Update app.py

app.py

@@ -0,0 +1,75 @@
+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression, Ridge
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.metrics import mean_squared_error
+
+st.subheader("Ridge Demo")
+col1, col2 = st.columns(2)
+
+degree = st.slider('Degree', 2, 40, 1)
+alpha = st.slider('Lambda (Regularisation)', 0, 500, 1)
+
+
+with col1:
+    st.markdown("#### Un-regularized")
+    
+with col2:
+    st.markdown("#### Regularized")
+    
+x = np.linspace(-1., 1., 100)
+y = 4 + 3*x + 2*np.sin(x) + 2*np.random.randn(len(x))
+
+
+poly = PolynomialFeatures(degree=degree, include_bias=False)
+x_new = poly.fit_transform(x.reshape(-1, 1))
+
+lr = LinearRegression()
+lr.fit(x_new, y)
+y_pred = lr.predict(x_new)
+
+
+ri = Ridge(alpha = alpha)
+ri.fit(x_new, y)
+y_pred_ri = ri.predict(x_new)
+
+
+fig1, ax1 = plt.subplots()
+fig2, ax2 = plt.subplots()
+
+ax1.scatter(x, y)
+ax1.plot(x, y_pred)
+
+ax2.scatter(x, y)
+ax2.plot(x, y_pred_ri)
+
+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)
+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(fig1)
+    
+with col2:
+    st.pyplot(fig2)
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)