| 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) | |