commit

Makefile

@@ -1,2 +1,2 @@
-edits: app.py
-	black app.py
+black app.py
+isort app.py

app.py

@@ -1,10 +1,9 @@
-import streamlit as st
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import streamlit as st
 from sklearn.datasets import make_regression
-from sklearn.neighbors import KNeighborsRegressor
 from sklearn.metrics import mean_squared_error
-
+from sklearn.neighbors import KNeighborsRegressor
 
 st.subheader("K nearest neighbor (KNN) Regressor")
 
@@ -15,7 +14,7 @@ K = st.slider(
 )
 
 
-X, y = make_regression(n_samples=1000, n_features=1,random_state=42)
+X, y = make_regression(n_samples=1000, n_features=1, random_state=42)
 
 ntrain = 700
 
@@ -28,14 +27,14 @@ knn = KNeighborsRegressor(n_neighbors=K)
 knn.fit(x_train, y_train)
 plt.figure()
 y_pred = knn.predict(x_test)
-plt.scatter(x_test,y_test)
-plt.plot(x_test,y_pred)
+plt.scatter(x_test, y_test)
+plt.plot(x_test, y_pred)
 plt.xlabel("Datapoints")
 plt.ylabel("Predictions")
 
 with st_col:
     st.pyplot(plt)
-error = mean_squared_error(y_test,y_pred)
+error = mean_squared_error(y_test, y_pred)
 st.write("The error is", error)
 
 hide_streamlit_style = """