Update app.py

app.py

@@ -5,7 +5,7 @@ from sklearn.linear_model import LinearRegression, Ridge
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.metrics import mean_squared_error
 
-st.title("Ridge Demo")
+st.subheader("Ridge Demo")
 col1, col2 = st.columns(2)
 
 degree = st.slider('Degree', 2, 40, 1)
@@ -13,10 +13,10 @@ alpha = st.slider('Lambda (Regularisation)', 0, 500, 1)
 
 
 with col1:
-    st.subheader("Un-regularized")
+    st.markdown("#### Un-regularized")
     
 with col2:
-    st.subheader("Regularized")
+    st.markdown("#### Regularized")
     
 x = np.linspace(-1., 1., 100)
 y = 4 + 3*x + 2*np.sin(x) + 2*np.random.randn(len(x))