Finance

workshop-resources/data-science-and-machine-learning/Data_Science_1/loan-project/loan-notebook.ipynb

@@ -63,7 +63,7 @@
     "\n",
     "### Removing the Gender and Married fields\n",
     "\n",
-    "Since we would not discriminate against people based on their sex, we wil remove that field. Also, instead of basing our decision on whether or not someone is married, we'll remove it and use something like income since it would be a more objective variable to base a loan decision on."
+    "Since we would not discriminate against people based on their gender, we wil remove that field. Also, instead of basing our decision on whether or not someone is married, we'll remove it and use something like income since it would be a more objective variable to base a loan decision on."
    ]
   },
   {
@@ -880,14 +880,6 @@
     "This is the last step that we'll do in exploring our dataset. From the exploration we know that we need to deal with some messy data. Let's move on to some **Data Munging.**"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Note from Sarah to Dan*  \n",
-    "You said `We just created two basic classification algorithms!` Can we be more speicific here? Can you describe the fact that the \"algorithm\" you mean is that they grouped/analyzed data by certain types/values which helped predict whether a loan would be approved? Maybe mention that this is a simple \"algorithm\" but the first steps to the more advanced machine learning algorithms that they will explore in the future, and help unveil what data science/ml really is with this?"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},