Skip to content

Instantly share code, notes, and snippets.

@Katba-Caroline

Katba-Caroline/Code4PA_Overdose_NN.ipynb

Created October 23, 2018 20:25
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save Katba-Caroline/e9dc33a73e4efeed059e5118ad16d69d to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save Katba-Caroline/e9dc33a73e4efeed059e5118ad16d69d to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Code4PA_Overdose_NN.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Incident ID</th>\n",
" <th>Incident Date</th>\n",
" <th>Incident Time</th>\n",
" <th>Day</th>\n",
" <th>Incident County Name</th>\n",
" <th>Incident State</th>\n",
" <th>Victim ID</th>\n",
" <th>Gender Desc</th>\n",
" <th>Age Start Description</th>\n",
" <th>Age End Desc</th>\n",
" <th>...</th>\n",
" <th>Naloxone Administered</th>\n",
" <th>Administration ID</th>\n",
" <th>Dose Count</th>\n",
" <th>Dose Unit</th>\n",
" <th>Dose Desc</th>\n",
" <th>Response Time Desc</th>\n",
" <th>Survive</th>\n",
" <th>Response_Desc</th>\n",
" <th>Revive_Action_Desc</th>\n",
" <th>Third_Party_Admin_Desc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>UNKNOWN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>GOOD SAMARITAN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>Y</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>MG</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>UNKNOWN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>GOOD SAMARITAN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>Y</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>MG</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>26</td>\n",
" <td>01/26/2018</td>\n",
" <td>9:14:00</td>\n",
" <td>Friday</td>\n",
" <td>Chester</td>\n",
" <td>Pennsylvania</td>\n",
" <td>5</td>\n",
" <td>Male</td>\n",
" <td>25</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>27</td>\n",
" <td>01/24/2018</td>\n",
" <td>23:32:00</td>\n",
" <td>Wednesday</td>\n",
" <td>Beaver</td>\n",
" <td>Pennsylvania</td>\n",
" <td>4</td>\n",
" <td>Male</td>\n",
" <td>30</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Y</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>28</td>\n",
" <td>01/15/2018</td>\n",
" <td>23:41:00</td>\n",
" <td>Monday</td>\n",
" <td>Bucks</td>\n",
" <td>Pennsylvania</td>\n",
" <td>6</td>\n",
" <td>Male</td>\n",
" <td>25</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Y</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>29</td>\n",
" <td>01/15/2018</td>\n",
" <td>10:54:00</td>\n",
" <td>Monday</td>\n",
" <td>Bucks</td>\n",
" <td>Pennsylvania</td>\n",
" <td>7</td>\n",
" <td>Female</td>\n",
" <td>30</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Y</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>30</td>\n",
" <td>01/01/2018</td>\n",
" <td>13:07:00</td>\n",
" <td>Monday</td>\n",
" <td>Philadelphia</td>\n",
" <td>Pennsylvania</td>\n",
" <td>8</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Y</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>32</td>\n",
" <td>01/24/2018</td>\n",
" <td>0:01:00</td>\n",
" <td>Wednesday</td>\n",
" <td>Cumberland</td>\n",
" <td>Pennsylvania</td>\n",
" <td>14</td>\n",
" <td>Male</td>\n",
" <td>30</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>32</td>\n",
" <td>01/24/2018</td>\n",
" <td>0:01:00</td>\n",
" <td>Wednesday</td>\n",
" <td>Cumberland</td>\n",
" <td>Pennsylvania</td>\n",
" <td>14</td>\n",
" <td>Male</td>\n",
" <td>30</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>33</td>\n",
" <td>01/13/2018</td>\n",
" <td>0:30:00</td>\n",
" <td>Saturday</td>\n",
" <td>Northumberland</td>\n",
" <td>Pennsylvania</td>\n",
" <td>10</td>\n",
" <td>Male</td>\n",
" <td>30</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>34</td>\n",
" <td>01/22/2018</td>\n",
" <td>NaN</td>\n",
" <td>Monday</td>\n",
" <td>Montgomery</td>\n",
" <td>Pennsylvania</td>\n",
" <td>23</td>\n",
" <td>Male</td>\n",
" <td>30</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>35</td>\n",
" <td>01/12/2018</td>\n",
" <td>11:00:00</td>\n",
" <td>Friday</td>\n",
" <td>Pike</td>\n",
" <td>Pennsylvania</td>\n",
" <td>11</td>\n",
" <td>Male</td>\n",
" <td>40</td>\n",
" <td>49</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>36</td>\n",
" <td>01/10/2018</td>\n",
" <td>18:00:00</td>\n",
" <td>Wednesday</td>\n",
" <td>Armstrong</td>\n",
" <td>Pennsylvania</td>\n",
" <td>12</td>\n",
" <td>Male</td>\n",
" <td>40</td>\n",
" <td>49</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>37</td>\n",
" <td>01/01/2018</td>\n",
" <td>19:10:00</td>\n",
" <td>Monday</td>\n",
" <td>Carbon</td>\n",
" <td>Pennsylvania</td>\n",
" <td>13</td>\n",
" <td>Female</td>\n",
" <td>30</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>38</td>\n",
" <td>01/23/2018</td>\n",
" <td>NaN</td>\n",
" <td>Tuesday</td>\n",
" <td>Carbon</td>\n",
" <td>Pennsylvania</td>\n",
" <td>15</td>\n",
" <td>Male</td>\n",
" <td>30</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Y</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>39</td>\n",
" <td>01/15/2018</td>\n",
" <td>NaN</td>\n",
" <td>Monday</td>\n",
" <td>Montgomery</td>\n",
" <td>Pennsylvania</td>\n",
" <td>18</td>\n",
" <td>Female</td>\n",
" <td>25</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Y</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>39</td>\n",
" <td>01/15/2018</td>\n",
" <td>NaN</td>\n",
" <td>Monday</td>\n",
" <td>Montgomery</td>\n",
" <td>Pennsylvania</td>\n",
" <td>17</td>\n",
" <td>Female</td>\n",
" <td>30</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Y</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>39</td>\n",
" <td>01/15/2018</td>\n",
" <td>NaN</td>\n",
" <td>Monday</td>\n",
" <td>Montgomery</td>\n",
" <td>Pennsylvania</td>\n",
" <td>16</td>\n",
" <td>Male</td>\n",
" <td>20</td>\n",
" <td>24</td>\n",
" <td>...</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" Incident ID Incident Date Incident Time Day Incident County Name \\\n",
"0 1 01/04/2018 0:42:00 Thursday Delaware \n",
"1 1 01/04/2018 0:42:00 Thursday Delaware \n",
"2 1 01/04/2018 0:42:00 Thursday Delaware \n",
"3 1 01/04/2018 0:42:00 Thursday Delaware \n",
"4 26 01/26/2018 9:14:00 Friday Chester \n",
"5 27 01/24/2018 23:32:00 Wednesday Beaver \n",
"6 28 01/15/2018 23:41:00 Monday Bucks \n",
"7 29 01/15/2018 10:54:00 Monday Bucks \n",
"8 30 01/01/2018 13:07:00 Monday Philadelphia \n",
"9 32 01/24/2018 0:01:00 Wednesday Cumberland \n",
"10 32 01/24/2018 0:01:00 Wednesday Cumberland \n",
"11 33 01/13/2018 0:30:00 Saturday Northumberland \n",
"12 34 01/22/2018 NaN Monday Montgomery \n",
"13 35 01/12/2018 11:00:00 Friday Pike \n",
"14 36 01/10/2018 18:00:00 Wednesday Armstrong \n",
"15 37 01/01/2018 19:10:00 Monday Carbon \n",
"16 38 01/23/2018 NaN Tuesday Carbon \n",
"17 39 01/15/2018 NaN Monday Montgomery \n",
"18 39 01/15/2018 NaN Monday Montgomery \n",
"19 39 01/15/2018 NaN Monday Montgomery \n",
"\n",
" Incident State Victim ID Gender Desc Age Start Description Age End Desc \\\n",
"0 Pennsylvania 1 Male 50 59 \n",
"1 Pennsylvania 1 Male 50 59 \n",
"2 Pennsylvania 1 Male 50 59 \n",
"3 Pennsylvania 1 Male 50 59 \n",
"4 Pennsylvania 5 Male 25 29 \n",
"5 Pennsylvania 4 Male 30 39 \n",
"6 Pennsylvania 6 Male 25 29 \n",
"7 Pennsylvania 7 Female 30 39 \n",
"8 Pennsylvania 8 Male 50 59 \n",
"9 Pennsylvania 14 Male 30 39 \n",
"10 Pennsylvania 14 Male 30 39 \n",
"11 Pennsylvania 10 Male 30 39 \n",
"12 Pennsylvania 23 Male 30 39 \n",
"13 Pennsylvania 11 Male 40 49 \n",
"14 Pennsylvania 12 Male 40 49 \n",
"15 Pennsylvania 13 Female 30 39 \n",
"16 Pennsylvania 15 Male 30 39 \n",
"17 Pennsylvania 18 Female 25 29 \n",
"18 Pennsylvania 17 Female 30 39 \n",
"19 Pennsylvania 16 Male 20 24 \n",
"\n",
" ... Naloxone Administered Administration ID Dose Count \\\n",
"0 ... Y 1.0 1.0 \n",
"1 ... Y 2.0 1.0 \n",
"2 ... Y 1.0 1.0 \n",
"3 ... Y 2.0 1.0 \n",
"4 ... N NaN NaN \n",
"5 ... N NaN NaN \n",
"6 ... N NaN NaN \n",
"7 ... N NaN NaN \n",
"8 ... N NaN NaN \n",
"9 ... N NaN NaN \n",
"10 ... N NaN NaN \n",
"11 ... N NaN NaN \n",
"12 ... N NaN NaN \n",
"13 ... N NaN NaN \n",
"14 ... N NaN NaN \n",
"15 ... N NaN NaN \n",
"16 ... N NaN NaN \n",
"17 ... N NaN NaN \n",
"18 ... N NaN NaN \n",
"19 ... N NaN NaN \n",
"\n",
" Dose Unit Dose Desc Response Time Desc Survive Response_Desc \\\n",
"0 0.0 UNKNOWN NaN N NO RESPONSE TO NALOXONE \n",
"1 4.0 MG NaN N NO RESPONSE TO NALOXONE \n",
"2 0.0 UNKNOWN NaN N NO RESPONSE TO NALOXONE \n",
"3 4.0 MG NaN N NO RESPONSE TO NALOXONE \n",
"4 NaN NaN NaN N NaN \n",
"5 NaN NaN NaN Y NaN \n",
"6 NaN NaN NaN Y NaN \n",
"7 NaN NaN NaN Y NaN \n",
"8 NaN NaN NaN Y NaN \n",
"9 NaN NaN NaN N NaN \n",
"10 NaN NaN NaN N NaN \n",
"11 NaN NaN NaN N NaN \n",
"12 NaN NaN NaN N NaN \n",
"13 NaN NaN NaN N NaN \n",
"14 NaN NaN NaN N NaN \n",
"15 NaN NaN NaN N NaN \n",
"16 NaN NaN NaN Y NaN \n",
"17 NaN NaN NaN Y NaN \n",
"18 NaN NaN NaN Y NaN \n",
"19 NaN NaN NaN N NaN \n",
"\n",
" Revive_Action_Desc Third_Party_Admin_Desc \n",
"0 NaN GOOD SAMARITAN \n",
"1 NaN NaN \n",
"2 NaN GOOD SAMARITAN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
"6 NaN NaN \n",
"7 NaN NaN \n",
"8 NaN NaN \n",
"9 NaN NaN \n",
"10 NaN NaN \n",
"11 NaN NaN \n",
"12 NaN NaN \n",
"13 NaN NaN \n",
"14 NaN NaN \n",
"15 NaN NaN \n",
"16 NaN NaN \n",
"17 NaN NaN \n",
"18 NaN NaN \n",
"19 NaN NaN \n",
"\n",
"[20 rows x 27 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import libraries necessary for this project\n",
"import numpy as np\n",
"import pandas as pd\n",
"from time import time\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as mpatches\n",
"\n",
"\n",
"# Pretty display for notebooks\n",
"%matplotlib inline\n",
"\n",
"# Load the Census dataset\n",
"data = pd.read_csv(\"Overdose_info.csv\")\n",
"\n",
"# Success - Display the first record\n",
"display(data.head(n=20))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4625, 27)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#getting info on the data\n",
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Incident ID</th>\n",
" <th>Incident Date</th>\n",
" <th>Incident Time</th>\n",
" <th>Day</th>\n",
" <th>Incident County Name</th>\n",
" <th>Incident State</th>\n",
" <th>Victim ID</th>\n",
" <th>Gender Desc</th>\n",
" <th>Age Start Description</th>\n",
" <th>Age End Desc</th>\n",
" <th>...</th>\n",
" <th>Administration ID</th>\n",
" <th>Dose Count</th>\n",
" <th>Dose Unit</th>\n",
" <th>Dose Desc</th>\n",
" <th>Response Time Desc</th>\n",
" <th>Survive</th>\n",
" <th>Response_Desc</th>\n",
" <th>Revive_Action_Desc</th>\n",
" <th>Third_Party_Admin_Desc</th>\n",
" <th>Avg_Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>UNKNOWN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>GOOD SAMARITAN</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>MG</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>UNKNOWN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>GOOD SAMARITAN</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>MG</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>26</td>\n",
" <td>01/26/2018</td>\n",
" <td>9:14:00</td>\n",
" <td>Friday</td>\n",
" <td>Chester</td>\n",
" <td>Pennsylvania</td>\n",
" <td>5</td>\n",
" <td>Male</td>\n",
" <td>25</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" Incident ID Incident Date Incident Time Day Incident County Name \\\n",
"0 1 01/04/2018 0:42:00 Thursday Delaware \n",
"1 1 01/04/2018 0:42:00 Thursday Delaware \n",
"2 1 01/04/2018 0:42:00 Thursday Delaware \n",
"3 1 01/04/2018 0:42:00 Thursday Delaware \n",
"4 26 01/26/2018 9:14:00 Friday Chester \n",
"\n",
" Incident State Victim ID Gender Desc Age Start Description Age End Desc \\\n",
"0 Pennsylvania 1 Male 50 59 \n",
"1 Pennsylvania 1 Male 50 59 \n",
"2 Pennsylvania 1 Male 50 59 \n",
"3 Pennsylvania 1 Male 50 59 \n",
"4 Pennsylvania 5 Male 25 29 \n",
"\n",
" ... Administration ID Dose Count Dose Unit Dose Desc \\\n",
"0 ... 1.0 1.0 0.0 UNKNOWN \n",
"1 ... 2.0 1.0 4.0 MG \n",
"2 ... 1.0 1.0 0.0 UNKNOWN \n",
"3 ... 2.0 1.0 4.0 MG \n",
"4 ... NaN NaN NaN NaN \n",
"\n",
" Response Time Desc Survive Response_Desc Revive_Action_Desc \\\n",
"0 NaN N NO RESPONSE TO NALOXONE NaN \n",
"1 NaN N NO RESPONSE TO NALOXONE NaN \n",
"2 NaN N NO RESPONSE TO NALOXONE NaN \n",
"3 NaN N NO RESPONSE TO NALOXONE NaN \n",
"4 NaN N NaN NaN \n",
"\n",
" Third_Party_Admin_Desc Avg_Age \n",
"0 GOOD SAMARITAN 80.0 \n",
"1 NaN 80.0 \n",
"2 GOOD SAMARITAN 80.0 \n",
"3 NaN 80.0 \n",
"4 NaN 40.0 \n",
"\n",
"[5 rows x 28 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#creating new calculated column for average age\n",
"data['Avg_Age'] = round(data['Age Start Description'] + data['Age End Desc']/2)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Incident ID</th>\n",
" <th>Incident Date</th>\n",
" <th>Incident Time</th>\n",
" <th>Day</th>\n",
" <th>Incident County Name</th>\n",
" <th>Incident State</th>\n",
" <th>Victim ID</th>\n",
" <th>Gender Desc</th>\n",
" <th>Age Start Description</th>\n",
" <th>Age End Desc</th>\n",
" <th>...</th>\n",
" <th>Dose Count</th>\n",
" <th>Dose Unit</th>\n",
" <th>Dose Desc</th>\n",
" <th>Response Time Desc</th>\n",
" <th>Survive</th>\n",
" <th>Response_Desc</th>\n",
" <th>Revive_Action_Desc</th>\n",
" <th>Third_Party_Admin_Desc</th>\n",
" <th>Avg_Age</th>\n",
" <th>Incident_Month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>UNKNOWN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>GOOD SAMARITAN</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>MG</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>UNKNOWN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>GOOD SAMARITAN</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>01/04/2018</td>\n",
" <td>0:42:00</td>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Pennsylvania</td>\n",
" <td>1</td>\n",
" <td>Male</td>\n",
" <td>50</td>\n",
" <td>59</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>MG</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NO RESPONSE TO NALOXONE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>26</td>\n",
" <td>01/26/2018</td>\n",
" <td>9:14:00</td>\n",
" <td>Friday</td>\n",
" <td>Chester</td>\n",
" <td>Pennsylvania</td>\n",
" <td>5</td>\n",
" <td>Male</td>\n",
" <td>25</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" Incident ID Incident Date Incident Time Day Incident County Name \\\n",
"0 1 01/04/2018 0:42:00 Thursday Delaware \n",
"1 1 01/04/2018 0:42:00 Thursday Delaware \n",
"2 1 01/04/2018 0:42:00 Thursday Delaware \n",
"3 1 01/04/2018 0:42:00 Thursday Delaware \n",
"4 26 01/26/2018 9:14:00 Friday Chester \n",
"\n",
" Incident State Victim ID Gender Desc Age Start Description Age End Desc \\\n",
"0 Pennsylvania 1 Male 50 59 \n",
"1 Pennsylvania 1 Male 50 59 \n",
"2 Pennsylvania 1 Male 50 59 \n",
"3 Pennsylvania 1 Male 50 59 \n",
"4 Pennsylvania 5 Male 25 29 \n",
"\n",
" ... Dose Count Dose Unit Dose Desc Response Time Desc Survive \\\n",
"0 ... 1.0 0.0 UNKNOWN NaN N \n",
"1 ... 1.0 4.0 MG NaN N \n",
"2 ... 1.0 0.0 UNKNOWN NaN N \n",
"3 ... 1.0 4.0 MG NaN N \n",
"4 ... NaN NaN NaN NaN N \n",
"\n",
" Response_Desc Revive_Action_Desc Third_Party_Admin_Desc Avg_Age \\\n",
"0 NO RESPONSE TO NALOXONE NaN GOOD SAMARITAN 80.0 \n",
"1 NO RESPONSE TO NALOXONE NaN NaN 80.0 \n",
"2 NO RESPONSE TO NALOXONE NaN GOOD SAMARITAN 80.0 \n",
"3 NO RESPONSE TO NALOXONE NaN NaN 80.0 \n",
"4 NaN NaN NaN 40.0 \n",
"\n",
" Incident_Month \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Extracting only the month out of that data\n",
"data['Incident_Month'] = pd.DatetimeIndex(data['Incident Date']).month\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Day</th>\n",
" <th>Incident County Name</th>\n",
" <th>Gender Desc</th>\n",
" <th>Race</th>\n",
" <th>Ethnicity Description</th>\n",
" <th>Victim State</th>\n",
" <th>Victim County</th>\n",
" <th>Accidental Exposure</th>\n",
" <th>Susp OD Drug Desc</th>\n",
" <th>Naloxone Administered</th>\n",
" <th>Survive</th>\n",
" <th>Avg_Age</th>\n",
" <th>Incident_Month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>COCAINE/CRACK</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>COCAINE/CRACK</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>HEROIN</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>HEROIN</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Friday</td>\n",
" <td>Chester</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Chester</td>\n",
" <td>N</td>\n",
" <td>HEROIN</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>40.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Day Incident County Name Gender Desc Race Ethnicity Description \\\n",
"0 Thursday Delaware Male White Not Hispanic \n",
"1 Thursday Delaware Male White Not Hispanic \n",
"2 Thursday Delaware Male White Not Hispanic \n",
"3 Thursday Delaware Male White Not Hispanic \n",
"4 Friday Chester Male White Not Hispanic \n",
"\n",
" Victim State Victim County Accidental Exposure Susp OD Drug Desc \\\n",
"0 Pennsylvania Delaware N COCAINE/CRACK \n",
"1 Pennsylvania Delaware N COCAINE/CRACK \n",
"2 Pennsylvania Delaware N HEROIN \n",
"3 Pennsylvania Delaware N HEROIN \n",
"4 Pennsylvania Chester N HEROIN \n",
"\n",
" Naloxone Administered Survive Avg_Age Incident_Month \n",
"0 Y N 80.0 1 \n",
"1 Y N 80.0 1 \n",
"2 Y N 80.0 1 \n",
"3 Y N 80.0 1 \n",
"4 N N 40.0 1 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Removing columns in place here\n",
"#FYI it would be great if we could use Response Time, please fill it out!\n",
"data.drop(columns = ['Incident ID','Incident Time','Incident Date','Incident State',\n",
" 'Victim ID','Age Start Description','Age End Desc',\n",
" 'Administration ID', 'Dose Count', 'Dose Unit','Dose Desc',\n",
" 'Response Time Desc', 'Victim OD Drug ID', 'Response_Desc',\n",
" 'Revive_Action_Desc', 'Third_Party_Admin_Desc'], inplace=True )\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Day 0\n",
"Incident County Name 0\n",
"Gender Desc 0\n",
"Race 0\n",
"Ethnicity Description 0\n",
"Victim State 0\n",
"Victim County 0\n",
"Accidental Exposure 0\n",
"Susp OD Drug Desc 0\n",
"Naloxone Administered 0\n",
"Survive 0\n",
"Avg_Age 0\n",
"Incident_Month 0\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#checking how many null values are available\n",
"data.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4625, 13)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Day</th>\n",
" <th>Incident_County_Name</th>\n",
" <th>Gender</th>\n",
" <th>Race</th>\n",
" <th>Ethnicity</th>\n",
" <th>Victim_State</th>\n",
" <th>Victim_County</th>\n",
" <th>Accidental_Exposure</th>\n",
" <th>Susp_OD_Drug</th>\n",
" <th>Naloxone_Administered</th>\n",
" <th>Survive</th>\n",
" <th>Avg_Age</th>\n",
" <th>Incident_Month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>COCAINE/CRACK</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>COCAINE/CRACK</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>HEROIN</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>HEROIN</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Friday</td>\n",
" <td>Chester</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Chester</td>\n",
" <td>N</td>\n",
" <td>HEROIN</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>40.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Day Incident_County_Name Gender Race Ethnicity Victim_State \\\n",
"0 Thursday Delaware Male White Not Hispanic Pennsylvania \n",
"1 Thursday Delaware Male White Not Hispanic Pennsylvania \n",
"2 Thursday Delaware Male White Not Hispanic Pennsylvania \n",
"3 Thursday Delaware Male White Not Hispanic Pennsylvania \n",
"4 Friday Chester Male White Not Hispanic Pennsylvania \n",
"\n",
" Victim_County Accidental_Exposure Susp_OD_Drug Naloxone_Administered \\\n",
"0 Delaware N COCAINE/CRACK Y \n",
"1 Delaware N COCAINE/CRACK Y \n",
"2 Delaware N HEROIN Y \n",
"3 Delaware N HEROIN Y \n",
"4 Chester N HEROIN N \n",
"\n",
" Survive Avg_Age Incident_Month \n",
"0 N 80.0 1 \n",
"1 N 80.0 1 \n",
"2 N 80.0 1 \n",
"3 N 80.0 1 \n",
"4 N 40.0 1 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# renaming the columns for easier handling\n",
"data.rename(columns={'Incident County Name': 'Incident_County_Name', \n",
" 'Gender Desc': 'Gender',\n",
" 'Ethnicity Description': 'Ethnicity', \n",
" 'Victim State': 'Victim_State', \n",
" 'Victim County': 'Victim_County',\n",
" 'Accidental Exposure': 'Accidental_Exposure', \n",
" 'Susp OD Drug Desc': 'Susp_OD_Drug',\n",
" 'Naloxone Administered': 'Naloxone_Administered'}, inplace=True)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2bd412a9cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# here we set the figure size to 15x10\n",
"plt.figure(figsize=(15, 10))\n",
"\n",
"plt.scatter(data.Avg_Age, data.Susp_OD_Drug)\n",
"plt.xlabel(\"Average Age\", fontsize=20)\n",
"plt.ylabel(\"Drug\", fontsize=20)\n",
"plt.title(\"Scatter plot of Suspected Drugs and Age\",fontsize=22)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Y 2993\n",
"N 1632\n",
"Name: Naloxone_Administered, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Naloxone_Administered'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2bd4190da58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(18,10))\n",
"data.Naloxone_Administered.value_counts().nlargest(20).plot(kind='barh')\n",
"plt.xlabel('Naloxone Administered', fontsize=20)\n",
"plt.title(\"Number of Naloxone Doses Administered\",fontsize=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Y 3445\n",
"N 929\n",
"U 251\n",
"Name: Survive, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Survive'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4374, 13)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Dropping the U (unknown if survive or no)\n",
"\n",
"data.drop(data[data['Survive']==\"U\"].index, inplace=True)\n",
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2bd3f6bec50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(17,8))\n",
"data.Survive.value_counts().plot(kind='bar')\n",
"plt.xlabel('Survive', fontsize=20)\n",
"plt.title(\"Number of Survivers of Overdoses\",fontsize=20)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Correlation Matrix"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Avg_Age Incident_Month\n",
"Avg_Age 1.00000 0.03179\n",
"Incident_Month 0.03179 1.00000\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2bd439f9860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.set_style(\"white\")\n",
"#Correlation Matrix\n",
"# Generate a custom diverging colormap\n",
"cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
"# Set up the matplotlib figure\n",
"f, ax = plt.subplots(figsize=(15, 10))\n",
"# Compute the correlation matrix\n",
"corr = data.corr()\n",
"print(corr)\n",
"# Generate a mask for the upper triangle\n",
"mask = np.zeros_like(corr, dtype=np.bool)\n",
"mask[np.triu_indices_from(mask)] = True\n",
"# Draw the heatmap with the mask and correct aspect ratio\n",
"sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
" square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
"plt.title('Correlation matrix', \n",
" fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2bd41965908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.matshow(data.corr())\n",
"plt.xticks(range(len(data.columns)), data.columns)\n",
"plt.yticks(range(len(data.columns)), data.columns)\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### these graphs above show us that there really isn't any correlation so we need to explore different avenue to understand the data"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Day</th>\n",
" <th>Incident_County_Name</th>\n",
" <th>Gender</th>\n",
" <th>Race</th>\n",
" <th>Ethnicity</th>\n",
" <th>Victim_State</th>\n",
" <th>Victim_County</th>\n",
" <th>Accidental_Exposure</th>\n",
" <th>Susp_OD_Drug</th>\n",
" <th>Naloxone_Administered</th>\n",
" <th>Survive</th>\n",
" <th>Avg_Age</th>\n",
" <th>Incident_Month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>COCAINE/CRACK</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>COCAINE/CRACK</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>HEROIN</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Thursday</td>\n",
" <td>Delaware</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Delaware</td>\n",
" <td>N</td>\n",
" <td>HEROIN</td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Friday</td>\n",
" <td>Chester</td>\n",
" <td>Male</td>\n",
" <td>White</td>\n",
" <td>Not Hispanic</td>\n",
" <td>Pennsylvania</td>\n",
" <td>Chester</td>\n",
" <td>N</td>\n",
" <td>HEROIN</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>40.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Day Incident_County_Name Gender Race Ethnicity Victim_State \\\n",
"0 Thursday Delaware Male White Not Hispanic Pennsylvania \n",
"1 Thursday Delaware Male White Not Hispanic Pennsylvania \n",
"2 Thursday Delaware Male White Not Hispanic Pennsylvania \n",
"3 Thursday Delaware Male White Not Hispanic Pennsylvania \n",
"4 Friday Chester Male White Not Hispanic Pennsylvania \n",
"\n",
" Victim_County Accidental_Exposure Susp_OD_Drug Naloxone_Administered \\\n",
"0 Delaware N COCAINE/CRACK Y \n",
"1 Delaware N COCAINE/CRACK Y \n",
"2 Delaware N HEROIN Y \n",
"3 Delaware N HEROIN Y \n",
"4 Chester N HEROIN N \n",
"\n",
" Survive Avg_Age Incident_Month \n",
"0 N 80.0 1 \n",
"1 N 80.0 1 \n",
"2 N 80.0 1 \n",
"3 N 80.0 1 \n",
"4 N 40.0 1 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dealing with categorical Values\n",
"## Preprocessing to get it cleaned up"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"X = data[['Day', 'Incident_County_Name', 'Gender', 'Race', 'Ethnicity',\n",
" 'Victim_State', 'Victim_County', 'Accidental_Exposure',\n",
" 'Susp_OD_Drug','Naloxone_Administered', 'Avg_Age', 'Incident_Month' ]]\n",
"Y = data[['Survive']]\n",
"\n",
"X = pd.get_dummies(data=X)\n",
"Y = pd.get_dummies(data=Y)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Avg_Age</th>\n",
" <th>Incident_Month</th>\n",
" <th>Day_Friday</th>\n",
" <th>Day_Monday</th>\n",
" <th>Day_Saturday</th>\n",
" <th>Day_Sunday</th>\n",
" <th>Day_Thursday</th>\n",
" <th>Day_Tuesday</th>\n",
" <th>Day_Wednesday</th>\n",
" <th>Incident_County_Name_Adams</th>\n",
" <th>...</th>\n",
" <th>Susp_OD_Drug_METHAMPHETAMINE</th>\n",
" <th>Susp_OD_Drug_OTHER</th>\n",
" <th>Susp_OD_Drug_PHARMACEUTICAL OPIOID</th>\n",
" <th>Susp_OD_Drug_PHARMACEUTICAL OTHER</th>\n",
" <th>Susp_OD_Drug_PHARMACEUTICAL STIMULANT</th>\n",
" <th>Susp_OD_Drug_SUBOXONE</th>\n",
" <th>Susp_OD_Drug_SYNTHETIC MARIJUANA</th>\n",
" <th>Susp_OD_Drug_UNKNOWN</th>\n",
" <th>Naloxone_Administered_N</th>\n",
" <th>Naloxone_Administered_Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>40.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 185 columns</p>\n",
"</div>"
],
"text/plain": [
" Avg_Age Incident_Month Day_Friday Day_Monday Day_Saturday Day_Sunday \\\n",
"0 80.0 1 0 0 0 0 \n",
"1 80.0 1 0 0 0 0 \n",
"2 80.0 1 0 0 0 0 \n",
"3 80.0 1 0 0 0 0 \n",
"4 40.0 1 1 0 0 0 \n",
"\n",
" Day_Thursday Day_Tuesday Day_Wednesday Incident_County_Name_Adams \\\n",
"0 1 0 0 0 \n",
"1 1 0 0 0 \n",
"2 1 0 0 0 \n",
"3 1 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" ... Susp_OD_Drug_METHAMPHETAMINE Susp_OD_Drug_OTHER \\\n",
"0 ... 0 0 \n",
"1 ... 0 0 \n",
"2 ... 0 0 \n",
"3 ... 0 0 \n",
"4 ... 0 0 \n",
"\n",
" Susp_OD_Drug_PHARMACEUTICAL OPIOID Susp_OD_Drug_PHARMACEUTICAL OTHER \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Susp_OD_Drug_PHARMACEUTICAL STIMULANT Susp_OD_Drug_SUBOXONE \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Susp_OD_Drug_SYNTHETIC MARIJUANA Susp_OD_Drug_UNKNOWN \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Naloxone_Administered_N Naloxone_Administered_Y \n",
"0 0 1 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 1 \n",
"4 1 0 \n",
"\n",
"[5 rows x 185 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survive_N</th>\n",
" <th>Survive_Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survive_N Survive_Y\n",
"0 1 0\n",
"1 1 0\n",
"2 1 0\n",
"3 1 0\n",
"4 1 0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survive</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survive\n",
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y = Y.drop(['Survive_N'], axis=1)\n",
"Y.rename(columns={'Survive_Y': 'Survive'}, inplace=True)\n",
"Y.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preparing and Training the Model"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 3445\n",
"0 929\n",
"Name: Survive, dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y.Survive.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Frank the Tank\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n",
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[80. 1. 0. ... 0. 0. 1.]\n",
" [80. 1. 0. ... 0. 0. 1.]\n",
" [80. 1. 0. ... 0. 0. 1.]\n",
" ...\n",
" [50. 1. 0. ... 0. 1. 0.]\n",
" [80. 1. 0. ... 0. 1. 0.]\n",
" [50. 1. 0. ... 0. 1. 0.]]\n",
"[[1. 0.]\n",
" [1. 0.]\n",
" [1. 0.]\n",
" [1. 0.]\n",
" [1. 0.]\n",
" [0. 1.]\n",
" [0. 1.]\n",
" [0. 1.]\n",
" [0. 1.]\n",
" [1. 0.]]\n"
]
}
],
"source": [
"import keras\n",
"\n",
"# Separate data and one-hot encode the output\n",
"# Note: We're also turning the data into numpy arrays, in order to train the model in Keras\n",
"X = np.array(X)\n",
"Y = np.array(keras.utils.to_categorical(Y, 2))\n",
"\n",
"\n",
"print(X[:10])\n",
"print(Y[:10])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_1 (Dense) (None, 286) 53196 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 286) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 64) 18368 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 64) 0 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 2) 130 \n",
"=================================================================\n",
"Total params: 71,694\n",
"Trainable params: 71,694\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# Imports\n",
"import numpy as np\n",
"from keras.models import Sequential\n",
"from keras.layers.core import Dense, Dropout, Activation\n",
"from keras.optimizers import SGD\n",
"from keras.utils import np_utils\n",
"\n",
"# Building the model\n",
"model = Sequential()\n",
"\n",
"#get number of columns in training data\n",
"n_cols = X.shape[1]\n",
"#rely is rectified linear, default go-to activiation function, can be tweeked\n",
"model.add(Dense(286, activation='relu', input_shape=(n_cols,)))\n",
"model.add(Dropout(.2))\n",
"model.add(Dense(64, activation='relu'))\n",
"model.add(Dropout(.1))\n",
"#this here is our output layer and will have our number of classifications\n",
"# so we will use softmas for a probability distribution\n",
"model.add(Dense(2, activation='softmax'))\n",
"\n",
"# Compiling the model\n",
"model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# Training the model\n",
"from keras.callbacks import EarlyStopping\n",
"#set early stopping monitor so the model stops training when it won't improve anymore\n",
"early_stopping_monitor = EarlyStopping(patience=3)\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 3061 samples, validate on 1313 samples\n",
"Epoch 1/200\n",
"3061/3061 [==============================] - 1s 322us/step - loss: 0.6806 - acc: 0.7119 - val_loss: 0.5522 - val_acc: 0.8111\n",
"Epoch 2/200\n",
"3061/3061 [==============================] - 0s 86us/step - loss: 0.5405 - acc: 0.7615 - val_loss: 0.4793 - val_acc: 0.8111\n",
"Epoch 3/200\n",
"3061/3061 [==============================] - 0s 98us/step - loss: 0.5075 - acc: 0.7805 - val_loss: 0.4310 - val_acc: 0.8119\n",
"Epoch 4/200\n",
"3061/3061 [==============================] - 0s 82us/step - loss: 0.4653 - acc: 0.7893 - val_loss: 0.4634 - val_acc: 0.8119\n",
"Epoch 5/200\n",
"3061/3061 [==============================] - 0s 77us/step - loss: 0.4537 - acc: 0.7890 - val_loss: 0.4139 - val_acc: 0.8111\n",
"Epoch 6/200\n",
"3061/3061 [==============================] - 0s 80us/step - loss: 0.4406 - acc: 0.8063 - val_loss: 0.3995 - val_acc: 0.8324\n",
"Epoch 7/200\n",
"3061/3061 [==============================] - 0s 72us/step - loss: 0.4270 - acc: 0.8122 - val_loss: 0.4095 - val_acc: 0.8180\n",
"Epoch 8/200\n",
"3061/3061 [==============================] - 0s 71us/step - loss: 0.4175 - acc: 0.8118 - val_loss: 0.4069 - val_acc: 0.8126\n",
"Epoch 9/200\n",
"3061/3061 [==============================] - 0s 66us/step - loss: 0.4186 - acc: 0.8053 - val_loss: 0.4093 - val_acc: 0.8210\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x2bd47b00a20>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X, Y, validation_split=0.3, epochs=200, batch_size=100, \n",
" verbose=1, callbacks=[early_stopping_monitor] )"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4374/4374 [==============================] - 0s 35us/step\n",
"\n",
" Model Accuracy: 0.8214449016645612\n"
]
}
],
"source": [
"#Scoring the model\n",
"# Evaluating the model on the training and testing set\n",
"score = model.evaluate(X, Y)\n",
"print(\"\\n Model Accuracy:\", score[1])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Save the Model to .h5 format"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<keras.engine.sequential.Sequential at 0x2bd494b37b8>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.save('OD_Survive2.h5')\n",
"Survive_model = keras.models.load_model('OD_Survive2.h5')\n",
"Survive_model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Making a new Prediction\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Avg_Age</th>\n",
" <th>Incident_Month</th>\n",
" <th>Day_Friday</th>\n",
" <th>Day_Monday</th>\n",
" <th>Day_Saturday</th>\n",
" <th>Day_Sunday</th>\n",
" <th>Day_Thursday</th>\n",
" <th>Day_Tuesday</th>\n",
" <th>Day_Wednesday</th>\n",
" <th>Incident_County_Name_Adams</th>\n",
" <th>...</th>\n",
" <th>Susp_OD_Drug_METHAMPHETAMINE</th>\n",
" <th>Susp_OD_Drug_OTHER</th>\n",
" <th>Susp_OD_Drug_PHARMACEUTICAL OPIOID</th>\n",
" <th>Susp_OD_Drug_PHARMACEUTICAL OTHER</th>\n",
" <th>Susp_OD_Drug_PHARMACEUTICAL STIMULANT</th>\n",
" <th>Susp_OD_Drug_SUBOXONE</th>\n",
" <th>Susp_OD_Drug_SYNTHETIC MARIJUANA</th>\n",
" <th>Susp_OD_Drug_UNKNOWN</th>\n",
" <th>Naloxone_Administered_N</th>\n",
" <th>Naloxone_Administered_Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>40.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 185 columns</p>\n",
"</div>"
],
"text/plain": [
" Avg_Age Incident_Month Day_Friday Day_Monday Day_Saturday Day_Sunday \\\n",
"0 80.0 1 0 0 0 0 \n",
"1 80.0 1 0 0 0 0 \n",
"2 80.0 1 0 0 0 0 \n",
"3 80.0 1 0 0 0 0 \n",
"4 40.0 1 1 0 0 0 \n",
"\n",
" Day_Thursday Day_Tuesday Day_Wednesday Incident_County_Name_Adams \\\n",
"0 1 0 0 0 \n",
"1 1 0 0 0 \n",
"2 1 0 0 0 \n",
"3 1 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" ... Susp_OD_Drug_METHAMPHETAMINE Susp_OD_Drug_OTHER \\\n",
"0 ... 0 0 \n",
"1 ... 0 0 \n",
"2 ... 0 0 \n",
"3 ... 0 0 \n",
"4 ... 0 0 \n",
"\n",
" Susp_OD_Drug_PHARMACEUTICAL OPIOID Susp_OD_Drug_PHARMACEUTICAL OTHER \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Susp_OD_Drug_PHARMACEUTICAL STIMULANT Susp_OD_Drug_SUBOXONE \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Susp_OD_Drug_SYNTHETIC MARIJUANA Susp_OD_Drug_UNKNOWN \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Naloxone_Administered_N Naloxone_Administered_Y \n",
"0 0 1 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 1 \n",
"4 1 0 \n",
"\n",
"[5 rows x 185 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a clean list of dummy variables for the prediction\n",
"X_use = data[['Day', 'Incident_County_Name', 'Gender', 'Race', 'Ethnicity',\n",
" 'Victim_State', 'Victim_County', 'Accidental_Exposure',\n",
" 'Susp_OD_Drug','Naloxone_Administered', 'Avg_Age', 'Incident_Month' ]]\n",
"Y_use = data.Survive\n",
"X_use = pd.get_dummies(data=X_use)\n",
"X_use.head()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"# a good chuck of this code was adapted from this code here, they did great workd\n",
"#https://github.com/PaacMaan/cars-price-predictor/blob/master/cars_price_predictor.ipynb\n",
"\n",
"#user_input = [1, 35, 'Monday', 'Delaware', 'Male', 'White', 'Not Hispanic',\n",
"# 'Pennsylvania', 'Delaware', 'N', 'COCAINE/CRACK', 'Y']\n",
"user_input = {'Incident_Month':1, 'Avg_Age':35, 'Day':'Monday', \n",
" 'Incident_County_Name':'Delaware', 'Gender':'Male',\n",
" 'Race': 'White', 'Ethnicity': 'Not Hispanic',\n",
" 'Victim_State': 'Pennsylvania','Victim_County': 'Delaware',\n",
" 'Accidental_Exposure':'N','Susp_OD_Drug':'COCAINE/CRACK',\n",
" 'Naloxone_Administered':'Y'}\n",
"\n",
"def input_to_one_hot(data):\n",
" #initialize the target vector with zero values\n",
" enc_input = np.zeros(185)\n",
" #set the numerical inputs as they are\n",
"\n",
" enc_input[0] = data['Incident_Month']\n",
" enc_input[1] = data['Avg_Age']\n",
" #Define the columns\n",
" cols = ['Day', 'Incident_County_Name', 'Gender', 'Race', 'Ethnicity',\n",
" 'Victim_State', 'Victim_County', 'Accidental_Exposure', 'Susp_OD_Drug',\n",
" 'Naloxone_Administered', 'Survive', 'Avg_Age', 'Incident_Month']\n",
" #get the array for categorical variables\n",
" Days = ['Thursday', 'Friday', 'Wednesday', 'Monday',\n",
" 'Saturday', 'Tuesday', 'Sunday']\n",
"\n",
" #redefine the user input to match the column name\n",
" redefined_user_input = 'Day_'+data['Day']\n",
" #search for the index in columns name list\n",
" Day_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" #print(Day_column_index)\n",
" #fulfill the found index with 1\n",
" enc_input[Day_column_index] = 1\n",
" \n",
" #repeat for all other categorical variables\n",
" Incident_County_Names = ['Delaware', 'Chester', 'Beaver', 'Bucks', 'Philadelphia',\n",
" 'Cumberland', 'Northumberland', 'Montgomery', 'Pike', 'Armstrong',\n",
" 'Carbon', 'Bradford', 'Dauphin', 'Lehigh', 'Erie', 'York',\n",
" 'Lebanon', 'Monroe', 'Franklin', 'Lancaster', 'Berks', 'Jefferson',\n",
" 'Mifflin', 'Westmoreland', 'Crawford', 'Blair', 'Allegheny',\n",
" 'Washington', 'Luzerne', 'Susquehanna', 'Elk', 'Lycoming',\n",
" 'Snyder', 'Lackawanna', 'Lawrence', 'Wayne', 'Potter', 'Centre',\n",
" 'Juniata', 'Perry', 'Northampton', 'Adams', 'Cambria', 'Wyoming',\n",
" 'Schuylkill', 'Clearfield', 'Tioga', 'Columbia', 'Fulton',\n",
" 'Mercer', 'Indiana', 'Union', 'Butler', 'Somerset', 'Fayette',\n",
" 'Clarion', 'Montour', 'Bedford', 'Greene', 'Huntingdon', 'McKean',\n",
" 'Forest', 'Clinton']\n",
" redefined_user_input = 'Incident_County_Name_'+data['Incident_County_Name']\n",
" Incident_County_Name_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" enc_input[Incident_County_Name_column_index] = 1\n",
" \n",
"\n",
" # Gender\n",
" Genders = ['Male', 'Female', 'Unknown']\n",
" redefined_user_input = 'Gender_'+data['Gender']\n",
" Gender_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" enc_input[Gender_column_index] = 1\n",
" \n",
" # Race\n",
" Races = ['White', 'Black', 'Unknown', 'Asian or Pacific Islander',\n",
" 'American Indian or Alaskan Native']\n",
" redefined_user_input = 'Race_'+data['Race']\n",
" Race_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" enc_input[Race_column_index] = 1\n",
" \n",
" # Ethnicity_Desc\n",
" Ethnicities = ['Not Hispanic', 'Unknown', 'Hispanic', 'Mongolian']\n",
" redefined_user_input = 'Ethnicity_'+data['Ethnicity']\n",
" Ethnicity_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" enc_input[Ethnicity_column_index] = 1\n",
" \n",
" # Victim_State\n",
" Victim_States = ['Pennsylvania', 'New Jersey', 'Delaware', 'Maryland', 'New York',\n",
" 'Ohio', 'West Virginia', 'South Carolina', 'Florida', 'Vermont',\n",
" 'Virginia', 'Arizona', 'Oklahoma']\n",
" redefined_user_input = 'Victim_State_'+data['Victim_State']\n",
" Victim_State_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" enc_input[Victim_State_column_index] = 1\n",
" \n",
" # Victim_County\n",
" Victim_Counties = ['Delaware', 'Chester', 'Beaver', 'Montgomery', 'Bucks',\n",
" 'Cumberland', 'Northumberland', 'Pike', 'Armstrong', 'Carbon',\n",
" 'Out of State', 'Bradford', 'Dauphin', 'Lehigh', 'Erie', 'York',\n",
" 'Adams', 'Lebanon', 'Monroe', 'Franklin', 'Lancaster', 'Berks',\n",
" 'Northampton', 'Jefferson', 'Mifflin', 'Allegheny', 'Westmoreland',\n",
" 'Crawford', 'Blair', 'Washington', 'Luzerne', 'Lackawanna', 'Elk',\n",
" 'Lycoming', 'Philadelphia', 'Snyder', 'Perry', 'Lawrence', 'Wayne',\n",
" 'Potter', 'Centre', 'Juniata', 'Cambria', 'Wyoming', 'Susquehanna',\n",
" 'Schuylkill', 'Clearfield', 'Tioga', 'Columbia', 'Fulton',\n",
" 'Mercer', 'Indiana', 'Union', 'Butler', 'Somerset', 'Fayette',\n",
" 'Clarion', 'Montour', 'Bedford', 'Greene', 'Huntingdon', 'McKean',\n",
" 'Forest', 'Clinton', 'Venango']\n",
" redefined_user_input = 'Victim_County_'+data['Victim_County']\n",
" Victim_County_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" enc_input[Victim_County_column_index] = 1\n",
" \n",
" # Accidental_Exposure\n",
" Accidental_Exposures = ['N', 'Y']\n",
" redefined_user_input = 'Accidental_Exposure_'+data['Accidental_Exposure']\n",
" Accidental_Exposure_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" enc_input[Accidental_Exposure_column_index] = 1\n",
" \n",
" # Susp_OD_Drug\n",
" Susp_OD_Drugs = ['COCAINE/CRACK', 'HEROIN', 'FENTANYL',\n",
" 'FENTANYL ANALOG/OTHER SYNTHETIC OPIOID', 'PHARMACEUTICAL OPIOID',\n",
" 'UNKNOWN', 'MARIJUANA', 'ALCOHOL', 'SYNTHETIC MARIJUANA',\n",
" 'PHARMACEUTICAL OTHER',\n",
" 'BENZODIAZEPINES (I.E.VALIUM, XANAX, ATIVAN, ETC)', 'OTHER',\n",
" 'BARBITURATES (I.E. AMYTAL, NEMBUTAL, ETC)', 'CARFENTANIL',\n",
" 'SUBOXONE', 'METHADONE', 'METHAMPHETAMINE', 'BATH SALTS',\n",
" 'PHARMACEUTICAL STIMULANT']\n",
" redefined_user_input = 'Susp_OD_Drug_'+data['Susp_OD_Drug']\n",
" Susp_OD_Drug_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" enc_input[Susp_OD_Drug_column_index] = 1\n",
" \n",
" # Naloxone_Administered\n",
" Naloxone_Administereds = ['Y', 'N']\n",
" redefined_user_input = 'Naloxone_Administered_'+data['Naloxone_Administered']\n",
" Naloxone_Administered_column_index = X_use.columns.tolist().index(redefined_user_input)\n",
" enc_input[Naloxone_Administered_column_index] = 1\n",
" return enc_input\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. 35. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 1.]\n"
]
}
],
"source": [
"print(input_to_one_hot(user_input))"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"a = input_to_one_hot(user_input)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
}
],
"source": [
"prediction1 = np.argmax([a])\n",
"print(prediction1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## we can see that with this new random prediction, our person will actually survive, great new!\n",
"\n",
"## Let's try for a different person"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"#user_input = [7, 25, 'Monday', 'Allegheny', 'Female', 'Black', 'Not Hispanic',\n",
"# 'Pennsylvania', 'Philadelphia', 'N', 'HEROIN', 'Y']\n",
"user_input2 = {'Incident_Month':7, 'Avg_Age':25, 'Day':'Monday', \n",
" 'Incident_County_Name':'Allegheny', 'Gender':'Female',\n",
" 'Race': 'Black', 'Ethnicity': 'Not Hispanic',\n",
" 'Victim_State': 'Pennsylvania','Victim_County': 'Philadelphia',\n",
" 'Accidental_Exposure':'N','Susp_OD_Drug':'HEROIN',\n",
" 'Naloxone_Administered':'N'}\n",
"\n",
"def input_to_one_hot(data):\n",
" #initialize the target vector with zero values\n",
" enc_input = np.zeros(185)\n",
" #set the numerical inputs as they are\n",
"\n",
" enc_input[0] = data['Incident_Month']\n",
" enc_input[1] = data['Avg_Age']\n",
" #Define the columns\n",
" cols = ['Day', 'Incident_County_Name', 'Gender', 'Race', 'Ethnicity',\n",
" 'Victim_State', 'Victim_County', 'Accidental_Exposure', 'Susp_OD_Drug',\n",
" 'Naloxone_Administered', 'Survive', 'Avg_Age', 'Incident_Month']\n",
" #get the array for categorical variables\n",
" Days = ['Thursday', 'Friday', 'Wednesday', 'Monday',\n",
" 'Saturday', 'Tuesday', 'Sunday']\n",
"\n",
" #redefine the user input to match the column name\n",
" redefined_user_input2 = 'Day_'+data['Day']\n",
" #search for the index in columns name list\n",
" Day_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" #print(Day_column_index)\n",
" #fulfill the found index with 1\n",
" enc_input[Day_column_index] = 1\n",
" \n",
" #repeat for all other categorical variables\n",
" Incident_County_Names = ['Delaware', 'Chester', 'Beaver', 'Bucks', 'Philadelphia',\n",
" 'Cumberland', 'Northumberland', 'Montgomery', 'Pike', 'Armstrong',\n",
" 'Carbon', 'Bradford', 'Dauphin', 'Lehigh', 'Erie', 'York',\n",
" 'Lebanon', 'Monroe', 'Franklin', 'Lancaster', 'Berks', 'Jefferson',\n",
" 'Mifflin', 'Westmoreland', 'Crawford', 'Blair', 'Allegheny',\n",
" 'Washington', 'Luzerne', 'Susquehanna', 'Elk', 'Lycoming',\n",
" 'Snyder', 'Lackawanna', 'Lawrence', 'Wayne', 'Potter', 'Centre',\n",
" 'Juniata', 'Perry', 'Northampton', 'Adams', 'Cambria', 'Wyoming',\n",
" 'Schuylkill', 'Clearfield', 'Tioga', 'Columbia', 'Fulton',\n",
" 'Mercer', 'Indiana', 'Union', 'Butler', 'Somerset', 'Fayette',\n",
" 'Clarion', 'Montour', 'Bedford', 'Greene', 'Huntingdon', 'McKean',\n",
" 'Forest', 'Clinton']\n",
" redefined_user_input2= 'Incident_County_Name_'+data['Incident_County_Name']\n",
" Incident_County_Name_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" enc_input[Incident_County_Name_column_index] = 1\n",
" \n",
"\n",
" # Gender\n",
" Genders = ['Male', 'Female', 'Unknown']\n",
" redefined_user_input2 = 'Gender_'+data['Gender']\n",
" Gender_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" enc_input[Gender_column_index] = 1\n",
" \n",
" # Race\n",
" Races = ['White', 'Black', 'Unknown', 'Asian or Pacific Islander',\n",
" 'American Indian or Alaskan Native']\n",
" redefined_user_input2 = 'Race_'+data['Race']\n",
" Race_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" enc_input[Race_column_index] = 1\n",
" \n",
" # Ethnicity_Desc\n",
" Ethnicities = ['Not Hispanic', 'Unknown', 'Hispanic', 'Mongolian']\n",
" redefined_user_input2 = 'Ethnicity_'+data['Ethnicity']\n",
" Ethnicity_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" enc_input[Ethnicity_column_index] = 1\n",
" \n",
" # Victim_State\n",
" Victim_States = ['Pennsylvania', 'New Jersey', 'Delaware', 'Maryland', 'New York',\n",
" 'Ohio', 'West Virginia', 'South Carolina', 'Florida', 'Vermont',\n",
" 'Virginia', 'Arizona', 'Oklahoma']\n",
" redefined_user_input2 = 'Victim_State_'+data['Victim_State']\n",
" Victim_State_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" enc_input[Victim_State_column_index] = 1\n",
" \n",
" # Victim_County\n",
" Victim_Counties = ['Delaware', 'Chester', 'Beaver', 'Montgomery', 'Bucks',\n",
" 'Cumberland', 'Northumberland', 'Pike', 'Armstrong', 'Carbon',\n",
" 'Out of State', 'Bradford', 'Dauphin', 'Lehigh', 'Erie', 'York',\n",
" 'Adams', 'Lebanon', 'Monroe', 'Franklin', 'Lancaster', 'Berks',\n",
" 'Northampton', 'Jefferson', 'Mifflin', 'Allegheny', 'Westmoreland',\n",
" 'Crawford', 'Blair', 'Washington', 'Luzerne', 'Lackawanna', 'Elk',\n",
" 'Lycoming', 'Philadelphia', 'Snyder', 'Perry', 'Lawrence', 'Wayne',\n",
" 'Potter', 'Centre', 'Juniata', 'Cambria', 'Wyoming', 'Susquehanna',\n",
" 'Schuylkill', 'Clearfield', 'Tioga', 'Columbia', 'Fulton',\n",
" 'Mercer', 'Indiana', 'Union', 'Butler', 'Somerset', 'Fayette',\n",
" 'Clarion', 'Montour', 'Bedford', 'Greene', 'Huntingdon', 'McKean',\n",
" 'Forest', 'Clinton', 'Venango']\n",
" redefined_user_input2 = 'Victim_County_'+data['Victim_County']\n",
" Victim_County_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" enc_input[Victim_County_column_index] = 1\n",
" \n",
" # Accidental_Exposure\n",
" Accidental_Exposures = ['N', 'Y']\n",
" redefined_user_input2 = 'Accidental_Exposure_'+data['Accidental_Exposure']\n",
" Accidental_Exposure_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" enc_input[Accidental_Exposure_column_index] = 1\n",
" \n",
" # Susp_OD_Drug\n",
" Susp_OD_Drugs = ['COCAINE/CRACK', 'HEROIN', 'FENTANYL',\n",
" 'FENTANYL ANALOG/OTHER SYNTHETIC OPIOID', 'PHARMACEUTICAL OPIOID',\n",
" 'UNKNOWN', 'MARIJUANA', 'ALCOHOL', 'SYNTHETIC MARIJUANA',\n",
" 'PHARMACEUTICAL OTHER',\n",
" 'BENZODIAZEPINES (I.E.VALIUM, XANAX, ATIVAN, ETC)', 'OTHER',\n",
" 'BARBITURATES (I.E. AMYTAL, NEMBUTAL, ETC)', 'CARFENTANIL',\n",
" 'SUBOXONE', 'METHADONE', 'METHAMPHETAMINE', 'BATH SALTS',\n",
" 'PHARMACEUTICAL STIMULANT']\n",
" redefined_user_input2 = 'Susp_OD_Drug_'+data['Susp_OD_Drug']\n",
" Susp_OD_Drug_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" enc_input[Susp_OD_Drug_column_index] = 1\n",
" \n",
" # Naloxone_Administered\n",
" Naloxone_Administereds = ['Y', 'N']\n",
" redefined_user_input2 = 'Naloxone_Administered_'+data['Naloxone_Administered']\n",
" Naloxone_Administered_column_index = X_use.columns.tolist().index(redefined_user_input2)\n",
" enc_input[Naloxone_Administered_column_index] = 1\n",
" return enc_input\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 7. 25. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 1. 0.]\n"
]
}
],
"source": [
"print(input_to_one_hot(user_input2))"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"b = input_to_one_hot(user_input2)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
}
],
"source": [
"prediction2 = np.argmax([b])\n",
"print(prediction2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:Anaconda3]",
"language": "python",
"name": "conda-env-Anaconda3-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment