Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import LinearRegression, Ridge
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.metrics import mean_squared_error
 
@@ -9,7 +9,7 @@ st.title("Ridge Demo")
 col1, col2 = st.columns(2)
 
 degree = st.slider('Degree', 2, 40, 1)
-lambda_ = st.slider('Lambda (Regularisation)', 0, 500, 1)
+alpha = st.slider('Lambda (Regularisation)', 0, 500, 1)
 
 
 with col1:
@@ -27,31 +27,45 @@ x_new = poly.fit_transform(x.reshape(-1, 1))
 
 lr = LinearRegression()
 lr.fit(x_new, y)
+y_pred = lr.predict(x_new)
 
 
-fig, ax = plt.subplots()
-ax.scatter(x, y)
-y_pred = lr.predict(x_new)
-ax.plot(x, y_pred)
+ri = Ridge(alpha = alpha)
+ri.fit(x_new, y)
+y_pred_ri = ri.predict(x_new)
+
+
+fig1, ax1 = plt.subplots()
+fig2, ax2 = plt.subplots()
 
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
+ax1.scatter(x, y)
+ax1.plot(x, y_pred)
 
-# Only show ticks on the left and bottom spines
-ax.yaxis.set_ticks_position('left')
-ax.xaxis.set_ticks_position('bottom')
+ax2.scatter(x, y)
+ax2.plot(x, y_pred_ri)
 
-ax.set_xlabel("x")
-ax.set_ylabel("y")
+for ax in [ax1, ax2]:
+    ax.spines['right'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+
+    # Only show ticks on the left and bottom spines
+    ax.yaxis.set_ticks_position('left')
+    ax.xaxis.set_ticks_position('bottom')
+
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
 
 rmse = np.round(np.sqrt(mean_squared_error(y_pred, y)), 2)
-ax.set_title(f"Train RMSE: {rmse}")
+ax1.set_title(f"Train RMSE: {rmse}")
+
+rmse_ri = np.round(np.sqrt(mean_squared_error(y_pred_ri, y)), 2)
+ax2.set_title(f"Train RMSE: {rmse_ri}")
 
 with col1:
-    st.pyplot(fig)
+    st.pyplot(fig1)
     
 with col2:
-    st.pyplot(fig)
+    st.pyplot(fig2)
 hide_streamlit_style = """
             <style>
             #MainMenu {visibility: hidden;}