Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+
+st.title("Ridge Demo")
+degree = st.slider('Degree', 2, 20, 1)
+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)
+
+
+fig, ax = plt.subplots()
+ax.scatter(x, y)
+y_pred = lr.predict(x_new)
+ax.plot(x, y_pred)
+
+
+st.pyplot(fig)