--- a
+++ b/Tabs/visualise.py
@@ -0,0 +1,83 @@
+"""This modules contains data about visualisation page"""
+
+# Import necessary modules
+import warnings
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import plot_confusion_matrix
+from sklearn import tree
+import streamlit as st
+
+
+# Import necessary functions from web_functions
+from web_functions import train_model
+
+def app(df, X, y):
+    """This function create the visualisation page"""
+    
+    # Remove the warnings
+    warnings.filterwarnings('ignore')
+    st.set_option('deprecation.showPyplotGlobalUse', False)
+
+    # Set the page title
+    st.title("Visualise the Parkinson's Prediction")
+
+    # Create a checkbox to show correlation heatmap
+    if st.checkbox("Show the correlation heatmap"):
+        st.subheader("Correlation Heatmap")
+
+        fig = plt.figure(figsize = (10, 6))
+        ax = sns.heatmap(df.iloc[:, 1:].corr(), annot = True)   # Creating an object of seaborn axis and storing it in 'ax' variable
+        bottom, top = ax.get_ylim()                             # Getting the top and bottom margin limits.
+        ax.set_ylim(bottom + 0.5, top - 0.5)                    # Increasing the bottom and decreasing the top margins respectively.
+        st.pyplot(fig)
+
+    if st.checkbox("Show Scatter Plot"):
+        
+        figure, axis = plt.subplots(2, 2,figsize=(15,10))
+
+        sns.scatterplot(ax=axis[0,0],data=df,x='AVFF',y='MAVFF',hue='status')
+        axis[0, 0].set_title("Oversampling Minority Scatter")
+  
+        sns.countplot(ax=axis[0, 1],x="status", data=df)
+        axis[0, 1].set_title("Oversampling Minority Count")
+  
+        sns.scatterplot(ax=axis[1, 0],data=df,x='AVFF',y='MAVFF',hue='status')
+        axis[1, 0].set_title("Undersampling Majority Scatter")
+  
+        sns.countplot(ax=axis[1, 1],x="status", data=df)
+        axis[1, 1].set_title("Undersampling Majority Count")
+        st.pyplot()
+
+    if st.checkbox("Display Boxplot"):
+        fig, ax = plt.subplots(figsize=(15,5))
+        df.boxplot(['AVFF', 'MAVFF', 'MIVFF','HNR'],ax=ax)
+        st.pyplot()
+
+    if st.checkbox("Show Sample Results"):
+        safe = (df['status'] == 0).sum()
+        prone = (df['status'] == 1).sum()
+        data = [safe,prone]
+        labels = ['Safe', 'Prone']
+        colors = sns.color_palette('pastel')[0:7]
+        plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%')
+        st.pyplot()
+
+    
+
+    if st.checkbox("Plot confusion matrix"):
+        model, score = train_model(X, y)
+        plt.figure(figsize = (10, 6))
+        plot_confusion_matrix(model, X, y, values_format='d')
+        st.pyplot()
+
+    if st.checkbox("Plot Decision Tree"):
+        model, score = train_model(X, y)
+        # Export decision tree in dot format and store in 'dot_data' variable.
+        dot_data = tree.export_graphviz(
+            decision_tree=model, max_depth=3, out_file=None, filled=True, rounded=True,
+            feature_names=X.columns, class_names=['0', '1']
+        )
+        # Plot the decision tree using the 'graphviz_chart' function of the 'streamlit' module.
+        st.graphviz_chart(dot_data)
+