diff --git a/Lessons/Lesson22_Basic_Stats_II_Percents.ipynb b/Lessons/Lesson22_Basic_Stats_II_Percents.ipynb
index 34261c6..2f323b5 100644
--- a/Lessons/Lesson22_Basic_Stats_II_Percents.ipynb
+++ b/Lessons/Lesson22_Basic_Stats_II_Percents.ipynb
@@ -83,7 +83,7 @@
     "id": "5ADm2TV-s7VG"
    },
    "source": [
-    "**Example 2:**  Let's learn to calculate percentages by using real world data. We will work with a dataset of Boston housing prices."
+    "**Example 2:**  Let's learn to calculate percentages by using real world data. We will work with a dataset of Ames, Iowa housing prices."
    ]
   },
   {
@@ -96,8 +96,9 @@
    },
    "outputs": [],
    "source": [
-    "# Import the load_boston method \n",
-    "from sklearn.datasets import load_boston"
+    "# Import the fetch_openml method \n",
+    "from sklearn.datasets import fetch_openml\n",
+    "housing = fetch_openml(name=\"house_prices\", as_frame=True, parser=\"auto\")"
    ]
   },
   {
@@ -110,7 +111,7 @@
    },
    "outputs": [],
    "source": [
-    "# Import pandas, so that we can work with the data frame version of the Boston housing data\n",
+    "# Import pandas, so that we can work with the data frame version of the Ames housing data\n",
     "import pandas as pd"
    ]
   },
@@ -125,12 +126,10 @@
    },
    "outputs": [],
    "source": [
-    "# Load the dataset of housing prices in Boston, and convert to\n",
+    "# Load the dataset of house prices in Ames, and convert to\n",
     "# a data frame format so it's easier to view and process\n",
-    "boston = load_boston()\n",
-    "boston_df = pd.DataFrame(boston['data'], columns = boston['feature_names'])\n",
-    "boston_df['PRICE'] = boston.target\n",
-    "boston_df"
+    "ames_df = pd.DataFrame(housing['data'])\n",
+    "ames_df"
    ]
   },
   {
@@ -140,7 +139,20 @@
     "id": "eyMUHGews7VZ"
    },
    "source": [
-    "CHAS is the indicator variable we used last week, where 1 indicates that the property (tract) is on the Charles River and 0 means otherwise."
+    "The `SaleCondition` column lists the condition of the house sale:\n",
+    "\n",
+    "\n",
+    "*   `Normal`: Normal Sale     \n",
+    "\n",
+    "* `Abnorml`: Abnormal Sale -  trade, foreclosure, short sale\n",
+    "\n",
+    "* `AdjLand`: Adjoining Land Purchase\n",
+    "\n",
+    "* `Alloca`: Allocation - two linked properties with separate deeds, typically condo with a garage unit\n",
+    "\n",
+    "* `Family`: Sale between family members   \n",
+    "\n",
+    "* `Partial`: Home was not completed when last assessed (associated with New Homes)\n"
    ]
   },
   {
@@ -150,7 +162,7 @@
     "id": "IMpeHBEzs7VZ"
    },
    "source": [
-    "What percentage of the tracts bound the Charles River? We'll see how to do this using the query method AND using boolean indexing."
+    "What percentage of the houses were sold normally? We'll see how to do this using the query method AND using boolean indexing."
    ]
   },
   {
@@ -200,10 +212,10 @@
    },
    "outputs": [],
    "source": [
-    "# Determine the total number of tracts in the dataset\n",
+    "# Determine the total number of houses in the dataset\n",
     "\n",
     "\n",
-    "# Now calculate the percentage of tracts that bounds the Charles River.\n"
+    "# Now calculate the percentage of houses sold normally.\n"
    ]
   },
   {
@@ -226,12 +238,12 @@
     "id": "kFGToww_s7Vg"
    },
    "source": [
-    "What percentage of tracts have a median price less than $10,000?"
+    "What percentage of houses have a price less than $200,000?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -239,10 +251,10 @@
    },
    "outputs": [],
    "source": [
-    "# Determine number of tracts that cost less than $10,000\n",
+    "# Determine number of houses that cost less than $200,000\n",
     "\n",
     "\n",
-    "# Calculate the percentage of tracts that cost less than $10k.\n"
+    "# Calculate the percentage of houses that cost less than $200k.\n"
    ]
   },
   {
@@ -252,7 +264,7 @@
     "id": "RLZ-k3L7s7Vq"
    },
    "source": [
-    "What percentage of tracts have a median price **between** \\$10,000 and \\$30,000?"
+    "What percentage of tracts have a median price **between** $200,000 and $500,000?"
    ]
   },
   {
@@ -265,13 +277,13 @@
    },
    "outputs": [],
    "source": [
-    "# Make an array of booleans with cost greater than $10,000 AND less than $30,000\n",
+    "# Make an array of booleans with cost greater than $200,000 AND less than $500,000\n",
     "\n",
     "\n",
-    "# Determine number of tracts that cost between $10,000 and $30,000\n",
+    "# Determine number of houses that cost between $200,000 and $500,000\n",
     "\n",
     "\n",
-    "# Calculate the percentage of tracts between $10,000 and $30,000\n"
+    "# Calculate the percentage of houses between $200,000 and $500,000\n"
    ]
   },
   {
@@ -301,7 +313,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,