diff --git a/introduction_to_applying_machine_learning/breast_cancer_prediction/Breast Cancer Prediction.ipynb b/introduction_to_applying_machine_learning/breast_cancer_prediction/Breast Cancer Prediction.ipynb
index e10654eb17..1c22899f1c 100644
--- a/introduction_to_applying_machine_learning/breast_cancer_prediction/Breast Cancer Prediction.ipynb	
+++ b/introduction_to_applying_machine_learning/breast_cancer_prediction/Breast Cancer Prediction.ipynb	
@@ -536,7 +536,7 @@
     "- Total Classification Accuracy \n",
     "- Mean Absolute Error\n",
     "\n",
-    "For our example, we'll keep things simple and use total clssification accuracy as our metric of choice. We will also evalute  Mean Absolute  Error (MAE) as the linear-learner has been optimized using this metric, not necessarily because it is a relevant metric from an application point of view. We'll compare the performance of the linear-learner against a naive benchmark prediction which uses majority class observed in the training data set for prediction on the test data.\n",
+    "For our example, we'll keep things simple and use total classification accuracy as our metric of choice. We will also evaluate  Mean Absolute  Error (MAE) as the linear-learner has been optimized using this metric, not necessarily because it is a relevant metric from an application point of view. We'll compare the performance of the linear-learner against a naive benchmark prediction which uses majority class observed in the training data set for prediction on the test data.\n",
     "\n",
     "\n"
    ]