Boston housing

projects/boston_housing/boston_housing.ipynb

@@ -33,9 +33,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Boston housing dataset has 489 data points with 4 variables each.\n"
+     ]
+    }
+   ],
    "source": [
     "# Import libraries necessary for this project\n",
     "import numpy as np\n",
@@ -81,24 +89,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Statistics for Boston housing dataset:\n",
+      "\n",
+      "Minimum price: $105000.0\n",
+      "Maximum price: $1024800.0\n",
+      "Mean price: $454342.9447852761\n",
+      "Median price $438900.0\n",
+      "Standard deviation of prices: $165171.13154429474\n"
+     ]
+    }
+   ],
    "source": [
     "# TODO: Minimum price of the data\n",
-    "minimum_price = None\n",
+    "minimum_price = min(prices)\n",
     "\n",
     "# TODO: Maximum price of the data\n",
-    "maximum_price = None\n",
+    "maximum_price = max(prices)\n",
     "\n",
     "# TODO: Mean price of the data\n",
-    "mean_price = None\n",
+    "mean_price = np.mean(prices)\n",
     "\n",
     "# TODO: Median price of the data\n",
-    "median_price = None\n",
+    "median_price = np.median(prices)\n",
     "\n",
     "# TODO: Standard deviation of prices of the data\n",
-    "std_price = None\n",
+    "std_price = np.std(prices)\n",
     "\n",
     "# Show the calculated statistics\n",
     "print(\"Statistics for Boston housing dataset:\\n\")\n",
@@ -657,7 +679,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,