diff --git a/frontend/pages/prediction_classification.py b/frontend/pages/prediction_classification.py
index c11d7ee..bb6bb22 100644
--- a/frontend/pages/prediction_classification.py
+++ b/frontend/pages/prediction_classification.py
@@ -67,7 +67,6 @@ if "data" in st.session_state:
             fig = plt.figure()
 
             y_pred = [model.predict(pd.DataFrame([pred_value[0]], columns=data_name)) for pred_value in X.values.tolist()]
-            print([x[0] for x in X.values.tolist()])
             cm = confusion_matrix(y, y_pred)
 
             sns.heatmap(cm, annot=True, fmt="d")
@@ -75,9 +74,6 @@ if "data" in st.session_state:
             plt.xlabel('Predicted')
             plt.ylabel('True')
 
-            st.pyplot(fig)
-
-
-
+            st.pyplot(fig, figsize=(1, 1))
 else:
     st.error("File not loaded")
diff --git a/frontend/pages/prediction_regression.py b/frontend/pages/prediction_regression.py
index e06fa12..35b648d 100644
--- a/frontend/pages/prediction_regression.py
+++ b/frontend/pages/prediction_regression.py
@@ -1,5 +1,6 @@
 import streamlit as st
 from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score
 import pandas as pd
 import matplotlib.pyplot as plt
 
@@ -21,6 +22,10 @@ if "data" in st.session_state:
         model = LinearRegression()
         model.fit(X, y)
         
+        y_pred = [model.predict(pd.DataFrame([pred_value[0]], columns=data_name)) for pred_value in X.values.tolist()]
+        r2 = r2_score(y, y_pred)
+        st.write('R-squared score:', r2)
+        
         st.subheader("Enter values for prediction")
         pred_values = [st.number_input(f"Value for {feature}", value=0.0) for feature in data_name]
         prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))