Enhance scatterplot and slider default

app.py

@@ -11,7 +11,7 @@ st.latex(r'''h(\boldsymbol x, \boldsymbol w)= \sum_{k=1}^{K}\boldsymbol w_{k} \p
 
 
 # Sidebar inputs
-number_of_observations = st.sidebar.slider('Number of observations', min_value=50, max_value=150, value=50)
+number_of_observations = st.sidebar.slider('Number of observations', min_value=50, max_value=150, value=75)
 noise_standard_deviation = st.sidebar.slider('Standard deviation of the noise', min_value = 0.0, max_value=2.0, value=0.25)
 cost_function = st.sidebar.radio('What cost function you want to use for the fitting?', options=('RMSE-Loss', 'Huber-Loss'))
 
@@ -30,8 +30,8 @@ y = np.dot(X, w) + noise_standard_deviation * np.random.randn(number_of_observat
 
 fig, ax = plt.subplots(dpi=320)
 ax.set_xlim((0,1))
-ax.set_ylim((-5,20))
-ax.scatter(X[:,1], y, c='r', edgecolors='black')
+ax.set_ylim((-5,26))
+ax.scatter(X[:,1], y, c='#e76254' ,edgecolors='firebrick')
 
 st.pyplot(fig)
 st.write(X[:5, :])