Exploring the Results.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the Results of Machine Learning #\n",
    "\n",
    "In this notebook I explore some of the implications of the machine learning results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary modules\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import graphviz\n",
    "import pydotplus\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn import tree\n",
    "from sklearn import ensemble\n",
    "\n",
    "from IPython.display import SVG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Decision Trees ##\n",
    "\n",
    "The decision tree was so effective, I thought it would be interesting to see if a single decision tree done on a subset of the data would still be interesting, perhaps for decision making for road designers.\n",
    "\n",
    "I created a data set where the only features were:\n",
    "- traffic_density: Average Annual Daily Traffic, measured by counting the total number of vehicles in a year and dividing by 365\n",
    "- bike_acc_score: the number of bike accidents that happened in that area within 50m of each other\n",
    "- ROW_Width: right of way width: includes road surface and property ceded by property owners\n",
    "- Struct_Cnd: Structural condition\n",
    "- Surface_Wd: Surface width\n",
    "- Surface_Tp: Surface Type\n",
    "- Rt_Sidewlk: right sidewalk width\n",
    "- Lt_Sidewlk: left sidewalk width\n",
    "- Terrain: terrain\n",
    "- Med_Width: median width\n",
    "- Op_Dir_SL: opposite direction speed limit\n",
    "\n",
    "And then created a decision tree from those features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "   car_acc_score  traffic_density  bike_acc_score  ROW_Width  Surface_Wd  \\\n",
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       "\n",
       "   Rt_Sidewlk  Lt_Sidewlk  Med_Width  Op_Dir_SL  Struct_Cnd_-1  ...  \\\n",
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       "\n",
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     "metadata": {},
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    }
   ],
   "source": [
    "#build smaller feature set\n",
    "#read in data set before making dummies\n",
    "sel_df = pd.read_csv('data/cleaned_data/model_df.csv')\n",
    "sel_df = sel_df[['car_acc_score', 'traffic_density', 'bike_acc_score', 'ROW_Width', 'Surface_Wd', 'Surface_Tp', \n",
    "                'Rt_Sidewlk', 'Lt_Sidewlk', 'Struct_Cnd', 'Terrain',\n",
    "                'Med_Width', 'Op_Dir_SL']]\n",
    "sel_dum = pd.get_dummies(sel_df, columns=['Struct_Cnd', 'Terrain', 'Surface_Tp'])\n",
    "sel_dum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_y = sel_dum.car_acc_score\n",
    "car_X = sel_dum.drop(columns=['car_acc_score'])\n",
    "car_c_y = sel_dum.car_acc_score.mask(sel_dum.car_acc_score > 0, 1)\n",
    "\n",
    "# do train test split\n",
    "X_car_c_train, X_car_c_test, y_car_c_train, y_car_c_test = train_test_split(car_X, car_c_y, test_size=0.3, \n",
    "                                                                            random_state=18,\n",
    "                                                                            shuffle=True, stratify=car_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dtc = tree.DecisionTreeClassifier(max_depth=5)\n",
    "dtc.fit(X_car_c_train, y_car_c_train)\n",
    "dtc.score(X_car_c_test, y_car_c_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even with only the features listed above, and limiting the depth to 5, a single decsion tree can still predict accidents with 87% accuracy. So the decision tree would be useful for road design decision-makers to help them predict accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "execution_count": 5,
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   ],
   "source": [
    "fn = X_car_c_train.columns\n",
    "cn = ['Accident', 'No Accident']\n",
    "dot_data = tree.export_graphviz(dtc, feature_names=fn, class_names=cn, out_file=None, filled=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "nodes = graph.get_node_list()\n",
    "graph.write_png('car_decision_tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "<text text-anchor=\"middle\" x=\"427.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.49</text>\r\n",
       "<text text-anchor=\"middle\" x=\"427.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 6060</text>\r\n",
       "<text text-anchor=\"middle\" x=\"427.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3464, 2596]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"427.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 3&#45;&gt;4 -->\r\n",
       "<g id=\"edge4\" class=\"edge\"><title>3&#45;&gt;4</title>\r\n",
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       "<!-- 11 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"865.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">bike_acc_score &lt;= 2.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"865.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.463</text>\r\n",
       "<text text-anchor=\"middle\" x=\"865.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3602</text>\r\n",
       "<text text-anchor=\"middle\" x=\"865.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1311, 2291]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"865.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 3&#45;&gt;11 -->\r\n",
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       "<g id=\"node6\" class=\"node\"><title>5</title>\r\n",
       "<polygon fill=\"#4da7e8\" stroke=\"black\" points=\"274.5,-187 134.5,-187 134.5,-104 274.5,-104 274.5,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"204.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">ROW_Width &lt;= 81.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.169</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 214</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [20, 194]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 4&#45;&gt;5 -->\r\n",
       "<g id=\"edge5\" class=\"edge\"><title>4&#45;&gt;5</title>\r\n",
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       "<!-- 8 -->\r\n",
       "<g id=\"node9\" class=\"node\"><title>8</title>\r\n",
       "<polygon fill=\"#f7d9c3\" stroke=\"black\" points=\"497.5,-187 357.5,-187 357.5,-104 497.5,-104 497.5,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"427.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">ROW_Width &lt;= 52.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"427.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.484</text>\r\n",
       "<text text-anchor=\"middle\" x=\"427.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 5846</text>\r\n",
       "<text text-anchor=\"middle\" x=\"427.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3444, 2402]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"427.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 4&#45;&gt;8 -->\r\n",
       "<g id=\"edge8\" class=\"edge\"><title>4&#45;&gt;8</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"65.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.148</text>\r\n",
       "<text text-anchor=\"middle\" x=\"65.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 211</text>\r\n",
       "<text text-anchor=\"middle\" x=\"65.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [17, 194]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"65.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 5&#45;&gt;6 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"204.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 5&#45;&gt;7 -->\r\n",
       "<g id=\"edge7\" class=\"edge\"><title>5&#45;&gt;7</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"343.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.449</text>\r\n",
       "<text text-anchor=\"middle\" x=\"343.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3336</text>\r\n",
       "<text text-anchor=\"middle\" x=\"343.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2200, 1136]</text>\r\n",
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       "</g>\r\n",
       "<!-- 8&#45;&gt;9 -->\r\n",
       "<g id=\"edge9\" class=\"edge\"><title>8&#45;&gt;9</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"492.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"492.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2510</text>\r\n",
       "<text text-anchor=\"middle\" x=\"492.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1244, 1266]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"492.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 8&#45;&gt;10 -->\r\n",
       "<g id=\"edge10\" class=\"edge\"><title>8&#45;&gt;10</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"786.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Op_Dir_SL &lt;= 32.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"786.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.493</text>\r\n",
       "<text text-anchor=\"middle\" x=\"786.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1809</text>\r\n",
       "<text text-anchor=\"middle\" x=\"786.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [795, 1014]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"786.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 11&#45;&gt;12 -->\r\n",
       "<g id=\"edge12\" class=\"edge\"><title>11&#45;&gt;12</title>\r\n",
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       "<g id=\"node16\" class=\"node\"><title>15</title>\r\n",
       "<polygon fill=\"#89c5f0\" stroke=\"black\" points=\"1019,-187 870,-187 870,-104 1019,-104 1019,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"944.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Surface_Wd &lt;= 27.063</text>\r\n",
       "<text text-anchor=\"middle\" x=\"944.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.41</text>\r\n",
       "<text text-anchor=\"middle\" x=\"944.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1793</text>\r\n",
       "<text text-anchor=\"middle\" x=\"944.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [516, 1277]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"944.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 11&#45;&gt;15 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"641.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.499</text>\r\n",
       "<text text-anchor=\"middle\" x=\"641.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1401</text>\r\n",
       "<text text-anchor=\"middle\" x=\"641.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [672, 729]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"641.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 12&#45;&gt;13 -->\r\n",
       "<g id=\"edge13\" class=\"edge\"><title>12&#45;&gt;13</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M732.507,-103.726C719.826,-94.1494 706.334,-83.9611 693.731,-74.4438\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"790.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.421</text>\r\n",
       "<text text-anchor=\"middle\" x=\"790.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 408</text>\r\n",
       "<text text-anchor=\"middle\" x=\"790.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [123, 285]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"790.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 12&#45;&gt;14 -->\r\n",
       "<g id=\"edge14\" class=\"edge\"><title>12&#45;&gt;14</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"939.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.451</text>\r\n",
       "<text text-anchor=\"middle\" x=\"939.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 976</text>\r\n",
       "<text text-anchor=\"middle\" x=\"939.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [335, 641]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"939.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 15&#45;&gt;16 -->\r\n",
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       "<path fill=\"none\" stroke=\"black\" d=\"M942.638,-103.726C942.263,-95.5175 941.868,-86.8595 941.489,-78.56\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"1088.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.345</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1088.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 817</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1088.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [181, 636]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1088.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
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       "<!-- 15&#45;&gt;17 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1583.5\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">bike_acc_score &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1583.5\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.107</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1583.5\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 21051</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1583.5\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 18&#45;&gt;19 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Surface_Tp_0 &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.461</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 7018</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [4493, 2525]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 18&#45;&gt;32 -->\r\n",
       "<g id=\"edge32\" class=\"edge\"><title>18&#45;&gt;32</title>\r\n",
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       "<polygon fill=\"black\" stroke=\"black\" points=\"2229.47,-400.799 2238.79,-395.75 2228.36,-393.888 2229.47,-400.799\"/>\r\n",
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       "<!-- 20 -->\r\n",
       "<g id=\"node21\" class=\"node\"><title>20</title>\r\n",
       "<polygon fill=\"#e68742\" stroke=\"black\" points=\"1584.5,-306 1412.5,-306 1412.5,-223 1584.5,-223 1584.5,-306\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1498.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">traffic_density &lt;= 2757.363</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1498.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.086</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1498.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 20585</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1498.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [19658, 927]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1498.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 19&#45;&gt;20 -->\r\n",
       "<g id=\"edge20\" class=\"edge\"><title>19&#45;&gt;20</title>\r\n",
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       "<!-- 25 -->\r\n",
       "<g id=\"node26\" class=\"node\"><title>25</title>\r\n",
       "<polygon fill=\"#cee7f9\" stroke=\"black\" points=\"1734,-306 1603,-306 1603,-223 1734,-223 1734,-306\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1668.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Rt_Sidewlk &lt;= 15.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1668.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.49</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1668.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 466</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1668.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [200, 266]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1668.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 19&#45;&gt;25 -->\r\n",
       "<g id=\"edge25\" class=\"edge\"><title>19&#45;&gt;25</title>\r\n",
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       "<!-- 21 -->\r\n",
       "<g id=\"node22\" class=\"node\"><title>21</title>\r\n",
       "<polygon fill=\"#e68641\" stroke=\"black\" points=\"1421,-187 1282,-187 1282,-104 1421,-104 1421,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1351.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Med_Width &lt;= 2.724</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1351.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.079</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1351.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 20498</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1351.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [19658, 840]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1351.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 20&#45;&gt;21 -->\r\n",
       "<g id=\"edge21\" class=\"edge\"><title>20&#45;&gt;21</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1447.5,-222.907C1435.53,-213.379 1422.67,-203.148 1410.39,-193.37\"/>\r\n",
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       "<!-- 24 -->\r\n",
       "<g id=\"node25\" class=\"node\"><title>24</title>\r\n",
       "<polygon fill=\"#399de5\" stroke=\"black\" points=\"1570,-179.5 1439,-179.5 1439,-111.5 1570,-111.5 1570,-179.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1504.5\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1504.5\" y=\"-149.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 87</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1504.5\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 87]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1504.5\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 20&#45;&gt;24 -->\r\n",
       "<g id=\"edge24\" class=\"edge\"><title>20&#45;&gt;24</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1500.58,-222.907C1501.13,-212.204 1501.72,-200.615 1502.28,-189.776\"/>\r\n",
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       "<!-- 22 -->\r\n",
       "<g id=\"node23\" class=\"node\"><title>22</title>\r\n",
       "<polygon fill=\"#e6843d\" stroke=\"black\" points=\"1303,-68 1172,-68 1172,-0 1303,-0 1303,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1237.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.041</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1237.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 17941</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1237.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [17567, 374]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1237.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 21&#45;&gt;22 -->\r\n",
       "<g id=\"edge22\" class=\"edge\"><title>21&#45;&gt;22</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1309.05,-103.726C1299.37,-94.423 1289.08,-84.5428 1279.42,-75.2612\"/>\r\n",
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       "<!-- 23 -->\r\n",
       "<g id=\"node24\" class=\"node\"><title>23</title>\r\n",
       "<polygon fill=\"#eb9d65\" stroke=\"black\" points=\"1446,-68 1321,-68 1321,-0 1446,-0 1446,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1383.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.298</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1383.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2557</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1383.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2091, 466]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1383.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 21&#45;&gt;23 -->\r\n",
       "<g id=\"edge23\" class=\"edge\"><title>21&#45;&gt;23</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1363.42,-103.726C1365.87,-95.3351 1368.46,-86.4745 1370.93,-78.0072\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1374.32,-78.8799 1373.77,-68.2996 1367.6,-76.9163 1374.32,-78.8799\"/>\r\n",
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       "<!-- 26 -->\r\n",
       "<g id=\"node27\" class=\"node\"><title>26</title>\r\n",
       "<polygon fill=\"#b9dcf6\" stroke=\"black\" points=\"1738.5,-187 1592.5,-187 1592.5,-104 1738.5,-104 1738.5,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1665.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">bike_acc_score &lt;= 2.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1665.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.477</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1665.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 420</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1665.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [165, 255]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1665.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 25&#45;&gt;26 -->\r\n",
       "<g id=\"edge26\" class=\"edge\"><title>25&#45;&gt;26</title>\r\n",
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       "<!-- 29 -->\r\n",
       "<g id=\"node30\" class=\"node\"><title>29</title>\r\n",
       "<polygon fill=\"#eda977\" stroke=\"black\" points=\"1902.5,-187 1756.5,-187 1756.5,-104 1902.5,-104 1902.5,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1829.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">bike_acc_score &lt;= 6.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1829.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.364</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1829.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 46</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1829.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [35, 11]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1829.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 25&#45;&gt;29 -->\r\n",
       "<g id=\"edge29\" class=\"edge\"><title>25&#45;&gt;29</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1724.36,-222.907C1737.6,-213.288 1751.82,-202.953 1765.39,-193.09\"/>\r\n",
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       "<g id=\"node28\" class=\"node\"><title>27</title>\r\n",
       "<polygon fill=\"#d6ebfa\" stroke=\"black\" points=\"1595,-68 1464,-68 1464,-0 1595,-0 1595,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1529.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.493</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1529.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 346</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1529.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [153, 193]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1529.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 26&#45;&gt;27 -->\r\n",
       "<g id=\"edge27\" class=\"edge\"><title>26&#45;&gt;27</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1614.86,-103.726C1603.08,-94.2406 1590.55,-84.1551 1578.83,-74.7159\"/>\r\n",
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       "<!-- 28 -->\r\n",
       "<g id=\"node29\" class=\"node\"><title>28</title>\r\n",
       "<polygon fill=\"#5fb0ea\" stroke=\"black\" points=\"1744,-68 1613,-68 1613,-0 1744,-0 1744,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1678.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.272</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1678.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 74</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1678.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [12, 62]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1678.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 26&#45;&gt;28 -->\r\n",
       "<g id=\"edge28\" class=\"edge\"><title>26&#45;&gt;28</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1670.34,-103.726C1671.33,-95.4263 1672.37,-86.6671 1673.36,-78.2834\"/>\r\n",
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       "<!-- 30 -->\r\n",
       "<g id=\"node31\" class=\"node\"><title>30</title>\r\n",
       "<polygon fill=\"#eb9e66\" stroke=\"black\" points=\"1872.5,-68 1762.5,-68 1762.5,-0 1872.5,-0 1872.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1817.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.303</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1817.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 43</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1817.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [35, 8]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1817.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 29&#45;&gt;30 -->\r\n",
       "<g id=\"edge30\" class=\"edge\"><title>29&#45;&gt;30</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1825.03,-103.726C1824.12,-95.4263 1823.16,-86.6671 1822.24,-78.2834\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"1956.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1956.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1956.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 3]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1956.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 29&#45;&gt;31 -->\r\n",
       "<g id=\"edge31\" class=\"edge\"><title>29&#45;&gt;31</title>\r\n",
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       "<polygon fill=\"#edf6fd\" stroke=\"black\" points=\"2378.5,-306 2238.5,-306 2238.5,-223 2378.5,-223 2378.5,-306\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">ROW_Width &lt;= 57.1</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.499</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2163</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1029, 1134]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2308.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 32&#45;&gt;33 -->\r\n",
       "<g id=\"edge33\" class=\"edge\"><title>32&#45;&gt;33</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2733.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">bike_acc_score &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2733.5\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.409</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2733.5\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4855</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2733.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3464, 1391]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2733.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 32&#45;&gt;40 -->\r\n",
       "<g id=\"edge40\" class=\"edge\"><title>32&#45;&gt;40</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2234.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Med_Width &lt;= 5.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2234.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.417</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2383.5\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.498</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2095.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.32</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2234.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.401</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2383.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2532.5\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.481</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2733.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Terrain_1 &lt;= 0.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2953.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">bike_acc_score &lt;= 2.5</text>\r\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x181030ff2c8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly for bike accidents, let's see if we can create a small(ish), meaningful decision tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3598.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3598.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_acc_score  bike_acc_score    AADT  ROW_Width  Surface_Wd  Rt_Sidewlk  \\\n",
       "0              1              10  1154.0       50.0        28.0         4.0   \n",
       "1              0               4  1154.0       50.0        28.0         4.0   \n",
       "2              0               1  1154.0       50.0        28.0         4.0   \n",
       "3              1              10  3598.0       55.0        30.0         5.0   \n",
       "4              0               1  3598.0       55.0        30.0         5.0   \n",
       "\n",
       "   Lt_Sidewlk  Med_Width  Op_Dir_SL  Struct_Cnd_-1  ...  Terrain_1  Terrain_2  \\\n",
       "0         4.0        0.0         30              0  ...          1          0   \n",
       "1         4.0        0.0         30              0  ...          1          0   \n",
       "2         4.0        0.0         30              0  ...          1          0   \n",
       "3         5.0        0.0         99              0  ...          1          0   \n",
       "4         5.0        0.0         99              0  ...          1          0   \n",
       "\n",
       "   Terrain_3  F_Class_-1  F_Class_0  F_Class_1  F_Class_2  F_Class_3  \\\n",
       "0          0           0          1          0          0          0   \n",
       "1          0           0          1          0          0          0   \n",
       "2          0           0          1          0          0          0   \n",
       "3          0           0          0          0          0          0   \n",
       "4          0           0          0          0          0          0   \n",
       "\n",
       "   F_Class_5  F_Class_6  \n",
       "0          0          0  \n",
       "1          0          0  \n",
       "2          0          0  \n",
       "3          0          1  \n",
       "4          0          1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build smaller feature set\n",
    "#read in data set before making dummies\n",
    "bike_df = pd.read_csv('data/cleaned_data/model_df.csv')\n",
    "bike_df = bike_df[['car_acc_score', 'bike_acc_score', 'AADT', 'ROW_Width', 'Surface_Wd', \n",
    "                'Rt_Sidewlk', 'Lt_Sidewlk', 'Med_Width',\n",
    "                 'Op_Dir_SL', 'Struct_Cnd', 'Operation', 'Terrain', 'F_Class']]\n",
    "bike_dum = pd.get_dummies(bike_df, columns=['Struct_Cnd',\n",
    "                                         'Operation', 'Terrain', 'F_Class'])\n",
    "bike_dum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "bike_X = bike_dum.drop(columns=['bike_acc_score'])\n",
    "bike_c_y = bike_dum.bike_acc_score.mask(bike_dum.bike_acc_score > 0, 1)\n",
    "\n",
    "# do train test split\n",
    "X_bike_c_train, X_bike_c_test, y_bike_c_train, y_bike_c_test = train_test_split(bike_X, bike_c_y, test_size=0.3, \n",
    "                                                                            random_state=18,\n",
    "                                                                            shuffle=True, stratify=bike_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7921931915276341"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc = tree.DecisionTreeClassifier(max_depth=5)\n",
    "dtc.fit(X_bike_c_train, y_bike_c_train)\n",
    "dtc.score(X_bike_c_test, y_bike_c_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17080   446]\n",
      " [ 4175   536]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = dtc.predict(X_bike_c_test)\n",
    "c=confusion_matrix(y_bike_c_test, pred)\n",
    "sns.heatmap(c,cmap='BrBG',annot=True)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = X_bike_c_train.columns\n",
    "cn = ['Accident', 'No Accident']\n",
    "#fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=600)\n",
    "#tree.plot_tree(dtc, feature_names=fn, class_names=cn, filled=True, impurity=False, fontsize=3)\n",
    "#fig.savefig('car_tree.png')\n",
    "#plt.show()\n",
    "dot_data = tree.export_graphviz(dtc, feature_names=fn, class_names=cn, out_file=None, filled=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "nodes = graph.get_node_list()\n",
    "graph.write_png('bike_decision_tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n",
       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\r\n",
       " -->\r\n",
       "<!-- Title: Tree Pages: 1 -->\r\n",
       "<svg width=\"4461pt\" height=\"671pt\"\r\n",
       " viewBox=\"0.00 0.00 4460.50 671.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 667)\">\r\n",
       "<title>Tree</title>\r\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-667 4456.5,-667 4456.5,4 -4,4\"/>\r\n",
       "<!-- 0 -->\r\n",
       "<g id=\"node1\" class=\"node\"><title>0</title>\r\n",
       "<polygon fill=\"#eca36e\" stroke=\"black\" points=\"2356.5,-663 2211.5,-663 2211.5,-580 2356.5,-580 2356.5,-663\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"2284\" y=\"-647.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Rt_Sidewlk &lt;= 6.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2284\" y=\"-632.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.334</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2284\" y=\"-617.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 51885</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2284\" y=\"-602.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [40892, 10993]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2284\" y=\"-587.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 1 -->\r\n",
       "<g id=\"node2\" class=\"node\"><title>1</title>\r\n",
       "<polygon fill=\"#e9965a\" stroke=\"black\" points=\"1749,-544 1595,-544 1595,-461 1749,-461 1749,-544\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1672\" y=\"-528.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">ROW_Width &lt;= 39.314</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1672\" y=\"-513.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.245</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1672\" y=\"-498.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 28676</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1672\" y=\"-483.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [24586, 4090]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1672\" y=\"-468.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 0&#45;&gt;1 -->\r\n",
       "<g id=\"edge1\" class=\"edge\"><title>0&#45;&gt;1</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2211.39,-606.619C2098.37,-585.012 1880.75,-543.409 1759.33,-520.195\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"1763.36\" y=\"-535.265\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">True</text>\r\n",
       "</g>\r\n",
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       "<polygon fill=\"#f0b68d\" stroke=\"black\" points=\"2835,-544 2697,-544 2697,-461 2835,-461 2835,-544\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"2766\" y=\"-528.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Rt_Sidewlk &lt;= 11.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2766\" y=\"-513.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.418</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2766\" y=\"-498.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 23209</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2766\" y=\"-483.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [16306, 6903]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2766\" y=\"-468.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 0&#45;&gt;32 -->\r\n",
       "<g id=\"edge32\" class=\"edge\"><title>0&#45;&gt;32</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2356.68,-602.857C2445.49,-581.299 2595.21,-544.957 2686.86,-522.71\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"2683.63\" y=\"-537.984\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">False</text>\r\n",
       "</g>\r\n",
       "<!-- 2 -->\r\n",
       "<g id=\"node3\" class=\"node\"><title>2</title>\r\n",
       "<polygon fill=\"#eda775\" stroke=\"black\" points=\"870.5,-425 739.5,-425 739.5,-342 870.5,-342 870.5,-425\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"805\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Rt_Sidewlk &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"805\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.356</text>\r\n",
       "<text text-anchor=\"middle\" x=\"805\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 7167</text>\r\n",
       "<text text-anchor=\"middle\" x=\"805\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [5507, 1660]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"805\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;2 -->\r\n",
       "<g id=\"edge2\" class=\"edge\"><title>1&#45;&gt;2</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1672\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">car_acc_score &lt;= 2.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1672\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.2</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1672\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 21509</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1672\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [19079, 2430]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1672\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;17 -->\r\n",
       "<g id=\"edge17\" class=\"edge\"><title>1&#45;&gt;17</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"404\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">ROW_Width &lt;= 24.961</text>\r\n",
       "<text text-anchor=\"middle\" x=\"404\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.234</text>\r\n",
       "<text text-anchor=\"middle\" x=\"404\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2023</text>\r\n",
       "<text text-anchor=\"middle\" x=\"404\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1749, 274]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"404\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;3 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"805\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">car_acc_score &lt;= 1.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"805\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.394</text>\r\n",
       "<text text-anchor=\"middle\" x=\"805\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 5144</text>\r\n",
       "<text text-anchor=\"middle\" x=\"805\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3758, 1386]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"805\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;10 -->\r\n",
       "<g id=\"edge10\" class=\"edge\"><title>2&#45;&gt;10</title>\r\n",
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       "<polygon fill=\"black\" stroke=\"black\" points=\"808.5,-316.021 805,-306.021 801.5,-316.021 808.5,-316.021\"/>\r\n",
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       "<!-- 4 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"191\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">car_acc_score &lt;= 1.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.311</text>\r\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 810</text>\r\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [654, 156]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 3&#45;&gt;4 -->\r\n",
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       "<!-- 7 -->\r\n",
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       "<polygon fill=\"#e88f4e\" stroke=\"black\" points=\"469,-187 339,-187 339,-104 469,-104 469,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"404\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">AADT &lt;= 152706.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"404\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.176</text>\r\n",
       "<text text-anchor=\"middle\" x=\"404\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1213</text>\r\n",
       "<text text-anchor=\"middle\" x=\"404\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1095, 118]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"404\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 3&#45;&gt;7 -->\r\n",
       "<g id=\"edge7\" class=\"edge\"><title>3&#45;&gt;7</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"59\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.283</text>\r\n",
       "<text text-anchor=\"middle\" x=\"59\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 703</text>\r\n",
       "<text text-anchor=\"middle\" x=\"59\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [583, 120]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"59\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 4&#45;&gt;5 -->\r\n",
       "<g id=\"edge5\" class=\"edge\"><title>4&#45;&gt;5</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"191\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.447</text>\r\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 107</text>\r\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [71, 36]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 4&#45;&gt;6 -->\r\n",
       "<g id=\"edge6\" class=\"edge\"><title>4&#45;&gt;6</title>\r\n",
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       "<g id=\"node9\" class=\"node\"><title>8</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"327\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.168</text>\r\n",
       "<text text-anchor=\"middle\" x=\"327\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1197</text>\r\n",
       "<text text-anchor=\"middle\" x=\"327\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1086, 111]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"327\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 7&#45;&gt;8 -->\r\n",
       "<g id=\"edge8\" class=\"edge\"><title>7&#45;&gt;8</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"463\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.492</text>\r\n",
       "<text text-anchor=\"middle\" x=\"463\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 16</text>\r\n",
       "<text text-anchor=\"middle\" x=\"463\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [9, 7]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"463\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 7&#45;&gt;9 -->\r\n",
       "<g id=\"edge9\" class=\"edge\"><title>7&#45;&gt;9</title>\r\n",
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       "<polygon fill=\"#eda978\" stroke=\"black\" points=\"793,-187 657,-187 657,-104 793,-104 793,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"725\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Surface_Wd &lt;= 10.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"725\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.367</text>\r\n",
       "<text text-anchor=\"middle\" x=\"725\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4406</text>\r\n",
       "<text text-anchor=\"middle\" x=\"725\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3340, 1066]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"725\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 10&#45;&gt;11 -->\r\n",
       "<g id=\"edge11\" class=\"edge\"><title>10&#45;&gt;11</title>\r\n",
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       "<g id=\"node15\" class=\"node\"><title>14</title>\r\n",
       "<polygon fill=\"#f9e1d1\" stroke=\"black\" points=\"958.5,-187 811.5,-187 811.5,-104 958.5,-104 958.5,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"885\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">car_acc_score &lt;= 70.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"885\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.491</text>\r\n",
       "<text text-anchor=\"middle\" x=\"885\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 738</text>\r\n",
       "<text text-anchor=\"middle\" x=\"885\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [418, 320]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"885\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 10&#45;&gt;14 -->\r\n",
       "<g id=\"edge14\" class=\"edge\"><title>10&#45;&gt;14</title>\r\n",
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       "<polygon fill=\"#fae6d7\" stroke=\"black\" points=\"654,-68 536,-68 536,-0 654,-0 654,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"595\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.494</text>\r\n",
       "<text text-anchor=\"middle\" x=\"595\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 331</text>\r\n",
       "<text text-anchor=\"middle\" x=\"595\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [184, 147]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"595\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 11&#45;&gt;12 -->\r\n",
       "<g id=\"edge12\" class=\"edge\"><title>11&#45;&gt;12</title>\r\n",
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       "<polygon fill=\"#eda673\" stroke=\"black\" points=\"797.5,-68 672.5,-68 672.5,-0 797.5,-0 797.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"735\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.349</text>\r\n",
       "<text text-anchor=\"middle\" x=\"735\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4075</text>\r\n",
       "<text text-anchor=\"middle\" x=\"735\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3156, 919]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"735\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
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       "<!-- 11&#45;&gt;13 -->\r\n",
       "<g id=\"edge13\" class=\"edge\"><title>11&#45;&gt;13</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"875\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.494</text>\r\n",
       "<text text-anchor=\"middle\" x=\"875\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 719</text>\r\n",
       "<text text-anchor=\"middle\" x=\"875\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [399, 320]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"875\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
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       "<!-- 14&#45;&gt;15 -->\r\n",
       "<g id=\"edge15\" class=\"edge\"><title>14&#45;&gt;15</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1007\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1007\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 19</text>\r\n",
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       "<!-- 14&#45;&gt;16 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1505\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">ROW_Width &lt;= 50.614</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1505\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.173</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1505\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 17942</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1771\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Op_Dir_SL &lt;= 32.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1771\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.321</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1771\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3567</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1771\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2852, 715]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1771\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 17&#45;&gt;25 -->\r\n",
       "<g id=\"edge25\" class=\"edge\"><title>17&#45;&gt;25</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1286\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Lt_Sidewlk &lt;= 5.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1286\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.105</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1286\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 6377</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1286\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [6024, 353]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1286\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 18&#45;&gt;19 -->\r\n",
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       "<!-- 22 -->\r\n",
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       "<polygon fill=\"#e89253\" stroke=\"black\" points=\"1574,-187 1436,-187 1436,-104 1574,-104 1574,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1505\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Med_Width &lt;= 7.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1505\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.208</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1505\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 11565</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1505\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [10203, 1362]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1505\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 18&#45;&gt;22 -->\r\n",
       "<g id=\"edge22\" class=\"edge\"><title>18&#45;&gt;22</title>\r\n",
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       "<!-- 20 -->\r\n",
       "<g id=\"node21\" class=\"node\"><title>20</title>\r\n",
       "<polygon fill=\"#e68641\" stroke=\"black\" points=\"1205.5,-68 1080.5,-68 1080.5,-0 1205.5,-0 1205.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1143\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.073</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1143\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4849</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1143\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [4664, 185]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1143\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 19&#45;&gt;20 -->\r\n",
       "<g id=\"edge20\" class=\"edge\"><title>19&#45;&gt;20</title>\r\n",
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       "<!-- 21 -->\r\n",
       "<g id=\"node22\" class=\"node\"><title>21</title>\r\n",
       "<polygon fill=\"#e89151\" stroke=\"black\" points=\"1348.5,-68 1223.5,-68 1223.5,-0 1348.5,-0 1348.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1286\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.196</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1286\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1528</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1286\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1360, 168]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1286\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 19&#45;&gt;21 -->\r\n",
       "<g id=\"edge21\" class=\"edge\"><title>19&#45;&gt;21</title>\r\n",
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       "<g id=\"node24\" class=\"node\"><title>23</title>\r\n",
       "<polygon fill=\"#e99356\" stroke=\"black\" points=\"1497.5,-68 1366.5,-68 1366.5,-0 1497.5,-0 1497.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1432\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.22</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1432\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 10422</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1432\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [9109, 1313]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1432\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 22&#45;&gt;23 -->\r\n",
       "<g id=\"edge23\" class=\"edge\"><title>22&#45;&gt;23</title>\r\n",
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       "<!-- 24 -->\r\n",
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       "<polygon fill=\"#e68742\" stroke=\"black\" points=\"1634,-68 1516,-68 1516,-0 1634,-0 1634,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1575\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.082</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1575\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1143</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1575\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1094, 49]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1575\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 22&#45;&gt;24 -->\r\n",
       "<g id=\"edge24\" class=\"edge\"><title>22&#45;&gt;24</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1531.07,-103.726C1536.66,-94.9703 1542.59,-85.7032 1548.21,-76.9051\"/>\r\n",
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       "<!-- 26 -->\r\n",
       "<g id=\"node27\" class=\"node\"><title>26</title>\r\n",
       "<polygon fill=\"#eeab7c\" stroke=\"black\" points=\"1844.5,-187 1697.5,-187 1697.5,-104 1844.5,-104 1844.5,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1771\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">car_acc_score &lt;= 13.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1771\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.377</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1771\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1903</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1771\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1424, 479]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1771\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 25&#45;&gt;26 -->\r\n",
       "<g id=\"edge26\" class=\"edge\"><title>25&#45;&gt;26</title>\r\n",
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       "<!-- 29 -->\r\n",
       "<g id=\"node30\" class=\"node\"><title>29</title>\r\n",
       "<polygon fill=\"#e9965a\" stroke=\"black\" points=\"2042.5,-187 1917.5,-187 1917.5,-104 2042.5,-104 2042.5,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1980\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">AADT &lt;= 125.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1980\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.243</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1980\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1664</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1980\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1428, 236]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1980\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 25&#45;&gt;29 -->\r\n",
       "<g id=\"edge29\" class=\"edge\"><title>25&#45;&gt;29</title>\r\n",
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       "<!-- 27 -->\r\n",
       "<g id=\"node28\" class=\"node\"><title>27</title>\r\n",
       "<polygon fill=\"#efb184\" stroke=\"black\" points=\"1777.5,-68 1652.5,-68 1652.5,-0 1777.5,-0 1777.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1715\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.399</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1715\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1597</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1715\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1158, 439]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1715\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 26&#45;&gt;27 -->\r\n",
       "<g id=\"edge27\" class=\"edge\"><title>26&#45;&gt;27</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1750.15,-103.726C1745.76,-95.1527 1741.13,-86.0891 1736.71,-77.4555\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"1851\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.227</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1851\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 306</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1851\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [266, 40]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1851\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 26&#45;&gt;28 -->\r\n",
       "<g id=\"edge28\" class=\"edge\"><title>26&#45;&gt;28</title>\r\n",
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       "<polygon fill=\"#f6d5bd\" stroke=\"black\" points=\"2035,-68 1925,-68 1925,-0 2035,-0 2035,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1980\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.48</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1980\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 85</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1980\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [51, 34]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1980\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 29&#45;&gt;30 -->\r\n",
       "<g id=\"edge30\" class=\"edge\"><title>29&#45;&gt;30</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2116\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.223</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2116\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1579</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2116\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1377, 202]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2116\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 29&#45;&gt;31 -->\r\n",
       "<g id=\"edge31\" class=\"edge\"><title>29&#45;&gt;31</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2030.64,-103.726C2042.42,-94.2406 2054.95,-84.1551 2066.67,-74.7159\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"2766\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">car_acc_score &lt;= 1.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2766\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.397</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2766\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 19914</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2766\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [14479, 5435]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2766\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 32&#45;&gt;33 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"3598\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">AADT &lt;= 19921.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3598\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.494</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3598\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3295</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3598\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1827, 1468]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3598\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 32&#45;&gt;48 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"2622\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Lt_Sidewlk &lt;= 7.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2622\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.371</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2622\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 15986</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2622\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [12055, 3931]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2622\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 33&#45;&gt;34 -->\r\n",
       "<g id=\"edge34\" class=\"edge\"><title>33&#45;&gt;34</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2903\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">F_Class_2 &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2903\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.473</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2903\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3928</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2903\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2424, 1504]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2903\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 33&#45;&gt;41 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"2405\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Rt_Sidewlk &lt;= 9.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2405\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.345</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2405\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 8658</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2405\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [6739, 1919]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2405\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 34&#45;&gt;35 -->\r\n",
       "<g id=\"edge35\" class=\"edge\"><title>34&#45;&gt;35</title>\r\n",
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       "<polygon fill=\"#efb184\" stroke=\"black\" points=\"2692,-187 2552,-187 2552,-104 2692,-104 2692,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"2622\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">ROW_Width &lt;= 44.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2622\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.398</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2622\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 7328</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2622\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [5316, 2012]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2622\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 34&#45;&gt;38 -->\r\n",
       "<g id=\"edge38\" class=\"edge\"><title>34&#45;&gt;38</title>\r\n",
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       "<polygon fill=\"#eca26c\" stroke=\"black\" points=\"2327.5,-68 2196.5,-68 2196.5,-0 2327.5,-0 2327.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"2262\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.327</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2262\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 7478</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2262\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [5937, 1541]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2262\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 35&#45;&gt;36 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"2405\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.435</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2405\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1180</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2405\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [802, 378]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2405\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 35&#45;&gt;37 -->\r\n",
       "<g id=\"edge37\" class=\"edge\"><title>35&#45;&gt;37</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2541\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.472</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2541\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 914</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2541\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [565, 349]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2541\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 38&#45;&gt;39 -->\r\n",
       "<g id=\"edge39\" class=\"edge\"><title>38&#45;&gt;39</title>\r\n",
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       "<!-- 40 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"2684\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.384</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2684\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 6414</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2684\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [4751, 1663]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2684\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 38&#45;&gt;40 -->\r\n",
       "<g id=\"edge40\" class=\"edge\"><title>38&#45;&gt;40</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2903\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">car_acc_score &lt;= 5.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2903\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.48</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2903\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3624</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2903\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2170, 1454]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2903\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 41&#45;&gt;42 -->\r\n",
       "<g id=\"edge42\" class=\"edge\"><title>41&#45;&gt;42</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3121\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Lt_Sidewlk &lt;= 9.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3121\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.275</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3121\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 304</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3121\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [254, 50]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3121\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 41&#45;&gt;45 -->\r\n",
       "<g id=\"edge45\" class=\"edge\"><title>41&#45;&gt;45</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"2833\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.467</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2833\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2868</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2833\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1801, 1067]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2833\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 42&#45;&gt;43 -->\r\n",
       "<g id=\"edge43\" class=\"edge\"><title>42&#45;&gt;43</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2876.93,-103.726C2871.34,-94.9703 2865.41,-85.7032 2859.79,-76.9051\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"2982\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2982\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 756</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2982\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [369, 387]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"2982\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 42&#45;&gt;44 -->\r\n",
       "<g id=\"edge44\" class=\"edge\"><title>42&#45;&gt;44</title>\r\n",
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       "<!-- 46 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"3121\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.186</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3121\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 279</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3121\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [250, 29]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3121\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 45&#45;&gt;46 -->\r\n",
       "<g id=\"edge46\" class=\"edge\"><title>45&#45;&gt;46</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"3260\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.269</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3260\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 25</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3260\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [4, 21]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3260\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 45&#45;&gt;47 -->\r\n",
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       "<path fill=\"none\" stroke=\"black\" d=\"M3172.76,-103.726C3184.8,-94.2406 3197.6,-84.1551 3209.58,-74.7159\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"3598\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">car_acc_score &lt;= 2.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3598\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.472</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3598\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1809</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3598\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1118, 691]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3598\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 48&#45;&gt;49 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"4011\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Rt_Sidewlk &lt;= 20.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4011\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.499</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4011\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1486</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4011\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [709, 777]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4011\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = No Accident</text>\r\n",
       "</g>\r\n",
       "<!-- 48&#45;&gt;56 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"3519\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Surface_Wd &lt;= 22.802</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3677\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">AADT &lt;= 2362.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3677\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.494</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3532\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.474</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3664\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.431</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3664\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 54</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3664\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [37, 17]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3803\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.479</text>\r\n",
       "<text text-anchor=\"middle\" x=\"3803\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 259</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4011\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">ROW_Width &lt;= 61.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4011\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"4238\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">ROW_Width &lt;= 160.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4238\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.367</text>\r\n",
       "<text text-anchor=\"middle\" x=\"4238\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 165</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"3946\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.467</text>\r\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x1810341aac8>"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = graphviz.Source(dot_data)\n",
    "graph"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Confusion Matrix ##\n",
    "\n",
    "The confusion matrices indicate how often the model predicts true and false negatives and positives. Because false positives may be places where accidents simply have not occured yet, I wanted to explore similarities between true and false negatives.\n",
    "\n",
    "First I want to create a dataframe that allows me to visualize the means of features for true positives and false positives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rebuild the reduced feature set, but not as small as for the decision tree above\n",
    "#read in data set before making dummies\n",
    "sel_df = pd.read_csv('data/cleaned_data/model_df.csv')\n",
    "sel_df = sel_df[['car_acc_score', 'traffic_density', 'bike_acc_score', 'AADT', 'ROW_Width', 'Surface_Wd', 'Surface_Tp', \n",
    "                'Rt_Sidewlk', 'Lt_Sidewlk', 'Route_System', 'Route_Direction', 'Struct_Cnd', 'Terrain',\n",
    "                'Route_System', 'Med_Width', 'City_Maint', 'Jurisdictn', 'Op_Dir_SL']]\n",
    "sel_dum = pd.get_dummies(sel_df, columns=['Route_System', 'Route_Direction', 'Struct_Cnd', 'Terrain', 'Surface_Tp',\n",
    "                'Route_System', 'City_Maint', 'Jurisdictn'])\n",
    "\n",
    "car_X = sel_dum.drop(columns=['car_acc_score'])\n",
    "car_c_y = sel_dum.car_acc_score.mask(sel_dum.car_acc_score > 0, 1)\n",
    "\n",
    "# do train test split\n",
    "X_car_c_train, X_car_c_test, y_car_c_train, y_car_c_test = train_test_split(car_X, car_c_y, test_size=0.3, \n",
    "                                                                            random_state=18,\n",
    "                                                                            shuffle=True, stratify=car_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1218.000000</td>\n",
       "      <td>1218.000000</td>\n",
       "      <td>1218.000000</td>\n",
       "      <td>1218.000000</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>1218.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.233798e+04</td>\n",
       "      <td>1.159278</td>\n",
       "      <td>13441.959290</td>\n",
       "      <td>61.405997</td>\n",
       "      <td>28.894991</td>\n",
       "      <td>6.028874</td>\n",
       "      <td>4.261698</td>\n",
       "      <td>3.345762</td>\n",
       "      <td>39.249589</td>\n",
       "      <td>0.224959</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004105</td>\n",
       "      <td>0.002463</td>\n",
       "      <td>0.143678</td>\n",
       "      <td>0.049261</td>\n",
       "      <td>0.577997</td>\n",
       "      <td>0.054187</td>\n",
       "      <td>0.002463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.081221e+06</td>\n",
       "      <td>1.629408</td>\n",
       "      <td>17623.221426</td>\n",
       "      <td>29.489594</td>\n",
       "      <td>9.013311</td>\n",
       "      <td>4.064991</td>\n",
       "      <td>3.868980</td>\n",
       "      <td>11.313982</td>\n",
       "      <td>24.215347</td>\n",
       "      <td>0.417727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063966</td>\n",
       "      <td>0.049588</td>\n",
       "      <td>0.350907</td>\n",
       "      <td>0.216502</td>\n",
       "      <td>0.494082</td>\n",
       "      <td>0.226480</td>\n",
       "      <td>0.049588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.797950e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.674861e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1154.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.393434e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13968.546893</td>\n",
       "      <td>58.090537</td>\n",
       "      <td>28.938072</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.168738</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.742477e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13968.546893</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.448446</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.773565e+07</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>196708.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       traffic_density  bike_acc_score           AADT    ROW_Width  \\\n",
       "count     1.218000e+03     1218.000000    1218.000000  1218.000000   \n",
       "mean      3.233798e+04        1.159278   13441.959290    61.405997   \n",
       "std       1.081221e+06        1.629408   17623.221426    29.489594   \n",
       "min       5.797950e-02        0.000000      10.000000    10.000000   \n",
       "25%       1.674861e+00        0.000000    1154.000000    42.000000   \n",
       "50%       4.393434e+01        1.000000   13968.546893    58.090537   \n",
       "75%       2.742477e+03        1.000000   13968.546893    65.000000   \n",
       "max       3.773565e+07       18.000000  196708.000000   200.000000   \n",
       "\n",
       "        Surface_Wd   Rt_Sidewlk   Lt_Sidewlk    Med_Width    Op_Dir_SL  \\\n",
       "count  1218.000000  1218.000000  1218.000000  1218.000000  1218.000000   \n",
       "mean     28.894991     6.028874     4.261698     3.345762    39.249589   \n",
       "std       9.013311     4.064991     3.868980    11.313982    24.215347   \n",
       "min       6.000000     0.000000     0.000000     0.000000    15.000000   \n",
       "25%      24.000000     4.000000     0.000000     0.000000    30.000000   \n",
       "50%      28.938072     6.000000     4.168738     0.000000    30.000000   \n",
       "75%      30.000000     8.000000     7.000000     2.448446    30.000000   \n",
       "max      80.000000    24.000000    27.000000   100.000000    99.000000   \n",
       "\n",
       "       Route_System_B  ...  City_Maint_274  City_Maint_314  Jurisdictn_0  \\\n",
       "count     1218.000000  ...     1218.000000     1218.000000   1218.000000   \n",
       "mean         0.224959  ...        0.004105        0.002463      0.143678   \n",
       "std          0.417727  ...        0.063966        0.049588      0.350907   \n",
       "min          0.000000  ...        0.000000        0.000000      0.000000   \n",
       "25%          0.000000  ...        0.000000        0.000000      0.000000   \n",
       "50%          0.000000  ...        0.000000        0.000000      0.000000   \n",
       "75%          0.000000  ...        0.000000        0.000000      0.000000   \n",
       "max          1.000000  ...        1.000000        1.000000      1.000000   \n",
       "\n",
       "       Jurisdictn_1  Jurisdictn_2  Jurisdictn_3  Jurisdictn_5  Jurisdictn_B  \\\n",
       "count   1218.000000   1218.000000   1218.000000   1218.000000        1218.0   \n",
       "mean       0.049261      0.577997      0.054187      0.002463           0.0   \n",
       "std        0.216502      0.494082      0.226480      0.049588           0.0   \n",
       "min        0.000000      0.000000      0.000000      0.000000           0.0   \n",
       "25%        0.000000      0.000000      0.000000      0.000000           0.0   \n",
       "50%        0.000000      1.000000      0.000000      0.000000           0.0   \n",
       "75%        0.000000      1.000000      0.000000      0.000000           0.0   \n",
       "max        1.000000      1.000000      1.000000      1.000000           0.0   \n",
       "\n",
       "       Jurisdictn_E  Jurisdictn_H  \n",
       "count        1218.0   1218.000000  \n",
       "mean            0.0      0.001642  \n",
       "std             0.0      0.040505  \n",
       "min             0.0      0.000000  \n",
       "25%             0.0      0.000000  \n",
       "50%             0.0      0.000000  \n",
       "75%             0.0      0.000000  \n",
       "max             0.0      1.000000  \n",
       "\n",
       "[8 rows x 87 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use the best performing classifier on the reduced feature set\n",
    "bc = ensemble.BaggingClassifier(random_state=18, n_estimators=100, max_features=0.75)\n",
    "bc.fit(X_car_c_train, y_car_c_train)\n",
    "pred = bc.predict(X_car_c_test)\n",
    "\n",
    "# create dataframe of false positives\n",
    "# create dataframe of true positives\n",
    "# compare on top 10 features\n",
    "car_false_pos = X_car_c_test[pred > y_car_c_test]\n",
    "car_true_pos = X_car_c_test[(pred == y_car_c_test) & (y_car_c_test == 1)]\n",
    "\n",
    "car_false_pos.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>traffic_density</th>\n",
       "      <th>bike_acc_score</th>\n",
       "      <th>AADT</th>\n",
       "      <th>ROW_Width</th>\n",
       "      <th>Surface_Wd</th>\n",
       "      <th>Rt_Sidewlk</th>\n",
       "      <th>Lt_Sidewlk</th>\n",
       "      <th>Med_Width</th>\n",
       "      <th>Op_Dir_SL</th>\n",
       "      <th>Route_System_B</th>\n",
       "      <th>...</th>\n",
       "      <th>City_Maint_274</th>\n",
       "      <th>City_Maint_314</th>\n",
       "      <th>Jurisdictn_0</th>\n",
       "      <th>Jurisdictn_1</th>\n",
       "      <th>Jurisdictn_2</th>\n",
       "      <th>Jurisdictn_3</th>\n",
       "      <th>Jurisdictn_5</th>\n",
       "      <th>Jurisdictn_B</th>\n",
       "      <th>Jurisdictn_E</th>\n",
       "      <th>Jurisdictn_H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.619000e+03</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "      <td>8619.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.734543e+03</td>\n",
       "      <td>0.544611</td>\n",
       "      <td>19683.406944</td>\n",
       "      <td>63.512345</td>\n",
       "      <td>29.714660</td>\n",
       "      <td>5.795548</td>\n",
       "      <td>4.172707</td>\n",
       "      <td>3.711305</td>\n",
       "      <td>44.079128</td>\n",
       "      <td>0.047569</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013111</td>\n",
       "      <td>0.005917</td>\n",
       "      <td>0.063929</td>\n",
       "      <td>0.162432</td>\n",
       "      <td>0.624318</td>\n",
       "      <td>0.080636</td>\n",
       "      <td>0.006265</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000232</td>\n",
       "      <td>0.000116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.161427e+05</td>\n",
       "      <td>1.584020</td>\n",
       "      <td>32399.399441</td>\n",
       "      <td>34.224296</td>\n",
       "      <td>9.302317</td>\n",
       "      <td>4.253983</td>\n",
       "      <td>4.262826</td>\n",
       "      <td>16.361918</td>\n",
       "      <td>27.673162</td>\n",
       "      <td>0.212866</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113755</td>\n",
       "      <td>0.076700</td>\n",
       "      <td>0.244640</td>\n",
       "      <td>0.368868</td>\n",
       "      <td>0.484327</td>\n",
       "      <td>0.272291</td>\n",
       "      <td>0.078909</td>\n",
       "      <td>0.010771</td>\n",
       "      <td>0.015232</td>\n",
       "      <td>0.010771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.238931e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.131909e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1154.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.334739e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10669.000000</td>\n",
       "      <td>58.090537</td>\n",
       "      <td>28.938072</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.168738</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.958027e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20943.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.883903e+07</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>224649.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       traffic_density  bike_acc_score           AADT    ROW_Width  \\\n",
       "count     8.619000e+03     8619.000000    8619.000000  8619.000000   \n",
       "mean      3.734543e+03        0.544611   19683.406944    63.512345   \n",
       "std       2.161427e+05        1.584020   32399.399441    34.224296   \n",
       "min       4.238931e-04        0.000000      10.000000     8.000000   \n",
       "25%       9.131909e-01        0.000000    1154.000000    41.000000   \n",
       "50%       3.334739e+00        0.000000   10669.000000    58.090537   \n",
       "75%       1.958027e+01        0.000000   20943.000000    73.000000   \n",
       "max       1.883903e+07       19.000000  224649.000000   300.000000   \n",
       "\n",
       "        Surface_Wd   Rt_Sidewlk   Lt_Sidewlk    Med_Width    Op_Dir_SL  \\\n",
       "count  8619.000000  8619.000000  8619.000000  8619.000000  8619.000000   \n",
       "mean     29.714660     5.795548     4.172707     3.711305    44.079128   \n",
       "std       9.302317     4.253983     4.262826    16.361918    27.673162   \n",
       "min       6.000000     0.000000     0.000000     0.000000    15.000000   \n",
       "25%      24.000000     0.000000     0.000000     0.000000    30.000000   \n",
       "50%      28.938072     6.000000     4.168738     0.000000    30.000000   \n",
       "75%      34.000000     8.000000     7.000000     0.000000    40.000000   \n",
       "max      80.000000    26.000000    27.000000   240.000000    99.000000   \n",
       "\n",
       "       Route_System_B  ...  City_Maint_274  City_Maint_314  Jurisdictn_0  \\\n",
       "count     8619.000000  ...     8619.000000     8619.000000   8619.000000   \n",
       "mean         0.047569  ...        0.013111        0.005917      0.063929   \n",
       "std          0.212866  ...        0.113755        0.076700      0.244640   \n",
       "min          0.000000  ...        0.000000        0.000000      0.000000   \n",
       "25%          0.000000  ...        0.000000        0.000000      0.000000   \n",
       "50%          0.000000  ...        0.000000        0.000000      0.000000   \n",
       "75%          0.000000  ...        0.000000        0.000000      0.000000   \n",
       "max          1.000000  ...        1.000000        1.000000      1.000000   \n",
       "\n",
       "       Jurisdictn_1  Jurisdictn_2  Jurisdictn_3  Jurisdictn_5  Jurisdictn_B  \\\n",
       "count   8619.000000   8619.000000   8619.000000   8619.000000   8619.000000   \n",
       "mean       0.162432      0.624318      0.080636      0.006265      0.000116   \n",
       "std        0.368868      0.484327      0.272291      0.078909      0.010771   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      1.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      1.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       Jurisdictn_E  Jurisdictn_H  \n",
       "count   8619.000000   8619.000000  \n",
       "mean       0.000232      0.000116  \n",
       "std        0.015232      0.010771  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max        1.000000      1.000000  \n",
       "\n",
       "[8 rows x 87 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_true_pos.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#construct summary dataframe\n",
    "car_false_means = car_false_pos[['traffic_density', 'bike_acc_score', 'AADT',\n",
    "                            'ROW_Width', 'Surface_Wd', \n",
    "                            'Rt_Sidewlk', 'Lt_Sidewlk', 'Med_Width', 'Op_Dir_SL']].describe().loc['mean']\n",
    "car_true_means = car_true_pos[['traffic_density', 'bike_acc_score', 'AADT',\n",
    "                            'ROW_Width', 'Surface_Wd',\n",
    "                            'Rt_Sidewlk', 'Lt_Sidewlk', 'Med_Width', 'Op_Dir_SL']].describe().loc['mean']\n",
    "car_false_summary = pd.DataFrame({\"means\": car_false_means})\n",
    "car_false_summary['Type'] = 'False Positive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_true_summary = pd.DataFrame({\"means\": car_true_means})\n",
    "car_true_summary['Type'] = 'True Positive'\n",
    "car_summary = car_false_summary.append(car_true_summary)\n",
    "car_summary = car_summary.reset_index()\n",
    "car_summary = car_summary.rename(columns={'index' : 'feature'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>means</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>traffic_density</td>\n",
       "      <td>32337.980440</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bike_acc_score</td>\n",
       "      <td>1.159278</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AADT</td>\n",
       "      <td>13441.959290</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ROW_Width</td>\n",
       "      <td>61.405997</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Surface_Wd</td>\n",
       "      <td>28.894991</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rt_Sidewlk</td>\n",
       "      <td>6.028874</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lt_Sidewlk</td>\n",
       "      <td>4.261698</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Med_Width</td>\n",
       "      <td>3.345762</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Op_Dir_SL</td>\n",
       "      <td>39.249589</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>traffic_density</td>\n",
       "      <td>3734.543477</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bike_acc_score</td>\n",
       "      <td>0.544611</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AADT</td>\n",
       "      <td>19683.406944</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ROW_Width</td>\n",
       "      <td>63.512345</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Surface_Wd</td>\n",
       "      <td>29.714660</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Rt_Sidewlk</td>\n",
       "      <td>5.795548</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lt_Sidewlk</td>\n",
       "      <td>4.172707</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Med_Width</td>\n",
       "      <td>3.711305</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Op_Dir_SL</td>\n",
       "      <td>44.079128</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>traffic_density</td>\n",
       "      <td>2489.092322</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bike_acc_score</td>\n",
       "      <td>0.209232</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AADT</td>\n",
       "      <td>10127.984594</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ROW_Width</td>\n",
       "      <td>53.328944</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Surface_Wd</td>\n",
       "      <td>28.207446</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rt_Sidewlk</td>\n",
       "      <td>5.249640</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lt_Sidewlk</td>\n",
       "      <td>4.270560</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Med_Width</td>\n",
       "      <td>1.632960</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Op_Dir_SL</td>\n",
       "      <td>40.440036</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            feature         means            Type\n",
       "0   traffic_density  32337.980440  False Positive\n",
       "1    bike_acc_score      1.159278  False Positive\n",
       "2              AADT  13441.959290  False Positive\n",
       "3         ROW_Width     61.405997  False Positive\n",
       "4        Surface_Wd     28.894991  False Positive\n",
       "5        Rt_Sidewlk      6.028874  False Positive\n",
       "6        Lt_Sidewlk      4.261698  False Positive\n",
       "7         Med_Width      3.345762  False Positive\n",
       "8         Op_Dir_SL     39.249589  False Positive\n",
       "9   traffic_density   3734.543477   True Positive\n",
       "10   bike_acc_score      0.544611   True Positive\n",
       "11             AADT  19683.406944   True Positive\n",
       "12        ROW_Width     63.512345   True Positive\n",
       "13       Surface_Wd     29.714660   True Positive\n",
       "14       Rt_Sidewlk      5.795548   True Positive\n",
       "15       Lt_Sidewlk      4.172707   True Positive\n",
       "16        Med_Width      3.711305   True Positive\n",
       "17        Op_Dir_SL     44.079128   True Positive\n",
       "0   traffic_density   2489.092322   True Negative\n",
       "1    bike_acc_score      0.209232   True Negative\n",
       "2              AADT  10127.984594   True Negative\n",
       "3         ROW_Width     53.328944   True Negative\n",
       "4        Surface_Wd     28.207446   True Negative\n",
       "5        Rt_Sidewlk      5.249640   True Negative\n",
       "6        Lt_Sidewlk      4.270560   True Negative\n",
       "7         Med_Width      1.632960   True Negative\n",
       "8         Op_Dir_SL     40.440036   True Negative"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_true_neg = X_car_c_test[(pred == y_car_c_test) & (y_car_c_test == 0)]\n",
    "car_true_neg_means = car_true_neg[['traffic_density', 'bike_acc_score', 'AADT',\n",
    "                            'ROW_Width', 'Surface_Wd', \n",
    "                            'Rt_Sidewlk', 'Lt_Sidewlk', 'Med_Width', 'Op_Dir_SL']].describe().loc['mean']\n",
    "\n",
    "car_neg_summary = pd.DataFrame({\"means\": car_true_neg_means})\n",
    "car_neg_summary['Type'] = 'True Negative'\n",
    "car_neg_summary = car_neg_summary.reset_index()\n",
    "car_neg_summary = car_neg_summary.rename(columns={'index' : 'feature'})\n",
    "\n",
    "car_summary = car_summary.append(car_neg_summary)\n",
    "car_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_all = car_false_pos.copy()\n",
    "car_all['Type'] = 'False Positive'\n",
    "car_all = car_all.append(car_true_pos)\n",
    "car_all['Type'] = car_all['Type'].fillna(value='True Positive')\n",
    "car_all = car_all.append(car_true_neg)\n",
    "car_all['Type'] = car_all['Type'].fillna(value='True Negative')\n",
    "\n",
    "#car_true_pos['Type'] = 'True Positive'\n",
    "#car_true_neg['Type'] = 'True Negative'\n",
    "\n",
    "#car_all = car_all.append(car_true_pos).append(car_true_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18102f17f88>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='AADT', x='Type', showfliers=False, data=car_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18102b340c8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Type', y='bike_acc_score', data=car_all, showfliers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18102bb7e48>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Type', y='ROW_Width', data=car_all, showfliers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14b24469748>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "other_features = car_summary[(car_summary.feature != 'bike_acc_score') & \n",
    "                             (car_summary.feature != 'AADT') &\n",
    "                            (car_summary.feature != 'traffic_density')]\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(x=\"feature\", y=\"means\", hue=\"Type\", data=other_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So looking for high traffic, and potentially wide rights of way annd wide medians may reveal locations where accidents will happen in the future.\n",
    "\n",
    "Now exploring the same comparisons for bike accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build smaller feature set\n",
    "#read in data set before making dummies\n",
    "bike_df = pd.read_csv('data/cleaned_data/model_df.csv')\n",
    "bike_df = bike_df[['car_acc_score', 'bike_acc_score', 'AADT', 'City', 'ROW_Width', 'Surface_Wd', \n",
    "                 'Jurisdictn', 'Rt_Sidewlk', 'Lt_Sidewlk', 'Route_Direction', 'Med_Width',\n",
    "                 'Op_Dir_SL', 'Struct_Cnd', 'Operation', 'Terrain', 'F_Class']]\n",
    "bike_dum = pd.get_dummies(bike_df, columns=['City', 'Jurisdictn', 'Route_Direction', 'Struct_Cnd',\n",
    "                                         'Operation', 'Terrain', 'F_Class'])\n",
    "\n",
    "\n",
    "bike_c_y = bike_dum.bike_acc_score.mask(bike_dum.bike_acc_score > 0, 1)\n",
    "bike_X = bike_dum.drop(columns=['bike_acc_score'])\n",
    "# do train test split\n",
    "X_bike_c_train, X_bike_c_test, y_bike_c_train, y_bike_c_test = train_test_split(bike_X, bike_c_y, test_size=0.3, \n",
    "                                                                            random_state=18,\n",
    "                                                                            shuffle=True, stratify=bike_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_acc_score</th>\n",
       "      <th>AADT</th>\n",
       "      <th>ROW_Width</th>\n",
       "      <th>Surface_Wd</th>\n",
       "      <th>Rt_Sidewlk</th>\n",
       "      <th>Lt_Sidewlk</th>\n",
       "      <th>Med_Width</th>\n",
       "      <th>Op_Dir_SL</th>\n",
       "      <th>City_-1</th>\n",
       "      <th>City_26</th>\n",
       "      <th>...</th>\n",
       "      <th>Terrain_1</th>\n",
       "      <th>Terrain_2</th>\n",
       "      <th>Terrain_3</th>\n",
       "      <th>F_Class_-1</th>\n",
       "      <th>F_Class_0</th>\n",
       "      <th>F_Class_1</th>\n",
       "      <th>F_Class_2</th>\n",
       "      <th>F_Class_3</th>\n",
       "      <th>F_Class_5</th>\n",
       "      <th>F_Class_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "      <td>1211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.829893</td>\n",
       "      <td>15711.157877</td>\n",
       "      <td>59.814408</td>\n",
       "      <td>28.835661</td>\n",
       "      <td>7.729118</td>\n",
       "      <td>5.589874</td>\n",
       "      <td>2.165893</td>\n",
       "      <td>41.734104</td>\n",
       "      <td>0.000826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.887696</td>\n",
       "      <td>0.093311</td>\n",
       "      <td>0.017341</td>\n",
       "      <td>0.001652</td>\n",
       "      <td>0.377374</td>\n",
       "      <td>0.017341</td>\n",
       "      <td>0.028076</td>\n",
       "      <td>0.288192</td>\n",
       "      <td>0.221305</td>\n",
       "      <td>0.066061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.587152</td>\n",
       "      <td>24034.757834</td>\n",
       "      <td>30.423020</td>\n",
       "      <td>10.792082</td>\n",
       "      <td>4.239799</td>\n",
       "      <td>4.571020</td>\n",
       "      <td>11.146028</td>\n",
       "      <td>26.825090</td>\n",
       "      <td>0.028736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.315870</td>\n",
       "      <td>0.290988</td>\n",
       "      <td>0.130593</td>\n",
       "      <td>0.040622</td>\n",
       "      <td>0.484930</td>\n",
       "      <td>0.130593</td>\n",
       "      <td>0.165258</td>\n",
       "      <td>0.453108</td>\n",
       "      <td>0.415297</td>\n",
       "      <td>0.248492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1154.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10489.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>19191.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>168186.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       car_acc_score           AADT    ROW_Width   Surface_Wd   Rt_Sidewlk  \\\n",
       "count    1211.000000    1211.000000  1211.000000  1211.000000  1211.000000   \n",
       "mean        0.829893   15711.157877    59.814408    28.835661     7.729118   \n",
       "std         2.587152   24034.757834    30.423020    10.792082     4.239799   \n",
       "min         0.000000      10.000000     9.000000     7.000000     0.000000   \n",
       "25%         0.000000    1154.000000    40.000000    22.000000     6.000000   \n",
       "50%         0.000000   10489.000000    55.000000    26.000000     8.000000   \n",
       "75%         1.000000   19191.500000    80.000000    35.000000    10.000000   \n",
       "max        42.000000  168186.000000   200.000000    80.000000    24.000000   \n",
       "\n",
       "        Lt_Sidewlk    Med_Width    Op_Dir_SL      City_-1  City_26  ...  \\\n",
       "count  1211.000000  1211.000000  1211.000000  1211.000000   1211.0  ...   \n",
       "mean      5.589874     2.165893    41.734104     0.000826      0.0  ...   \n",
       "std       4.571020    11.146028    26.825090     0.028736      0.0  ...   \n",
       "min       0.000000     0.000000    20.000000     0.000000      0.0  ...   \n",
       "25%       0.000000     0.000000    30.000000     0.000000      0.0  ...   \n",
       "50%       6.000000     0.000000    30.000000     0.000000      0.0  ...   \n",
       "75%       8.000000     0.000000    30.000000     0.000000      0.0  ...   \n",
       "max      27.000000   180.000000    99.000000     1.000000      0.0  ...   \n",
       "\n",
       "         Terrain_1    Terrain_2    Terrain_3   F_Class_-1    F_Class_0  \\\n",
       "count  1211.000000  1211.000000  1211.000000  1211.000000  1211.000000   \n",
       "mean      0.887696     0.093311     0.017341     0.001652     0.377374   \n",
       "std       0.315870     0.290988     0.130593     0.040622     0.484930   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       1.000000     0.000000     0.000000     0.000000     1.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "         F_Class_1    F_Class_2    F_Class_3    F_Class_5    F_Class_6  \n",
       "count  1211.000000  1211.000000  1211.000000  1211.000000  1211.000000  \n",
       "mean      0.017341     0.028076     0.288192     0.221305     0.066061  \n",
       "std       0.130593     0.165258     0.453108     0.415297     0.248492  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     1.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 53 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use the best performing classifier on the reduced feature set\n",
    "gbc = ensemble.GradientBoostingClassifier(random_state=18, \n",
    "                                          n_estimators=500, \n",
    "                                          learning_rate=0.2, \n",
    "                                          max_features=None,\n",
    "                                         min_samples_split=10,\n",
    "                                         max_depth=5)\n",
    "bc.fit(X_bike_c_train, y_bike_c_train)\n",
    "pred = bc.predict(X_bike_c_test)\n",
    "\n",
    "# create dataframe of false positives\n",
    "# create dataframe of true positives\n",
    "# compare on top 10 features\n",
    "bike_false_pos = X_bike_c_test[pred > y_bike_c_test]\n",
    "bike_true_pos = X_bike_c_test[(pred == y_bike_c_test) & (y_bike_c_test == 1)]\n",
    "\n",
    "bike_false_pos.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_acc_score</th>\n",
       "      <th>AADT</th>\n",
       "      <th>ROW_Width</th>\n",
       "      <th>Surface_Wd</th>\n",
       "      <th>Rt_Sidewlk</th>\n",
       "      <th>Lt_Sidewlk</th>\n",
       "      <th>Med_Width</th>\n",
       "      <th>Op_Dir_SL</th>\n",
       "      <th>City_-1</th>\n",
       "      <th>City_26</th>\n",
       "      <th>...</th>\n",
       "      <th>Terrain_1</th>\n",
       "      <th>Terrain_2</th>\n",
       "      <th>Terrain_3</th>\n",
       "      <th>F_Class_-1</th>\n",
       "      <th>F_Class_0</th>\n",
       "      <th>F_Class_1</th>\n",
       "      <th>F_Class_2</th>\n",
       "      <th>F_Class_3</th>\n",
       "      <th>F_Class_5</th>\n",
       "      <th>F_Class_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1913.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.587559</td>\n",
       "      <td>18259.755678</td>\n",
       "      <td>66.988388</td>\n",
       "      <td>29.618614</td>\n",
       "      <td>8.678475</td>\n",
       "      <td>5.689323</td>\n",
       "      <td>3.016332</td>\n",
       "      <td>41.376372</td>\n",
       "      <td>0.002614</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.936749</td>\n",
       "      <td>0.055410</td>\n",
       "      <td>0.005227</td>\n",
       "      <td>0.004182</td>\n",
       "      <td>0.248824</td>\n",
       "      <td>0.016205</td>\n",
       "      <td>0.023001</td>\n",
       "      <td>0.417669</td>\n",
       "      <td>0.231051</td>\n",
       "      <td>0.059070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.592718</td>\n",
       "      <td>23009.455958</td>\n",
       "      <td>32.817126</td>\n",
       "      <td>10.027635</td>\n",
       "      <td>4.412135</td>\n",
       "      <td>5.161501</td>\n",
       "      <td>12.736405</td>\n",
       "      <td>26.643597</td>\n",
       "      <td>0.051071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243478</td>\n",
       "      <td>0.228839</td>\n",
       "      <td>0.072130</td>\n",
       "      <td>0.064549</td>\n",
       "      <td>0.432445</td>\n",
       "      <td>0.126296</td>\n",
       "      <td>0.149944</td>\n",
       "      <td>0.493304</td>\n",
       "      <td>0.421615</td>\n",
       "      <td>0.235816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3692.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11078.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>20951.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>168186.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       car_acc_score           AADT    ROW_Width   Surface_Wd   Rt_Sidewlk  \\\n",
       "count    1913.000000    1913.000000  1913.000000  1913.000000  1913.000000   \n",
       "mean        3.587559   18259.755678    66.988388    29.618614     8.678475   \n",
       "std         7.592718   23009.455958    32.817126    10.027635     4.412135   \n",
       "min         0.000000      10.000000    10.000000     8.000000     0.000000   \n",
       "25%         0.000000    3692.000000    46.000000    24.000000     7.000000   \n",
       "50%         1.000000   11078.000000    60.000000    28.000000     8.000000   \n",
       "75%         3.000000   20951.000000    81.000000    36.000000    10.000000   \n",
       "max        59.000000  168186.000000   200.000000    76.000000    26.000000   \n",
       "\n",
       "        Lt_Sidewlk    Med_Width    Op_Dir_SL      City_-1  City_26  ...  \\\n",
       "count  1913.000000  1913.000000  1913.000000  1913.000000   1913.0  ...   \n",
       "mean      5.689323     3.016332    41.376372     0.002614      0.0  ...   \n",
       "std       5.161501    12.736405    26.643597     0.051071      0.0  ...   \n",
       "min       0.000000     0.000000     5.000000     0.000000      0.0  ...   \n",
       "25%       0.000000     0.000000    30.000000     0.000000      0.0  ...   \n",
       "50%       6.000000     0.000000    30.000000     0.000000      0.0  ...   \n",
       "75%      10.000000     0.000000    30.000000     0.000000      0.0  ...   \n",
       "max      27.000000   240.000000    99.000000     1.000000      0.0  ...   \n",
       "\n",
       "         Terrain_1    Terrain_2    Terrain_3   F_Class_-1    F_Class_0  \\\n",
       "count  1913.000000  1913.000000  1913.000000  1913.000000  1913.000000   \n",
       "mean      0.936749     0.055410     0.005227     0.004182     0.248824   \n",
       "std       0.243478     0.228839     0.072130     0.064549     0.432445   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "         F_Class_1    F_Class_2    F_Class_3    F_Class_5    F_Class_6  \n",
       "count  1913.000000  1913.000000  1913.000000  1913.000000  1913.000000  \n",
       "mean      0.016205     0.023001     0.417669     0.231051     0.059070  \n",
       "std       0.126296     0.149944     0.493304     0.421615     0.235816  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     1.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 53 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_true_pos.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#construct summary dataframe\n",
    "bike_false_means = bike_false_pos[['car_acc_score', 'AADT', 'ROW_Width', 'Surface_Wd', \n",
    "                                 'Rt_Sidewlk', 'Lt_Sidewlk', 'Med_Width', \n",
    "                                 'Op_Dir_SL']].describe().loc['mean']\n",
    "bike_true_means = bike_true_pos[['car_acc_score', 'AADT', 'ROW_Width', 'Surface_Wd', \n",
    "                                 'Rt_Sidewlk', 'Lt_Sidewlk', 'Med_Width', \n",
    "                                 'Op_Dir_SL']].describe().loc['mean']\n",
    "bike_false_summary = pd.DataFrame({\"means\": bike_false_means})\n",
    "bike_false_summary['Type'] = 'False Positive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_true_summary = pd.DataFrame({\"means\": bike_true_means})\n",
    "bike_true_summary['Type'] = 'True Positive'\n",
    "bike_summary = bike_false_summary.append(bike_true_summary)\n",
    "bike_summary = bike_summary.reset_index()\n",
    "bike_summary = bike_summary.rename(columns={'index' : 'feature'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>means</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>car_acc_score</td>\n",
       "      <td>0.829893</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AADT</td>\n",
       "      <td>15711.157877</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ROW_Width</td>\n",
       "      <td>59.814408</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Surface_Wd</td>\n",
       "      <td>28.835661</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rt_Sidewlk</td>\n",
       "      <td>7.729118</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lt_Sidewlk</td>\n",
       "      <td>5.589874</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Med_Width</td>\n",
       "      <td>2.165893</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Op_Dir_SL</td>\n",
       "      <td>41.734104</td>\n",
       "      <td>False Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>car_acc_score</td>\n",
       "      <td>3.587559</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AADT</td>\n",
       "      <td>18259.755678</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ROW_Width</td>\n",
       "      <td>66.988388</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Surface_Wd</td>\n",
       "      <td>29.618614</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rt_Sidewlk</td>\n",
       "      <td>8.678475</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lt_Sidewlk</td>\n",
       "      <td>5.689323</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Med_Width</td>\n",
       "      <td>3.016332</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Op_Dir_SL</td>\n",
       "      <td>41.376372</td>\n",
       "      <td>True Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>car_acc_score</td>\n",
       "      <td>2.127245</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AADT</td>\n",
       "      <td>13699.719048</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ROW_Width</td>\n",
       "      <td>57.220953</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Surface_Wd</td>\n",
       "      <td>28.898646</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rt_Sidewlk</td>\n",
       "      <td>4.845877</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lt_Sidewlk</td>\n",
       "      <td>3.777276</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Med_Width</td>\n",
       "      <td>2.440373</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Op_Dir_SL</td>\n",
       "      <td>41.810849</td>\n",
       "      <td>True Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          feature         means            Type\n",
       "0   car_acc_score      0.829893  False Positive\n",
       "1            AADT  15711.157877  False Positive\n",
       "2       ROW_Width     59.814408  False Positive\n",
       "3      Surface_Wd     28.835661  False Positive\n",
       "4      Rt_Sidewlk      7.729118  False Positive\n",
       "5      Lt_Sidewlk      5.589874  False Positive\n",
       "6       Med_Width      2.165893  False Positive\n",
       "7       Op_Dir_SL     41.734104  False Positive\n",
       "8   car_acc_score      3.587559   True Positive\n",
       "9            AADT  18259.755678   True Positive\n",
       "10      ROW_Width     66.988388   True Positive\n",
       "11     Surface_Wd     29.618614   True Positive\n",
       "12     Rt_Sidewlk      8.678475   True Positive\n",
       "13     Lt_Sidewlk      5.689323   True Positive\n",
       "14      Med_Width      3.016332   True Positive\n",
       "15      Op_Dir_SL     41.376372   True Positive\n",
       "0   car_acc_score      2.127245   True Negative\n",
       "1            AADT  13699.719048   True Negative\n",
       "2       ROW_Width     57.220953   True Negative\n",
       "3      Surface_Wd     28.898646   True Negative\n",
       "4      Rt_Sidewlk      4.845877   True Negative\n",
       "5      Lt_Sidewlk      3.777276   True Negative\n",
       "6       Med_Width      2.440373   True Negative\n",
       "7       Op_Dir_SL     41.810849   True Negative"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_true_neg = X_bike_c_test[(pred == y_bike_c_test) & (y_bike_c_test == 0)]\n",
    "bike_true_neg_means = bike_true_neg[['car_acc_score', 'AADT', 'ROW_Width', 'Surface_Wd', \n",
    "                                 'Rt_Sidewlk', 'Lt_Sidewlk', 'Med_Width', \n",
    "                                 'Op_Dir_SL']].describe().loc['mean']\n",
    "\n",
    "bike_neg_summary = pd.DataFrame({\"means\": bike_true_neg_means})\n",
    "bike_neg_summary['Type'] = 'True Negative'\n",
    "bike_neg_summary = bike_neg_summary.reset_index()\n",
    "bike_neg_summary = bike_neg_summary.rename(columns={'index' : 'feature'})\n",
    "\n",
    "bike_summary = bike_summary.append(bike_neg_summary)\n",
    "bike_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_all = bike_false_pos.copy()\n",
    "bike_all['Type'] = 'False Positive'\n",
    "bike_all = bike_all.append(bike_true_pos)\n",
    "bike_all['Type'] = bike_all['Type'].fillna(value='True Positive')\n",
    "bike_all = bike_all.append(bike_true_neg)\n",
    "bike_all['Type'] = bike_all['Type'].fillna(value='True Negative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x181083b2a88>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='AADT', x='Type', showfliers=False, data=bike_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18103b7fd08>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='car_acc_score', x='Type', showfliers=False, data=bike_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14b260e4c08>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show traffic density comparison between true positives and false positives\n",
    "traffic = bike_summary[(bike_summary.feature == 'AADT')]\n",
    "sns.barplot(x=\"feature\", y=\"means\", hue='Type', data=traffic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14b267c1888>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_acc = bike_summary[bike_summary.feature == 'car_acc_score']\n",
    "sns.barplot(x=\"feature\", y=\"means\", hue=\"Type\", data=car_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14b24475048>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "other_features = bike_summary[(bike_summary.feature != 'car_acc_score') & \n",
    "                             (bike_summary.feature != 'AADT') &\n",
    "                            (bike_summary.feature != 'traffic_density')]\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(x=\"feature\", y=\"means\", hue=\"Type\", data=other_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Sensitivity ##\n",
    "\n",
    "Machine learning models can be used to predict how changes in features might predict changes in outcome. Since traffic is something that can change easily, I decided to look at the effect of increased traffic on predicted bike and car accidents.\n",
    "\n",
    "I want to do this on the best model, so I first I need to set that up for car accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in data set with categorical variables turned into dummy variablees\n",
    "df = pd.read_csv('data/cleaned_data/md_dum.csv')\n",
    "\n",
    "# create X and y values for modeling, this time using a mask to create 1 and 0 values for classification\n",
    "car_c_y = df.car_acc_score.mask(df.car_acc_score > 0, 1)\n",
    "car_X = df.drop(columns=['Unnamed: 0', 'car_acc_score', 'car_dens_score', 'bike_dens_score', 'AADT'])\n",
    "bike_c_y = df.bike_acc_score.mask(df.bike_acc_score > 0, 1)\n",
    "bike_X = df.drop(columns=['Unnamed: 0', 'bike_acc_score', 'car_dens_score', 'bike_dens_score', 'AADT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test split\n",
    "X_car_c_train, X_car_c_test, y_car_c_train, y_car_c_test = train_test_split(car_X, car_c_y, test_size=0.3, \n",
    "                                                                            random_state=18,\n",
    "                                                                            shuffle=True, stratify=car_c_y)\n",
    "X_bike_c_train, X_bike_c_test, y_bike_c_train, y_bike_c_test = train_test_split(bike_X, bike_c_y, test_size=0.3, \n",
    "                                                                            random_state=18,\n",
    "                                                                            shuffle=True, stratify=bike_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=65, max_features=None,\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=400,\n",
       "                       n_jobs=None, oob_score=False, random_state=18, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = ensemble.RandomForestClassifier(criterion='entropy', \n",
    "                                      n_estimators=400,\n",
    "                                      min_samples_split=2, \n",
    "                                      max_depth=65,\n",
    "                                      max_features=None,\n",
    "                                      random_state=18)\n",
    "rfc.fit(X_car_c_train, y_car_c_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=10,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=20,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=18, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc = ensemble.GradientBoostingClassifier(random_state=18, \n",
    "                                          n_estimators=100, \n",
    "                                          min_samples_split=20,\n",
    "                                          max_features=None, \n",
    "                                          max_depth=10, \n",
    "                                          learning_rate=0.1)\n",
    "gbc.fit(X_bike_c_train, y_bike_c_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will fake some data to show how increasing traffic increases predicted accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1% more traffic could lead to -2.0% more car accidents, or -182 additional accidents.\n",
      "0.1% more traffic could lead to 151.0% more bike accidents, or 7113 additional accidents.\n",
      "\n",
      "0.5% more traffic could lead to -2.0% more car accidents, or -200 additional accidents.\n",
      "0.5% more traffic could lead to 151.0% more bike accidents, or 7106 additional accidents.\n",
      "\n",
      "1% more traffic could lead to 82.0% more car accidents, or 7987 additional accidents.\n",
      "1% more traffic could lead to 155.0% more bike accidents, or 7317 additional accidents.\n",
      "\n",
      "2% more traffic could lead to 82.0% more car accidents, or 7977 additional accidents.\n",
      "2% more traffic could lead to 155.0% more bike accidents, or 7308 additional accidents.\n",
      "\n",
      "3% more traffic could lead to 82.0% more car accidents, or 7959 additional accidents.\n",
      "3% more traffic could lead to 155.0% more bike accidents, or 7284 additional accidents.\n",
      "\n",
      "4% more traffic could lead to 81.0% more car accidents, or 7932 additional accidents.\n",
      "4% more traffic could lead to 154.0% more bike accidents, or 7275 additional accidents.\n",
      "\n",
      "5% more traffic could lead to 81.0% more car accidents, or 7943 additional accidents.\n",
      "5% more traffic could lead to 193.0% more bike accidents, or 9091 additional accidents.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "current_car_acc = sum(y_car_c_test)\n",
    "current_bike_acc = sum(y_bike_c_test)\n",
    "car_pred_arr = []\n",
    "bike_pred_arr = []\n",
    "\n",
    "percents = [0.001, 0.005, 0.01, 0.02, 0.03, 0.04, 0.05]\n",
    "for p in percents:\n",
    "    X_car = X_car_c_test.copy()\n",
    "    X_bike = X_bike_c_test.copy()\n",
    "    X_car['traffic_density'] = X_car['traffic_density'] * (1 + p)\n",
    "    X_bike['traffic_density'] = X_bike['traffic_density'] * (1 + p)\n",
    "    pred_car = rfc.predict(X_car)\n",
    "    pred_bike = gbc.predict(X_bike)\n",
    "    if (p >= 0.01):\n",
    "        pct = round(p*100)\n",
    "    else:\n",
    "        pct = p*100\n",
    "    car_pct_more = round(((sum(pred_car) - current_car_acc)/current_car_acc)*100)\n",
    "    bike_pct_more = round(((sum(pred_bike) - current_bike_acc)/current_bike_acc)*100)\n",
    "    print(\"{}% more traffic could lead to {}% more car accidents, or {} additional accidents.\"\n",
    "          .format(pct, car_pct_more, sum(pred_car) - current_car_acc))\n",
    "    print(\"{}% more traffic could lead to {}% more bike accidents, or {} additional accidents.\"\n",
    "          .format(pct, bike_pct_more, sum(pred_bike) - current_bike_acc))\n",
    "    print()\n",
    "    car_pred_arr.append(car_pct_more)\n",
    "    bike_pred_arr.append(bike_pct_more)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prediction is pretty sensitive,  and once it jumps up, I'm not sure it's very accurate anymore, but within a few percent, it shows a believable level of increased accidents for additional traffic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inc_df = pd.DataFrame({'Traffic Increase': percents, 'Percent Accident Increase': car_pred_array})\n",
    "inc_df['Type'] = 'Car'\n",
    "temp_df = pd.DataFrame({'Traffic Increase': percents, 'Percent Accident Increase': bike_pred_array})\n",
    "temp_df['Type'] = 'Bike'\n",
    "inc_df = inc_df.append(temp_df)\n",
    "inc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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