model Testing:**
+            - The app allows the user to enter custom feature values.
+            - The model predicts the cluster and assigns a meaningful label to the input data.
+    
+        ## **Dataset Information**
+        """)
+        st.write(pd.DataFrame(load_iris()['data'], columns=load_iris()['feature_names']).head())
+        st.markdown("""
+            The Iris dataset contains **150 samples** of iris flowers.
+            Each sample includes the following features:
+            - 🌸 Sepal Length (cm)
+            - 🌸 Sepal Width (cm)
+            - 🌸 Petal Length (cm)
+            - 🌸 Petal Width (cm)
+    
+            The goal of clustering is to find natural patterns among these measurements.
     """)
 
 # ------------- Data Visualization Tab -------------