diff --git "a/pages/3_\360\237\223\212 Visualize.py" "b/pages/3_\360\237\223\212 Visualize.py"
index 29bd669..70d1201 100644
--- "a/pages/3_\360\237\223\212 Visualize.py"	
+++ "b/pages/3_\360\237\223\212 Visualize.py"	
@@ -5,69 +5,58 @@
 import seaborn as sns
 
 def show_visualize_page():
-    st.title("📊 Mining Site Visualization")
-    st.write("Visualize mining site data to gain insights.")
+    st.title("🌌 Mining Site Visualization 🌟")
+    st.write("Explore and visualize mining site data interactively!")
 
     # Load dataset
     df = pd.read_csv("space_mining_dataset.csv")
     
-    # Set seaborn style and palette for professional appearance
-    sns.set_style("darkgrid")
-    sns.set_palette("coolwarm_r")
-
-    # Select columns for visualizations
-    columns = df.columns.tolist()
-    numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns.tolist()
-    selected_columns = st.multiselect("Select Columns to Visualize", numeric_columns, default=numeric_columns[:3])
-
-    if not selected_columns:
-        st.warning("Please select at least one numeric column.")
-        return
-
-    # Scatter Plot with user-selected columns
-    if len(selected_columns) >= 2:
-        st.write(f"### 🧲 {selected_columns[0]} vs. {selected_columns[1]} Composition")
+    # Set seaborn style and palette
+    sns.set_style("whitegrid")
+    sns.set_palette("Set2")
+
+    # Plot 1: Scatter Plot with column selection
+    st.write("### 🧲 Scatter Plot")
+    scatter_x = st.selectbox("Choose X-axis for Scatter Plot", df.columns, index=0)
+    scatter_y = st.selectbox("Choose Y-axis for Scatter Plot", df.columns, index=1)
+    if scatter_x and scatter_y:
+        st.write(f"### {scatter_x} vs. {scatter_y} Composition 🌀")
         plt.figure(figsize=(10, 6))
-        sns.scatterplot(x=selected_columns[0], y=selected_columns[1], data=df, hue='Celestial Body', 
+        sns.scatterplot(x=scatter_x, y=scatter_y, data=df, hue='Celestial Body', 
                         palette='Spectral', s=100, edgecolor='black')
-        plt.title(f'{selected_columns[0]} vs. {selected_columns[1]} Composition', fontsize=16, fontweight='bold')
-        plt.xlabel(f'{selected_columns[0]}', fontsize=14)
-        plt.ylabel(f'{selected_columns[1]}', fontsize=14)
+        plt.title(f'{scatter_x} vs. {scatter_y} Composition', fontsize=16, fontweight='bold')
+        plt.xlabel(f'{scatter_x}', fontsize=14)
+        plt.ylabel(f'{scatter_y}', fontsize=14)
         plt.grid(True)
         st.pyplot(plt)
 
-    # Histogram of selected column
-    if len(selected_columns) >= 1:
-        st.write(f"### 🏭 Distribution of {selected_columns[0]}")
+    # Plot 2: Histogram with column selection
+    st.write("### 📊 Histogram")
+    hist_col = st.selectbox("Choose a Column for Histogram", df.columns, index=0)
+    if hist_col:
+        st.write(f"### Distribution of {hist_col} 📉")
         plt.figure(figsize=(10, 6))
-        sns.histplot(df[selected_columns[0]], kde=True, color='crimson', bins=20, edgecolor='black')
-        plt.title(f'Distribution of {selected_columns[0]}', fontsize=16, fontweight='bold')
-        plt.xlabel(f'{selected_columns[0]}', fontsize=14)
+        sns.histplot(df[hist_col], kde=True, color='crimson', bins=20, edgecolor='black')
+        plt.title(f'Distribution of {hist_col}', fontsize=16, fontweight='bold')
+        plt.xlabel(f'{hist_col}', fontsize=14)
         plt.ylabel('Frequency', fontsize=14)
         plt.grid(True)
         st.pyplot(plt)
 
-    # Violin Plot
-    if len(selected_columns) >= 1:
-        st.write(f"### 🎻 {selected_columns[0]} Distribution by Celestial Body (Violin Plot)")
+    # Plot 3: Violin Plot with column selection
+    st.write("### 🎻 Violin Plot")
+    violin_col = st.selectbox("Choose a Column for Violin Plot", df.columns, index=0)
+    if violin_col:
+        st.write(f"### {violin_col} Distribution by Celestial Body 🎶")
         plt.figure(figsize=(12, 6))
-        sns.violinplot(x='Celestial Body', y=selected_columns[0], data=df, palette='muted')
-        plt.title(f'{selected_columns[0]} Distribution by Celestial Body (Violin Plot)', fontsize=16, fontweight='bold')
+        sns.violinplot(x='Celestial Body', y=violin_col, data=df, palette='muted')
+        plt.title(f'{violin_col} Distribution by Celestial Body (Violin Plot)', fontsize=16, fontweight='bold')
         plt.xticks(rotation=45, fontsize=12)
         plt.grid(True)
         st.pyplot(plt)
 
-    # FacetGrid for selected column
-    if len(selected_columns) >= 1:
-        st.write(f"### 🪐 {selected_columns[0]} Distribution by Celestial Body (FacetGrid)")
-        g = sns.FacetGrid(df, col='Celestial Body', height=4, aspect=1.2)
-        g.map(sns.histplot, selected_columns[0], kde=True, bins=15, color='orange')
-        g.set_axis_labels(selected_columns[0], 'Frequency')
-        g.fig.suptitle(f'{selected_columns[0]} Distribution by Celestial Body (FacetGrid)', fontsize=16, fontweight='bold')
-        st.pyplot(g.fig)
-
-    # Pie Chart of Celestial Bodies
-    st.write("### 🌌 Celestial Body Distribution")
+    # Plot 4: Pie Chart for Celestial Body Distribution
+    st.write("### 🌌 Celestial Body Distribution (Pie Chart) 🍰")
     body_counts = df['Celestial Body'].value_counts()
     plt.figure(figsize=(8, 8))
     plt.pie(body_counts, labels=body_counts.index, autopct='%1.1f%%', startangle=140, 
@@ -75,79 +64,51 @@ def show_visualize_page():
     plt.title('Celestial Body Distribution', fontsize=16, fontweight='bold')
     st.pyplot(plt)
 
-    # Boxplot of Selected Columns by Celestial Body
-    if len(selected_columns) >= 1:
-        st.write(f"### 💵 {selected_columns[0]} by Celestial Body (Boxplot)")
-        plt.figure(figsize=(12, 6))
-        sns.boxplot(x='Celestial Body', y=selected_columns[0], data=df, palette='rocket')
-        plt.xticks(rotation=45, fontsize=12)
-        plt.title(f'{selected_columns[0]} by Celestial Body', fontsize=16, fontweight='bold')
-        plt.grid(True)
-        st.pyplot(plt)
-
-    # Correlation Heatmap
+    # Plot 5: Correlation Heatmap with selected numeric columns
     st.write("### 🔥 Correlation Heatmap")
-    numeric_df = df[selected_columns].select_dtypes(include=['float64', 'int64'])
-    if not numeric_df.empty:
-        plt.figure(figsize=(10, 8))
-        corr_matrix = numeric_df.corr()
-        mask = np.triu(np.ones_like(corr_matrix, dtype=bool))  # Mask upper triangle
-        sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5, 
-                    cbar_kws={'shrink': 0.5}, mask=mask)
-        plt.title('Correlation Heatmap of Selected Features', fontsize=16, fontweight='bold')
-        st.pyplot(plt)
-    else:
-        st.warning("No numeric columns selected for correlation heatmap.")
-
-    # Pairplot
-    if len(selected_columns) > 1:
-        st.write("### 🔗 Pairplot of Selected Features")
-        sns.pairplot(df[selected_columns], diag_kind='kde', palette='coolwarm', plot_kws={'edgecolor': 'black'})
+    selected_columns = st.multiselect("Choose Columns for Correlation Heatmap", df.columns)
+    if selected_columns:
+        numeric_df = df[selected_columns].select_dtypes(include=['float64', 'int64'])
+        if not numeric_df.empty:
+            plt.figure(figsize=(10, 8))
+            corr_matrix = numeric_df.corr()
+            sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5, 
+                        cbar_kws={'shrink': 0.5})
+            plt.title('Correlation Heatmap of Selected Features', fontsize=16, fontweight='bold')
+            st.pyplot(plt)
+        else:
+            st.warning("Please select numeric columns for the heatmap.")
+
+    # Plot 6: Pairplot with selected columns
+    st.write("### 🔗 Pairplot")
+    pairplot_columns = st.multiselect("Choose Columns for Pairplot", df.columns, default=df.columns[:3])
+    if pairplot_columns:
+        sns.pairplot(df[pairplot_columns], diag_kind='kde', palette='coolwarm', plot_kws={'edgecolor': 'black'})
         plt.suptitle('Pairplot of Selected Features', y=1.02, fontsize=16, fontweight='bold')
         st.pyplot(plt)
 
-    # Regression Plot: Iron vs Nickel with a trendline
-    if 'iron' in df.columns and 'nickel' in df.columns:
-        st.write("### 📈 Regression Plot: Iron vs Nickel")
-        plt.figure(figsize=(10, 6))
-        sns.regplot(x='iron', y='nickel', data=df, scatter_kws={'s': 50, 'color': 'green'}, line_kws={'color': 'red'})
-        plt.title('Regression Plot: Iron vs Nickel', fontsize=16, fontweight='bold')
-        plt.xlabel('Iron Content', fontsize=14)
-        plt.ylabel('Nickel Content', fontsize=14)
-        plt.grid(True)
-        st.pyplot(plt)
-
-    # Additional Visualizations
-    st.write("### 📊 More Visualizations")
-
-
-
-    # KDE Plot
-    if len(selected_columns) >= 1:
-        st.write(f"### 📈 KDE Plot of {selected_columns[0]}")
-        plt.figure(figsize=(10, 6))
-        sns.kdeplot(df[selected_columns[0]], shade=True, color='blue')
-        plt.title(f'KDE Plot of {selected_columns[0]}', fontsize=16, fontweight='bold')
-        plt.xlabel(f'{selected_columns[0]}', fontsize=14)
-        plt.grid(True)
-        st.pyplot(plt)
-
-    # Strip Plot
-    if len(selected_columns) >= 1:
-        st.write(f"### 🎋 Strip Plot of {selected_columns[0]} by Celestial Body")
-        plt.figure(figsize=(10, 6))
-        sns.stripplot(x='Celestial Body', y=selected_columns[0], data=df, jitter=True, palette='viridis', size=7)
-        plt.title(f'Strip Plot of {selected_columns[0]} by Celestial Body', fontsize=16, fontweight='bold')
+    # Plot 7: Boxplot with column selection
+    st.write("### 📦 Boxplot")
+    box_col = st.selectbox("Choose a Column for Boxplot", df.columns, index=0)
+    if box_col:
+        st.write(f"### {box_col} by Celestial Body 📦")
+        plt.figure(figsize=(12, 6))
+        sns.boxplot(x='Celestial Body', y=box_col, data=df, palette='rocket')
+        plt.xticks(rotation=45, fontsize=12)
+        plt.title(f'{box_col} by Celestial Body', fontsize=16, fontweight='bold')
         plt.grid(True)
         st.pyplot(plt)
 
-    # Hexbin Plot for continuous variables
-    if len(selected_columns) >= 2:
-        st.write(f"### 🧮 Hexbin Plot of {selected_columns[0]} vs {selected_columns[1]}")
+    # Plot 8: Hexbin Plot with column selection
+    st.write("### 🧮 Hexbin Plot")
+    hexbin_x = st.selectbox("Choose X-axis for Hexbin Plot", df.columns, index=0)
+    hexbin_y = st.selectbox("Choose Y-axis for Hexbin Plot", df.columns, index=1)
+    if hexbin_x and hexbin_y:
+        st.write(f"### {hexbin_x} vs. {hexbin_y} (Hexbin Plot) 🎲")
         plt.figure(figsize=(10, 6))
-        plt.hexbin(df[selected_columns[0]], df[selected_columns[1]], gridsize=30, cmap='Purples', edgecolors='black')
+        plt.hexbin(df[hexbin_x], df[hexbin_y], gridsize=30, cmap='Purples', edgecolors='black')
         plt.colorbar(label='Count')
-        plt.title(f'Hexbin Plot of {selected_columns[0]} vs {selected_columns[1]}', fontsize=16, fontweight='bold')
+        plt.title(f'Hexbin Plot of {hexbin_x} vs {hexbin_y}', fontsize=16, fontweight='bold')
         plt.grid(True)
         st.pyplot(plt)