diff --git a/.ipynb_checkpoints/1. Classifying Partial Permits-checkpoint.ipynb b/.ipynb_checkpoints/1. Classifying Partial Permits-checkpoint.ipynb
index c804982..ff51d56 100644
--- a/.ipynb_checkpoints/1. Classifying Partial Permits-checkpoint.ipynb	
+++ b/.ipynb_checkpoints/1. Classifying Partial Permits-checkpoint.ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 382,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
     "from keras.utils import np_utils\n",
     "from sklearn.model_selection import cross_val_score\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.model_selection import KFold\n",
     "from sklearn.preprocessing import LabelEncoder\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from sklearn.pipeline import Pipeline"
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 383,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 384,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1520,7 +1520,7 @@
        "[75891 rows x 16 columns]"
       ]
      },
-     "execution_count": 384,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1545,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 385,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -2884,7 +2884,7 @@
        "[75891 rows x 14 columns]"
       ]
      },
-     "execution_count": 385,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2905,7 +2905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 386,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -4244,7 +4244,7 @@
        "[75891 rows x 14 columns]"
       ]
      },
-     "execution_count": 386,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4265,7 +4265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 387,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -4301,7 +4301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 388,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -4371,7 +4371,7 @@
        "Name: Purpose, Length: 75891, dtype: object"
       ]
      },
-     "execution_count": 388,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4391,7 +4391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 389,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -5417,7 +5417,7 @@
        "[75891 rows x 12 columns]"
       ]
      },
-     "execution_count": 389,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5437,7 +5437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 390,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -7336,7 +7336,7 @@
        "[75891 rows x 9400 columns]"
       ]
      },
-     "execution_count": 390,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7356,7 +7356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 391,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -9003,7 +9003,7 @@
        "[75891 rows x 9400 columns]"
       ]
      },
-     "execution_count": 391,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9027,7 +9027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 392,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9036,7 +9036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 393,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -9068,7 +9068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 394,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9091,7 +9091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 395,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9108,7 +9108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 396,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -9116,24 +9116,24 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/5\n",
-      "60712/60712 [==============================] - 148s 2ms/step - loss: 0.7000 - acc: 0.7340\n",
+      "60712/60712 [==============================] - 150s 2ms/step - loss: 0.7000 - acc: 0.7341\n",
       "Epoch 2/5\n",
-      "60712/60712 [==============================] - 146s 2ms/step - loss: 0.3237 - acc: 0.8088\n",
+      "60712/60712 [==============================] - 147s 2ms/step - loss: 0.3236 - acc: 0.8089\n",
       "Epoch 3/5\n",
-      "60712/60712 [==============================] - 152s 3ms/step - loss: 0.2564 - acc: 0.8270\n",
+      "60712/60712 [==============================] - 143s 2ms/step - loss: 0.2564 - acc: 0.8270\n",
       "Epoch 4/5\n",
-      "60712/60712 [==============================] - 145s 2ms/step - loss: 0.2190 - acc: 0.8382\n",
+      "60712/60712 [==============================] - 143s 2ms/step - loss: 0.2190 - acc: 0.8382\n",
       "Epoch 5/5\n",
-      "60712/60712 [==============================] - 143s 2ms/step - loss: 0.1948 - acc: 0.8446\n"
+      "60712/60712 [==============================] - 148s 2ms/step - loss: 0.1948 - acc: 0.8447\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x1acb952b0>"
+       "<keras.callbacks.History at 0x118098940>"
       ]
      },
-     "execution_count": 396,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9145,15 +9145,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 397,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15179/15179 [==============================] - 31s 2ms/step\n",
-      "acc: 81.85%\n"
+      "15179/15179 [==============================] - 33s 2ms/step\n",
+      "acc: 81.84%\n"
      ]
     }
    ],
@@ -9174,7 +9174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 543,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -9261,7 +9261,7 @@
        "[1 rows x 9400 columns]"
       ]
      },
-     "execution_count": 543,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9275,7 +9275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 544,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -9338,7 +9338,7 @@
        "1  0  0  0  0  0  0  0  0  1  0  0  0"
       ]
      },
-     "execution_count": 544,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9350,7 +9350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 545,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -9376,42 +9376,37 @@
    "source": [
     "### Evaluating our model with K-Fold Cross Validation\n",
     "\n",
-    "We'll use k-fold validation to get a better representation of how our model did..."
+    "We'll use k-fold validation to get a better representation of how our model did.\n",
+    "We'll first build an estimator using `KerasClassifier` which is a wrapper to make our model work nicely with sci-kit learn's validators..."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 398,
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimator = KerasClassifier(build_fn=build_model, epochs=epochs, batch_size=batch_size, verbose=0)\n",
+    "k_fold    = KFold(n_splits=10, shuffle=True, random_state=seed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'[ 9400  9401  9402 ... 75888 75889 75890] not in index'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-398-5c8de40a14e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkfold\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s: %.2f%%\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2131\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2132\u001b[0m             \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2133\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2134\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2135\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2175\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2176\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2177\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2178\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[1;32m   1267\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1268\u001b[0m                     raise KeyError('{mask} not in index'\n\u001b[0;32m-> 1269\u001b[0;31m                                    .format(mask=objarr[mask]))\n\u001b[0m\u001b[1;32m   1270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1271\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: '[ 9400  9401  9402 ... 75888 75889 75890] not in index'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline: 82.23% (0.43%)\n"
      ]
     }
    ],
    "source": [
-    "k_fold    = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
-    "cv_scores = []\n",
+    "results = cross_val_score(estimator, data, labels, cv=k_fold)\n",
     "\n",
-    "for train, test in kfold.split(data, labels):\n",
-    "    model = build_model()\n",
-    "    model.fit(data[train], labels[train], epochs=epochs, batch_size=10, verbose=0)\n",
-    "    scores = model.evaluate(data[test], labels[test], verbose=0)\n",
-    "    print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
-    "    \n",
-    "    cv_scores.append(scores[1] * 100)\n",
-    "    \n",
-    "print(\"%.2f%% (+/- %.2f%%)\" % (numpy.mean(cv_scores), numpy.std(cv_scores)))"
+    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
    ]
   },
   {
diff --git a/1. Classifying Partial Permits.ipynb b/1. Classifying Partial Permits.ipynb
index c804982..ff51d56 100644
--- a/1. Classifying Partial Permits.ipynb	
+++ b/1. Classifying Partial Permits.ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 382,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
     "from keras.utils import np_utils\n",
     "from sklearn.model_selection import cross_val_score\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.model_selection import KFold\n",
     "from sklearn.preprocessing import LabelEncoder\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from sklearn.pipeline import Pipeline"
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 383,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 384,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1520,7 +1520,7 @@
        "[75891 rows x 16 columns]"
       ]
      },
-     "execution_count": 384,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1545,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 385,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -2884,7 +2884,7 @@
        "[75891 rows x 14 columns]"
       ]
      },
-     "execution_count": 385,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2905,7 +2905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 386,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -4244,7 +4244,7 @@
        "[75891 rows x 14 columns]"
       ]
      },
-     "execution_count": 386,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4265,7 +4265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 387,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -4301,7 +4301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 388,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -4371,7 +4371,7 @@
        "Name: Purpose, Length: 75891, dtype: object"
       ]
      },
-     "execution_count": 388,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4391,7 +4391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 389,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -5417,7 +5417,7 @@
        "[75891 rows x 12 columns]"
       ]
      },
-     "execution_count": 389,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5437,7 +5437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 390,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -7336,7 +7336,7 @@
        "[75891 rows x 9400 columns]"
       ]
      },
-     "execution_count": 390,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7356,7 +7356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 391,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -9003,7 +9003,7 @@
        "[75891 rows x 9400 columns]"
       ]
      },
-     "execution_count": 391,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9027,7 +9027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 392,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9036,7 +9036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 393,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -9068,7 +9068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 394,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9091,7 +9091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 395,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9108,7 +9108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 396,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -9116,24 +9116,24 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/5\n",
-      "60712/60712 [==============================] - 148s 2ms/step - loss: 0.7000 - acc: 0.7340\n",
+      "60712/60712 [==============================] - 150s 2ms/step - loss: 0.7000 - acc: 0.7341\n",
       "Epoch 2/5\n",
-      "60712/60712 [==============================] - 146s 2ms/step - loss: 0.3237 - acc: 0.8088\n",
+      "60712/60712 [==============================] - 147s 2ms/step - loss: 0.3236 - acc: 0.8089\n",
       "Epoch 3/5\n",
-      "60712/60712 [==============================] - 152s 3ms/step - loss: 0.2564 - acc: 0.8270\n",
+      "60712/60712 [==============================] - 143s 2ms/step - loss: 0.2564 - acc: 0.8270\n",
       "Epoch 4/5\n",
-      "60712/60712 [==============================] - 145s 2ms/step - loss: 0.2190 - acc: 0.8382\n",
+      "60712/60712 [==============================] - 143s 2ms/step - loss: 0.2190 - acc: 0.8382\n",
       "Epoch 5/5\n",
-      "60712/60712 [==============================] - 143s 2ms/step - loss: 0.1948 - acc: 0.8446\n"
+      "60712/60712 [==============================] - 148s 2ms/step - loss: 0.1948 - acc: 0.8447\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x1acb952b0>"
+       "<keras.callbacks.History at 0x118098940>"
       ]
      },
-     "execution_count": 396,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9145,15 +9145,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 397,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15179/15179 [==============================] - 31s 2ms/step\n",
-      "acc: 81.85%\n"
+      "15179/15179 [==============================] - 33s 2ms/step\n",
+      "acc: 81.84%\n"
      ]
     }
    ],
@@ -9174,7 +9174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 543,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -9261,7 +9261,7 @@
        "[1 rows x 9400 columns]"
       ]
      },
-     "execution_count": 543,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9275,7 +9275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 544,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -9338,7 +9338,7 @@
        "1  0  0  0  0  0  0  0  0  1  0  0  0"
       ]
      },
-     "execution_count": 544,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9350,7 +9350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 545,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -9376,42 +9376,37 @@
    "source": [
     "### Evaluating our model with K-Fold Cross Validation\n",
     "\n",
-    "We'll use k-fold validation to get a better representation of how our model did..."
+    "We'll use k-fold validation to get a better representation of how our model did.\n",
+    "We'll first build an estimator using `KerasClassifier` which is a wrapper to make our model work nicely with sci-kit learn's validators..."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 398,
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimator = KerasClassifier(build_fn=build_model, epochs=epochs, batch_size=batch_size, verbose=0)\n",
+    "k_fold    = KFold(n_splits=10, shuffle=True, random_state=seed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'[ 9400  9401  9402 ... 75888 75889 75890] not in index'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-398-5c8de40a14e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkfold\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s: %.2f%%\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2131\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2132\u001b[0m             \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2133\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2134\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2135\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2175\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2176\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2177\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2178\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[1;32m   1267\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1268\u001b[0m                     raise KeyError('{mask} not in index'\n\u001b[0;32m-> 1269\u001b[0;31m                                    .format(mask=objarr[mask]))\n\u001b[0m\u001b[1;32m   1270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1271\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: '[ 9400  9401  9402 ... 75888 75889 75890] not in index'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline: 82.23% (0.43%)\n"
      ]
     }
    ],
    "source": [
-    "k_fold    = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
-    "cv_scores = []\n",
+    "results = cross_val_score(estimator, data, labels, cv=k_fold)\n",
     "\n",
-    "for train, test in kfold.split(data, labels):\n",
-    "    model = build_model()\n",
-    "    model.fit(data[train], labels[train], epochs=epochs, batch_size=10, verbose=0)\n",
-    "    scores = model.evaluate(data[test], labels[test], verbose=0)\n",
-    "    print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
-    "    \n",
-    "    cv_scores.append(scores[1] * 100)\n",
-    "    \n",
-    "print(\"%.2f%% (+/- %.2f%%)\" % (numpy.mean(cv_scores), numpy.std(cv_scores)))"
+    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
    ]
   },
   {