Chapel_Hill_Vehicle_Pedestrian_Accidents.ipynb

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    "colab": {
      "name": "Chapel_Hill_Vehicle_Pedestrian_Accidents.ipynb",
      "provenance": [],
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    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "E97gDChX_fHO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDW56jg5_fHk",
        "colab_type": "code",
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      },
      "source": [
        "# Import the Chapel Hill data\n",
        "df_work = pd.read_csv(\"pedestrian-crashes-chapel-hill-region_imported.txt\",\n",
        "                     sep='\\t')\n",
        "\n",
        "# df_work = pd.read_csv(\"/Users/danoand/Documents/Companies/LambdaSchool/Build_Project_02/data/pedestrian-crashes-chapel-hill-region_imported.txt\",\n",
        "#                    sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhDyk_Uu_fIM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Profile the \"work\" dataframe\n",
        "from pandas_profiling import ProfileReport\n",
        "\n",
        "profile = ProfileReport(df_work, title='Pandas Profiling Report', html={'style':{'full_width':True}})\n",
        "profile.to_file(output_file=\"/Users/danoand/Documents/Companies/LambdaSchool/Build_Project_02/data/ChapelHillAccidents.html\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_HjF7ZY_fIT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define \"ante\" columns (data that can be reasonably known prior to the event)\n",
        "# Notes:\n",
        "#   - removed the lat/long column \n",
        "categories_ante = [\n",
        "    'City',\n",
        "    'County',\n",
        "    'CrashHour',\n",
        "    'CrashMonth',\n",
        "    'Development',\n",
        "    'LightCond',\n",
        "    'Locality',\n",
        "    'NumLanes',\n",
        "    'RdCharacte',\n",
        "    'RdClass',\n",
        "    'RdConditio',\n",
        "    'RdConfig',\n",
        "    'RdDefects',\n",
        "    'RdFeature',\n",
        "    'RdSurface',\n",
        "    'Region',\n",
        "    'RuralUrban',\n",
        "    'SpeedLimit',\n",
        "    'TraffCntrl',\n",
        "    'Weather',\n",
        "    'Workzone',\n",
        "    'PedInjury']        # Source for the target data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USwoXO_h_fIZ",
        "colab_type": "code",
        "colab": {
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          "height": 804
        },
        "outputId": "938a73cf-8cd6-484c-de54-4fa8b24621da"
      },
      "source": [
        "# Define a dataframe with \"ante\" data\n",
        "df_ante = df_work[categories_ante]\n",
        "\n",
        "df_ante.sample(10)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>County</th>\n",
              "      <th>CrashHour</th>\n",
              "      <th>CrashMonth</th>\n",
              "      <th>Development</th>\n",
              "      <th>LightCond</th>\n",
              "      <th>Locality</th>\n",
              "      <th>NumLanes</th>\n",
              "      <th>RdCharacte</th>\n",
              "      <th>RdClass</th>\n",
              "      <th>RdConditio</th>\n",
              "      <th>RdConfig</th>\n",
              "      <th>RdDefects</th>\n",
              "      <th>RdFeature</th>\n",
              "      <th>RdSurface</th>\n",
              "      <th>Region</th>\n",
              "      <th>RuralUrban</th>\n",
              "      <th>SpeedLimit</th>\n",
              "      <th>TraffCntrl</th>\n",
              "      <th>Weather</th>\n",
              "      <th>Workzone</th>\n",
              "      <th>PedInjury</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22839</th>\n",
              "      <td>Raleigh</td>\n",
              "      <td>Wake</td>\n",
              "      <td>17</td>\n",
              "      <td>June</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>Daylight</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>2 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Public Vehicular Area</td>\n",
              "      <td>Dry</td>\n",
              "      <td>Two-Way, Not Divided</td>\n",
              "      <td>None</td>\n",
              "      <td>No Special Feature</td>\n",
              "      <td>Smooth Asphalt</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Urban</td>\n",
              "      <td>5 - 15 MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>B: Suspected Minor Injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4757</th>\n",
              "      <td>Charlotte</td>\n",
              "      <td>Mecklenburg</td>\n",
              "      <td>6</td>\n",
              "      <td>November</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>Dark - Lighted Roadway</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Straight - Grade</td>\n",
              "      <td>Public Vehicular Area</td>\n",
              "      <td>Dry</td>\n",
              "      <td>Two-Way, Not Divided</td>\n",
              "      <td>None</td>\n",
              "      <td>Driveway, Public</td>\n",
              "      <td>Coarse Asphalt</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Urban</td>\n",
              "      <td>20 - 25  MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>B: Suspected Minor Injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25563</th>\n",
              "      <td>None - Rural Crash</td>\n",
              "      <td>Orange</td>\n",
              "      <td>14</td>\n",
              "      <td>July</td>\n",
              "      <td>Farms, Woods, Pastures</td>\n",
              "      <td>Daylight</td>\n",
              "      <td>Rural (&lt;30% Developed)</td>\n",
              "      <td>2 lanes</td>\n",
              "      <td>Straight - Grade</td>\n",
              "      <td>Interstate</td>\n",
              "      <td>Wet</td>\n",
              "      <td>Two-Way, Divided, Positive Median Barrier</td>\n",
              "      <td>Obstruction In Roadway</td>\n",
              "      <td>No Special Feature</td>\n",
              "      <td>Grooved Concrete</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Rural</td>\n",
              "      <td>60 - 75 MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Rain</td>\n",
              "      <td>No</td>\n",
              "      <td>B: Suspected Minor Injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21623</th>\n",
              "      <td>Lillington</td>\n",
              "      <td>Harnett</td>\n",
              "      <td>13</td>\n",
              "      <td>August</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>Daylight</td>\n",
              "      <td>Mixed (30% To 70% Developed)</td>\n",
              "      <td>4 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Local Street</td>\n",
              "      <td>Dry</td>\n",
              "      <td>Two-Way, Not Divided</td>\n",
              "      <td>None</td>\n",
              "      <td>Four-Way Intersection</td>\n",
              "      <td>Smooth Asphalt</td>\n",
              "      <td>Coastal</td>\n",
              "      <td>Rural</td>\n",
              "      <td>20 - 25  MPH</td>\n",
              "      <td>Stop And Go Signal</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>A: Suspected Serious Injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4215</th>\n",
              "      <td>Charlotte</td>\n",
              "      <td>Mecklenburg</td>\n",
              "      <td>16</td>\n",
              "      <td>September</td>\n",
              "      <td>Farms, Woods, Pastures</td>\n",
              "      <td>Daylight</td>\n",
              "      <td>Rural (&lt;30% Developed)</td>\n",
              "      <td>2 lanes</td>\n",
              "      <td>Straight - Hillcrest</td>\n",
              "      <td>Public Vehicular Area</td>\n",
              "      <td>Dry</td>\n",
              "      <td>Two-Way, Not Divided</td>\n",
              "      <td>None</td>\n",
              "      <td>No Special Feature</td>\n",
              "      <td>Smooth Asphalt</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Urban</td>\n",
              "      <td>20 - 25  MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>No</td>\n",
              "      <td>C: Possible Injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8436</th>\n",
              "      <td>None - Rural Crash</td>\n",
              "      <td>Gaston</td>\n",
              "      <td>20</td>\n",
              "      <td>November</td>\n",
              "      <td>Farms, Woods, Pastures</td>\n",
              "      <td>Dark - Roadway Not Lighted</td>\n",
              "      <td>Rural (&lt;30% Developed)</td>\n",
              "      <td>2 lanes</td>\n",
              "      <td>Straight - Grade</td>\n",
              "      <td>NC Route</td>\n",
              "      <td>Dry</td>\n",
              "      <td>Two-Way, Not Divided</td>\n",
              "      <td>None</td>\n",
              "      <td>No Special Feature</td>\n",
              "      <td>Smooth Asphalt</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Rural</td>\n",
              "      <td>40 - 45  MPH</td>\n",
              "      <td>Double Yellow Line, No Passing Zone</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>C: Possible Injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28041</th>\n",
              "      <td>Monroe</td>\n",
              "      <td>Union</td>\n",
              "      <td>22</td>\n",
              "      <td>December</td>\n",
              "      <td>Residential</td>\n",
              "      <td>Dark - Lighted Roadway</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>2 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Local Street</td>\n",
              "      <td>Dry</td>\n",
              "      <td>Two-Way, Not Divided</td>\n",
              "      <td>None</td>\n",
              "      <td>Four-Way Intersection</td>\n",
              "      <td>Smooth Asphalt</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Urban</td>\n",
              "      <td>30 - 35  MPH</td>\n",
              "      <td>Stop Sign</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>C: Possible Injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18346</th>\n",
              "      <td>Greenville</td>\n",
              "      <td>Pitt</td>\n",
              "      <td>13</td>\n",
              "      <td>May</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>Daylight</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>2 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Local Street</td>\n",
              "      <td>Dry</td>\n",
              "      <td>Two-Way, Divided, Unprotected Median</td>\n",
              "      <td>None</td>\n",
              "      <td>T-Intersection</td>\n",
              "      <td>Coarse Asphalt</td>\n",
              "      <td>Coastal</td>\n",
              "      <td>Urban</td>\n",
              "      <td>30 - 35  MPH</td>\n",
              "      <td>Stop Sign</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>B: Suspected Minor Injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9168</th>\n",
              "      <td>Raleigh</td>\n",
              "      <td>Wake</td>\n",
              "      <td>8</td>\n",
              "      <td>October</td>\n",
              "      <td>Residential</td>\n",
              "      <td>Daylight</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>4 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Local Street</td>\n",
              "      <td>Dry</td>\n",
              "      <td>Two-Way, Not Divided</td>\n",
              "      <td>None</td>\n",
              "      <td>Four-Way Intersection</td>\n",
              "      <td>Coarse Asphalt</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Urban</td>\n",
              "      <td>40 - 45  MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>B: Suspected Minor Injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31582</th>\n",
              "      <td>Greensboro</td>\n",
              "      <td>Guilford</td>\n",
              "      <td>11</td>\n",
              "      <td>September</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>Daylight</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>2 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Local Street</td>\n",
              "      <td>Dry</td>\n",
              "      <td>Two-Way, Not Divided</td>\n",
              "      <td>None</td>\n",
              "      <td>Driveway, Private</td>\n",
              "      <td>Smooth Asphalt</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Urban</td>\n",
              "      <td>20 - 25  MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>C: Possible Injury</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     City       County  ...  Workzone                    PedInjury\n",
              "22839             Raleigh         Wake  ...        No    B: Suspected Minor Injury\n",
              "4757            Charlotte  Mecklenburg  ...        No    B: Suspected Minor Injury\n",
              "25563  None - Rural Crash       Orange  ...        No    B: Suspected Minor Injury\n",
              "21623          Lillington      Harnett  ...        No  A: Suspected Serious Injury\n",
              "4215            Charlotte  Mecklenburg  ...        No           C: Possible Injury\n",
              "8436   None - Rural Crash       Gaston  ...        No           C: Possible Injury\n",
              "28041              Monroe        Union  ...        No           C: Possible Injury\n",
              "18346          Greenville         Pitt  ...        No    B: Suspected Minor Injury\n",
              "9168              Raleigh         Wake  ...        No    B: Suspected Minor Injury\n",
              "31582          Greensboro     Guilford  ...        No           C: Possible Injury\n",
              "\n",
              "[10 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swecKesvC2CR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2cc5e33d-a3a3-43fa-c219-8f5ebeb95428"
      },
      "source": [
        "df_ante['CrashHour'].unique()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([22,  8, 19, 13, 15, 20, 14,  7, 18, 10, 16, 11, 12,  6, 21,  9,  5,\n",
              "        0, 17,  2, 23,  1,  4,  3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJjq1GfJ_fIm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Display some exploratory plots\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "tmp_outcm = df_ante['PedInjury'].replace({\n",
        "    \"K: Killed\": \"Death or Serious Injury\",\n",
        "    \"Unknown Injury\": \"Unknown\",\n",
        "    \"C: Possible Injury\": \"Possible Injury\",\n",
        "    \"B: Suspected Minor Injury\": \"Minor Injury\",\n",
        "    \"O: No Injury\": \"No Injury\",\n",
        "    \"A: Suspected Serious Injury\": \"Death or Serious Injury\"})\n",
        "\n",
        "tmp_wthr = df_ante['Weather'].replace({\n",
        "    \"Snow, Sleet, Hail, Freezing Rain/Drizzle\": \"Snow/Mix\",\n",
        "    \"Fog, Smog, Smoke\": \"Fog/Smog\"})\n",
        "\n",
        "tmp_wthr.fillna(\"Other\", inplace=True)\n",
        "\n",
        "tmp_hour = df_ante['CrashHour']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJKSx_a1THJO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "25339da1-bec1-403e-a705-e6d86eadbe5e"
      },
      "source": [
        "tmp_outcm.value_counts()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Possible Injury            13787\n",
              "Minor Injury               11952\n",
              "Death or Serious Injury     4587\n",
              "No Injury                   2021\n",
              "Unknown                     1360\n",
              "Name: PedInjury, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8VipCymANna",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "de2b2156-628f-48de-938d-73547e4c1c64"
      },
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.title(f'Pedestrian Outcomes')\n",
        "\n",
        "plt.grid(True)\n",
        "plt.hist(tmp_outcm);"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SpE4MYpIkSZ0YxCRJkjoxiEmSJHViEJMkSerEICZJktSJQUySJKkTg5gkSVInBjFJkqRO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\nJEmSOjGISZIkdfL/AKWN4qTegFs6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pko7jTT2LeuL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "acf0204e-26ac-4b57-cbc5-7edcbf122735"
      },
      "source": [
        "tmp_df_wthr_outc = pd.DataFrame({\"Weather\": tmp_wthr, \"Outcome\": tmp_outcm})\n",
        "\n",
        "tmp_srs_killed_by_weather = tmp_df_wthr_outc[tmp_df_wthr_outc['Outcome']=='Death or Serious Injury']['Weather'].value_counts()\n",
        "\n",
        "pd.DataFrame(tmp_srs_killed_by_weather)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Clear</th>\n",
              "      <td>3474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cloudy</th>\n",
              "      <td>691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rain</th>\n",
              "      <td>346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fog/Smog</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Snow/Mix</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Weather\n",
              "Clear        3474\n",
              "Cloudy        691\n",
              "Rain          346\n",
              "Fog/Smog       48\n",
              "Snow/Mix       17\n",
              "Other          11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYMCQQYxfXir",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "outputId": "facc11b5-4bfe-4a41-9a98-1f4c1817a74d"
      },
      "source": [
        "labels = list(tmp_srs_killed_by_weather.index)\n",
        "counts = list(tmp_srs_killed_by_weather)\n",
        "\n",
        "patches, texts = plt.pie(counts, startangle=90)\n",
        "plt.legend(patches, labels, loc=\"best\")\n",
        "\n",
        "# Set aspect ratio to be equal so that pie is drawn as a circle.\n",
        "plt.axis('equal')\n",
        "plt.tight_layout()\n",
        "plt.title('Pedestrian Deaths/Serious Injures by\\nWeather Type')\n",
        "plt.show()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EtOhJC+iowYMHV0k4hYfD4dCDBw+uxGqRCiGEsPUkoBwSTuFlv57SXSqEEK3Ih6IQQoio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\notL/B0+9IWvjUwA4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qV_xzKCjn898",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tmp_dict = {}\n",
        "\n",
        "# count_badoutcomes_by_hour tallies up the deaths/serious injuries by hour and\n",
        "#   stores those counts in a pyton dict\n",
        "def count_badoutcomes_by_hour(row):\n",
        "  if row['Outcome'] == \"Death or Serious Injury\":\n",
        "    key = row['Hour']\n",
        "    if key in tmp_dict:\n",
        "      tmp_dict[key] = tmp_dict[key] + 1\n",
        "\n",
        "    if not(key in tmp_dict):\n",
        "      tmp_dict[key] = 0\n",
        "\n",
        "  return\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdbS93eQvrdp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "2cf90a69-a484-4c7b-9769-9a6cea91ef84"
      },
      "source": [
        "df_tmp_wth_hour_otcm = pd.DataFrame({\n",
        "    \"Weather\": tmp_wthr,\n",
        "    \"Hour\": tmp_hour,\n",
        "    \"Outcome\": tmp_outcm})\n",
        "\n",
        "tmp_dict_hour_bad_otcms = df_tmp_wth_hour_otcm.apply(count_badoutcomes_by_hour, axis=1)\n",
        "\n",
        "tmp_dict"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 160,\n",
              " 1: 158,\n",
              " 2: 172,\n",
              " 3: 107,\n",
              " 4: 108,\n",
              " 5: 159,\n",
              " 6: 175,\n",
              " 7: 142,\n",
              " 8: 108,\n",
              " 9: 77,\n",
              " 10: 76,\n",
              " 11: 111,\n",
              " 12: 107,\n",
              " 13: 137,\n",
              " 14: 125,\n",
              " 15: 172,\n",
              " 16: 148,\n",
              " 17: 241,\n",
              " 18: 311,\n",
              " 19: 374,\n",
              " 20: 406,\n",
              " 21: 429,\n",
              " 22: 330,\n",
              " 23: 230}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41z_DBApzimC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "outputId": "d4087929-65bb-4dbe-c63f-08f513b2663f"
      },
      "source": [
        "df_temp_hour_badotcm_count = pd.DataFrame(columns=['Hour', 'BadCount'])\n",
        "for item in tmp_dict.items():\n",
        "    df_temp_hour_badotcm_count = df_temp_hour_badotcm_count.append({'Hour': item[0], 'BadCount': item[1]}, ignore_index=True)\n",
        "\n",
        "df_temp_hour_badotcm_count"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Hour</th>\n",
              "      <th>BadCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22</td>\n",
              "      <td>330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16</td>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>21</td>\n",
              "      <td>429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>14</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>11</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>18</td>\n",
              "      <td>311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>6</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>17</td>\n",
              "      <td>241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>10</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>19</td>\n",
              "      <td>374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5</td>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>23</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>13</td>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>12</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>3</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>4</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hour BadCount\n",
              "0    22      330\n",
              "1    20      406\n",
              "2     7      142\n",
              "3    15      172\n",
              "4    16      148\n",
              "5     0      160\n",
              "6     2      172\n",
              "7    21      429\n",
              "8     8      108\n",
              "9    14      125\n",
              "10    9       77\n",
              "11   11      111\n",
              "12   18      311\n",
              "13    6      175\n",
              "14    1      158\n",
              "15   17      241\n",
              "16   10       76\n",
              "17   19      374\n",
              "18    5      159\n",
              "19   23      230\n",
              "20   13      137\n",
              "21   12      107\n",
              "22    3      107\n",
              "23    4      108"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-9pQ3y92qjI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6e7c1b2c-6430-4948-af9a-ad822779480a"
      },
      "source": [
        "plt.scatter(df_temp_hour_badotcm_count['Hour'], df_temp_hour_badotcm_count['BadCount']);"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afGwHvd__fIq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Profile the df_ante dataframe\n",
        "# from pandas_profiling import ProfileReport\n",
        "\n",
        "# profile = ProfileReport(df_work, title='Pandas Profiling Report', html={'style':{'full_width':True}})\n",
        "# profile.to_file(output_file=\"/Users/danoand/Documents/Companies/LambdaSchool/Build_Project_02/data/ChapelHillAccidents.html\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDdmrmHI_fIv",
        "colab_type": "code",
        "colab": {},
        "outputId": "88bbc8da-f8b1-418a-ca4e-58483938bc24"
      },
      "source": [
        "# Split the data into training, validation, and testing datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "df_ante_train, df_ante_test = train_test_split(df_ante, train_size=0.85, test_size=0.15, \n",
        "                              stratify=df_ante['PedInjury'], random_state=42)\n",
        "\n",
        "print(f'Shape of df_ante_train: {df_ante_train.shape}; Shape of df_ante_test: {df_ante_test.shape}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of df_ante_train: (28650, 22); Shape of df_ante_test: (5057, 22)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7WZzQ6y_fI1",
        "colab_type": "code",
        "colab": {},
        "outputId": "121219e8-36cd-4bdb-f3c9-54152b5feb94"
      },
      "source": [
        "# Split the train data into a training subset and a validation subset\n",
        "df_ante_train, df_ante_val = train_test_split(df_ante_train, test_size=len(df_ante_test), \n",
        "                              stratify=df_ante_train['PedInjury'], random_state=42)\n",
        "\n",
        "print(f'Shape of df_ante_train: {df_ante_train.shape}; Shape of df_ante_val: {df_ante_val.shape}; Shape of df_ante_test: {df_ante_test.shape}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of df_ante_train: (23593, 22); Shape of df_ante_val: (5057, 22); Shape of df_ante_test: (5057, 22)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwbVyhwz_fI8",
        "colab_type": "code",
        "colab": {},
        "outputId": "0dbe3a3e-211b-4f6b-fa71-f9e724fcadfe"
      },
      "source": [
        "# Breakdown the target attribute by class value\n",
        "df_ante_train['PedInjury'].value_counts(normalize=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C: Possible Injury             0.409020\n",
              "B: Suspected Minor Injury      0.354597\n",
              "A: Suspected Serious Injury    0.072140\n",
              "K: Killed                      0.063917\n",
              "O: No Injury                   0.059975\n",
              "Unknown Injury                 0.040351\n",
              "Name: PedInjury, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xtv8Mu3u_fJE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Wrangle the modeling data\n",
        "outcome_serious = ['K: Killed', 'A: Suspected Serious Injury']\n",
        "\n",
        "# has_fatality is a function that returns a boolean value if the inbound value represents a serious outcome (True) or not (False)\n",
        "def has_serious_outcome(val):\n",
        "  if (val in outcome_serious): \n",
        "    return True\n",
        "\n",
        "  return False\n",
        "\n",
        "# wrangle returns a dataframe with updated/created columns for a given input dataframe \n",
        "def wrangle(DF):\n",
        "  X = DF.copy()\n",
        "\n",
        "  # Create a boolean target column reflecting an outcome (serious injury or not)\n",
        "  X['ped_serious_outcome'] = X['PedInjury'].apply(has_serious_outcome)\n",
        "\n",
        "  # Convert the CrashHour column to a string (from an int)\n",
        "  X['CrashHour'] = X['CrashHour'].apply(str)\n",
        "\n",
        "  # List columns to be dropped\n",
        "  cols_drop = ['PedInjury'] # PedInjury - use the engineered 'ped_fatality' column\n",
        "\n",
        "  # Drop undesired columns\n",
        "  X = X.drop(columns=cols_drop)\n",
        "\n",
        "  return X"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqzjRvpZ_fJJ",
        "colab_type": "code",
        "colab": {},
        "outputId": "a04a60bb-cbe5-4255-f351-d95c7b5650d7"
      },
      "source": [
        "# Wrangle the training, validation, and test datasets\n",
        "df_wrgl_train = wrangle(df_ante_train)\n",
        "df_wrgl_val   = wrangle(df_ante_val)\n",
        "df_wrgl_test  = wrangle(df_ante_test)\n",
        "\n",
        "print(f'Shape of df_wrgl_train: {df_wrgl_train.shape}; Shape of df_wrgl_val: {df_wrgl_val.shape}; Shape of df_wrgl_test: {df_wrgl_test.shape}\\n')\n",
        "\n",
        "print(f'\\nColumn Data Types:\\n{df_wrgl_val.dtypes}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of df_wrgl_train: (23593, 22); Shape of df_wrgl_val: (5057, 22); Shape of df_wrgl_test: (5057, 22)\n",
            "\n",
            "\n",
            "Column Data Types:\n",
            "City                   object\n",
            "County                 object\n",
            "CrashHour              object\n",
            "CrashMonth             object\n",
            "Development            object\n",
            "LightCond              object\n",
            "Locality               object\n",
            "NumLanes               object\n",
            "RdCharacte             object\n",
            "RdClass                object\n",
            "RdConditio             object\n",
            "RdConfig               object\n",
            "RdDefects              object\n",
            "RdFeature              object\n",
            "RdSurface              object\n",
            "Region                 object\n",
            "RuralUrban             object\n",
            "SpeedLimit             object\n",
            "TraffCntrl             object\n",
            "Weather                object\n",
            "Workzone               object\n",
            "ped_serious_outcome      bool\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOVFrcyK_fJP",
        "colab_type": "code",
        "colab": {},
        "outputId": "73dc09fe-9e0a-4ff1-d3d8-6e692bfcf782"
      },
      "source": [
        "df_wrgl_train.sample(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>City</th>\n",
              "      <th>County</th>\n",
              "      <th>CrashHour</th>\n",
              "      <th>CrashMonth</th>\n",
              "      <th>Development</th>\n",
              "      <th>LightCond</th>\n",
              "      <th>Locality</th>\n",
              "      <th>NumLanes</th>\n",
              "      <th>RdCharacte</th>\n",
              "      <th>RdClass</th>\n",
              "      <th>...</th>\n",
              "      <th>RdDefects</th>\n",
              "      <th>RdFeature</th>\n",
              "      <th>RdSurface</th>\n",
              "      <th>Region</th>\n",
              "      <th>RuralUrban</th>\n",
              "      <th>SpeedLimit</th>\n",
              "      <th>TraffCntrl</th>\n",
              "      <th>Weather</th>\n",
              "      <th>Workzone</th>\n",
              "      <th>ped_serious_outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>835</th>\n",
              "      <td>Fayetteville</td>\n",
              "      <td>Cumberland</td>\n",
              "      <td>16</td>\n",
              "      <td>October</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>Daylight</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>4 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Local Street</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>No Special Feature</td>\n",
              "      <td>Coarse Asphalt</td>\n",
              "      <td>Coastal</td>\n",
              "      <td>Urban</td>\n",
              "      <td>50 - 55  MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2381</th>\n",
              "      <td>Charlotte</td>\n",
              "      <td>Mecklenburg</td>\n",
              "      <td>18</td>\n",
              "      <td>February</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>Dark - Lighted Roadway</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>2 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Public Vehicular Area</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>No Special Feature</td>\n",
              "      <td>Smooth Asphalt</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25925</th>\n",
              "      <td>Charlotte</td>\n",
              "      <td>Mecklenburg</td>\n",
              "      <td>3</td>\n",
              "      <td>August</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>Dark - Lighted Roadway</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>4 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Local Street</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>No Special Feature</td>\n",
              "      <td>Smooth Asphalt</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Urban</td>\n",
              "      <td>40 - 45  MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2530</th>\n",
              "      <td>Rocky Mount</td>\n",
              "      <td>Edgecombe</td>\n",
              "      <td>18</td>\n",
              "      <td>June</td>\n",
              "      <td>Residential</td>\n",
              "      <td>Daylight</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>2 lanes</td>\n",
              "      <td>Straight - Level</td>\n",
              "      <td>Local Street</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>No Special Feature</td>\n",
              "      <td>Coarse Asphalt</td>\n",
              "      <td>Coastal</td>\n",
              "      <td>Urban</td>\n",
              "      <td>30 - 35  MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20150</th>\n",
              "      <td>Raleigh</td>\n",
              "      <td>Wake</td>\n",
              "      <td>20</td>\n",
              "      <td>October</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>Dark - Lighted Roadway</td>\n",
              "      <td>Urban (&gt;70% Developed)</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Missing</td>\n",
              "      <td>Local Street</td>\n",
              "      <td>...</td>\n",
              "      <td>None</td>\n",
              "      <td>Missing</td>\n",
              "      <td>Missing</td>\n",
              "      <td>Piedmont</td>\n",
              "      <td>Urban</td>\n",
              "      <td>40 - 45  MPH</td>\n",
              "      <td>No Control Present</td>\n",
              "      <td>Clear</td>\n",
              "      <td>No</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               City       County CrashHour CrashMonth  Development  \\\n",
              "835    Fayetteville   Cumberland        16    October   Commercial   \n",
              "2381      Charlotte  Mecklenburg        18   February   Commercial   \n",
              "25925     Charlotte  Mecklenburg         3     August   Commercial   \n",
              "2530    Rocky Mount    Edgecombe        18       June  Residential   \n",
              "20150       Raleigh         Wake        20    October   Commercial   \n",
              "\n",
              "                    LightCond                Locality NumLanes  \\\n",
              "835                  Daylight  Urban (>70% Developed)  4 lanes   \n",
              "2381   Dark - Lighted Roadway  Urban (>70% Developed)  2 lanes   \n",
              "25925  Dark - Lighted Roadway  Urban (>70% Developed)  4 lanes   \n",
              "2530                 Daylight  Urban (>70% Developed)  2 lanes   \n",
              "20150  Dark - Lighted Roadway  Urban (>70% Developed)  Unknown   \n",
              "\n",
              "             RdCharacte                RdClass  ... RdDefects  \\\n",
              "835    Straight - Level           Local Street  ...      None   \n",
              "2381   Straight - Level  Public Vehicular Area  ...      None   \n",
              "25925  Straight - Level           Local Street  ...      None   \n",
              "2530   Straight - Level           Local Street  ...      None   \n",
              "20150           Missing           Local Street  ...      None   \n",
              "\n",
              "                RdFeature       RdSurface    Region RuralUrban    SpeedLimit  \\\n",
              "835    No Special Feature  Coarse Asphalt   Coastal      Urban  50 - 55  MPH   \n",
              "2381   No Special Feature  Smooth Asphalt  Piedmont      Urban       Unknown   \n",
              "25925  No Special Feature  Smooth Asphalt  Piedmont      Urban  40 - 45  MPH   \n",
              "2530   No Special Feature  Coarse Asphalt   Coastal      Urban  30 - 35  MPH   \n",
              "20150             Missing         Missing  Piedmont      Urban  40 - 45  MPH   \n",
              "\n",
              "               TraffCntrl Weather Workzone ped_serious_outcome  \n",
              "835    No Control Present   Clear       No               False  \n",
              "2381   No Control Present   Clear       No               False  \n",
              "25925  No Control Present   Clear       No               False  \n",
              "2530   No Control Present   Clear       No               False  \n",
              "20150  No Control Present   Clear       No               False  \n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nb21gn9__fJV",
        "colab_type": "code",
        "colab": {},
        "outputId": "b2d526c3-66f8-4d5b-e949-de61b301a2ea"
      },
      "source": [
        "# Construct the modeling datasets\n",
        "target = 'ped_serious_outcome'\n",
        "X_train = df_wrgl_train.drop(columns=target)\n",
        "y_train = df_wrgl_train[target]\n",
        "\n",
        "X_val   = df_wrgl_val.drop(columns=target)\n",
        "y_val   = df_wrgl_val[target]\n",
        "\n",
        "X_test  = df_wrgl_test.drop(columns=target)\n",
        "y_test  = df_wrgl_test[target]\n",
        "\n",
        "\n",
        "print(f'\\nValidation Column Data Types:\\n{X_val.dtypes}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation Column Data Types:\n",
            "City           object\n",
            "County         object\n",
            "CrashHour      object\n",
            "CrashMonth     object\n",
            "Development    object\n",
            "LightCond      object\n",
            "Locality       object\n",
            "NumLanes       object\n",
            "RdCharacte     object\n",
            "RdClass        object\n",
            "RdConditio     object\n",
            "RdConfig       object\n",
            "RdDefects      object\n",
            "RdFeature      object\n",
            "RdSurface      object\n",
            "Region         object\n",
            "RuralUrban     object\n",
            "SpeedLimit     object\n",
            "TraffCntrl     object\n",
            "Weather        object\n",
            "Workzone       object\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGRsyAZj_fJb",
        "colab_type": "code",
        "colab": {},
        "outputId": "d2a3b0ae-1cbc-4d97-ddd4-9790df6fe977"
      },
      "source": [
        "# Create a baseline prediction using a dummy classifier\n",
        "from sklearn.dummy import DummyClassifier\n",
        "clfr_dummy = DummyClassifier(strategy=\"stratified\")\n",
        "\n",
        "clfr_dummy.fit(X_train, y_train)\n",
        "clfr_dummy.predict(X_train)\n",
        "\n",
        "dummy_train_y_pred = clfr_dummy.predict(X_train)\n",
        "\n",
        "print(f'Baseline prediction score for the training set is: {round(clfr_dummy.score(X_train, y_train), 4)}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Baseline prediction score for the training set is: 0.7663\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D28ma3vJ_fJr",
        "colab_type": "code",
        "colab": {},
        "outputId": "833bd62a-bfbb-49ec-e7ef-9eb66c570500"
      },
      "source": [
        "# Generate the classification report for the Dummy Classifier model\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(f'Dummy (Baseline) Classification Report:\\n\\n{classification_report(y_train, dummy_train_y_pred)}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dummy (Baseline) Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.86      0.86      0.86     20383\n",
            "        True       0.14      0.14      0.14      3210\n",
            "\n",
            "    accuracy                           0.77     23593\n",
            "   macro avg       0.50      0.50      0.50     23593\n",
            "weighted avg       0.77      0.77      0.77     23593\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjIru11d_fJz",
        "colab_type": "code",
        "colab": {},
        "outputId": "6db5ab5e-b903-488d-d3c3-0cc1202f079a"
      },
      "source": [
        "import category_encoders as ce \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "print(X_train.dtypes)\n",
        "\n",
        "# Construct a modeling pipeline\n",
        "pipeline = make_pipeline(\n",
        "    ce.OrdinalEncoder(),\n",
        "    RandomForestClassifier(n_jobs=-1, random_state=42)\n",
        ")\n",
        "\n",
        "# Fit the pipeline to the training dataset\n",
        "pipeline.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "City           object\n",
            "County         object\n",
            "CrashHour      object\n",
            "CrashMonth     object\n",
            "Development    object\n",
            "LightCond      object\n",
            "Locality       object\n",
            "NumLanes       object\n",
            "RdCharacte     object\n",
            "RdClass        object\n",
            "RdConditio     object\n",
            "RdConfig       object\n",
            "RdDefects      object\n",
            "RdFeature      object\n",
            "RdSurface      object\n",
            "Region         object\n",
            "RuralUrban     object\n",
            "SpeedLimit     object\n",
            "TraffCntrl     object\n",
            "Weather        object\n",
            "Workzone       object\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('ordinalencoder',\n",
              "                 OrdinalEncoder(cols=['City', 'County', 'CrashHour',\n",
              "                                      'CrashMonth', 'Development', 'LightCond',\n",
              "                                      'Locality', 'NumLanes', 'RdCharacte',\n",
              "                                      'RdClass', 'RdConditio', 'RdConfig',\n",
              "                                      'RdDefects', 'RdFeature', 'RdSurface',\n",
              "                                      'Region', 'RuralUrban', 'SpeedLimit',\n",
              "                                      'TraffCntrl', 'Weather', 'Workzone'],\n",
              "                                drop_invariant=False, handle_missing='valu...\n",
              "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                        class_weight=None, criterion='gini',\n",
              "                                        max_depth=None, max_features='auto',\n",
              "                                        max_leaf_nodes=None, max_samples=None,\n",
              "                                        min_impurity_decrease=0.0,\n",
              "                                        min_impurity_split=None,\n",
              "                                        min_samples_leaf=1, min_samples_split=2,\n",
              "                                        min_weight_fraction_leaf=0.0,\n",
              "                                        n_estimators=100, n_jobs=-1,\n",
              "                                        oob_score=False, random_state=42,\n",
              "                                        verbose=0, warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lr2iMUe-_fKG",
        "colab_type": "code",
        "colab": {},
        "outputId": "7e7265d7-6ba0-4336-c985-231d9846ca2a"
      },
      "source": [
        "# Calculate the accuracy using the validation data\n",
        "print(f'The Validation Accuracy is: {round(pipeline.score(X_val, y_val), 4)}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Validation Accuracy is: 0.8655\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gecvmjnx_fKL",
        "colab_type": "code",
        "colab": {},
        "outputId": "35da96f4-748c-43f7-fcd4-5684b679be8b"
      },
      "source": [
        "# Generate the classification report for the Random Forest Classifier model\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "y_val_pred = pipeline.predict(X_val)\n",
        "\n",
        "print(f'Random Forest Classification Report:\\n\\n{classification_report(y_val, y_val_pred)}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.87      0.99      0.93      4368\n",
            "        True       0.53      0.11      0.18       689\n",
            "\n",
            "    accuracy                           0.87      5057\n",
            "   macro avg       0.70      0.55      0.55      5057\n",
            "weighted avg       0.83      0.87      0.82      5057\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sdnhWbtF_fKQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "e3bf2997-0803-4de0-cb4c-770e5f8d649c"
      },
      "source": [
        "# Construct the encoding and modeling objects for use in generating a pdp plot\n",
        "trans_encode = make_pipeline(\n",
        "    ce.OrdinalEncoder()\n",
        ")\n",
        "\n",
        "# Encode the train and validation attributes\n",
        "X_train_trans_encode = trans_encode.fit_transform(X_train)\n",
        "X_val_trans_encode   = trans_encode.fit_transform(X_val)\n",
        "\n",
        "# Recreate the model\n",
        "tmp_mdl = RandomForestClassifier(n_jobs=-1, random_state=42)\n",
        "tmp_mdl.fit(X_train_trans_encode, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\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=100,\n",
              "                       n_jobs=-1, oob_score=False, random_state=42, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AFQZoM-h_fKb",
        "colab_type": "code",
        "colab": {},
        "outputId": "6bd7dd2d-087e-4557-f679-d98885da7270"
      },
      "source": [
        "# Calculate the permutation importance - Random Forest Classifier\n",
        "import eli5\n",
        "from eli5.sklearn import PermutationImportance\n",
        "\n",
        "# Create a permutation importance object\n",
        "prmuter = PermutationImportance(\n",
        "    tmp_mdl,\n",
        "    scoring='accuracy',\n",
        "    n_iter=5,\n",
        "    random_state=42)\n",
        "\n",
        "# Compute feature importance\n",
        "prmuter.fit(X_val_trans_encode, y_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PermutationImportance(cv='prefit',\n",
              "                      estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                       ccp_alpha=0.0,\n",
              "                                                       class_weight=None,\n",
              "                                                       criterion='gini',\n",
              "                                                       max_depth=None,\n",
              "                                                       max_features='auto',\n",
              "                                                       max_leaf_nodes=None,\n",
              "                                                       max_samples=None,\n",
              "                                                       min_impurity_decrease=0.0,\n",
              "                                                       min_impurity_split=None,\n",
              "                                                       min_samples_leaf=1,\n",
              "                                                       min_samples_split=2,\n",
              "                                                       min_weight_fraction_leaf=0.0,\n",
              "                                                       n_estimators=100,\n",
              "                                                       n_jobs=-1,\n",
              "                                                       oob_score=False,\n",
              "                                                       random_state=42,\n",
              "                                                       verbose=0,\n",
              "                                                       warm_start=False),\n",
              "                      n_iter=5, random_state=42, refit=True,\n",
              "                      scoring='accuracy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZuTgrVq_fKi",
        "colab_type": "code",
        "colab": {},
        "outputId": "4b1a8fd5-fbcf-4087-e8d3-94a17845f0ed"
      },
      "source": [
        "# List out the independent variables by importance\n",
        "feature_names = X_val.columns.tolist()\n",
        "\n",
        "# Display permutation importances\n",
        "eli5.show_weights(\n",
        "    prmuter,\n",
        "    top=None,\n",
        "    feature_names=feature_names)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "    <style>\n",
              "    table.eli5-weights tr:hover {\n",
              "        filter: brightness(85%);\n",
              "    }\n",
              "</style>\n",
              "\n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
              "    <thead>\n",
              "    <tr style=\"border: none;\">\n",
              "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
              "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0036\n",
              "                \n",
              "                    &plusmn; 0.0008\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                LightCond\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 84.03%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0026\n",
              "                \n",
              "                    &plusmn; 0.0019\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                TraffCntrl\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 86.66%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0020\n",
              "                \n",
              "                    &plusmn; 0.0015\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                CrashMonth\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 87.59%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0018\n",
              "                \n",
              "                    &plusmn; 0.0028\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                RdClass\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 89.75%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0014\n",
              "                \n",
              "                    &plusmn; 0.0014\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                SpeedLimit\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 90.38%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0013\n",
              "                \n",
              "                    &plusmn; 0.0014\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                RdConfig\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 91.24%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0011\n",
              "                \n",
              "                    &plusmn; 0.0011\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Locality\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 91.90%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0010\n",
              "                \n",
              "                    &plusmn; 0.0014\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                NumLanes\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 92.83%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0008\n",
              "                \n",
              "                    &plusmn; 0.0028\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Development\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 93.07%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0008\n",
              "                \n",
              "                    &plusmn; 0.0026\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                CrashHour\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 93.57%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0007\n",
              "                \n",
              "                    &plusmn; 0.0012\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                RdConditio\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 94.08%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0006\n",
              "                \n",
              "                    &plusmn; 0.0031\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                County\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 94.88%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0005\n",
              "                \n",
              "                    &plusmn; 0.0026\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                City\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 96.04%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0004\n",
              "                \n",
              "                    &plusmn; 0.0003\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                RdDefects\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.02%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0002\n",
              "                \n",
              "                    &plusmn; 0.0012\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Region\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 99.15%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0000\n",
              "                \n",
              "                    &plusmn; 0.0002\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Workzone\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                -0.0000\n",
              "                \n",
              "                    &plusmn; 0.0015\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Weather\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(0, 100.00%, 93.57%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                -0.0007\n",
              "                \n",
              "                    &plusmn; 0.0029\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                RdFeature\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(0, 100.00%, 93.57%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                -0.0007\n",
              "                \n",
              "                    &plusmn; 0.0006\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                RdSurface\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(0, 100.00%, 92.83%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                -0.0008\n",
              "                \n",
              "                    &plusmn; 0.0015\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                RuralUrban\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(0, 100.00%, 91.90%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                -0.0010\n",
              "                \n",
              "                    &plusmn; 0.0019\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                RdCharacte\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "    \n",
              "    </tbody>\n",
              "</table>\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQVq9XLv_fK2",
        "colab_type": "code",
        "colab": {},
        "outputId": "0e0314a0-1b63-4f1e-b9b9-4c8c9eff55f5"
      },
      "source": [
        "# Generate a logistic regression model\n",
        "from sklearn.linear_model import LogisticRegressionCV\n",
        "\n",
        "# Construct a modeling pipeline\n",
        "pipeline_logstc = make_pipeline(\n",
        "    ce.OrdinalEncoder(),\n",
        "    LogisticRegressionCV(cv=5, n_jobs=-1, random_state=42)\n",
        ")\n",
        "\n",
        "# Fit the pipeline to the training dataset\n",
        "pipeline_logstc.fit(X_train, y_train)\n",
        "\n",
        "# Calculate the accuracy using the validation data\n",
        "print(f'The Validation Accuracy is: {round(pipeline_logstc.score(X_val, y_val), 4)}')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Validation Accuracy is: 0.8638\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKl33ARD_fK7",
        "colab_type": "code",
        "colab": {},
        "outputId": "9ba27bf5-03fb-4141-841f-bc34d11dd28d"
      },
      "source": [
        "pipeline_logstc[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegressionCV(Cs=10, class_weight=None, cv=5, dual=False,\n",
              "                     fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
              "                     max_iter=100, multi_class='auto', n_jobs=-1, penalty='l2',\n",
              "                     random_state=42, refit=True, scoring=None, solver='lbfgs',\n",
              "                     tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O79U8Exy_fLA",
        "colab_type": "code",
        "colab": {},
        "outputId": "a0e711c1-df38-47c7-dd25-4c618acef348"
      },
      "source": [
        "# Generate the classification report for the Logistic Regression model\n",
        "y_val_pred_lgtc = pipeline_logstc.predict(X_val)\n",
        "\n",
        "print(f'Logistic Regression Classification Report:\\n\\n{classification_report(y_val, y_val_pred_lgtc)}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic Regression Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.86      1.00      0.93      4368\n",
            "        True       0.00      0.00      0.00       689\n",
            "\n",
            "    accuracy                           0.86      5057\n",
            "   macro avg       0.43      0.50      0.46      5057\n",
            "weighted avg       0.75      0.86      0.80      5057\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGkd0Mwc_fLJ",
        "colab_type": "code",
        "colab": {},
        "outputId": "9225b2fb-3987-4266-9bef-ea49f1dfbf11"
      },
      "source": [
        "# Render the regression expression terms (coefficients, variables, and intercept)\n",
        "%matplotlib inline\n",
        "coefficients = pd.Series(pipeline_logstc[1].coef_[0], X_train.columns)\n",
        "coefficients.sort_values()\n",
        "\n",
        "tmp_series = pd.Series([pipeline_logstc[1].intercept_[0]], ['(y intercept)'])\n",
        "coefficients = coefficients.append(tmp_series)\n",
        "coefficients.plot.barh();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxQixCDh_fLO",
        "colab_type": "code",
        "colab": {},
        "outputId": "c2f3bba1-ecd6-4c1d-82a5-3703a9d5ddeb"
      },
      "source": [
        "coefficients"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City            -0.001330\n",
              "County           0.008261\n",
              "CrashHour       -0.010968\n",
              "CrashMonth      -0.009142\n",
              "Development      0.033361\n",
              "LightCond       -0.068097\n",
              "Locality         0.016585\n",
              "NumLanes         0.050360\n",
              "RdCharacte       0.027702\n",
              "RdClass          0.128074\n",
              "RdConditio       0.001557\n",
              "RdConfig         0.031567\n",
              "RdDefects       -0.001759\n",
              "RdFeature       -0.015087\n",
              "RdSurface       -0.004395\n",
              "Region           0.014589\n",
              "RuralUrban       0.031191\n",
              "SpeedLimit       0.066307\n",
              "TraffCntrl      -0.001861\n",
              "Weather          0.002165\n",
              "Workzone        -0.000079\n",
              "(y intercept)   -2.531621\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8YiMi0C_fLS",
        "colab_type": "code",
        "colab": {},
        "outputId": "61500f30-0968-4a99-e047-45b79a2a4800"
      },
      "source": [
        "coefficients.sort_values(ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RdClass          0.128074\n",
              "SpeedLimit       0.066307\n",
              "NumLanes         0.050360\n",
              "Development      0.033361\n",
              "RdConfig         0.031567\n",
              "RuralUrban       0.031191\n",
              "RdCharacte       0.027702\n",
              "Locality         0.016585\n",
              "Region           0.014589\n",
              "County           0.008261\n",
              "Weather          0.002165\n",
              "RdConditio       0.001557\n",
              "Workzone        -0.000079\n",
              "City            -0.001330\n",
              "RdDefects       -0.001759\n",
              "TraffCntrl      -0.001861\n",
              "RdSurface       -0.004395\n",
              "CrashMonth      -0.009142\n",
              "CrashHour       -0.010968\n",
              "RdFeature       -0.015087\n",
              "LightCond       -0.068097\n",
              "(y intercept)   -2.531621\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDg0U7O__fLV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}