Schrick-Noah_Learning-Practice-8.ipynb

@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -109,8 +109,7 @@
       "0  1  0.643141\n",
       "1  2  0.720241\n",
       "2  3  0.710570\n",
-      "3  4  0.676896\n",
-      "4  5  0.664856\n"
+      "3  4  0.676896\n"
      ]
     }
    ],
@@ -122,7 +121,7 @@
     "valid_X = validNorm[predictors]\n",
     "valid_y = validNorm['MEDV']\n",
     "\n",
-    "for k in range(1,6):\n",
+    "for k in range(1,5):\n",
     "    knn = KNeighborsRegressor(n_neighbors=k).fit(train_X, train_y)\n",
     "    results.append({\n",
     "        'k': k,\n",
@@ -495,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [
     {
@@ -518,7 +517,7 @@
     "# iv.\n",
     "print(weather_freq)\n",
     "print(traf_freq)\n",
-    "print(2/3 * 0/3 * 3/12)"
+    "print(((3 / 12) * ((2 / 3) * (0 / 3))) / (((3 / 12) * ((2 / 3) * (0 / 3))) + ((9 / 12) * ((3 / 9) * (2 / 9)))))\n"
    ]
   },
   {