Finalizing Dummies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pprint import pprint\n",
    "\n",
    "df = pd.read_csv('Speeding/Violations_2017_Speeding_after_dummies.csv', parse_dates=['time_of_stop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_of_stop</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>belts_No</th>\n",
       "      <th>belts_Yes</th>\n",
       "      <th>personal_injury_No</th>\n",
       "      <th>personal_injury_Yes</th>\n",
       "      <th>property_damage_No</th>\n",
       "      <th>property_damage_Yes</th>\n",
       "      <th>fatal_No</th>\n",
       "      <th>...</th>\n",
       "      <th>make_SATURN</th>\n",
       "      <th>make_SCION</th>\n",
       "      <th>make_SMART</th>\n",
       "      <th>make_SUBARU</th>\n",
       "      <th>make_SUZUKI</th>\n",
       "      <th>make_TESLA</th>\n",
       "      <th>make_TOYOTA</th>\n",
       "      <th>make_VOLKSWAGEN</th>\n",
       "      <th>make_VOLVO</th>\n",
       "      <th>make_YAMAHA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-28 23:13:00</td>\n",
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       "      <td>2017-08-28 22:53:00</td>\n",
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       "      <td>2017-08-29 01:44:00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-29 07:55:00</td>\n",
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       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         time_of_stop   latitude  longitude  belts_No  belts_Yes  \\\n",
       "0 2017-08-28 23:13:00  39.157853 -77.204095         1          0   \n",
       "1 2017-08-28 22:53:00  39.152928 -77.171317         1          0   \n",
       "2 2017-08-29 01:44:00  39.083948 -77.153498         1          0   \n",
       "3 2017-08-29 07:55:00  39.033387 -77.072528         1          0   \n",
       "4 2017-08-29 08:07:00  39.032983 -77.073839         1          0   \n",
       "\n",
       "   personal_injury_No  personal_injury_Yes  property_damage_No  \\\n",
       "0                   1                    0                   1   \n",
       "1                   1                    0                   1   \n",
       "2                   1                    0                   1   \n",
       "3                   1                    0                   1   \n",
       "4                   1                    0                   1   \n",
       "\n",
       "   property_damage_Yes  fatal_No     ...       make_SATURN  make_SCION  \\\n",
       "0                    0         1     ...                 0           0   \n",
       "1                    0         1     ...                 0           0   \n",
       "2                    0         1     ...                 0           0   \n",
       "3                    0         1     ...                 0           0   \n",
       "4                    0         1     ...                 0           0   \n",
       "\n",
       "   make_SMART  make_SUBARU  make_SUZUKI  make_TESLA  make_TOYOTA  \\\n",
       "0           0            0            0           0            0   \n",
       "1           0            1            0           0            0   \n",
       "2           0            0            0           0            1   \n",
       "3           0            0            0           0            0   \n",
       "4           0            1            0           0            0   \n",
       "\n",
       "   make_VOLKSWAGEN  make_VOLVO  make_YAMAHA  \n",
       "0                0           0            0  \n",
       "1                0           0            0  \n",
       "2                0           0            0  \n",
       "3                0           0            0  \n",
       "4                0           0            0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['time_of_stop', 'latitude', 'longitude', 'belts_No', 'belts_Yes',\n",
       "       'personal_injury_No', 'personal_injury_Yes', 'property_damage_No',\n",
       "       'property_damage_Yes', 'fatal_No', 'fatal_Yes', 'commercial_license_No',\n",
       "       'commercial_license_Yes', 'alcohol_No', 'alcohol_Yes', 'work_zone_No',\n",
       "       'work_zone_Yes', 'contributed_to_accident_No',\n",
       "       'contributed_to_accident_Yes', 'violation_type_Citation',\n",
       "       'violation_type_Warning', 'make_ACURA', 'make_AUDI', 'make_BENTLEY',\n",
       "       'make_BMW', 'make_BUICK', 'make_CADILLAC', 'make_CHEVROLET',\n",
       "       'make_CHRYSLER', 'make_DODGE', 'make_DUCATI', 'make_FIAT', 'make_FORD',\n",
       "       'make_FREIGHTLINER', 'make_GEO', 'make_GILLIG', 'make_GMC',\n",
       "       'make_GRUMMAN', 'make_HARLEY', 'make_HINO', 'make_HONDA', 'make_HUMMER',\n",
       "       'make_HYUNDAI', 'make_INFINITI', 'make_INTERNATIONAL', 'make_ISUZU',\n",
       "       'make_JAGUAR', 'make_JEEP', 'make_KAWASAKI', 'make_KENWORTH',\n",
       "       'make_KIA', 'make_LAND_ROVER', 'make_LEXUS', 'make_LINCOLN',\n",
       "       'make_MACK', 'make_MASERATI', 'make_MAZDA', 'make_MERCEDES',\n",
       "       'make_MERCURY', 'make_MINI', 'make_MITSUBISHI', 'make_NISSAN',\n",
       "       'make_OLDSMOBILE', 'make_PETERBILT', 'make_PLYMOUTH', 'make_POLARIS',\n",
       "       'make_PONTIAC', 'make_PORSCHE', 'make_RANGE_ROVER', 'make_SAAB',\n",
       "       'make_SATURN', 'make_SCION', 'make_SMART', 'make_SUBARU', 'make_SUZUKI',\n",
       "       'make_TESLA', 'make_TOYOTA', 'make_VOLKSWAGEN', 'make_VOLVO',\n",
       "       'make_YAMAHA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combining columns down into binary, remove one of the car makes\n",
    "df = df.drop(['belts_No', 'personal_injury_No', 'property_damage_No', 'fatal_No', \n",
    "              'commercial_license_No', 'alcohol_No', 'work_zone_No', 'contributed_to_accident_No',\n",
    "             'violation_type_Warning'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={\"belts_Yes\": \"belts\", \"personal_injury_Yes\" : \"personal_injury\", \n",
    "                        \"property_damage_Yes\" : \"property_damage\", \"fatal_Yes\" : \"fatal\", \n",
    "                        \"commercial_license_Yes\" : \"commercial_license\", \"alcohol_Yes\" : \"alcohol\", \n",
    "                        \"work_zone_Yes\" : \"work_zone\", \n",
    "                        \"contributed_to_accident_Yes\" : \"contributed_to_accident\",\n",
    "                        \"violation_type_Citation\" : \"citation\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = df.drop(['time_of_stop', 'latitude', 'longitude'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>make_ACURA</th>\n",
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       "      <th>make_SATURN</th>\n",
       "      <th>make_SCION</th>\n",
       "      <th>make_SMART</th>\n",
       "      <th>make_SUBARU</th>\n",
       "      <th>make_SUZUKI</th>\n",
       "      <th>make_TESLA</th>\n",
       "      <th>make_TOYOTA</th>\n",
       "      <th>make_VOLKSWAGEN</th>\n",
       "      <th>make_VOLVO</th>\n",
       "      <th>make_YAMAHA</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   belts  personal_injury  property_damage  fatal  commercial_license  \\\n",
       "0      0                0                0      0                   0   \n",
       "1      0                0                0      0                   0   \n",
       "2      0                0                0      0                   0   \n",
       "3      0                0                0      0                   0   \n",
       "4      0                0                0      0                   0   \n",
       "\n",
       "   alcohol  work_zone  contributed_to_accident  citation  make_ACURA  \\\n",
       "0        0          0                        0         1           0   \n",
       "1        0          0                        0         1           0   \n",
       "2        0          0                        0         1           0   \n",
       "3        0          0                        0         1           0   \n",
       "4        0          0                        0         1           0   \n",
       "\n",
       "      ...       make_SATURN  make_SCION  make_SMART  make_SUBARU  make_SUZUKI  \\\n",
       "0     ...                 0           0           0            0            0   \n",
       "1     ...                 0           0           0            1            0   \n",
       "2     ...                 0           0           0            0            0   \n",
       "3     ...                 0           0           0            0            0   \n",
       "4     ...                 0           0           0            1            0   \n",
       "\n",
       "   make_TESLA  make_TOYOTA  make_VOLKSWAGEN  make_VOLVO  make_YAMAHA  \n",
       "0           0            0                0           0            0  \n",
       "1           0            0                0           0            0  \n",
       "2           0            1                0           0            0  \n",
       "3           0            0                0           0            0  \n",
       "4           0            0                0           0            0  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['belts', 'personal_injury', 'property_damage', 'fatal',\n",
       "       'commercial_license', 'alcohol', 'work_zone', 'contributed_to_accident',\n",
       "       'citation', 'make_ACURA', 'make_AUDI', 'make_BENTLEY', 'make_BMW',\n",
       "       'make_BUICK', 'make_CADILLAC', 'make_CHEVROLET', 'make_CHRYSLER',\n",
       "       'make_DODGE', 'make_DUCATI', 'make_FIAT', 'make_FORD',\n",
       "       'make_FREIGHTLINER', 'make_GEO', 'make_GILLIG', 'make_GMC',\n",
       "       'make_GRUMMAN', 'make_HARLEY', 'make_HINO', 'make_HONDA', 'make_HUMMER',\n",
       "       'make_HYUNDAI', 'make_INFINITI', 'make_INTERNATIONAL', 'make_ISUZU',\n",
       "       'make_JAGUAR', 'make_JEEP', 'make_KAWASAKI', 'make_KENWORTH',\n",
       "       'make_KIA', 'make_LAND_ROVER', 'make_LEXUS', 'make_LINCOLN',\n",
       "       'make_MACK', 'make_MASERATI', 'make_MAZDA', 'make_MERCEDES',\n",
       "       'make_MERCURY', 'make_MINI', 'make_MITSUBISHI', 'make_NISSAN',\n",
       "       'make_OLDSMOBILE', 'make_PETERBILT', 'make_PLYMOUTH', 'make_POLARIS',\n",
       "       'make_PONTIAC', 'make_PORSCHE', 'make_RANGE_ROVER', 'make_SAAB',\n",
       "       'make_SATURN', 'make_SCION', 'make_SMART', 'make_SUBARU', 'make_SUZUKI',\n",
       "       'make_TESLA', 'make_TOYOTA', 'make_VOLKSWAGEN', 'make_VOLVO',\n",
       "       'make_YAMAHA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "X = dataset.drop(['citation'], axis=1)\n",
    "Y = dataset.citation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'citations.pdf'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "dot_data = tree.export_graphviz(clf, out_file=None)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render('citations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58013059701492542"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X=X, y=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset2 = df\n",
    "dataset2.to_csv('Speeding/Violations_2017_Speeding_after_dummies_dr.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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