Upload app.py

app.py

@@ -43,13 +43,15 @@ user_input = {}
 for feature in xtrain.columns:
     user_input[feature] = st.checkbox(feature)
 
-# Function to predict the prognosis based on selected features
-def predict_prognosis(user_input):
-    features = [1 if user_input[feature] else 0 for feature in xtrain.columns]
-    predicted_prognosis = model.predict([features])[0]
-    return predicted_prognosis
-
-# Button to predict the prognosis
-if st.button('Predict Prognosis'):
-    predicted_prognosis = predict_prognosis(user_input)
-    st.write("Predicted Prognosis:", predicted_prognosis)
+# Calculate the correlation matrix
+corr = df_train.corr()
+
+# Create a heatmap to visualize the correlation
+plt.figure(figsize=(10, 8))
+sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')
+plt.title('Correlation Matrix')
+st.pyplot()
+
+# Display the correlation values
+st.write("Correlation Values:")
+st.write(corr)