diff --git a/README.md b/README.md
index 7db3459..1f221f5 100644
--- a/README.md
+++ b/README.md
@@ -32,11 +32,15 @@ Probabilistic regression example on the Boston housing dataset:
 ```python
 from ngboost import NGBRegressor
 
-from sklearn.datasets import load_boston
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import mean_squared_error
 
-X, Y = load_boston(True)
+#Load Boston housing dataset
+data_url = "http://lib.stat.cmu.edu/datasets/boston"
+raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
+X = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
+Y = raw_df.values[1::2, 2]
+
 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
 
 ngb = NGBRegressor().fit(X_train, Y_train)
diff --git a/examples/regression.py b/examples/regression.py
index ee06ade..971a7b2 100644
--- a/examples/regression.py
+++ b/examples/regression.py
@@ -1,4 +1,5 @@
-from sklearn.datasets import load_boston
+import numpy as np
+import pandas as pd
 from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import train_test_split
 
@@ -6,8 +7,12 @@
 from ngboost.distns import Normal
 
 if __name__ == "__main__":
+    # Load Boston housing dataset
+    data_url = "http://lib.stat.cmu.edu/datasets/boston"
+    raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
+    X = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
+    Y = raw_df.values[1::2, 2]
 
-    X, Y = load_boston(return_X_y=True)
     X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
 
     ngb = NGBRegressor(Dist=Normal).fit(X_train, Y_train)
diff --git a/examples/survival.py b/examples/survival.py
index c830db2..32be7a5 100644
--- a/examples/survival.py
+++ b/examples/survival.py
@@ -1,5 +1,5 @@
 import numpy as np
-from sklearn.datasets import load_boston
+import pandas as pd
 from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import train_test_split
 
@@ -7,8 +7,12 @@
 from ngboost.distns import LogNormal
 
 if __name__ == "__main__":
+    # Load Boston housing dataset
+    data_url = "http://lib.stat.cmu.edu/datasets/boston"
+    raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
+    X = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
+    Y = raw_df.values[1::2, 2]
 
-    X, Y = load_boston(return_X_y=True)
     X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
 
     # introduce administrative censoring