diff --git a/week 8/Untitled.ipynb b/week 8/Untitled.ipynb
index 3302c06..8ca58f3 100644
--- a/week 8/Untitled.ipynb	
+++ b/week 8/Untitled.ipynb	
@@ -221,9 +221,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ML",
+   "display_name": "APIs_geospatial",
    "language": "python",
-   "name": "ml"
+   "name": "apis_geospatial"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/week 8/week_8_demo.ipynb b/week 8/week_8_demo.ipynb
index 51081f0..d0a4b30 100644
--- a/week 8/week_8_demo.ipynb	
+++ b/week 8/week_8_demo.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -18,7 +18,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/nicholasjones/anaconda3/envs/GPD/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "/Users/qyinhelena/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
       "`%matplotlib` prevents importing * from pylab and numpy\n",
       "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
      ]
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
@@ -58,7 +58,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 3,
      "metadata": {
       "image/png": {
        "width": 600
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 306,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
@@ -229,7 +229,7 @@
        "4     Classical music   8:30:00 AM   Walk or bike  "
       ]
      },
-     "execution_count": 306,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -255,22 +255,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 307,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f6f82b0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11f44fd68>"
       ]
      },
-     "execution_count": 307,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -290,12 +290,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 308,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x504 with 4 Axes>"
       ]
@@ -328,12 +328,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 309,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 310,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 311,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 312,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -407,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 313,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -424,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 319,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 333,
+   "execution_count": 24,
    "metadata": {
     "scrolled": true
    },
@@ -583,7 +583,7 @@
        "[2 rows x 21 columns]"
       ]
      },
-     "execution_count": 333,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -602,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 334,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -615,7 +615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 335,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 337,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -654,7 +654,7 @@
        "                       random_state=None, splitter='best')"
       ]
      },
-     "execution_count": 337,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -671,9 +671,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'export_graphviz' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-31-88c408d96ad0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# the .dot file can be pasted into WebGraphViz to produce the tree graphics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m export_graphviz(tree_transport, out_file='tree.dot', \n\u001b[0m\u001b[1;32m      4\u001b[0m                 \u001b[0mfeature_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\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[0mclass_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Train/car'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'walk/bike'\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[0;31mNameError\u001b[0m: name 'export_graphviz' is not defined"
+     ]
+    }
+   ],
    "source": [
     "# the .dot file can be pasted into WebGraphViz to produce the tree graphics\n",
     "\n",
@@ -686,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 340,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -696,7 +708,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 340,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -715,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 356,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -724,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 357,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -738,7 +750,7 @@
        "                       random_state=None, splitter='best')"
       ]
      },
-     "execution_count": 357,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -750,9 +762,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'export_graphviz' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-38-0488077f8d2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m export_graphviz(music_tree, out_file='tree2.dot', \n\u001b[0m\u001b[1;32m      2\u001b[0m                 \u001b[0mfeature_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                 \u001b[0mclass_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Classical'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Metal'\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      4\u001b[0m                 \u001b[0mrounded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproportion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\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                 precision = 2, filled = True)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'export_graphviz' is not defined"
+     ]
+    }
+   ],
    "source": [
     "export_graphviz(music_tree, out_file='tree2.dot', \n",
     "                feature_names = X,\n",
@@ -763,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 345,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -773,7 +797,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 345,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -791,14 +815,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 347,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "TRANSPORT: First 10 predictions: [1, 1, 0, 1, 0, 1, 0, 1, 0, 0]\n"
+      "TRANSPORT: First 10 predictions: [1, 1, 1, 1, 1, 1, 1, 0, 1, 1]\n"
      ]
     }
    ],
@@ -810,14 +834,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 350,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "TRANSPORT - We predicted 15 right out of 18 examples. That's a 83.3 % accuracy rate.\n"
+      "TRANSPORT - We predicted 16 right out of 18 examples. That's a 88.9 % accuracy rate.\n"
      ]
     }
    ],
@@ -833,14 +857,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 358,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MUSIC - We predicted 11 right out of 18 examples. That's a 61.1 % accuracy rate.\n"
+      "MUSIC - We predicted 6 right out of 18 examples. That's a 33.3 % accuracy rate.\n"
      ]
     }
    ],
@@ -900,7 +924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 369,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -910,7 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 370,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -1013,60 +1037,156 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>94947</td>\n",
+       "      <td>21</td>\n",
+       "      <td>363</td>\n",
+       "      <td>8973</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>t</td>\n",
+       "      <td>r</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>590882</td>\n",
+       "      <td>22</td>\n",
+       "      <td>418</td>\n",
+       "      <td>10694</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>t</td>\n",
+       "      <td>r</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>201944</td>\n",
+       "      <td>11</td>\n",
+       "      <td>131</td>\n",
+       "      <td>1488</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>t</td>\n",
+       "      <td>r</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2 rows × 39 columns</p>\n",
+       "<p>5 rows × 39 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "   building_id  geo_level_1_id  geo_level_2_id  geo_level_3_id  \\\n",
        "0       802906               6             487           12198   \n",
        "1        28830               8             900            2812   \n",
+       "2        94947              21             363            8973   \n",
+       "3       590882              22             418           10694   \n",
+       "4       201944              11             131            1488   \n",
        "\n",
        "   count_floors_pre_eq  age  area_percentage  height_percentage  \\\n",
        "0                    2   30                6                  5   \n",
        "1                    2   10                8                  7   \n",
+       "2                    2   10                5                  5   \n",
+       "3                    2   10                6                  5   \n",
+       "4                    3   30                8                  9   \n",
        "\n",
        "  land_surface_condition foundation_type  ... has_secondary_use_agriculture  \\\n",
        "0                      t               r  ...                             0   \n",
        "1                      o               r  ...                             0   \n",
+       "2                      t               r  ...                             0   \n",
+       "3                      t               r  ...                             0   \n",
+       "4                      t               r  ...                             0   \n",
        "\n",
        "  has_secondary_use_hotel has_secondary_use_rental  \\\n",
        "0                       0                        0   \n",
        "1                       0                        0   \n",
+       "2                       0                        0   \n",
+       "3                       0                        0   \n",
+       "4                       0                        0   \n",
        "\n",
        "  has_secondary_use_institution has_secondary_use_school  \\\n",
        "0                             0                        0   \n",
        "1                             0                        0   \n",
+       "2                             0                        0   \n",
+       "3                             0                        0   \n",
+       "4                             0                        0   \n",
        "\n",
        "   has_secondary_use_industry  has_secondary_use_health_post  \\\n",
        "0                           0                              0   \n",
        "1                           0                              0   \n",
+       "2                           0                              0   \n",
+       "3                           0                              0   \n",
+       "4                           0                              0   \n",
        "\n",
        "   has_secondary_use_gov_office  has_secondary_use_use_police  \\\n",
        "0                             0                             0   \n",
        "1                             0                             0   \n",
+       "2                             0                             0   \n",
+       "3                             0                             0   \n",
+       "4                             0                             0   \n",
        "\n",
        "   has_secondary_use_other  \n",
        "0                        0  \n",
        "1                        0  \n",
+       "2                        0  \n",
+       "3                        0  \n",
+       "4                        0  \n",
        "\n",
-       "[2 rows x 39 columns]"
+       "[5 rows x 39 columns]"
       ]
      },
-     "execution_count": 370,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "print(\"Dataset has {} rows and {} columns.\".format(*X.shape))\n",
-    "X.head(2)"
+    "X.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 371,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1082,7 +1202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 372,
+   "execution_count": 89,
    "metadata": {
     "scrolled": true
    },
@@ -1091,7 +1211,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/nicholasjones/anaconda3/envs/GPD/lib/python3.7/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "/Users/qyinhelena/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
       "of pandas will change to not sort by default.\n",
       "\n",
       "To accept the future behavior, pass 'sort=False'.\n",
@@ -1112,7 +1232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 373,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1123,13 +1243,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 374,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
+       "                       max_features=None, max_leaf_nodes=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=3, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tree = DecisionTreeClassifier(max_depth=3,min_samples_leaf=3)\n",
+    "tree.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndentationError",
+     "evalue": "unexpected indent (<ipython-input-93-0bfa0729d61f>, line 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-93-0bfa0729d61f>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    feature_names = X,\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export_graphviz(tree, out_file='tree2.dot', \n",
+    "                feature_names = X,\n",
+    "                class_names = [3,2,1],\n",
+    "                rounded = True, proportion = False, \n",
+    "                precision = 2, filled = True)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1165,7 +1333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 384,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -1179,7 +1347,7 @@
        "                       random_state=None, splitter='best')"
       ]
      },
-     "execution_count": 384,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1191,7 +1359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 385,
+   "execution_count": 95,
    "metadata": {
     "scrolled": true
    },
@@ -1208,7 +1376,7 @@
        "                       verbose=0, warm_start=False)"
       ]
      },
-     "execution_count": 385,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1227,16 +1395,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 386,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7429970376509953"
+       "0.7450384491412257"
       ]
      },
-     "execution_count": 386,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1250,14 +1418,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 387,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9338245075466871 0.7597581004128869\n"
+      "0.934203121002814 0.7596199597857285\n"
      ]
     }
    ],
@@ -1269,18 +1437,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.6632898957805713"
-      ]
-     },
-     "execution_count": 236,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'log_reg' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-98-ef6f339cceee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlog_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdamage_grade\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'log_reg' is not defined"
+     ]
     }
    ],
    "source": [
@@ -1296,7 +1465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1308,9 +1477,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 267,
+   "execution_count": 81,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Series' object has no attribute 'damage_grade'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-81-95698c401832>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mn_estimators\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mrf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdamage_grade\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mIS_accuracy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOS_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate_accuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mIS_accuracy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIS_accuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5065\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\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   5066\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5067\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5069\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'damage_grade'"
+     ]
+    }
+   ],
    "source": [
     "# Accuracy versus number of trees\n",
     "\n",
@@ -1328,24 +1510,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
+   "execution_count": 82,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Accuracy versus number of trees')"
-      ]
-     },
-     "execution_count": 268,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ValueError",
+     "evalue": "arrays must all be same length",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-82-262b34c42044>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\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[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_estimators\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIS_accuracy_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"in-sample\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_estimators\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOS_accuracy_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"out-of-sample\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy versus number of trees'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1076\u001b[0m         \u001b[0mdashes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdashes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarkers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle_order\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m         \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1078\u001b[0;31m         \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_style\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_style\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1079\u001b[0m     )\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend)\u001b[0m\n\u001b[1;32m    661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         plot_data = self.establish_variables(\n\u001b[0;32m--> 663\u001b[0;31m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    664\u001b[0m         )\n\u001b[1;32m    665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, size, style, units, data)\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m             )\n\u001b[0;32m--> 157\u001b[0;31m             \u001b[0mplot_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0;31m# Option 3:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    390\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[1;32m    391\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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[0;32m--> 392\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    393\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\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    394\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[0;34m(data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    315\u001b[0m             \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_lengths\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    316\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'arrays must all be same length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: arrays must all be same length"
+     ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1365,19 +1555,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 245,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([  1000.        ,  29428.57142857,  57857.14285714,  86285.71428571,\n",
-       "       114714.28571429, 143142.85714286, 171571.42857143, 200000.        ])"
-      ]
-     },
-     "execution_count": 245,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'n_rows' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-83-09633ed1dcc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn_rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'n_rows' is not defined"
+     ]
     }
    ],
    "source": [
@@ -1386,9 +1576,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 253,
+   "execution_count": 84,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Series' object has no attribute 'damage_grade'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-84-cc650e624b4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mrf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mdc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDummyClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstrategy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'most_frequent'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdamage_grade\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\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[0m\u001b[1;32m     10\u001b[0m     \u001b[0mdc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdamage_grade\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\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     11\u001b[0m     \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5065\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\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   5066\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5067\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5069\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'damage_grade'"
+     ]
+    }
+   ],
    "source": [
     "# Accuracy versus amount of training data\n",
     "n_rows = np.linspace(1000, X.shape[0], 14).astype(int)\n",
@@ -1411,22 +1614,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Accuracy versus number of rows')"
-      ]
-     },
-     "execution_count": 254,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ValueError",
+     "evalue": "arrays must all be same length",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-85-d5d58aca5fa9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\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[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_list_dummy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy versus number of rows'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1076\u001b[0m         \u001b[0mdashes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdashes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmarkers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle_order\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m         \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1078\u001b[0;31m         \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_style\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_style\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1079\u001b[0m     )\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, sort, err_style, err_kws, legend)\u001b[0m\n\u001b[1;32m    661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         plot_data = self.establish_variables(\n\u001b[0;32m--> 663\u001b[0;31m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    664\u001b[0m         )\n\u001b[1;32m    665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, size, style, units, data)\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m             )\n\u001b[0;32m--> 157\u001b[0;31m             \u001b[0mplot_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0;31m# Option 3:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    390\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[1;32m    391\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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[0;32m--> 392\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    393\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\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    394\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[0;34m(data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    315\u001b[0m             \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_lengths\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    316\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'arrays must all be same length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: arrays must all be same length"
+     ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1469,7 +1680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 269,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1478,7 +1689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1487,9 +1698,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'X_train_transport' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-102-f19347c0a25b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mgrid_search\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_transport\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train_transport\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'X_train_transport' is not defined"
+     ]
+    }
+   ],
    "source": [
     "grid_search = GridSearchCV(RandomForestClassifier(),param_grid,cv=5)\n",
     "grid_search.fit(X_train_transport,y_train_transport)"
@@ -1497,9 +1720,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 103,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'GridSearchCV' object has no attribute 'best_params_'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-103-161fb2de36a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The best parameters were: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\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[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The best model classified {} percent of examples correctly.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\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[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_params_'"
+     ]
+    }
+   ],
    "source": [
     "print(\"The best parameters were: {}\".format(grid_search.best_params_))\n",
     "print(\"The best model classified {} percent of examples correctly.\".format((grid_search.best_score_*100).round(1)))"
@@ -1514,20 +1749,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 388,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/nicholasjones/anaconda3/envs/GPD/lib/python3.7/site-packages/pandas/plotting/_core.py:185: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
+      "/Users/qyinhelena/anaconda3/envs/APIs_geospatial/lib/python3.7/site-packages/pandas/plotting/_core.py:185: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
       "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1543,13 +1778,20 @@
     "feat_importance.nlargest(8).plot(kind='barh',title='Earthquake damage: Feature importances',colors='b')\n",
     "plt.gca().invert_yaxis();"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ML",
+   "display_name": "APIs_geospatial",
    "language": "python",
-   "name": "ml"
+   "name": "apis_geospatial"
   },
   "language_info": {
    "codemirror_mode": {