Incident_event_log.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset(https://archive.ics.uci.edu/ml/datasets/Incident+management+process+enriched+event+log) is from UCI ML repository  event log was extracted from data gathered from the audit system of an instance of the ServiceNow platform used by an IT company and enriched with data loaded from a relational database.In order to get a better understanding of the data we will be looking at throughout this lesson, let's take a look at defnition of each column of the dataset:-\n",
    "\n",
    "1. number: incident identifier (24,918 different values);\n",
    "2. incident state: eight levels controlling the incident management process transitions from opening until closing the case;\n",
    "3. active: boolean attribute that shows whether the record is active or closed/canceled;\n",
    "4. reassignment_count: number of times the incident has the group or the support analysts changed;\n",
    "5. reopen_count: number of times the incident resolution was rejected by the caller;\n",
    "6. sys_mod_count: number of incident updates until that moment;\n",
    "7. made_sla: boolean attribute that shows whether the incident exceeded the target SLA;\n",
    "8. caller_id: identifier of the user affected;\n",
    "9. opened_by: identifier of the user who reported the incident;\n",
    "10. opened_at: incident user opening date and time;\n",
    "11. sys_created_by: identifier of the user who registered the incident;\n",
    "12. sys_created_at: incident system creation date and time;\n",
    "13. sys_updated_by: identifier of the user who updated the incident and generated the current log record;\n",
    "14. sys_updated_at: incident system update date and time;\n",
    "15. contact_type: categorical attribute that shows by what means the incident was reported;\n",
    "16. location: identifier of the location of the place affected;\n",
    "17. category: first-level description of the affected service;\n",
    "18. subcategory: second-level description of the affected service (related to the first level description, i.e., to category);\n",
    "19. u_symptom: description of the user perception about service availability;\n",
    "20. cmdb_ci: (confirmation item) identifier used to report the affected item (not mandatory);\n",
    "21. impact: description of the impact caused by the incident (values: 1â€“High; 2â€“Medium; 3â€“Low);\n",
    "22. urgency: description of the urgency informed by the user for the incident resolution (values: 1â€“High; 2â€“Medium; 3â€“Low);\n",
    "23. priority: calculated by the system based on 'impact' and 'urgency';\n",
    "24. assignment_group: identifier of the support group in charge of the incident;\n",
    "25. assigned_to: identifier of the user in charge of the incident;\n",
    "26. knowledge: boolean attribute that shows whether a knowledge base document was used to resolve the incident;\n",
    "27. u_priority_confirmation: boolean attribute that shows whether the priority field has been double-checked;\n",
    "28. notify: categorical attribute that shows whether notifications were generated for the incident;\n",
    "29. problem_id: identifier of the problem associated with the incident;\n",
    "30. rfc: (request for change) identifier of the change request associated with the incident;\n",
    "31. vendor: identifier of the vendor in charge of the incident;\n",
    "32. caused_by: identifier of the RFC responsible by the incident;\n",
    "33. close_code: identifier of the resolution of the incident;\n",
    "34. resolved_by: identifier of the user who resolved the incident;\n",
    "35. resolved_at: incident user resolution date and time (dependent variable);\n",
    "36. closed_at: incident user close date and time (dependent variable).\n",
    "\n",
    "First, let's read in the data and necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>number</th>\n",
       "      <th>incident_state</th>\n",
       "      <th>active</th>\n",
       "      <th>reassignment_count</th>\n",
       "      <th>reopen_count</th>\n",
       "      <th>sys_mod_count</th>\n",
       "      <th>made_sla</th>\n",
       "      <th>caller_id</th>\n",
       "      <th>opened_by</th>\n",
       "      <th>opened_at</th>\n",
       "      <th>...</th>\n",
       "      <th>u_priority_confirmation</th>\n",
       "      <th>notify</th>\n",
       "      <th>problem_id</th>\n",
       "      <th>rfc</th>\n",
       "      <th>vendor</th>\n",
       "      <th>caused_by</th>\n",
       "      <th>closed_code</th>\n",
       "      <th>resolved_by</th>\n",
       "      <th>resolved_at</th>\n",
       "      <th>closed_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INC0000045</td>\n",
       "      <td>New</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>Caller 2403</td>\n",
       "      <td>Opened by  8</td>\n",
       "      <td>29/2/2016 01:16</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>Do Not Notify</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>code 5</td>\n",
       "      <td>Resolved by 149</td>\n",
       "      <td>29/2/2016 11:29</td>\n",
       "      <td>5/3/2016 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INC0000045</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>Caller 2403</td>\n",
       "      <td>Opened by  8</td>\n",
       "      <td>29/2/2016 01:16</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>Do Not Notify</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>code 5</td>\n",
       "      <td>Resolved by 149</td>\n",
       "      <td>29/2/2016 11:29</td>\n",
       "      <td>5/3/2016 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INC0000045</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>Caller 2403</td>\n",
       "      <td>Opened by  8</td>\n",
       "      <td>29/2/2016 01:16</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>Do Not Notify</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>code 5</td>\n",
       "      <td>Resolved by 149</td>\n",
       "      <td>29/2/2016 11:29</td>\n",
       "      <td>5/3/2016 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>INC0000045</td>\n",
       "      <td>Closed</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>Caller 2403</td>\n",
       "      <td>Opened by  8</td>\n",
       "      <td>29/2/2016 01:16</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>Do Not Notify</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>code 5</td>\n",
       "      <td>Resolved by 149</td>\n",
       "      <td>29/2/2016 11:29</td>\n",
       "      <td>5/3/2016 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>INC0000047</td>\n",
       "      <td>New</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>Caller 2403</td>\n",
       "      <td>Opened by  397</td>\n",
       "      <td>29/2/2016 04:40</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>Do Not Notify</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>code 5</td>\n",
       "      <td>Resolved by 81</td>\n",
       "      <td>1/3/2016 09:52</td>\n",
       "      <td>6/3/2016 10:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       number incident_state  active  reassignment_count  reopen_count  \\\n",
       "0  INC0000045            New    True                   0             0   \n",
       "1  INC0000045       Resolved    True                   0             0   \n",
       "2  INC0000045       Resolved    True                   0             0   \n",
       "3  INC0000045         Closed   False                   0             0   \n",
       "4  INC0000047            New    True                   0             0   \n",
       "\n",
       "   sys_mod_count  made_sla    caller_id       opened_by        opened_at  \\\n",
       "0              0      True  Caller 2403    Opened by  8  29/2/2016 01:16   \n",
       "1              2      True  Caller 2403    Opened by  8  29/2/2016 01:16   \n",
       "2              3      True  Caller 2403    Opened by  8  29/2/2016 01:16   \n",
       "3              4      True  Caller 2403    Opened by  8  29/2/2016 01:16   \n",
       "4              0      True  Caller 2403  Opened by  397  29/2/2016 04:40   \n",
       "\n",
       "        ...       u_priority_confirmation         notify problem_id rfc  \\\n",
       "0       ...                         False  Do Not Notify          ?   ?   \n",
       "1       ...                         False  Do Not Notify          ?   ?   \n",
       "2       ...                         False  Do Not Notify          ?   ?   \n",
       "3       ...                         False  Do Not Notify          ?   ?   \n",
       "4       ...                         False  Do Not Notify          ?   ?   \n",
       "\n",
       "  vendor caused_by closed_code      resolved_by      resolved_at  \\\n",
       "0      ?         ?      code 5  Resolved by 149  29/2/2016 11:29   \n",
       "1      ?         ?      code 5  Resolved by 149  29/2/2016 11:29   \n",
       "2      ?         ?      code 5  Resolved by 149  29/2/2016 11:29   \n",
       "3      ?         ?      code 5  Resolved by 149  29/2/2016 11:29   \n",
       "4      ?         ?      code 5   Resolved by 81   1/3/2016 09:52   \n",
       "\n",
       "        closed_at  \n",
       "0  5/3/2016 12:00  \n",
       "1  5/3/2016 12:00  \n",
       "2  5/3/2016 12:00  \n",
       "3  5/3/2016 12:00  \n",
       "4  6/3/2016 10:00  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('./incident_event_log.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us examine the rows and columns in the dataset,There are 141712 rows with 36 columns and there is no missing values as checked below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141712, 36)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number                     0\n",
       "incident_state             0\n",
       "active                     0\n",
       "reassignment_count         0\n",
       "reopen_count               0\n",
       "sys_mod_count              0\n",
       "made_sla                   0\n",
       "caller_id                  0\n",
       "opened_by                  0\n",
       "opened_at                  0\n",
       "sys_created_by             0\n",
       "sys_created_at             0\n",
       "sys_updated_by             0\n",
       "sys_updated_at             0\n",
       "contact_type               0\n",
       "location                   0\n",
       "category                   0\n",
       "subcategory                0\n",
       "u_symptom                  0\n",
       "cmdb_ci                    0\n",
       "impact                     0\n",
       "urgency                    0\n",
       "priority                   0\n",
       "assignment_group           0\n",
       "assigned_to                0\n",
       "knowledge                  0\n",
       "u_priority_confirmation    0\n",
       "notify                     0\n",
       "problem_id                 0\n",
       "rfc                        0\n",
       "vendor                     0\n",
       "caused_by                  0\n",
       "closed_code                0\n",
       "resolved_by                0\n",
       "resolved_at                0\n",
       "closed_at                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the distribution of different state of incidents in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7b3a933c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Active                38716\n",
       "New                   36407\n",
       "Resolved              25751\n",
       "Closed                24985\n",
       "Awaiting User Info    14642\n",
       "Awaiting Vendor         707\n",
       "Awaiting Problem        461\n",
       "Awaiting Evidence        38\n",
       "-100                      5\n",
       "Name: incident_state, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('incident_state') \\\n",
    "    .count()['number'] \\\n",
    "    .plot(kind='barh',\n",
    "          title='Distribution of incident_state',\n",
    "          figsize=(15, 3))\n",
    "plt.show()\n",
    "df['incident_state'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering out only closed incidents for our analysis as the incident can be in other state for multiple time and closed will be the final state which will give us the unique incident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_closed = df[\"incident_state\"] == \"Closed\"\n",
    "df_closed = df[df_closed]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see the data types of each variable, only three variables are numeric rest are charatcer and bool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number                     object\n",
       "incident_state             object\n",
       "active                       bool\n",
       "reassignment_count          int64\n",
       "reopen_count                int64\n",
       "sys_mod_count               int64\n",
       "made_sla                     bool\n",
       "caller_id                  object\n",
       "opened_by                  object\n",
       "opened_at                  object\n",
       "sys_created_by             object\n",
       "sys_created_at             object\n",
       "sys_updated_by             object\n",
       "sys_updated_at             object\n",
       "contact_type               object\n",
       "location                   object\n",
       "category                   object\n",
       "subcategory                object\n",
       "u_symptom                  object\n",
       "cmdb_ci                    object\n",
       "impact                     object\n",
       "urgency                    object\n",
       "priority                   object\n",
       "assignment_group           object\n",
       "assigned_to                object\n",
       "knowledge                    bool\n",
       "u_priority_confirmation      bool\n",
       "notify                     object\n",
       "problem_id                 object\n",
       "rfc                        object\n",
       "vendor                     object\n",
       "caused_by                  object\n",
       "closed_code                object\n",
       "resolved_by                object\n",
       "resolved_at                object\n",
       "closed_at                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed.dtypes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see the stats of numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>reassignment_count</th>\n",
       "      <th>reopen_count</th>\n",
       "      <th>sys_mod_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24985.000000</td>\n",
       "      <td>24985.000000</td>\n",
       "      <td>24985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.941765</td>\n",
       "      <td>0.013368</td>\n",
       "      <td>6.694417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.573962</td>\n",
       "      <td>0.149116</td>\n",
       "      <td>8.548757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>129.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reassignment_count  reopen_count  sys_mod_count\n",
       "count        24985.000000  24985.000000   24985.000000\n",
       "mean             0.941765      0.013368       6.694417\n",
       "std              1.573962      0.149116       8.548757\n",
       "min              0.000000      0.000000       1.000000\n",
       "25%              0.000000      0.000000       2.000000\n",
       "50%              0.000000      0.000000       4.000000\n",
       "75%              1.000000      0.000000       8.000000\n",
       "max             27.000000      8.000000     129.000000"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1 - What types of incidents are getting reopened?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Incidents are reassigned and reopend sometimes, maximum reassigment is 27 whereas there are incidents which are reopened 8 times.Let us first see that with what close codes incidents are getting closed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5QAAADSCAYAAAA134+kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3X/c1mV9///HMzEbQSj+aEqCVC6MFMxTNz/4oY02qrXVbls5GophG5/t44JWsdVsn3DEaG5p8nUjWLkFotVobtq2sF2VS8HGBchFZE1zpqSmgL/IfiA9v3+8j0tOT68f53nBxSnX9bzfbueN9/l6H+/jeL3x3ZmHx/E+DtkmIiIiIiIiolUvaHcCERERERERcXhKhzIiIiIiIiIGJB3KiIiIiIiIGJB0KCMiIiIiImJA0qGMiIiIiIiIAUmHMiIiIiIiIgYkHcqIiIiIiIgYkHQoIyKiLSR9QtKfHaS6xkvaI+mI8v2rkn73YNRd6vt3SRcdrPpaaPcjknZKeqjF6w7q/TfR3imSLGnEIWrvkN5fRET07pD88EdExPAi6V7gpcDTwD7gm8AqYKXtnwLY/v0W6vpd2//RWxnb9wGjDizrZ9pbBLzS9gV19b/pYNTdYh4nA+8DJth++FC3HxER0YyMUEZExGD5ddujgQnAR4E/AT51sBs5VKNibTAB2JXOZEREPJ+lQxkREYPK9uO2bwR+G7hI0msAJP2DpI+U4+MkfUHSY5J2S/qapBdIWg2MB24qU1r/uG565bsk3Qd8uZcpl6+Q9F+SHpf0L5LGlrZ+UdKO+hwl3SvplyW9EfhT4LdLe1vL+WemWJa8PiTpu5IelrRK0phyrjuPiyTdV6arXtrb342kMeX6R0p9Hyr1/zLwJeCkksc/9HL9WyXdIekJSd8p+TeW6SvfF0m6VtKu8ne/UdJL63L7lKQHJX2vTL/tnlJ8hKS/Lvd3D/Dmvp6BulzGSvp7SQ9IelTSP9ed+z1Jd5d//jdKOqnu3K9I+lb5Z3k1oIZ6L5Z0Z6lznaQJzeQTEREHLh3KiIg4JGz/F7AD+N89nH5fOXc81VTZP60u8YXAfVSjnaNsX153zeuA04A39NLkHOBi4CSqqbfLmsjxi8BfAJ8t7U3podg7y+eXgJdTTbW9uqHMecCrgNcD/0/Sab00+f8BY0o9rys5zy3Te98EPFDyeGfjhZLOoZpGvBA4GpgO3NtivheV9k8GjgV+H/hhOfdpqr+3VwJnAjOB7vcWfw/4tRKvAW/r5f4arQZGApOBE4Ary73MAJYC5wMnAt8FPlPOHQd8HvgQcBzwHWBa3d/Db1A9L79J9fx8Dbi+yXwiIuIApUMZERGH0gPA2B7ie6k6EhNs77X9Ndvup65Ftn9g+4e9nF9t+xu2fwD8GXB+9wjbAZoNXGH7Htt7gA8CsxpGRy+z/UPbW4GtwHM6piWX3wY+aPtJ2/cCHwMubDKPdwHX2P6S7Z/a/p7tb7WY716qjuQrbe+zvcn2E2WU8k3Ae8rf8cNUnb9Zpc7zgY/bvt/2bqrOYJ8knVjq/H3bj5Z/zrfU5XiN7c22f1xyPFfSKcCvAt+0vdb2XuDjQP0iRf8HWGr7TttPU/0HgakZpYyIODTSoYyIiENpHLC7h/hfAXcDN0u6R9IHmqjr/hbOfxc4kmqE60CdVOqrr3sE1chqt/oOz1P0vGDQccALe6hrXJN5nEw1WtefvvJdDawDPlOmoV4u6Uiq9zePBB4sU2EfA1ZQjSp219n499tMvrttP9pfjqXju4vq7+JZbZX/0FDf9gTgqro8d1NNiW327zEiIg5AOpQREXFISDqb6l/yb208V0bo3mf75cCvA++V9Pru071U2d8I5sl1x+OpRuN2Aj+gmnbZndcRVFMlm633AapOTH3dTwPf7+e6RjtLTo11fa/J6+8HXtFEuV7zLaOEl9l+NfC/qKaxzil1/xg4zvbR5fMS25NLHQ/y3L/fZvIdK+no/nKU9GKqkdPvNbYlSQ1t3w/8n7o8j7b9M7bXN5FTREQcoHQoIyJiUEl6iaRfo3on7lrb23oo82uSXlk6C09QbTWyr5z+PtW7f626QNKrJY0E/hxYa3sf8N/AiyS9uYzGfQg4qu667wOnSOrt/yOvB/5I0kRJo9j/zuXTrSRXcvkcsETS6DJF873AtU1W8SlgrqTXl4V3xkma1Eq+kn5J0umlU/0EVQd3n+0HgZuBj5V/fi+Q9ApJryt1fg6YL+llko4B+h1RLnX+O/C3ko6RdKSk6eX0deVepko6quT49TIN+F+ByZJ+s0zTnQ/8bF3VnwA+KGkyPLOY0Nub/DuMiIgDlA5lREQMlpskPUk1gnQpcAUwt5eypwL/AewBNgB/a/ur5dxS4ENlSuP7W2h/NfAPVNNPX0TVEcH248D/BT5JNQL2A6oFgbr9Y/lzl6TNPdR7Tan7P4H/AX4EvLuFvOq9u7R/D9XI7XWl/n6VRY7mUr3b+DhwC88eiWwm358F1lJ1Ju8sdXR3aOdQTcn9JvBoKXdiOfd3VFNltwKbgX9qJmeq90P3At8CHgbeU+6lg+o9189TjUi+gvK+pu2dwNuptp7ZRfWs3Fb393AD8JdU03afAL5B9a5mREQcAup/zYOIiIiIiIiI58oIZURERERERAzIiP6LRERERDRH0p5eTr3J9tcOaTIRETHoMuU1IiIiIiIiBiRTXiMiIiIiImJAMuW1B8cdd5xPOeWUdqcRERERERHRFps2bdpp+/j+yh12HUpJe2yPaqH8b1MtV38E8K+2/7i/a0455RQ6OzsPIMuIiIiIiIjDl6TvNlPusOtQtkLSscBfAWfZfkTSpyW9vux31auffG8POz6QdQOGk5d99H+3O4WIiIiIiMNOW96hlDRHUpekrZJWl9gESR0l3iFpfIlPlLRB0kZJixvqWVjiXZIu66GplwP/bfuR8v0/gN8azHuLiIiIiIgYLg55h1LSZKopqDNsTwEWlFNXA6tsnwGsAZaV+FXActtnAw/V1TMTOBU4B5gKnCVpekNzdwOTJJ0iaQTwG8DJveQ1T1KnpM7dTz12MG41IiIiIiJiSGvHCOUMYK3tnQC2d5f4ucB15Xg1cF45ngZcXxfvNrN8tgCbgUlUHcxn2H4U+APgs8DXgHuBp3tKyvZK2zXbtbEjjx7ovUVERERERAwb7XiHUkAzm1+6l+P6epbaXtFnJfZNwE1QjUIC+5rMMyIiIiIiIvrQjg5lB3CDpCtt75I0toxSrgdmUY1CzgZuLeVvK/FrS7zbOmCxpDW290gaB+y1/XB9Y5JOsP2wpGOA/wuc31+CLxw3Kou0RERERERE9OOQdyhtb5e0BLhF0j6qKavvBOYD10haCDwCzC2XLACuk7QA+HxdPTdLOg3YIAlgD3AB8KwOJXCVpCnl+M9t//fg3FlERERERMTwIruZ2afDS61Wc/ahjIiIiIiI4UrSJtu1/sq1ZduQAyVpT4vl3yFpW9le5IuSjhus3CIiIiIiIoaLw3KEUtIe26OaLDsCeAB4te2dki4HnrK9qLdrTh57tN/zK+f1dvqw877PfqHdKURERERExGHkeT9CKWlOGTHcKml1iU2Q1FHiHZLGl/hESRskbZS0uKGehSXeJemynpoqnxeretnyJVQdzIiIiIiIiDgAbelQSpoMXArMsD2FauEdgKuBVbbPANYAy0r8KmC57bOBh+rqmUm19+Q5wFTgLEnT69uyvZdqL8ptlJFK4FM95DRPUqekzh/8+CcH7V4jIiIiIiKGqnaNUM4A1treCVC2DQE4F7iuHK8GuuedTgOur4t3m1k+W4DNwCSqDuYzJB1J1aE8EzgJ6AI+2JiQ7ZW2a7ZrLz7qhQd0cxEREREREcNBO/ahhGoKajMvb7qX4/p6ltpe0UcdUwFsfwdA0ueADzSZZ0RERERERPSiXR3KDuAGSVfa3iVpbBmlXA/MohqFnA3cWsrfVuLXlni3dcBiSWts75E0Dthru34vyu8Br5Z0vO1HgF8B7uwruZe+/JVZyCYiIiIiIqIfbelQ2t4uaQlwi6R9VFNW3wnMB66RtBB4BJhbLlkAXCdpAfD5unpulnQasKFab4c9wAXAw3VlHiiL9fynpL3Ad0tbERERERERcQAOy21DBlutVnNnZ2e704iIiIiIiGiL5/22IQMlaU8LZUdLuqPus1PSxwczv4iIiIiIiOGiXe9QHhK2n6QsygNVLxv4p/6ue/i7T/I3v//lwUwt4nnpkk/MaHcKEREREXEYadc+lHMkdUnaKml1iU2Q1FHiHZLGl/hESRskbZS0uKGehSXeVd6T7KvNU4ETgK8N1n1FREREREQMJ4e8QylpMnApMMP2FKoFdwCuBlbZPgNYAywr8auA5bbPBh6qq2cm1Z6T51CNQp4laXofTb8D+Kx7eWlU0jxJnZI69/zosYHfYERERERExDDRjhHKGcBa2zsBynYhAOcC15Xj1cB55XgacH1dvNvM8tkCbAYmUXUwezOrrp7nsL3Sds12bdSLjm7+biIiIiIiIoapdrxDKaCZpWXdy3F9PUttr+i3QWkKMML2puZSjIiIiIiIiP60o0PZAdwg6UrbuySNLaOU66lGEVcDs4FbS/nbSvzaEu+2DlgsaY3tPZLGAXttP8xzvYM+RicbnTBhdBYniYiIiIiI6Mchn/JqezuwBLhF0lbginJqPjBXUhdwIfvfrVwAXCJpIzCmrp6bqabIbpC0DVgLjO6l2fNpoUMZERERERER/VMva9QMa7VazZ2dne1OIyIiIiIioi0kbbJd669cW7YNORCS9rRY/oWSVkr6b0nfkvRbg5VbRERERETEcNKOdygPtUuBh23/nKQXAGP7u+BH39jOnZNOG/zMIiIiIiJiWDrtW3e2O4WDoi0jlJLmSOqStFXS6hKbIKmjxDskjS/xiZI2SNooaXFDPQtLvEvSZb00dzGwFMD2T7u3K4mIiIiIiIgDc8g7lJImU40azrA9hf2L71wNrLJ9BrAGWFbiVwHLbZ8NPFRXz0yqfSfPAaYCZ0ma3tBW94aSiyVtlvSPkl7aS17zJHVK6ty97+mDcq8RERERERFDWTtGKGcAa7tHCsuWIQDnUq3aCtXWIeeV42nsX6F1dV09M8tnC7AZmETVwaw3AngZcJvt1wIbgL/uKSnbK23XbNfGHjEcZgJHREREREQcmHb0nAQ0s7Ssezmur2ep7RV91LELeAq4oXz/R+BdzSQZERERERERfWtHh7IDuEHSlbZ3SRpbRinXA7OoRiFnA7eW8reV+LUl3m0d1VTWNbb3SBoH7LX9cHcB25Z0E/CLwJeB1wPf7C/BF71mMqdl25CIiIiIiIg+HfIOpe3tkpYAt0jaRzVl9Z3AfOAaSQuBR4C55ZIFwHWSFgCfr6vnZkmnARskAewBLgCe6VAWfwKslvTxhnojIiIiIiLiAMhuZvbp8FKr1dyZEcqIiIiIiBimJG2yXeuv3GG3+oykPbZHtVD+q8CJwA9LaGb9tNiIiIiIiIgYmMOuQzlAs203PeS4fdd2Tv/06YOZzwHbdtG2dqcQERERERHDXDu2DUHSHEldkrZKWl1iEyR1lHiHpPElPlHSBkkbJS1uqGdhiXdJuqwd9xIRERERETFcHfIOpaTJwKXADNtTqBbdAbgaWGX7DGANsKzErwKW2z4beKiunplU+06eA0wFzpI0vZdm/17SHZL+TGUFnx7ymiepU1Lnvif3HeBdRkREREREDH3tGKGcAay1vROgbBkCcC5wXTleDZxXjqcB19fFu80sny3AZmASVQez0WzbpwP/u3wu7Ckp2ytt12zXjhh9xEDuKyIiIiIiYlhpxzuUAppZWta9HNfXs9T2ij4rsb9X/nxS0nVUI5qrmsw1IiIiIiIietGODmUHcIOkK23vkjS2jFKuB2ZRjULOBm4t5W8r8WtLvNs6YLGkNbb3SBoH7K1fwVXSCOBo2zslHQn8GvAf/SU4+djJdF6UbUMiIiIiIiL6csg7lLa3S1oC3CJpH9WU1XcC84FrJC0EHgHmlksWANdJWgB8vq6emyWdBmwor0XuAS4A6rcEOQpYVzqTR1B1Jv9uEG8vIiIiIiJi2JDdzOzT4aVWq7mzMyOUERERERExPEnaZLvWX7m2bBtyoCTtGeB1N0r6xsHOJyIiIiIiYjhqxzuUbSHpN6mmxfbvgS2waEz/5RY9fmBJRUREREREHMbaNkIpaY6kLklbJa0usQmSOkq8Q9L4Ep8oaYOkjZIWN9SzsMS7JF3WS1ujgPcCHxns+4qIiIiIiBgu2tKhlDQZuBSYYXsK1cI7AFcDq2yfAawBlpX4VcBy22cDD9XVM5Nq78lzgKnAWZKm99DkYuBjwFODcDsRERERERHDUrtGKGcAa23vBCjbhgCcC1xXjlcD55XjacD1dfFuM8tnC7AZmETVwXyGpKnAK23f0FdCkuZJ6pTU+chTWagoIiIiIiKiP+16h1JAM70293JcX89S2yv6qONcqpHLe6nu9wRJX7X9i89qyF4JrASonXREepQRERERERH9aFeHsgO4QdKVtndJGltGKdcDs6hGIWcDt5byt5X4tSXebR2wWNIa23skjQP22n5mL0rby4HlAJJOAb7Q2Jl8jpPOhEXZNiQiIiIiIqIvTU95VeUCSf+vfB8v6ZyBNGp7O7AEuEXSVuCKcmo+MFdSF3Ah+9+tXABcImkjMKaunpuppshukLQNWAuMHkhOERERERER0RrZzc3ulLQc+CnVQjqnSToGuLkslDOk1Go1d3ZmhDIiIiIiIoYnSZts1/or18qU15+3/VpJWwBsPyrphQPO8ABI2mN7VAvlvwicSHW/XwMusb1vsPKLiIiIiIgYDlrpUO6VdARlcRxJx1ONWB4Ozrf9hCRRTYt9O/CZ3gpv+97jnPKBf31O/N6PvnnwMoyIiIiIiDjMtLJtyDLgBqpVUpdQLZjzFwNtWNIcSV2StkpaXWITJHWUeIek8SU+UdIGSRslLW6oZ2GJd0m6rKe2bD9RDkcAL6S5FWYjIiIiIiKiD013KG2vAf4YWAo8CPyG7X8cSKOSJgOXUr2POYX9i+9cDayyfQawhqoTC3AVsLy8r/lQXT0zqfadPAeYSrU9yPRe2lwHPAw8STVKGREREREREQeg3w6lpLHdH6oO2fVUK6t+v8QGYgaw1vZOgLJlCFR7Rl5XjlcD55XjaaXd7ni3meWzBdgMTKLqYD6H7TdQvUd5VGn/WSTNk9QpqXPfU48P8LYiIiIiIiKGj2beodxENUVUwHjg0XJ8NHAfMHEA7Yrmpp26l+P6epbaXtFMo7Z/JOlG4K3AlxrOrQRWAhx14qmZEhsREREREdGPfjuUticCSPoEcKPtfyvf3wT88gDb7QBukHSl7V2SxpZRyvXALKpRyNlU72kC3Fbi15Z4t3XAYklrbO+RNA7Ya/vh7gKSRgGjbT8oaQTwq1Qrvfbq9HFj6MwCPBEREREREX1qZVGes7s7kwC2/x143UAatb0dWALcImkrcEU5NR+YK6kLuJD971YuAC6RtBEYU1fPzVRTZDdI2kb1buTohuZeDNxY6txKNW33EwPJOyIiIiIiIvaT3dzszrKozdeoRgkNXABML+8mDim1Ws2dnZ3tTiMiIiIiIqItJG2yXeuvXCsjlO8AjqfaOuSfgRNK7JCStKeFsiMl/aukb0naLumjg5lbRERERETEcNL0COUzF0gvAX5qu+mO3cEkaY/tUU2WHQn8vO2vSHoh1bubf1Gm6/bqqBNP9YkXfRyAe/MuZUREREREDDMHfYRS0umStgDbgO2SNkl6zQCTmyOpS9JWSatLbIKkjhLvkDS+xCdK2iBpo6TFDfUsLPEuSZc1tmP7KdtfKcc/odpa5GUDyTkiIiIiIiKerZUpryuA99qeYHsC8D7KNhutkDQZuBSYYXsK+xfeuRpYZfsMYA2wrMSvApbbPht4qK6emVR7Tp4DTAXOkjS9j3aPBn6dapQyIiIiIiIiDlArHcoXd4/2Adj+KtUKqq2aAay1vbPUs7vEz6VasRWqbUPOK8fTgOvr4t1mls8WqpHHSVQdzOco24VcDyyzfU8vZeZJ6pTUue+pxwdwWxEREREREcNLv/tQ1rlH0p+xv1N3AfA/A2hTVKvE9se9HNfXs9T2iibqWgncZfvjvTZmryzlOOrEU1t7sTQiIiIiImIYamWE8mKqVV7/qXyOA+YOoM0O4HxJxwJIGlvi64FZ5Xg2cGs5vq0h3m0dcLGkUaWecZJOaGxM0keo9q58T7MJnj5uDPd+9M1ZkCciIiIiIqIPLa/yelAalS4CFgL7gC223ynpFOAaqo7qI8Bc2/dJmkg1FXYE8HngQ92rvEpaAPxuqXYPcIHt79S18zLgfuBbwI9L+Grbn+wrv+xDGRERERERw1mzq7w23aGU9CXg7bYfK9+PAT5j+w0HlOnzUDqUERERERExnB30bUOA47o7kwC2HwWeM8X0YJHU0j6XkpZIur/xOklXSrqjfP5b0mO91RERERERERHNa2VRnp9KGm/7Pqj2jaS5xXUOlZuoth65qz5o+4+6jyW9Gzizv4q2PvnUQU8uIiIiIiJiqGmlQ3kpcKukW8r36cC8vi6QNAd4P1XHs8v2haUjeg3VAj+9vSv5xYZ6FgLnA0cBN9j+cGNbtm8vZftK6R3Ac66NiIiIiIiI1jU95dX2F4HXAp8FPgecZXtd93lJk+vLl++XAjNsTwEWlFNXA6tsnwGsAZaV+FXActtnAw/V1TOTan/Jc4CpwFmSprdyk6WeCcBE4MutXhsRERERERHP1co7lNjeafsLtm+yvbPh9OqG7zOAtd3lbO8u8XOpRiK7rzmvHE8Dru+hrpnlswXYDEyi6mC2albJZ19PJyXNk9QpqfOnj+c1y4iIiIiIiP60MuW1P41zTUVz71i6l+P6epbaXjHQxIpZwCW9JmGvBFYCHPmqVz+f3g2NiIiIiIh4XmpphLIfjZ2wDuB8SccCSBpb4uupOncAs4Fby/FtDfFu64CLJXXvPTlOUkury0p6FXAMsKGZ8lNGj2yl+oiIiIiIiGHpYHYon8X2dmAJcIukrcAV5dR8YK6kLuBC9r9buQC4RNJGYExdPTdTTZHdIGkbsBYY3diepMsl7QBGStohaVHd6XdQ7ZmZkceIiIiIiIiDRAerjyXpdtu/cFAqa7NarebOzs52pxEREREREdEWkjbZrvVXrt93KCW9tq/ztjeXPw9qZ1LSHtujWii/BJgDHNN4naTzgUVU03K32v6dg5lrRERERETEcNTMojwfK3++CKgBW6kWyjkD+Dr7V2ltt5uotiS5qz4o6VTgg8A024+2+v5lRERERERE9KzfDqXtXwKQ9Blgnu1t5ftrgPf3da2kOaWMgS7bF5b9IK8BjgceAebavk/SRKp3JUcAX2yoZyFwPnAUcIPtD/eQ5+2lbOOp3wP+xvajpdzD/d3zk09uo+PLr+ivGK+f8Z1+y0RERERERAxVrSzKM6m7Mwlg+xvA1N4KS5oMXArMsD2F/YvvXA2ssn0GsAZYVuJXActtnw08VFfPTKp9J88p7Z0laXoLef8c8HOSbpN0u6Q3tnBtRERERERE9KKVDuWdkj4p6RclvU7S3wF39lF+BrDW9k4A27tL/FyqkUiA1eyfMjsNuL4u3m1m+WwBNgOTqDqYzRpRyv8i1Wqvn5R0dGMhSfMkdUrqfOyxn7ZQfURERERExPDUzDuU3eYCf8D+kcb/BJb3UV48d2/KnriX4/p6ltpe0UySPdgB3G57L/A/kr5N1cHc+Kwk7JXASoBXveqobC8SERERERHRj6Y7lLZ/JOkTwL/Z/nYTl3QAN0i60vYuSWPLKOV6YBbVKORs4NZS/rYSv7bEu60DFktaY3uPpHHA3mbehSz+mWpk8h8kHUc1Bfaevi4YPfp0Xj8j24ZERERERET0pekpr5LeAtxBWTBH0lRJN/ZW3vZ2YAlwi6StwBXl1HxgrqQu4EL2j3guAC6RtBEYU1fPzVRTZDdI2gasBUb3kN/lknYAIyXtkLSonFoH7JL0TeArwELbu5q974iIiIiIiOiZ7OZmd0raRPVe5Fdtn1liXWVxnSGlVqu5szMjlBERERERMTxJ2mS71l+5Vhbledr24weQU0sk7Wmx/BJJ9zdeJ+mdkh6RdEf5/O7BzTQiIiIiImJ4amVRnm9I+h3gCEmnUk1dXT84aQ3ITVRbktzVw7nP2v7DQ5xPRERERETEkNbKCOW7gcnAj6m293gCeE9fF0iaI6lL0lZJq0tsgqSOEu+QNL7EJ0raIGmjpMUN9Sws8S5Jl/XUlu3bbT/Ywv306oEHHmDRokUHo6qIiIiIiIghq+kOpe2nbF9q+2zg54G/tP2j3spLmgxcCsywPYX9i+9cDawq716uAZaV+FXA8lL/Q3X1zKTa5uMcYCpwlqTpzeZd/FbpjK6VdHKL10ZEREREREQPWlnl9TpJL5H0YmA78G1JC/u4ZAaw1vZOgLJlCMC5VKu2QrV1yHnleBrVyGd3vNvM8tkCbAYmUXUwm3UTcErpwP4H8OmeCkmaJ6lTUudTTz3VQvURERERERHDUytTXl9t+wngN4B/A8ZTbfvRGwHNLCHrXo7r61lqe2r5vNL2p5pN2vYu2z8uX/8OOKuXcitt12zXRo4c2Wz1ERERERERw1YrHcojJR1J1aH8F9t76bvD2AGcL+lYAEljS3w9MKsczwZuLce3NcS7rQMuljSq1DNO0gnNJi3pxLqvbwHu7O+ak046Ke9QRkRERERE9KOVDuUK4F7gxcB/SppAtTBPj2xvB5YAt0jaClxRTs0H5krqohrh7H63cgFwiaSNwJi6em6mmiK7QdI2YC0wurE9SZdL2gGMlLRD0qLu9iRtLznMB97Zwj1HREREREREL2Q3Myu1l4ulEbafPoj5PC/UajV3dna2O42IiIiIiIi2kLTJdq2/cq0syjNG0hXdC9dI+hjVaOWgkLSnxfJLJN3f23WS3ibJkvr9S4mIiIiIiIj+tTLl9RrgSeD88nkC+PvBSGqAbqLaWuQ5JI2mmu769UOaUUREREQ9uocdAAAR9klEQVRExBDWSofyFbY/bPue8rkMeHlfF0iaU/Z/3CppdYlNkNRR4h2Sxpf4REkbJG2UtLihnoUl3iXpsp7asn277Qd7SWUxcDnQ676ZERERERER0ZpWOpQ/lNS9ZySSpgE/7K2wpMnApcAM21PYv/jO1cCqsi/kGmBZiV8FLLd9NvBQXT0zqfadPAeYCpwlaXqzSUs6EzjZ9heavSYiIiIiIiL6N6KFsn8AfFrSGKq9IXfT94qpM4C1tncC2N5d4ucCv1mOV1ONHAJMA36rLv6X5Xhm+Wwp30dRdTD/s7+EJb0AuLKfPLvLzgPmAYwfP76/4hEREREREcNe0x1K23cAUyS9pHzvdcuQQvS9T+UzVfdyXF/PUtsrmkr02UYDrwG+KgngZ4EbJb3F9rOWcbW9ElgJ1SqvA2grIiIiIiJiWOm3Qynpvb3EAbB9RU/ngQ7gBklX2t4laWwZpVwPzKIahZwN3FrK31bi15Z4t3XAYklrbO+RNA7Ya/vh/nK3/ThwXF3OXwXe39iZjIiIiIiIiNY18w7l6PIZVXdcH+uR7e3AEuAWSVuB7o7nfGCupC7gQva/W7kAuETSRmBMXT03A9cBGyRtA9aWtp9F0uWSdgAjJe2QtKiJe4uIiIiIiIgBkt3c7E5JnwYW2H6sfD8G+Jjtiwcxv7ao1Wru7MwgZkREREREDE+SNtmu9VeulVVez+juTALYfhQ4cyDJNUPSnhbLL5F0f+N1kn5f0jZJd0i6VdKrD26mERERERERw1MrHcoXlFFJACSNpbVVYgfbTVRbizS6zvbptqdSrSjb2zufERERERER0YJWOoQfA9ZLWku1Guv5VO9I9krSHOD9pXyX7QslTQCuAY4HHgHm2r5P0kSqdyVHAF9sqGdhae8o4AbbH25sy/btpWxjvH412hfTxMqz37/n7v6KREREREREDHtNj1DaXkW1T+T3qTqCv2l7dW/lJU0GLgVm2J7C/sV3rgZW2T4DWAMsK/GrgOW2zwYeqqtnJtW+k+cAU4GzJE1vNu9SxyWSvkM1Qjm/lWsjIiIiIiKiZ00vytNyxdK7gZ+1fWlDfCdwou29ko4EHrR9nKRdpfzestflA7ZHSfpr4G1A9/ubo6j2pfxUL+3usd3j6rOSfgd4g+2Lejg3D5gHcMzInzlr9w+eGtB9R0REREREHO6aXZRnMN+BFE1ML20o01N5UXUgVxyEnD4DLO8xCXslsBLg5LFHD04vOyIiIiIiYghpZVGeVnUA50s6Fp5ZxAdgPTCrHM8Gbi3HtzXEu60DLpY0qtQzTtIJzSYh6dS6r28G7urvmpe+/JXNVh8RERERETFsDVqH0vZ2qkV7bpG0lf2rq84H5krqAi5k/7uVC4BLJG0ExtTVczPVYj0bJG0D1gKjG9uTdLmkHcBISTskLSqn/lDSdkl3AO8FnjPdNSIiIiIiIlo3aO9QHs5qtZo7OzvbnUZERERERERbNPsO5WBOeY2IiIiIiIgh7HnboZS0p8XySyTd33idpPdK+qakLkkdZR/MiIiIiIiIOEDP2w7lANxEtVdloy1Arex7uZZqL8qIiIiIiIg4QIPaoZQ0p4wMbpW0usQmlJHC7hHD8SU+UdIGSRslLW6oZ2GJd0m6rKe2bN9u+8Ee4l+x3b2p5O3Ayw7uXUZERERERAxPg9ahlDQZuBSYYXsK+1dzvRpYVUYM1wDLSvwqYLnts4GH6uqZCZxKNfo4FThL0vQBpvUu4N97yXeepE5JnY888sgAq4+IiIiIiBg+BnOEcgaw1vZOANu7S/xcqm1AAFYD55XjacD1dfFuM8tnC7AZmETVwWyJpAuAGvBXPZ23vdJ2zXbt+OOPb7X6iIiIiIiIYWfEINYtoJk9SdzLcX09S22vGHAi0i9TjZa+zvaPB1pPRERERERE7DeYI5QdwPmSjgWQNLbE1wOzyvFs4NZyfFtDvNs64GJJo0o94ySd0GwSks4EVgBvsf3wQG4kIiIiIiIinmvQOpS2twNLgFskbQWuKKfmA3MldQEXsv/dygXAJZI2AmPq6rmZaorsBknbqFZqHd3YnqTLJe0ARkraIWlROfVXwCjgHyXdIenGg3yrERERERERw5LsZmalDi+1Ws2dnZ3tTiMiIiIiIqItJG2yXeuv3PN2H0pJe1osv0TS/Y3XSZouabOkpyW97eBmGRERERERMXwN5qI8h9pNVFuS3NUQvw94J/D+Ziv60Te2c+ek01pO4LRv3dnyNREREREREYerQR2hlDRHUpekrZJWl9gESR0l3iFpfIlPlLRB0kZJixvqWVjiXZIu66kt27fbfrCH+L22u4CfDsItRkREREREDFuD1qGUNJlqq44Ztqewf/Gdq4FVts8A1gDLSvwqYLnts4GH6uqZSbXv5DnAVOAsSdMHId95kjolde7e9/TBrj4iIiIiImLIGcwRyhnAWts7AWzvLvFzqVZtBVgNnFeOpwHX18W7zSyfLcBmYBJVB/Ogsr3Sds12bewRQ2kmcERERERExOAYzJ6TgGaWkHUvx/X1LLW94qBk1YQXvWYyp2WV14iIiIiIiD4N5ghlB3C+pGMBJI0t8fXArHI8G7i1HN/WEO+2DrhY0qhSzzhJJwxi3hEREREREdGEQetQ2t4OLAFukbQVuKKcmg/MldQFXMj+dysXAJdI2giMqavnZqopshskbQPWAqMb25N0uaQdwEhJOyQtKvGzS/ztwApJ2w/+3UZERERERAw/spuZlTq81Go1d2bKa0REREREDFOSNtmu9VduULcNORCS9rRYfomk+xuvk3SUpM9KulvS1yWdcjDzjIiIiIiIGK6G0nKmN1FtSXJXQ/xdwKO2XylpFvCXwG/3VdH2Xds5/dOnDyiJbRdtG9B1ERERERERh5tBHaGUNEdSl6StklaX2ARJHSXeIWl8iU+UtEHSRkmLG+pZWOJdki7rqS3bt9t+sIdTbwU+XY7XAq+XpIN3lxEREREREcPToHUoJU0GLgVm2J7C/sV3rgZW2T4DWAMsK/GrgOW2zwYeqqtnJtW+k+cAU4GzJE1vIZVxwP0Atp8GHgeO7SHfeZI6JXXue3JfC9VHREREREQMT4M5QjkDWGt7J4Dt3SV+LtWqrQCrgfPK8TTg+rp4t5nlswXYDEyi6mA2q6fRyOesRGR7pe2a7doRo49oofqIiIiIiIjhaTDfoRQ9dNx64F6O6+tZanvFAPPYAZwM7JA0gmpLkt19XTD52Ml0XpRVXiMiIiIiIvoymCOUHcD5ko4FkDS2xNcDs8rxbODWcnxbQ7zbOuBiSaNKPeMkndBCHjcCF5XjtwFfdvZKiYiIiIiIOGCD1qG0vR1YAtwiaStwRTk1H5grqQu4kP3vVi4ALpG0kWoUsbuem6mmyG6QtI1qYZ3Rje1JulzSDmCkpB2SFpVTnwKOlXQ38F7gAwf3TiMiIiIiIoYnZbDuuWq1mjs7M+U1IiIiIiKGJ0mbbNf6Kzeo24YMBkl7Wiy/RNL9rV4XERERERERfRvMRXmeL26i2qrkrqaveGALLBrTf7mDbdHjh77NiIiIiIiIAWrLCKWkOZK6JG2VtLrEJkjqKPEOSeNLfKKkDZI2SlrcUM/CEu+SdFlPbdm+3faDg39XERERERERw8sh71BKmgxcCsywPYX9i/JcDayyfQawBlhW4lcBy22fDTxUV89Mqv0ozwGmAmdJmn4Aec2T1Cmp85Gn8l5pREREREREf9oxQjkDWGt7J4Dt7j0hz6VazRVgNXBeOZ4GXF8X7zazfLYAm4FJVB3MAbG90nbNdu34kRpoNREREREREcNGO96hFNDMEKB7Oa6vZ6ntFQclq3onnQmLssprREREREREX9oxQtkBnC/pWABJY0t8PTCrHM8Gbi3HtzXEu60DLpY0qtQzTtIJg5l4RERERERE7HfIO5S2twNLgFskbQWuKKfmA3MldQEXsv/dygXAJZI2AmPq6rmZaorsBknbgLXA6Mb2JF0uaQcwUtIOSYsG584iIiIiIiKGF9lZgKZRrVZzZ2emvEZERERExPAkaZPtWn/l2rJtyKEmaZKk9ZK2SbpF0nHtzikiIiIiIuJwNyxGKCVNAn5i+x5JS4Ef2P5Ib+WPOvFUn3jRx3s8d+9H3zxIWUZERERERDw/NDtC2Y5VXg8529+q+/oiYFe7comIiIiIiBgqhkWHspukNwBvpNrzsvHcPGAewBEvOf4QZxYREREREXH4GRbvUAJIegHwKeAtth9rPG97pe2a7doRI8c8t4KIiIiIiIh4lmHToQROAh63fVe7E4mIiIiIiBgKhtOU10eB9zVT8PRxY+jM4jsRERERERF9Gk4jlGOA3213EhEREREREUPFsNg2pFWSngS+3e48IgbJccDOdicRMUjyfMdQluc7hro8488vE2z3u1rpcJry2opvN7PnSsThSFJnnu8YqvJ8x1CW5zuGujzjh6fhNOU1IiIiIiIiDqJ0KCMiIiIiImJA0qHs2cp2JxAxiPJ8x1CW5zuGsjzfMdTlGT8MZVGeiIiIiIiIGJCMUEZERERERMSApENZR9IbJX1b0t2SPtDufCJ6I+lkSV+RdKek7ZIWlPhYSV+SdFf585gSl6Rl5dnukvTaurouKuXvknRRXfwsSdvKNcsk6dDfaQxnko6QtEXSF8r3iZK+Xp7Vz0p6YYkfVb7fXc6fUlfHB0v825LeUBfP7320jaSjJa2V9K3yO35ufr9jKJH0R+XfT74h6XpJL8pv+NCVDmUh6Qjgb4A3Aa8G3iHp1e3NKqJXTwPvs30a8AvAJeV5/QDQYftUoKN8h+q5PrV85gHLoeqAAh8Gfh44B/hw97/ElDLz6q574yG4r4h6C4A7677/JXBleb4fBd5V4u8CHrX9SuDKUo7yv4lZwGSq5/dvSyc1v/fRblcBX7Q9CZhC9Zzn9zuGBEnjgPlAzfZrgCOofovzGz5EpUO53znA3bbvsf0T4DPAW9ucU0SPbD9oe3M5fpLqX0bGUT2zny7FPg38Rjl+K7DKlduBoyWdCLwB+JLt3bYfBb4EvLGce4ntDa5etF5VV1fEoJP0MuDNwCfLdwEzgLWlSOPz3f3crwVeX8q/FfiM7R/b/h/gbqrf+vzeR9tIegkwHfgUgO2f2H6M/H7H0DIC+BlJI4CRwIPkN3zISodyv3HA/XXfd5RYxPNamRpyJvB14KW2H4Sq0wmcUIr19nz3Fd/RQzziUPk48MfAT8v3Y4HHbD9dvtc/k888x+X846V8q899xKHwcuAR4O/LlO5PSnox+f2OIcL294C/Bu6j6kg+Dmwiv+FDVjqU+/X0fkGWwI3nNUmjgM8D77H9RF9Fe4h5APGIQSfp14CHbW+qD/dQ1P2cy/Mdz0cjgNcCy22fCfyA/dNbe5LnOw4rZer1W4GJwEnAi6mmpzbKb/gQkQ7lfjuAk+u+vwx4oE25RPRL0pFUnck1tv+phL9fpjtR/ny4xHt7vvuKv6yHeMShMA14i6R7qaYyzaAasTy6TJ+CZz+TzzzH5fwYYDetP/cRh8IOYIftr5fva6k6mPn9jqHil4H/sf2I7b3APwH/i/yGD1npUO63ETi1rED1QqqXgG9sc04RPSrvFnwKuNP2FXWnbgS6V/q7CPiXuvicslrgLwCPlylV64CZko4p/0VxJrCunHtS0i+UtubU1RUxqGx/0PbLbJ9C9Vv8Zduzga8AbyvFGp/v7uf+baW8S3xWWUFwItXiJP9Ffu+jjWw/BNwv6VUl9Hrgm+T3O4aO+4BfkDSyPIPdz3h+w4eoEf0XGR5sPy3pD6l+oI8ArrG9vc1pRfRmGnAhsE3SHSX2p8BHgc9JehfVD/rby7l/A36V6oX2p4C5ALZ3S1pM9eMM8Oe2d5fjPwD+AfgZ4N/LJ6Kd/gT4jKSPAFsoi5qUP1dLupvqv2rPArC9XdLnqP5F5mngEtv7APJ7H232bmBN+Zfhe6h+k19Afr9jCLD9dUlrgc1Uv71bgJXAv5Lf8CFJ1X8AiIiIiIiIiGhNprxGRERERETEgKRDGREREREREQOSDmVEREREREQMSDqUERERERERMSDpUEZERERERMSApEMZERERERERA5IOZURERERERAxIOpQRERERERExIP8/z0T5YnfbUQUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7b4444f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "code 6     86583\n",
       "code 7     20733\n",
       "code 9     13562\n",
       "code 8      5646\n",
       "code 5      4469\n",
       "code 1      3265\n",
       "code 10     1678\n",
       "code 11     1493\n",
       "code 4      1139\n",
       "code 16     1091\n",
       "?            714\n",
       "code 3       608\n",
       "code 2       349\n",
       "code 15      183\n",
       "code 17      115\n",
       "code 13       59\n",
       "code 12       13\n",
       "code 14       12\n",
       "Name: closed_code, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('closed_code') \\\n",
    "    .count()['number'] \\\n",
    "    .plot(kind='barh',\n",
    "          title='Distribution of closed_code',\n",
    "          figsize=(15, 3))\n",
    "plt.show()\n",
    "df['closed_code'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close code 6,7,8,9 are the major ones with which the incidents are getting closed.Now let us check if that happens for the ones which are getting reopened or it is different?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_closed_reo = df_closed[\"reopen_count\"] > 0\n",
    "df_closed_reo = df_closed[df_closed_reo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7b4b48908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "code 6     170\n",
       "code 7      28\n",
       "code 9      27\n",
       "code 1      11\n",
       "code 8      10\n",
       "code 4       9\n",
       "code 5       8\n",
       "code 16      5\n",
       "code 2       3\n",
       "code 11      2\n",
       "code 10      2\n",
       "code 3       2\n",
       "?            1\n",
       "Name: closed_code, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed_reo.groupby('closed_code') \\\n",
    "    .count()['number'] \\\n",
    "    .plot(kind='barh',\n",
    "          title='Distribution of closed_code',\n",
    "          figsize=(15, 3))\n",
    "plt.show()\n",
    "df_closed_reo['closed_code'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the close code are similar for reopened tickets but we see that there are other close codes too which are ending as reason to close the reopened tickets. Let us now see if these reopened has any pattern with SLA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     15831\n",
       "False     9154\n",
       "Name: made_sla, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed['made_sla'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    210\n",
       "True      68\n",
       "Name: made_sla, dtype: int64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed_reo['made_sla'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here that SLAs are missed in most of the reopened incidents so if the tickets are getting reopened then there is high chance that SLAs will be missed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now examine if there are different symptoms for reopened incidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Symptom 491    9272\n",
       "?              5856\n",
       "Symptom 534    1670\n",
       "Symptom 116     485\n",
       "Symptom 387     426\n",
       "Symptom 4       375\n",
       "Symptom 506     355\n",
       "Symptom 532     353\n",
       "Symptom 571     282\n",
       "Symptom 296     207\n",
       "Symptom 105     200\n",
       "Symptom 118     195\n",
       "Symptom 208     173\n",
       "Symptom 470     165\n",
       "Symptom 10      158\n",
       "Symptom 295     154\n",
       "Symptom 120     150\n",
       "Symptom 517     142\n",
       "Symptom 102     141\n",
       "Symptom 127     136\n",
       "Symptom 54      135\n",
       "Symptom 87      129\n",
       "Symptom 458     125\n",
       "Symptom 494     123\n",
       "Symptom 6        94\n",
       "Symptom 70       93\n",
       "Symptom 117      87\n",
       "Symptom 592      86\n",
       "Symptom 65       85\n",
       "Symptom 119      84\n",
       "               ... \n",
       "Symptom 362       1\n",
       "Symptom 207       1\n",
       "Symptom 309       1\n",
       "Symptom 545       1\n",
       "Symptom 330       1\n",
       "Symptom 210       1\n",
       "Symptom 179       1\n",
       "Symptom 29        1\n",
       "Symptom 400       1\n",
       "Symptom 450       1\n",
       "Symptom 275       1\n",
       "Symptom 314       1\n",
       "Symptom 73        1\n",
       "Symptom 204       1\n",
       "Symptom 327       1\n",
       "Symptom 496       1\n",
       "Symptom 555       1\n",
       "Symptom 542       1\n",
       "Symptom 12        1\n",
       "Symptom 238       1\n",
       "Symptom 428       1\n",
       "Symptom 353       1\n",
       "Symptom 124       1\n",
       "Symptom 551       1\n",
       "Symptom 322       1\n",
       "Symptom 165       1\n",
       "Symptom 449       1\n",
       "Symptom 302       1\n",
       "Symptom 326       1\n",
       "Symptom 156       1\n",
       "Name: u_symptom, Length: 398, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed['u_symptom'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Symptom 491    104\n",
       "?               58\n",
       "Symptom 532     12\n",
       "Symptom 534      9\n",
       "Symptom 4        7\n",
       "Symptom 296      5\n",
       "Symptom 458      5\n",
       "Symptom 208      4\n",
       "Symptom 10       4\n",
       "Symptom 465      4\n",
       "Symptom 114      4\n",
       "Symptom 387      3\n",
       "Symptom 116      3\n",
       "Symptom 119      3\n",
       "Symptom 9        3\n",
       "Symptom 134      2\n",
       "Symptom 315      2\n",
       "Symptom 120      2\n",
       "Symptom 117      2\n",
       "Symptom 87       2\n",
       "Symptom 455      2\n",
       "Symptom 494      2\n",
       "Symptom 6        2\n",
       "Symptom 65       2\n",
       "Symptom 54       2\n",
       "Symptom 596      1\n",
       "Symptom 463      1\n",
       "Symptom 118      1\n",
       "Symptom 127      1\n",
       "Symptom 474      1\n",
       "Symptom 592      1\n",
       "Symptom 544      1\n",
       "Symptom 8        1\n",
       "Symptom 410      1\n",
       "Symptom 218      1\n",
       "Symptom 381      1\n",
       "Symptom 517      1\n",
       "Symptom 256      1\n",
       "Symptom 273      1\n",
       "Symptom 462      1\n",
       "Symptom 506      1\n",
       "Symptom 548      1\n",
       "Symptom 531      1\n",
       "Symptom 486      1\n",
       "Symptom 70       1\n",
       "Symptom 471      1\n",
       "Symptom 217      1\n",
       "Symptom 102      1\n",
       "Symptom 198      1\n",
       "Symptom 358      1\n",
       "Symptom 257      1\n",
       "Symptom 371      1\n",
       "Symptom 105      1\n",
       "Symptom 524      1\n",
       "Symptom 20       1\n",
       "Name: u_symptom, dtype: int64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed_reo['u_symptom'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symptoms are also very similar to other incidents as for reopened so there is no clear pattern with close code and symptoms but we can see that incase of reopened incidents the SLAs get missed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2 - What kind of incidents are missing the SLAs?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now as we see that reopened incidents are missing SLAs so now let us exmaine distribution of incidents which are missing SLAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7aedd50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True     15831\n",
       "False     9154\n",
       "Name: made_sla, dtype: int64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed_reo.groupby('made_sla') \\\n",
    "    .count()['number'] \\\n",
    "    .plot(kind='barh',\n",
    "          title='Distribution of closed_code',\n",
    "          figsize=(15, 3))\n",
    "plt.show()\n",
    "df_closed['made_sla'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Around 37% incidents are missing SLAs so now let us see what kind of incidents usually miss SLAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_closed_sla = df_closed[\"made_sla\"] == 1\n",
    "df_closed_sla = df_closed[df_closed_sla]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>priority</th>\n",
       "      <th>1 - Critical</th>\n",
       "      <th>2 - High</th>\n",
       "      <th>3 - Moderate</th>\n",
       "      <th>4 - Low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>made_sla</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>265</td>\n",
       "      <td>406</td>\n",
       "      <td>8358</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>15171</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "priority  1 - Critical  2 - High  3 - Moderate  4 - Low\n",
       "made_sla                                               \n",
       "False              265       406          8358      125\n",
       "True                 6         2         15171      652"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_closed['made_sla'],df_closed['priority'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is very clear pattern that if SLAs mostly get missed for critical and high priortiy incidents whereas SLAs are met for Moderate and Low priortiy incidents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 - What features are important to predict the close code?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the given incident data, let us see which features can help us to predict the close code so that incidents can be assigned to relevant team based on the predicted close code. First let us filter out the relevant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_closed_1 = df_closed.loc[:,['reassignment_count','reopen_count','sys_mod_count','made_sla','location','category','subcategory','u_symptom','priority','vendor']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have only categorical variables s0 for each of the categorical variables, we now need to create dummy columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_closed_dummy = pd.get_dummies(df_closed_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>reassignment_count</th>\n",
       "      <th>reopen_count</th>\n",
       "      <th>sys_mod_count</th>\n",
       "      <th>made_sla</th>\n",
       "      <th>location_?</th>\n",
       "      <th>location_Location 10</th>\n",
       "      <th>location_Location 100</th>\n",
       "      <th>location_Location 101</th>\n",
       "      <th>location_Location 102</th>\n",
       "      <th>location_Location 105</th>\n",
       "      <th>...</th>\n",
       "      <th>u_symptom_Symptom 96</th>\n",
       "      <th>u_symptom_Symptom 97</th>\n",
       "      <th>u_symptom_Symptom 99</th>\n",
       "      <th>priority_1 - Critical</th>\n",
       "      <th>priority_2 - High</th>\n",
       "      <th>priority_3 - Moderate</th>\n",
       "      <th>priority_4 - Low</th>\n",
       "      <th>vendor_?</th>\n",
       "      <th>vendor_Vendor 1</th>\n",
       "      <th>vendor_code 8s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>False</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 934 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    reassignment_count  reopen_count  sys_mod_count  made_sla  location_?  \\\n",
       "3                    0             0              4      True           0   \n",
       "12                   1             0              8      True           0   \n",
       "19                   0             0              6      True           0   \n",
       "23                   0             0              3      True           0   \n",
       "31                   1             0              7     False           0   \n",
       "\n",
       "    location_Location 10  location_Location 100  location_Location 101  \\\n",
       "3                      0                      0                      0   \n",
       "12                     0                      0                      0   \n",
       "19                     0                      0                      0   \n",
       "23                     0                      0                      0   \n",
       "31                     0                      0                      0   \n",
       "\n",
       "    location_Location 102  location_Location 105       ...        \\\n",
       "3                       0                      0       ...         \n",
       "12                      0                      0       ...         \n",
       "19                      0                      0       ...         \n",
       "23                      0                      0       ...         \n",
       "31                      0                      0       ...         \n",
       "\n",
       "    u_symptom_Symptom 96  u_symptom_Symptom 97  u_symptom_Symptom 99  \\\n",
       "3                      0                     0                     0   \n",
       "12                     0                     0                     0   \n",
       "19                     0                     0                     0   \n",
       "23                     0                     0                     0   \n",
       "31                     0                     0                     0   \n",
       "\n",
       "    priority_1 - Critical  priority_2 - High  priority_3 - Moderate  \\\n",
       "3                       0                  0                      1   \n",
       "12                      0                  0                      1   \n",
       "19                      0                  0                      1   \n",
       "23                      0                  0                      1   \n",
       "31                      0                  1                      0   \n",
       "\n",
       "    priority_4 - Low  vendor_?  vendor_Vendor 1  vendor_code 8s  \n",
       "3                  0         1                0               0  \n",
       "12                 0         1                0               0  \n",
       "19                 0         1                0               0  \n",
       "23                 0         1                0               0  \n",
       "31                 0         1                0               0  \n",
       "\n",
       "[5 rows x 934 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_closed_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_closed_dummy['closed_code']= df_closed['closed_code']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us Split into explanatory and response variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df_closed_dummy['closed_code']\n",
    "X = df_closed_dummy.drop(columns=['closed_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>reassignment_count</th>\n",
       "      <th>reopen_count</th>\n",
       "      <th>sys_mod_count</th>\n",
       "      <th>made_sla</th>\n",
       "      <th>location_?</th>\n",
       "      <th>location_Location 10</th>\n",
       "      <th>location_Location 100</th>\n",
       "      <th>location_Location 101</th>\n",
       "      <th>location_Location 102</th>\n",
       "      <th>location_Location 105</th>\n",
       "      <th>...</th>\n",
       "      <th>u_symptom_Symptom 96</th>\n",
       "      <th>u_symptom_Symptom 97</th>\n",
       "      <th>u_symptom_Symptom 99</th>\n",
       "      <th>priority_1 - Critical</th>\n",
       "      <th>priority_2 - High</th>\n",
       "      <th>priority_3 - Moderate</th>\n",
       "      <th>priority_4 - Low</th>\n",
       "      <th>vendor_?</th>\n",
       "      <th>vendor_Vendor 1</th>\n",
       "      <th>vendor_code 8s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>False</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 934 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    reassignment_count  reopen_count  sys_mod_count  made_sla  location_?  \\\n",
       "3                    0             0              4      True           0   \n",
       "12                   1             0              8      True           0   \n",
       "19                   0             0              6      True           0   \n",
       "23                   0             0              3      True           0   \n",
       "31                   1             0              7     False           0   \n",
       "\n",
       "    location_Location 10  location_Location 100  location_Location 101  \\\n",
       "3                      0                      0                      0   \n",
       "12                     0                      0                      0   \n",
       "19                     0                      0                      0   \n",
       "23                     0                      0                      0   \n",
       "31                     0                      0                      0   \n",
       "\n",
       "    location_Location 102  location_Location 105       ...        \\\n",
       "3                       0                      0       ...         \n",
       "12                      0                      0       ...         \n",
       "19                      0                      0       ...         \n",
       "23                      0                      0       ...         \n",
       "31                      0                      0       ...         \n",
       "\n",
       "    u_symptom_Symptom 96  u_symptom_Symptom 97  u_symptom_Symptom 99  \\\n",
       "3                      0                     0                     0   \n",
       "12                     0                     0                     0   \n",
       "19                     0                     0                     0   \n",
       "23                     0                     0                     0   \n",
       "31                     0                     0                     0   \n",
       "\n",
       "    priority_1 - Critical  priority_2 - High  priority_3 - Moderate  \\\n",
       "3                       0                  0                      1   \n",
       "12                      0                  0                      1   \n",
       "19                      0                  0                      1   \n",
       "23                      0                  0                      1   \n",
       "31                      0                  1                      0   \n",
       "\n",
       "    priority_4 - Low  vendor_?  vendor_Vendor 1  vendor_code 8s  \n",
       "3                  0         1                0               0  \n",
       "12                 0         1                0               0  \n",
       "19                 0         1                0               0  \n",
       "23                 0         1                0               0  \n",
       "31                 0         1                0               0  \n",
       "\n",
       "[5 rows x 934 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will fit random forest model to check which features are coming as important for predicting the close code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=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=200, n_jobs=1,\n",
       "            oob_score=False, random_state=100, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators=200\n",
    "                               ,random_state=100)\n",
    "\n",
    "model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f7b937bc18>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7b937b5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most important feature that will help us in our prediction is sys_mod_count that is number of incident updates until that moment which makes sense as based on the system updates there will some pattern that what would be best resolution for the given incidents. Next features in reassignment count and if the incident has met SLA or not, symptom and location also coming as important features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "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
}