diff --git a/Regression_model.ipynb b/Regression_model.ipynb
index 71a3782f39339a75f7ca22ead132664506594d37..94fc2d2f5b966df2b10c5144e82af6589439570e 100644
--- a/Regression_model.ipynb
+++ b/Regression_model.ipynb
@@ -1938,7 +1938,7 @@
    "cell_type": "markdown",
    "source": [
     "#### Over all performance of the model is 75% ie,our model correctly predicts 75% of all customers.For non churning customers Precision: 92% - When you predict \"won't churn\", we are almost always right.Recall: 72% - You catch 72% of customers who actually stay.Our Model is giving Strong performance for identifying loyal customers.For Churned customers Precision: 51% - Only half your churn predictions are correct Recall: 82% - our  catches 82% of customers who actually churn Good at finding churners, but many false alarms.\n",
-    "#### Strong ares of our mode ls are\n",
+    "#### Strong areas of our model are\n",
     "##### 1).Excellent churn detection (82% recall) - You won't miss many leaving customers\n",
     "##### 2). High confidence in loyal customers (92% precision for non-churn)\n",
     "##### 3).Actionable for retention campaigns\n"