eda-comcast-consumer-complaints-analysis-using-r.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.040182,
     "end_time": "2020-11-21T21:15:50.811406",
     "exception": false,
     "start_time": "2020-11-21T21:15:50.771224",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Description:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.0396,
     "end_time": "2020-11-21T21:15:50.889431",
     "exception": false,
     "start_time": "2020-11-21T21:15:50.849831",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve. Only last month (October 2016) the authority fined them a $2.3 million, after receiving over 1000 consumer complaints.\n",
    "The existing database will serve as a repository of public customer complaints filed against Comcast.\n",
    "It will help to pin down what is wrong with Comcast's customer service.\n",
    "Data Dictionary\n",
    "Ticket #: Ticket number assigned to each complaint\n",
    "Customer Complaint: Description of complaint\n",
    "Date: Date of complaint\n",
    "Time: Time of complaint\n",
    "Received Via: Mode of communication of the complaint\n",
    "City: Customer city\n",
    "State: Customer state\n",
    "Zipcode: Customer zip\n",
    "Status: Status of complaint\n",
    "Filing on behalf of someone\n",
    "Analysis Task\n",
    "- Import data into R environment.\n",
    "- Provide the trend chart for the number of complaints at monthly and daily granularity levels.\n",
    "- Provide a table with the frequency of complaint types.\n",
    "Which complaint types are maximum i.e., around internet, network issues, or across any other domains.\n",
    "- Create a new categorical variable with value as Open and Closed. Open & Pending is to be categorized as Open and Closed & Solved is to be categorized as Closed.\n",
    "- Provide state wise status of complaints in a stacked bar chart. Use the categorized variable from Q3. Provide insights on:\n",
    "Which state has the maximum complaints\n",
    "Which state has the highest percentage of unresolved complaints\n",
    "- Provide the percentage of complaints resolved till date, which were received through the Internet and customer care calls.\n",
    "The analysis results to be provided with insights wherever applicable.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDA Performed by: Anik Chakraborty (waytoanik@outlook.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.038216,
     "end_time": "2020-11-21T21:15:50.965981",
     "exception": false,
     "start_time": "2020-11-21T21:15:50.927765",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# EDA (Exploratory Data Analysis):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "051d70d956493feee0c6d64651c6a088724dca2a",
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:51.076384Z",
     "iopub.status.busy": "2020-11-21T21:15:51.074532Z",
     "iopub.status.idle": "2020-11-21T21:15:51.940113Z",
     "shell.execute_reply": "2020-11-21T21:15:51.938594Z"
    },
    "papermill": {
     "duration": 0.936242,
     "end_time": "2020-11-21T21:15:51.940452",
     "exception": false,
     "start_time": "2020-11-21T21:15:51.004210",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t2225 obs. of  11 variables:\n",
      " $ Ticket..                   : Factor w/ 2225 levels \"211255\",\"211472\",..: 371 124 307 611 849 1214 1763 1590 967 2111 ...\n",
      " $ Customer.Complaint         : Factor w/ 1842 levels \"(Comcast is not my complaint!) Cyber Tele-marketing is my complaint!\",..: 307 1520 1661 499 647 1354 1716 712 447 696 ...\n",
      " $ Date                       : Factor w/ 92 levels \"4/1/2015\",\"4/10/2015\",..: 15 29 10 59 49 65 78 77 62 83 ...\n",
      " $ Time                       : Factor w/ 2191 levels \"1:00:18 AM\",\"1:00:32 PM\",..: 1198 291 2166 652 89 2190 252 1666 594 1648 ...\n",
      " $ Received.Via               : Factor w/ 1 level \"Internet\": 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ City                       : Factor w/ 928 levels \"Abingdon\",\"Acworth\",..: 1 2 2 2 2 2 2 3 4 4 ...\n",
      " $ State                      : Factor w/ 43 levels \"Alabama\",\"Arizona\",..: 19 11 11 11 11 11 11 21 4 4 ...\n",
      " $ Zip.code                   : int  21009 30102 30101 30101 30101 30101 30101 49221 94502 94501 ...\n",
      " $ Status                     : Factor w/ 4 levels \"Closed\",\"Open\",..: 1 1 1 2 4 4 3 4 1 2 ...\n",
      " $ Filing.on.Behalf.of.Someone: Factor w/ 2 levels \"No\",\"Yes\": 1 1 2 2 1 1 1 1 1 2 ...\n",
      " $ Description                : Factor w/ 2214 levels \".follow up complain false service\\n waiting for six months for repair outside my house and being charge for max\"| __truncated__,..: 968 117 1806 292 951 2045 599 2176 416 81 ...\n"
     ]
    }
   ],
   "source": [
    "# This R environment comes with many helpful analytics packages installed\n",
    "# It is defined by the kaggle/rstats Docker image: https://github.com/kaggle/docker-rstats\n",
    "# For example, here's a helpful package to load\n",
    "\n",
    "#Loading Data\n",
    "df <- read.csv(\"../input/comcastcomplaints/comcast_fcc_complaints_2015.csv\")\n",
    "str(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.039106,
     "end_time": "2020-11-21T21:15:52.020200",
     "exception": false,
     "start_time": "2020-11-21T21:15:51.981094",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now preparing the data for further processing.\n",
    "Checked if there is any NA value in the data frame?\n",
    "There is no NA value present as output is 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.040116,
     "end_time": "2020-11-21T21:15:52.100137",
     "exception": false,
     "start_time": "2020-11-21T21:15:52.060021",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Data Preparation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:52.212033Z",
     "iopub.status.busy": "2020-11-21T21:15:52.183041Z",
     "iopub.status.idle": "2020-11-21T21:15:52.230617Z",
     "shell.execute_reply": "2020-11-21T21:15:52.229144Z"
    },
    "papermill": {
     "duration": 0.091118,
     "end_time": "2020-11-21T21:15:52.230760",
     "exception": false,
     "start_time": "2020-11-21T21:15:52.139642",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0"
      ],
      "text/latex": [
       "0"
      ],
      "text/markdown": [
       "0"
      ],
      "text/plain": [
       "[1] 0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking if NA present\n",
    "na_value <- is.na(df)\n",
    "length(na_value[na_value==TRUE])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.039973,
     "end_time": "2020-11-21T21:15:52.310973",
     "exception": false,
     "start_time": "2020-11-21T21:15:52.271000",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Date value is stored in data frame as character and is in different format. Converted it into Date with a specific format and stored into the same data frame as Date_new.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:52.396382Z",
     "iopub.status.busy": "2020-11-21T21:15:52.394811Z",
     "iopub.status.idle": "2020-11-21T21:15:52.415646Z",
     "shell.execute_reply": "2020-11-21T21:15:52.414150Z"
    },
    "papermill": {
     "duration": 0.064966,
     "end_time": "2020-11-21T21:15:52.415790",
     "exception": false,
     "start_time": "2020-11-21T21:15:52.350824",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>4/22/2015</li><li>4/8/2015</li><li>4/18/2015</li><li>5/7/2015</li><li>5/26/2015</li><li>6/12/2015</li></ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<style>\n",
       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
       "\t.list-inline>li {display: inline-block}\n",
       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "\t</style>\n",
       "\t<ol class=list-inline><li>'4/1/2015'</li><li>'4/10/2015'</li><li>'4/11/2015'</li><li>'4/12/2015'</li><li>'4/13/2015'</li><li>'4/14/2015'</li><li>'4/15/2015'</li><li>'4/16/2015'</li><li>'4/17/2015'</li><li>'4/18/2015'</li><li>'4/19/2015'</li><li>'4/2/2015'</li><li>'4/20/2015'</li><li>'4/21/2015'</li><li>'4/22/2015'</li><li>'4/23/2015'</li><li>'4/24/2015'</li><li>'4/25/2015'</li><li>'4/26/2015'</li><li>'4/27/2015'</li><li>'4/28/2015'</li><li>'4/29/2015'</li><li>'4/3/2015'</li><li>'4/30/2015'</li><li>'4/4/2015'</li><li>'4/5/2015'</li><li>'4/6/2015'</li><li>'4/7/2015'</li><li>'4/8/2015'</li><li>'4/9/2015'</li><li>'5/1/2015'</li><li>'5/10/2015'</li><li>'5/11/2015'</li><li>'5/12/2015'</li><li>'5/13/2015'</li><li>'5/14/2015'</li><li>'5/15/2015'</li><li>'5/16/2015'</li><li>'5/17/2015'</li><li>'5/18/2015'</li><li>'5/19/2015'</li><li>'5/2/2015'</li><li>'5/20/2015'</li><li>'5/21/2015'</li><li>'5/22/2015'</li><li>'5/23/2015'</li><li>'5/24/2015'</li><li>'5/25/2015'</li><li>'5/26/2015'</li><li>'5/27/2015'</li><li>'5/28/2015'</li><li>'5/29/2015'</li><li>'5/3/2015'</li><li>'5/30/2015'</li><li>'5/31/2015'</li><li>'5/4/2015'</li><li>'5/5/2015'</li><li>'5/6/2015'</li><li>'5/7/2015'</li><li>'5/8/2015'</li><li>'5/9/2015'</li><li>'6/1/2015'</li><li>'6/10/2015'</li><li>'6/11/2015'</li><li>'6/12/2015'</li><li>'6/13/2015'</li><li>'6/14/2015'</li><li>'6/15/2015'</li><li>'6/16/2015'</li><li>'6/17/2015'</li><li>'6/18/2015'</li><li>'6/19/2015'</li><li>'6/2/2015'</li><li>'6/20/2015'</li><li>'6/21/2015'</li><li>'6/22/2015'</li><li>'6/23/2015'</li><li>'6/24/2015'</li><li>'6/24/2015 -'</li><li>'6/25/2015'</li><li>'6/26/2015'</li><li>'6/27/2015'</li><li>'6/28/2015'</li><li>'6/29/2015'</li><li>'6/3/2015'</li><li>'6/30/2015'</li><li>'6/4/2015'</li><li>'6/5/2015'</li><li>'6/6/2015'</li><li>'6/7/2015'</li><li>'6/8/2015'</li><li>'6/9/2015'</li></ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 4/22/2015\n",
       "\\item 4/8/2015\n",
       "\\item 4/18/2015\n",
       "\\item 5/7/2015\n",
       "\\item 5/26/2015\n",
       "\\item 6/12/2015\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item '4/1/2015'\n",
       "\\item '4/10/2015'\n",
       "\\item '4/11/2015'\n",
       "\\item '4/12/2015'\n",
       "\\item '4/13/2015'\n",
       "\\item '4/14/2015'\n",
       "\\item '4/15/2015'\n",
       "\\item '4/16/2015'\n",
       "\\item '4/17/2015'\n",
       "\\item '4/18/2015'\n",
       "\\item '4/19/2015'\n",
       "\\item '4/2/2015'\n",
       "\\item '4/20/2015'\n",
       "\\item '4/21/2015'\n",
       "\\item '4/22/2015'\n",
       "\\item '4/23/2015'\n",
       "\\item '4/24/2015'\n",
       "\\item '4/25/2015'\n",
       "\\item '4/26/2015'\n",
       "\\item '4/27/2015'\n",
       "\\item '4/28/2015'\n",
       "\\item '4/29/2015'\n",
       "\\item '4/3/2015'\n",
       "\\item '4/30/2015'\n",
       "\\item '4/4/2015'\n",
       "\\item '4/5/2015'\n",
       "\\item '4/6/2015'\n",
       "\\item '4/7/2015'\n",
       "\\item '4/8/2015'\n",
       "\\item '4/9/2015'\n",
       "\\item '5/1/2015'\n",
       "\\item '5/10/2015'\n",
       "\\item '5/11/2015'\n",
       "\\item '5/12/2015'\n",
       "\\item '5/13/2015'\n",
       "\\item '5/14/2015'\n",
       "\\item '5/15/2015'\n",
       "\\item '5/16/2015'\n",
       "\\item '5/17/2015'\n",
       "\\item '5/18/2015'\n",
       "\\item '5/19/2015'\n",
       "\\item '5/2/2015'\n",
       "\\item '5/20/2015'\n",
       "\\item '5/21/2015'\n",
       "\\item '5/22/2015'\n",
       "\\item '5/23/2015'\n",
       "\\item '5/24/2015'\n",
       "\\item '5/25/2015'\n",
       "\\item '5/26/2015'\n",
       "\\item '5/27/2015'\n",
       "\\item '5/28/2015'\n",
       "\\item '5/29/2015'\n",
       "\\item '5/3/2015'\n",
       "\\item '5/30/2015'\n",
       "\\item '5/31/2015'\n",
       "\\item '5/4/2015'\n",
       "\\item '5/5/2015'\n",
       "\\item '5/6/2015'\n",
       "\\item '5/7/2015'\n",
       "\\item '5/8/2015'\n",
       "\\item '5/9/2015'\n",
       "\\item '6/1/2015'\n",
       "\\item '6/10/2015'\n",
       "\\item '6/11/2015'\n",
       "\\item '6/12/2015'\n",
       "\\item '6/13/2015'\n",
       "\\item '6/14/2015'\n",
       "\\item '6/15/2015'\n",
       "\\item '6/16/2015'\n",
       "\\item '6/17/2015'\n",
       "\\item '6/18/2015'\n",
       "\\item '6/19/2015'\n",
       "\\item '6/2/2015'\n",
       "\\item '6/20/2015'\n",
       "\\item '6/21/2015'\n",
       "\\item '6/22/2015'\n",
       "\\item '6/23/2015'\n",
       "\\item '6/24/2015'\n",
       "\\item '6/24/2015 -'\n",
       "\\item '6/25/2015'\n",
       "\\item '6/26/2015'\n",
       "\\item '6/27/2015'\n",
       "\\item '6/28/2015'\n",
       "\\item '6/29/2015'\n",
       "\\item '6/3/2015'\n",
       "\\item '6/30/2015'\n",
       "\\item '6/4/2015'\n",
       "\\item '6/5/2015'\n",
       "\\item '6/6/2015'\n",
       "\\item '6/7/2015'\n",
       "\\item '6/8/2015'\n",
       "\\item '6/9/2015'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 4/22/2015\n",
       "2. 4/8/2015\n",
       "3. 4/18/2015\n",
       "4. 5/7/2015\n",
       "5. 5/26/2015\n",
       "6. 6/12/2015\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. '4/1/2015'\n",
       "2. '4/10/2015'\n",
       "3. '4/11/2015'\n",
       "4. '4/12/2015'\n",
       "5. '4/13/2015'\n",
       "6. '4/14/2015'\n",
       "7. '4/15/2015'\n",
       "8. '4/16/2015'\n",
       "9. '4/17/2015'\n",
       "10. '4/18/2015'\n",
       "11. '4/19/2015'\n",
       "12. '4/2/2015'\n",
       "13. '4/20/2015'\n",
       "14. '4/21/2015'\n",
       "15. '4/22/2015'\n",
       "16. '4/23/2015'\n",
       "17. '4/24/2015'\n",
       "18. '4/25/2015'\n",
       "19. '4/26/2015'\n",
       "20. '4/27/2015'\n",
       "21. '4/28/2015'\n",
       "22. '4/29/2015'\n",
       "23. '4/3/2015'\n",
       "24. '4/30/2015'\n",
       "25. '4/4/2015'\n",
       "26. '4/5/2015'\n",
       "27. '4/6/2015'\n",
       "28. '4/7/2015'\n",
       "29. '4/8/2015'\n",
       "30. '4/9/2015'\n",
       "31. '5/1/2015'\n",
       "32. '5/10/2015'\n",
       "33. '5/11/2015'\n",
       "34. '5/12/2015'\n",
       "35. '5/13/2015'\n",
       "36. '5/14/2015'\n",
       "37. '5/15/2015'\n",
       "38. '5/16/2015'\n",
       "39. '5/17/2015'\n",
       "40. '5/18/2015'\n",
       "41. '5/19/2015'\n",
       "42. '5/2/2015'\n",
       "43. '5/20/2015'\n",
       "44. '5/21/2015'\n",
       "45. '5/22/2015'\n",
       "46. '5/23/2015'\n",
       "47. '5/24/2015'\n",
       "48. '5/25/2015'\n",
       "49. '5/26/2015'\n",
       "50. '5/27/2015'\n",
       "51. '5/28/2015'\n",
       "52. '5/29/2015'\n",
       "53. '5/3/2015'\n",
       "54. '5/30/2015'\n",
       "55. '5/31/2015'\n",
       "56. '5/4/2015'\n",
       "57. '5/5/2015'\n",
       "58. '5/6/2015'\n",
       "59. '5/7/2015'\n",
       "60. '5/8/2015'\n",
       "61. '5/9/2015'\n",
       "62. '6/1/2015'\n",
       "63. '6/10/2015'\n",
       "64. '6/11/2015'\n",
       "65. '6/12/2015'\n",
       "66. '6/13/2015'\n",
       "67. '6/14/2015'\n",
       "68. '6/15/2015'\n",
       "69. '6/16/2015'\n",
       "70. '6/17/2015'\n",
       "71. '6/18/2015'\n",
       "72. '6/19/2015'\n",
       "73. '6/2/2015'\n",
       "74. '6/20/2015'\n",
       "75. '6/21/2015'\n",
       "76. '6/22/2015'\n",
       "77. '6/23/2015'\n",
       "78. '6/24/2015'\n",
       "79. '6/24/2015 -'\n",
       "80. '6/25/2015'\n",
       "81. '6/26/2015'\n",
       "82. '6/27/2015'\n",
       "83. '6/28/2015'\n",
       "84. '6/29/2015'\n",
       "85. '6/3/2015'\n",
       "86. '6/30/2015'\n",
       "87. '6/4/2015'\n",
       "88. '6/5/2015'\n",
       "89. '6/6/2015'\n",
       "90. '6/7/2015'\n",
       "91. '6/8/2015'\n",
       "92. '6/9/2015'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 4/22/2015 4/8/2015  4/18/2015 5/7/2015  5/26/2015 6/12/2015\n",
       "92 Levels: 4/1/2015 4/10/2015 4/11/2015 4/12/2015 4/13/2015 ... 6/9/2015"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(df$Date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.041154,
     "end_time": "2020-11-21T21:15:52.498250",
     "exception": false,
     "start_time": "2020-11-21T21:15:52.457096",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Formating date and storing in dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:52.586295Z",
     "iopub.status.busy": "2020-11-21T21:15:52.584673Z",
     "iopub.status.idle": "2020-11-21T21:15:52.801925Z",
     "shell.execute_reply": "2020-11-21T21:15:52.800609Z"
    },
    "papermill": {
     "duration": 0.262692,
     "end_time": "2020-11-21T21:15:52.802119",
     "exception": false,
     "start_time": "2020-11-21T21:15:52.539427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘lubridate’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    date, intersect, setdiff, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(lubridate)\n",
    "df$Date_new<- mdy(df$Date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:52.892875Z",
     "iopub.status.busy": "2020-11-21T21:15:52.891235Z",
     "iopub.status.idle": "2020-11-21T21:15:52.908446Z",
     "shell.execute_reply": "2020-11-21T21:15:52.907265Z"
    },
    "papermill": {
     "duration": 0.063536,
     "end_time": "2020-11-21T21:15:52.908610",
     "exception": false,
     "start_time": "2020-11-21T21:15:52.845074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li><time datetime=\"2015-04-22\">2015-04-22</time></li><li><time datetime=\"2015-04-08\">2015-04-08</time></li><li><time datetime=\"2015-04-18\">2015-04-18</time></li><li><time datetime=\"2015-05-07\">2015-05-07</time></li><li><time datetime=\"2015-05-26\">2015-05-26</time></li><li><time datetime=\"2015-06-12\">2015-06-12</time></li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2015-04-22\n",
       "\\item 2015-04-08\n",
       "\\item 2015-04-18\n",
       "\\item 2015-05-07\n",
       "\\item 2015-05-26\n",
       "\\item 2015-06-12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2015-04-22\n",
       "2. 2015-04-08\n",
       "3. 2015-04-18\n",
       "4. 2015-05-07\n",
       "5. 2015-05-26\n",
       "6. 2015-06-12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"2015-04-22\" \"2015-04-08\" \"2015-04-18\" \"2015-05-07\" \"2015-05-26\"\n",
       "[6] \"2015-06-12\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(df$Date_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.043305,
     "end_time": "2020-11-21T21:15:52.995066",
     "exception": false,
     "start_time": "2020-11-21T21:15:52.951761",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Checking if we have data of single year or multiple years. And the answer is yes, It's only 2015. Storing Months number in df$month from df$Data_new."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:53.094908Z",
     "iopub.status.busy": "2020-11-21T21:15:53.086356Z",
     "iopub.status.idle": "2020-11-21T21:15:53.107488Z",
     "shell.execute_reply": "2020-11-21T21:15:53.106475Z"
    },
    "papermill": {
     "duration": 0.068761,
     "end_time": "2020-11-21T21:15:53.107635",
     "exception": false,
     "start_time": "2020-11-21T21:15:53.038874",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "2015"
      ],
      "text/latex": [
       "2015"
      ],
      "text/markdown": [
       "2015"
      ],
      "text/plain": [
       "[1] 2015"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique(as.integer(year(df$Date_new)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:53.201815Z",
     "iopub.status.busy": "2020-11-21T21:15:53.200237Z",
     "iopub.status.idle": "2020-11-21T21:15:53.212169Z",
     "shell.execute_reply": "2020-11-21T21:15:53.210803Z"
    },
    "papermill": {
     "duration": 0.060063,
     "end_time": "2020-11-21T21:15:53.212317",
     "exception": false,
     "start_time": "2020-11-21T21:15:53.152254",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df$month = as.integer(month(df$Date_new))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.044592,
     "end_time": "2020-11-21T21:15:53.302454",
     "exception": false,
     "start_time": "2020-11-21T21:15:53.257862",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, Calculating Daily and Monthly number of complaints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:53.396717Z",
     "iopub.status.busy": "2020-11-21T21:15:53.395118Z",
     "iopub.status.idle": "2020-11-21T21:15:53.452533Z",
     "shell.execute_reply": "2020-11-21T21:15:53.451152Z"
    },
    "papermill": {
     "duration": 0.105859,
     "end_time": "2020-11-21T21:15:53.452691",
     "exception": false,
     "start_time": "2020-11-21T21:15:53.346832",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "`summarise()` ungrouping output (override with `.groups` argument)\n",
      "\n",
      "`summarise()` ungrouping output (override with `.groups` argument)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(dplyr)\n",
    "count_d <- summarise(group_by(df, Date_new), Count = n())\n",
    "count_m <- summarise(group_by(df, month), Count = n())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:53.551895Z",
     "iopub.status.busy": "2020-11-21T21:15:53.550221Z",
     "iopub.status.idle": "2020-11-21T21:15:53.613680Z",
     "shell.execute_reply": "2020-11-21T21:15:53.612160Z"
    },
    "papermill": {
     "duration": 0.114212,
     "end_time": "2020-11-21T21:15:53.613836",
     "exception": false,
     "start_time": "2020-11-21T21:15:53.499624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 91 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Date_new</th><th scope=col>Count</th></tr>\n",
       "\t<tr><th scope=col>&lt;date&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>2015-04-01</td><td>18</td></tr>\n",
       "\t<tr><td>2015-04-02</td><td>27</td></tr>\n",
       "\t<tr><td>2015-04-03</td><td>15</td></tr>\n",
       "\t<tr><td>2015-04-04</td><td>12</td></tr>\n",
       "\t<tr><td>2015-04-05</td><td> 6</td></tr>\n",
       "\t<tr><td>2015-04-06</td><td>13</td></tr>\n",
       "\t<tr><td>2015-04-07</td><td>25</td></tr>\n",
       "\t<tr><td>2015-04-08</td><td>28</td></tr>\n",
       "\t<tr><td>2015-04-09</td><td>21</td></tr>\n",
       "\t<tr><td>2015-04-10</td><td>21</td></tr>\n",
       "\t<tr><td>2015-04-11</td><td> 5</td></tr>\n",
       "\t<tr><td>2015-04-12</td><td>15</td></tr>\n",
       "\t<tr><td>2015-04-13</td><td>24</td></tr>\n",
       "\t<tr><td>2015-04-14</td><td>23</td></tr>\n",
       "\t<tr><td>2015-04-15</td><td>12</td></tr>\n",
       "\t<tr><td>2015-04-16</td><td>20</td></tr>\n",
       "\t<tr><td>2015-04-17</td><td>21</td></tr>\n",
       "\t<tr><td>2015-04-18</td><td> 8</td></tr>\n",
       "\t<tr><td>2015-04-19</td><td> 9</td></tr>\n",
       "\t<tr><td>2015-04-20</td><td>20</td></tr>\n",
       "\t<tr><td>2015-04-21</td><td>12</td></tr>\n",
       "\t<tr><td>2015-04-22</td><td>22</td></tr>\n",
       "\t<tr><td>2015-04-23</td><td>23</td></tr>\n",
       "\t<tr><td>2015-04-24</td><td>24</td></tr>\n",
       "\t<tr><td>2015-04-25</td><td>16</td></tr>\n",
       "\t<tr><td>2015-04-26</td><td> 8</td></tr>\n",
       "\t<tr><td>2015-04-27</td><td>25</td></tr>\n",
       "\t<tr><td>2015-04-28</td><td>26</td></tr>\n",
       "\t<tr><td>2015-04-29</td><td>22</td></tr>\n",
       "\t<tr><td>2015-04-30</td><td>24</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>2015-06-01</td><td> 25</td></tr>\n",
       "\t<tr><td>2015-06-02</td><td> 25</td></tr>\n",
       "\t<tr><td>2015-06-03</td><td> 25</td></tr>\n",
       "\t<tr><td>2015-06-04</td><td> 12</td></tr>\n",
       "\t<tr><td>2015-06-05</td><td> 29</td></tr>\n",
       "\t<tr><td>2015-06-06</td><td> 11</td></tr>\n",
       "\t<tr><td>2015-06-07</td><td>  9</td></tr>\n",
       "\t<tr><td>2015-06-08</td><td> 24</td></tr>\n",
       "\t<tr><td>2015-06-09</td><td> 23</td></tr>\n",
       "\t<tr><td>2015-06-10</td><td> 25</td></tr>\n",
       "\t<tr><td>2015-06-11</td><td> 21</td></tr>\n",
       "\t<tr><td>2015-06-12</td><td> 43</td></tr>\n",
       "\t<tr><td>2015-06-13</td><td> 32</td></tr>\n",
       "\t<tr><td>2015-06-14</td><td> 16</td></tr>\n",
       "\t<tr><td>2015-06-15</td><td> 34</td></tr>\n",
       "\t<tr><td>2015-06-16</td><td> 29</td></tr>\n",
       "\t<tr><td>2015-06-17</td><td> 32</td></tr>\n",
       "\t<tr><td>2015-06-18</td><td> 47</td></tr>\n",
       "\t<tr><td>2015-06-19</td><td> 29</td></tr>\n",
       "\t<tr><td>2015-06-20</td><td> 16</td></tr>\n",
       "\t<tr><td>2015-06-21</td><td> 12</td></tr>\n",
       "\t<tr><td>2015-06-22</td><td> 30</td></tr>\n",
       "\t<tr><td>2015-06-23</td><td>190</td></tr>\n",
       "\t<tr><td>2015-06-24</td><td>219</td></tr>\n",
       "\t<tr><td>2015-06-25</td><td> 98</td></tr>\n",
       "\t<tr><td>2015-06-26</td><td> 55</td></tr>\n",
       "\t<tr><td>2015-06-27</td><td> 39</td></tr>\n",
       "\t<tr><td>2015-06-28</td><td> 27</td></tr>\n",
       "\t<tr><td>2015-06-29</td><td> 51</td></tr>\n",
       "\t<tr><td>2015-06-30</td><td> 53</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 91 × 2\n",
       "\\begin{tabular}{ll}\n",
       " Date\\_new & Count\\\\\n",
       " <date> & <int>\\\\\n",
       "\\hline\n",
       "\t 2015-04-01 & 18\\\\\n",
       "\t 2015-04-02 & 27\\\\\n",
       "\t 2015-04-03 & 15\\\\\n",
       "\t 2015-04-04 & 12\\\\\n",
       "\t 2015-04-05 &  6\\\\\n",
       "\t 2015-04-06 & 13\\\\\n",
       "\t 2015-04-07 & 25\\\\\n",
       "\t 2015-04-08 & 28\\\\\n",
       "\t 2015-04-09 & 21\\\\\n",
       "\t 2015-04-10 & 21\\\\\n",
       "\t 2015-04-11 &  5\\\\\n",
       "\t 2015-04-12 & 15\\\\\n",
       "\t 2015-04-13 & 24\\\\\n",
       "\t 2015-04-14 & 23\\\\\n",
       "\t 2015-04-15 & 12\\\\\n",
       "\t 2015-04-16 & 20\\\\\n",
       "\t 2015-04-17 & 21\\\\\n",
       "\t 2015-04-18 &  8\\\\\n",
       "\t 2015-04-19 &  9\\\\\n",
       "\t 2015-04-20 & 20\\\\\n",
       "\t 2015-04-21 & 12\\\\\n",
       "\t 2015-04-22 & 22\\\\\n",
       "\t 2015-04-23 & 23\\\\\n",
       "\t 2015-04-24 & 24\\\\\n",
       "\t 2015-04-25 & 16\\\\\n",
       "\t 2015-04-26 &  8\\\\\n",
       "\t 2015-04-27 & 25\\\\\n",
       "\t 2015-04-28 & 26\\\\\n",
       "\t 2015-04-29 & 22\\\\\n",
       "\t 2015-04-30 & 24\\\\\n",
       "\t ⋮ & ⋮\\\\\n",
       "\t 2015-06-01 &  25\\\\\n",
       "\t 2015-06-02 &  25\\\\\n",
       "\t 2015-06-03 &  25\\\\\n",
       "\t 2015-06-04 &  12\\\\\n",
       "\t 2015-06-05 &  29\\\\\n",
       "\t 2015-06-06 &  11\\\\\n",
       "\t 2015-06-07 &   9\\\\\n",
       "\t 2015-06-08 &  24\\\\\n",
       "\t 2015-06-09 &  23\\\\\n",
       "\t 2015-06-10 &  25\\\\\n",
       "\t 2015-06-11 &  21\\\\\n",
       "\t 2015-06-12 &  43\\\\\n",
       "\t 2015-06-13 &  32\\\\\n",
       "\t 2015-06-14 &  16\\\\\n",
       "\t 2015-06-15 &  34\\\\\n",
       "\t 2015-06-16 &  29\\\\\n",
       "\t 2015-06-17 &  32\\\\\n",
       "\t 2015-06-18 &  47\\\\\n",
       "\t 2015-06-19 &  29\\\\\n",
       "\t 2015-06-20 &  16\\\\\n",
       "\t 2015-06-21 &  12\\\\\n",
       "\t 2015-06-22 &  30\\\\\n",
       "\t 2015-06-23 & 190\\\\\n",
       "\t 2015-06-24 & 219\\\\\n",
       "\t 2015-06-25 &  98\\\\\n",
       "\t 2015-06-26 &  55\\\\\n",
       "\t 2015-06-27 &  39\\\\\n",
       "\t 2015-06-28 &  27\\\\\n",
       "\t 2015-06-29 &  51\\\\\n",
       "\t 2015-06-30 &  53\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 91 × 2\n",
       "\n",
       "| Date_new &lt;date&gt; | Count &lt;int&gt; |\n",
       "|---|---|\n",
       "| 2015-04-01 | 18 |\n",
       "| 2015-04-02 | 27 |\n",
       "| 2015-04-03 | 15 |\n",
       "| 2015-04-04 | 12 |\n",
       "| 2015-04-05 |  6 |\n",
       "| 2015-04-06 | 13 |\n",
       "| 2015-04-07 | 25 |\n",
       "| 2015-04-08 | 28 |\n",
       "| 2015-04-09 | 21 |\n",
       "| 2015-04-10 | 21 |\n",
       "| 2015-04-11 |  5 |\n",
       "| 2015-04-12 | 15 |\n",
       "| 2015-04-13 | 24 |\n",
       "| 2015-04-14 | 23 |\n",
       "| 2015-04-15 | 12 |\n",
       "| 2015-04-16 | 20 |\n",
       "| 2015-04-17 | 21 |\n",
       "| 2015-04-18 |  8 |\n",
       "| 2015-04-19 |  9 |\n",
       "| 2015-04-20 | 20 |\n",
       "| 2015-04-21 | 12 |\n",
       "| 2015-04-22 | 22 |\n",
       "| 2015-04-23 | 23 |\n",
       "| 2015-04-24 | 24 |\n",
       "| 2015-04-25 | 16 |\n",
       "| 2015-04-26 |  8 |\n",
       "| 2015-04-27 | 25 |\n",
       "| 2015-04-28 | 26 |\n",
       "| 2015-04-29 | 22 |\n",
       "| 2015-04-30 | 24 |\n",
       "| ⋮ | ⋮ |\n",
       "| 2015-06-01 |  25 |\n",
       "| 2015-06-02 |  25 |\n",
       "| 2015-06-03 |  25 |\n",
       "| 2015-06-04 |  12 |\n",
       "| 2015-06-05 |  29 |\n",
       "| 2015-06-06 |  11 |\n",
       "| 2015-06-07 |   9 |\n",
       "| 2015-06-08 |  24 |\n",
       "| 2015-06-09 |  23 |\n",
       "| 2015-06-10 |  25 |\n",
       "| 2015-06-11 |  21 |\n",
       "| 2015-06-12 |  43 |\n",
       "| 2015-06-13 |  32 |\n",
       "| 2015-06-14 |  16 |\n",
       "| 2015-06-15 |  34 |\n",
       "| 2015-06-16 |  29 |\n",
       "| 2015-06-17 |  32 |\n",
       "| 2015-06-18 |  47 |\n",
       "| 2015-06-19 |  29 |\n",
       "| 2015-06-20 |  16 |\n",
       "| 2015-06-21 |  12 |\n",
       "| 2015-06-22 |  30 |\n",
       "| 2015-06-23 | 190 |\n",
       "| 2015-06-24 | 219 |\n",
       "| 2015-06-25 |  98 |\n",
       "| 2015-06-26 |  55 |\n",
       "| 2015-06-27 |  39 |\n",
       "| 2015-06-28 |  27 |\n",
       "| 2015-06-29 |  51 |\n",
       "| 2015-06-30 |  53 |\n",
       "\n"
      ],
      "text/plain": [
       "   Date_new   Count\n",
       "1  2015-04-01 18   \n",
       "2  2015-04-02 27   \n",
       "3  2015-04-03 15   \n",
       "4  2015-04-04 12   \n",
       "5  2015-04-05  6   \n",
       "6  2015-04-06 13   \n",
       "7  2015-04-07 25   \n",
       "8  2015-04-08 28   \n",
       "9  2015-04-09 21   \n",
       "10 2015-04-10 21   \n",
       "11 2015-04-11  5   \n",
       "12 2015-04-12 15   \n",
       "13 2015-04-13 24   \n",
       "14 2015-04-14 23   \n",
       "15 2015-04-15 12   \n",
       "16 2015-04-16 20   \n",
       "17 2015-04-17 21   \n",
       "18 2015-04-18  8   \n",
       "19 2015-04-19  9   \n",
       "20 2015-04-20 20   \n",
       "21 2015-04-21 12   \n",
       "22 2015-04-22 22   \n",
       "23 2015-04-23 23   \n",
       "24 2015-04-24 24   \n",
       "25 2015-04-25 16   \n",
       "26 2015-04-26  8   \n",
       "27 2015-04-27 25   \n",
       "28 2015-04-28 26   \n",
       "29 2015-04-29 22   \n",
       "30 2015-04-30 24   \n",
       "⋮  ⋮          ⋮    \n",
       "62 2015-06-01  25  \n",
       "63 2015-06-02  25  \n",
       "64 2015-06-03  25  \n",
       "65 2015-06-04  12  \n",
       "66 2015-06-05  29  \n",
       "67 2015-06-06  11  \n",
       "68 2015-06-07   9  \n",
       "69 2015-06-08  24  \n",
       "70 2015-06-09  23  \n",
       "71 2015-06-10  25  \n",
       "72 2015-06-11  21  \n",
       "73 2015-06-12  43  \n",
       "74 2015-06-13  32  \n",
       "75 2015-06-14  16  \n",
       "76 2015-06-15  34  \n",
       "77 2015-06-16  29  \n",
       "78 2015-06-17  32  \n",
       "79 2015-06-18  47  \n",
       "80 2015-06-19  29  \n",
       "81 2015-06-20  16  \n",
       "82 2015-06-21  12  \n",
       "83 2015-06-22  30  \n",
       "84 2015-06-23 190  \n",
       "85 2015-06-24 219  \n",
       "86 2015-06-25  98  \n",
       "87 2015-06-26  55  \n",
       "88 2015-06-27  39  \n",
       "89 2015-06-28  27  \n",
       "90 2015-06-29  51  \n",
       "91 2015-06-30  53  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>month</th><th scope=col>Count</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>4</td><td> 545</td></tr>\n",
       "\t<tr><td>5</td><td> 399</td></tr>\n",
       "\t<tr><td>6</td><td>1281</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 2\n",
       "\\begin{tabular}{ll}\n",
       " month & Count\\\\\n",
       " <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 4 &  545\\\\\n",
       "\t 5 &  399\\\\\n",
       "\t 6 & 1281\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 2\n",
       "\n",
       "| month &lt;int&gt; | Count &lt;int&gt; |\n",
       "|---|---|\n",
       "| 4 |  545 |\n",
       "| 5 |  399 |\n",
       "| 6 | 1281 |\n",
       "\n"
      ],
      "text/plain": [
       "  month Count\n",
       "1 4      545 \n",
       "2 5      399 \n",
       "3 6     1281 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_d\n",
    "count_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.048824,
     "end_time": "2020-11-21T21:15:53.719262",
     "exception": false,
     "start_time": "2020-11-21T21:15:53.670438",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Creating Monthly trend chart for number of complains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:53.821115Z",
     "iopub.status.busy": "2020-11-21T21:15:53.819957Z",
     "iopub.status.idle": "2020-11-21T21:15:54.555172Z",
     "shell.execute_reply": "2020-11-21T21:15:54.553817Z"
    },
    "papermill": {
     "duration": 0.787482,
     "end_time": "2020-11-21T21:15:54.555328",
     "exception": false,
     "start_time": "2020-11-21T21:15:53.767846",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nAAA6QbADAADQif8BrSC679ElYdMAAAAASUVORK5CYII="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "ggplot(data = count_m, aes(x= month, y= Count))+\n",
    "  geom_line(color=\"blue\")+\n",
    "  geom_point()+\n",
    "  geom_text(label= count_m$Count, nudge_x = 0.2, nudge_y = 0.3, check_overlap = T)+\n",
    "  scale_x_continuous(breaks = count_m$month)+\n",
    "  labs(title = \"Monthly Trend for the Number of Complaints\",x= \"Months\",y =\"No. of Complaints\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.050193,
     "end_time": "2020-11-21T21:15:54.656976",
     "exception": false,
     "start_time": "2020-11-21T21:15:54.606783",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "It can be observed that In June number of complaints are comparatively higher than in other months. In June probably there was some major issue because of that number of complaints reached the highest number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.050099,
     "end_time": "2020-11-21T21:15:54.757658",
     "exception": false,
     "start_time": "2020-11-21T21:15:54.707559",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Creating Daily trend chart for number of complains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:54.863541Z",
     "iopub.status.busy": "2020-11-21T21:15:54.862053Z",
     "iopub.status.idle": "2020-11-21T21:15:55.201900Z",
     "shell.execute_reply": "2020-11-21T21:15:55.200669Z"
    },
    "papermill": {
     "duration": 0.394102,
     "end_time": "2020-11-21T21:15:55.202092",
     "exception": false,
     "start_time": "2020-11-21T21:15:54.807990",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\n7AAAABTxHcbUZ2XtSb+QAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Daily trend chart for the number of complaints \n",
    "ggplot(data = count_d, aes(x= as.POSIXct(Date_new), y= Count))+\n",
    "  geom_line(color=\"blue\")+\n",
    "  geom_point()+\n",
    "  scale_x_datetime(breaks = \"1 weeks\", date_labels = \"%d/%m\")+\n",
    "  theme(axis.text.x = element_text(angle = 0))+\n",
    "  labs(title = \"Daily Trend for the Number of Complaints\", x = \"Date\", y =\"No. of Complaints\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.053039,
     "end_time": "2020-11-21T21:15:55.308475",
     "exception": false,
     "start_time": "2020-11-21T21:15:55.255436",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In the daily chart of complaints, it can be seen that cmpains are higher between 12th June and 30th June. To be more precise on 23rd, 24th and 25th June there are sudden spikes of daily complaints, there could be some major reason behind that. We will do further analysis below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.052787,
     "end_time": "2020-11-21T21:15:55.414107",
     "exception": false,
     "start_time": "2020-11-21T21:15:55.361320",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, calculating frequency of different types of complaints. To do so first we are using NLP to get the highest frequency words/tags that appeared in Customer.Complaint\n",
    "To do so I have converted Customer.Complaint column into a corpus (collection of documents). Then removed punctuation, converted everything to lowercase.\n",
    "After that I have removed all stop words and also the word “comcast” (as “comcast” is the company name and is appearing in most of the complaints text).\n",
    "Then performed stemming and removed whitespaces."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.052839,
     "end_time": "2020-11-21T21:15:55.519724",
     "exception": false,
     "start_time": "2020-11-21T21:15:55.466885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Frequency of complaint types Calculation:**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:55.630965Z",
     "iopub.status.busy": "2020-11-21T21:15:55.629666Z",
     "iopub.status.idle": "2020-11-21T21:15:55.791200Z",
     "shell.execute_reply": "2020-11-21T21:15:55.790172Z"
    },
    "papermill": {
     "duration": 0.218773,
     "end_time": "2020-11-21T21:15:55.791378",
     "exception": false,
     "start_time": "2020-11-21T21:15:55.572605",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: NLP\n",
      "\n",
      "\n",
      "Attaching package: ‘NLP’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:ggplot2’:\n",
      "\n",
      "    annotate\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:httr’:\n",
      "\n",
      "    content\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#using NLP to get highest frequency words\n",
    "library(tm)\n",
    "library(SnowballC)\n",
    "corp_com = Corpus(VectorSource(df$Customer.Complaint))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:55.907353Z",
     "iopub.status.busy": "2020-11-21T21:15:55.906143Z",
     "iopub.status.idle": "2020-11-21T21:15:55.948261Z",
     "shell.execute_reply": "2020-11-21T21:15:55.947104Z"
    },
    "papermill": {
     "duration": 0.101546,
     "end_time": "2020-11-21T21:15:55.948417",
     "exception": false,
     "start_time": "2020-11-21T21:15:55.846871",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in tm_map.SimpleCorpus(corp_com, removePunctuation):\n",
      "“transformation drops documents”\n",
      "Warning message in tm_map.SimpleCorpus(corp_com, PlainTextDocument):\n",
      "“transformation drops documents”\n",
      "Warning message in tm_map.SimpleCorpus(corp_com, tolower):\n",
      "“transformation drops documents”\n",
      "Warning message in tm_map.SimpleCorpus(corp_com, removeWords, c(\"comcast\", stopwords(\"english\"))):\n",
      "“transformation drops documents”\n"
     ]
    }
   ],
   "source": [
    "#remove punctuations\n",
    "corp_com <- tm_map(corp_com, removePunctuation)\n",
    "#to lowercase\n",
    "corp_com <- tm_map(corp_com, PlainTextDocument)\n",
    "corp_com <- tm_map(corp_com, tolower)\n",
    "#remove stopwords and the word \"comcast\"\n",
    "corp_com <- tm_map(corp_com, removeWords, c(\"comcast\", stopwords(\"english\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.056369,
     "end_time": "2020-11-21T21:15:56.061145",
     "exception": false,
     "start_time": "2020-11-21T21:15:56.004776",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Created termed document matrix and stored the frequency of the tags/words into df1 dataframe. Displaying top 25 tags/words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:56.179917Z",
     "iopub.status.busy": "2020-11-21T21:15:56.178644Z",
     "iopub.status.idle": "2020-11-21T21:15:56.398022Z",
     "shell.execute_reply": "2020-11-21T21:15:56.396867Z"
    },
    "papermill": {
     "duration": 0.279675,
     "end_time": "2020-11-21T21:15:56.398170",
     "exception": false,
     "start_time": "2020-11-21T21:15:56.118495",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 25 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>word</th><th scope=col>freq</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>internet</th><td>internet  </td><td>517</td></tr>\n",
       "\t<tr><th scope=row>service</th><td>service   </td><td>444</td></tr>\n",
       "\t<tr><th scope=row>billing</th><td>billing   </td><td>282</td></tr>\n",
       "\t<tr><th scope=row>data</th><td>data      </td><td>220</td></tr>\n",
       "\t<tr><th scope=row>speed</th><td>speed     </td><td>116</td></tr>\n",
       "\t<tr><th scope=row>cap</th><td>cap       </td><td>103</td></tr>\n",
       "\t<tr><th scope=row>issues</th><td>issues    </td><td> 90</td></tr>\n",
       "\t<tr><th scope=row>customer</th><td>customer  </td><td> 88</td></tr>\n",
       "\t<tr><th scope=row>caps</th><td>caps      </td><td> 79</td></tr>\n",
       "\t<tr><th scope=row>practices</th><td>practices </td><td> 73</td></tr>\n",
       "\t<tr><th scope=row>speeds</th><td>speeds    </td><td> 71</td></tr>\n",
       "\t<tr><th scope=row>charges</th><td>charges   </td><td> 71</td></tr>\n",
       "\t<tr><th scope=row>complaint</th><td>complaint </td><td> 70</td></tr>\n",
       "\t<tr><th scope=row>bill</th><td>bill      </td><td> 64</td></tr>\n",
       "\t<tr><th scope=row>xfinity</th><td>xfinity   </td><td> 62</td></tr>\n",
       "\t<tr><th scope=row>unfair</th><td>unfair    </td><td> 58</td></tr>\n",
       "\t<tr><th scope=row>throttling</th><td>throttling</td><td> 53</td></tr>\n",
       "\t<tr><th scope=row>services</th><td>services  </td><td> 52</td></tr>\n",
       "\t<tr><th scope=row>cable</th><td>cable     </td><td> 50</td></tr>\n",
       "\t<tr><th scope=row>slow</th><td>slow      </td><td> 49</td></tr>\n",
       "\t<tr><th scope=row>usage</th><td>usage     </td><td> 47</td></tr>\n",
       "\t<tr><th scope=row>poor</th><td>poor      </td><td> 43</td></tr>\n",
       "\t<tr><th scope=row>pricing</th><td>pricing   </td><td> 39</td></tr>\n",
       "\t<tr><th scope=row>price</th><td>price     </td><td> 39</td></tr>\n",
       "\t<tr><th scope=row>connection</th><td>connection</td><td> 34</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 25 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & word & freq\\\\\n",
       "  & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\tinternet & internet   & 517\\\\\n",
       "\tservice & service    & 444\\\\\n",
       "\tbilling & billing    & 282\\\\\n",
       "\tdata & data       & 220\\\\\n",
       "\tspeed & speed      & 116\\\\\n",
       "\tcap & cap        & 103\\\\\n",
       "\tissues & issues     &  90\\\\\n",
       "\tcustomer & customer   &  88\\\\\n",
       "\tcaps & caps       &  79\\\\\n",
       "\tpractices & practices  &  73\\\\\n",
       "\tspeeds & speeds     &  71\\\\\n",
       "\tcharges & charges    &  71\\\\\n",
       "\tcomplaint & complaint  &  70\\\\\n",
       "\tbill & bill       &  64\\\\\n",
       "\txfinity & xfinity    &  62\\\\\n",
       "\tunfair & unfair     &  58\\\\\n",
       "\tthrottling & throttling &  53\\\\\n",
       "\tservices & services   &  52\\\\\n",
       "\tcable & cable      &  50\\\\\n",
       "\tslow & slow       &  49\\\\\n",
       "\tusage & usage      &  47\\\\\n",
       "\tpoor & poor       &  43\\\\\n",
       "\tpricing & pricing    &  39\\\\\n",
       "\tprice & price      &  39\\\\\n",
       "\tconnection & connection &  34\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 25 × 2\n",
       "\n",
       "| <!--/--> | word &lt;fct&gt; | freq &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| internet | internet   | 517 |\n",
       "| service | service    | 444 |\n",
       "| billing | billing    | 282 |\n",
       "| data | data       | 220 |\n",
       "| speed | speed      | 116 |\n",
       "| cap | cap        | 103 |\n",
       "| issues | issues     |  90 |\n",
       "| customer | customer   |  88 |\n",
       "| caps | caps       |  79 |\n",
       "| practices | practices  |  73 |\n",
       "| speeds | speeds     |  71 |\n",
       "| charges | charges    |  71 |\n",
       "| complaint | complaint  |  70 |\n",
       "| bill | bill       |  64 |\n",
       "| xfinity | xfinity    |  62 |\n",
       "| unfair | unfair     |  58 |\n",
       "| throttling | throttling |  53 |\n",
       "| services | services   |  52 |\n",
       "| cable | cable      |  50 |\n",
       "| slow | slow       |  49 |\n",
       "| usage | usage      |  47 |\n",
       "| poor | poor       |  43 |\n",
       "| pricing | pricing    |  39 |\n",
       "| price | price      |  39 |\n",
       "| connection | connection |  34 |\n",
       "\n"
      ],
      "text/plain": [
       "           word       freq\n",
       "internet   internet   517 \n",
       "service    service    444 \n",
       "billing    billing    282 \n",
       "data       data       220 \n",
       "speed      speed      116 \n",
       "cap        cap        103 \n",
       "issues     issues      90 \n",
       "customer   customer    88 \n",
       "caps       caps        79 \n",
       "practices  practices   73 \n",
       "speeds     speeds      71 \n",
       "charges    charges     71 \n",
       "complaint  complaint   70 \n",
       "bill       bill        64 \n",
       "xfinity    xfinity     62 \n",
       "unfair     unfair      58 \n",
       "throttling throttling  53 \n",
       "services   services    52 \n",
       "cable      cable       50 \n",
       "slow       slow        49 \n",
       "usage      usage       47 \n",
       "poor       poor        43 \n",
       "pricing    pricing     39 \n",
       "price      price       39 \n",
       "connection connection  34 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating term document matrix and calculating tags frequency\n",
    "m <- TermDocumentMatrix(corp_com)\n",
    "mat <- as.matrix(m)\n",
    "f <- sort(rowSums(mat),decreasing=TRUE)\n",
    "df1 <- data.frame(word = names(f),freq=f)\n",
    "head(df1, 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.057171,
     "end_time": "2020-11-21T21:15:56.512800",
     "exception": false,
     "start_time": "2020-11-21T21:15:56.455629",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From above tags and their frequency, I have derived complain types. Now, getting the index of df$Customer.Complaint rows that contain below match words (in case insensitive form)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.057842,
     "end_time": "2020-11-21T21:15:56.628022",
     "exception": false,
     "start_time": "2020-11-21T21:15:56.570180",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Deriving complain types:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:56.748200Z",
     "iopub.status.busy": "2020-11-21T21:15:56.746904Z",
     "iopub.status.idle": "2020-11-21T21:15:56.819080Z",
     "shell.execute_reply": "2020-11-21T21:15:56.818093Z"
    },
    "papermill": {
     "duration": 0.133403,
     "end_time": "2020-11-21T21:15:56.819234",
     "exception": false,
     "start_time": "2020-11-21T21:15:56.685831",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "internet <- contains(df$Customer.Complaint, match = 'internet',ignore.case = T)\n",
    "billing <- contains(df$Customer.Complaint, match = 'bill',ignore.case = T)\n",
    "data <- contains(df$Customer.Complaint, match = 'data',ignore.case = T)\n",
    "cap <- contains(df$Customer.Complaint, match = 'cap',ignore.case = T)\n",
    "throttle <- contains(df$Customer.Complaint, match = 'throttl',ignore.case = T)\n",
    "price <- contains(df$Customer.Complaint, match = 'price',ignore.case = T)\n",
    "charge <- contains(df$Customer.Complaint, match = 'charge',ignore.case = T)\n",
    "cable <- contains(df$Customer.Complaint, match = 'cable',ignore.case = T)\n",
    "connect <- contains(df$Customer.Complaint, match = 'connect',ignore.case = T)\n",
    "contract <- contains(df$Customer.Complaint, match = 'contract',ignore.case = T)\n",
    "account <- contains(df$Customer.Complaint, match = 'account',ignore.case = T)\n",
    "email <- contains(df$Customer.Complaint, match = 'email',ignore.case = T)\n",
    "xfinity <- contains(df$Customer.Complaint, match = 'xfinit',ignore.case = T)\n",
    "hbo <- contains(df$Customer.Complaint, match = 'hbo',ignore.case = T)\n",
    "modem <- contains(df$Customer.Complaint, match = 'modem',ignore.case = T)\n",
    "service <- contains(df$Customer.Complaint, match = 'service',ignore.case = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.057341,
     "end_time": "2020-11-21T21:15:56.934269",
     "exception": false,
     "start_time": "2020-11-21T21:15:56.876928",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Complaint.Types column has been created. Initially all values in  Complaint.Types is NA. \n",
    "\n",
    "Internet has the highest tag frequency. Now, assigning category “Internet” to the rows of df$Complaint.Types having the same indexes that we have derived in last step.\n",
    "\n",
    "Second one having high frequency is “billing”. Now it may happen that, internet and billing may have some common indexes. To avoid that, I have decided that if one complaint is already assigned to a category then it will not be changed anymore. So below it can be seen that for “billing” we have assigned category= Billing to only those rows whose indexes are present in billing and existing value is NA.\n",
    "\n",
    "We have set all the category tags frequency wise. Then applied above technique for all  identified category tags.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.058177,
     "end_time": "2020-11-21T21:15:57.049828",
     "exception": false,
     "start_time": "2020-11-21T21:15:56.991651",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Creating new column and adding complaint type serially:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:57.175366Z",
     "iopub.status.busy": "2020-11-21T21:15:57.174080Z",
     "iopub.status.idle": "2020-11-21T21:15:57.208154Z",
     "shell.execute_reply": "2020-11-21T21:15:57.207046Z"
    },
    "papermill": {
     "duration": 0.100126,
     "end_time": "2020-11-21T21:15:57.208309",
     "exception": false,
     "start_time": "2020-11-21T21:15:57.108183",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df$Complaint.Types[internet] <- \"Internet\"\n",
    "df$Complaint.Types[billing] <- ifelse(is.na(df$Complaint.Types[billing]),\"Billing\",df$Complaint.Types[billing])\n",
    "df$Complaint.Types[data] <- ifelse(is.na(df$Complaint.Types[data]),\"Data\",df$Complaint.Types[data])\n",
    "df$Complaint.Types[cap] <- ifelse(is.na(df$Complaint.Types[cap]),\"Usage Cap\",df$Complaint.Types[cap])\n",
    "df$Complaint.Types[throttle] <- ifelse(is.na(df$Complaint.Types[throttle]),\"Throttling\",df$Complaint.Types[throttle])\n",
    "df$Complaint.Types[price] <- ifelse(is.na(df$Complaint.Types[price]),\"Price\",df$Complaint.Types[price])\n",
    "df$Complaint.Types[charge] <- ifelse(is.na(df$Complaint.Types[charge]),\"Charges\",df$Complaint.Types[charge])\n",
    "df$Complaint.Types[cable] <- ifelse(is.na(df$Complaint.Types[cable]),\"Cable\",df$Complaint.Types[cable])\n",
    "df$Complaint.Types[connect] <- ifelse(is.na(df$Complaint.Types[connect]),\"Connect\",df$Complaint.Types[connect])\n",
    "df$Complaint.Types[contract] <- ifelse(is.na(df$Complaint.Types[contract]),\"Contract\",df$Complaint.Types[contract])\n",
    "df$Complaint.Types[account] <- ifelse(is.na(df$Complaint.Types[account]),\"Account\",df$Complaint.Types[account])\n",
    "df$Complaint.Types[email] <- ifelse(is.na(df$Complaint.Types[email]),\"Email\",df$Complaint.Types[email])\n",
    "df$Complaint.Types[xfinity] <- ifelse(is.na(df$Complaint.Types[xfinity]),\"Xfinity\",df$Complaint.Types[xfinity])\n",
    "df$Complaint.Types[hbo] <- ifelse(is.na(df$Complaint.Types[hbo]),\"HBO\",df$Complaint.Types[hbo])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.05786,
     "end_time": "2020-11-21T21:15:57.324988",
     "exception": false,
     "start_time": "2020-11-21T21:15:57.267128",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now assigning Type= Others to rest of the complaints. This Others complains are very important to study. Above it can be seen that there are 16 Categories identified. Initially number of categories were lesser than that. Those incidents were part of Others category. Then I have checked if there are any such incident type that has multiple incidents and those are in Others category. A new tag/category has been created for such incidents. It is an iterative process. Thus number of incident categories have been increases and number of complaints/incidents in Others category reduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:57.446117Z",
     "iopub.status.busy": "2020-11-21T21:15:57.444784Z",
     "iopub.status.idle": "2020-11-21T21:15:57.456754Z",
     "shell.execute_reply": "2020-11-21T21:15:57.455593Z"
    },
    "papermill": {
     "duration": 0.07403,
     "end_time": "2020-11-21T21:15:57.456895",
     "exception": false,
     "start_time": "2020-11-21T21:15:57.382865",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#assigning \"Others\" for rest of the complaints\n",
    "df$Complaint.Types[-c(internet,billing,data,cap,throttle,price,charge,cable,connect,contract,account,email,xfinity,hbo,modem,service)]<- \"Others\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:57.577631Z",
     "iopub.status.busy": "2020-11-21T21:15:57.576421Z",
     "iopub.status.idle": "2020-11-21T21:15:57.592844Z",
     "shell.execute_reply": "2020-11-21T21:15:57.591615Z"
    },
    "papermill": {
     "duration": 0.078264,
     "end_time": "2020-11-21T21:15:57.593028",
     "exception": false,
     "start_time": "2020-11-21T21:15:57.514764",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "   Account    Billing      Cable    Charges    Connect   Contract       Data \n",
       "        17        334         23         97         21         22        197 \n",
       "     Email        HBO   Internet     Others      Price Throttling  Usage Cap \n",
       "        12         25        532        560         36         41         15 \n",
       "   Xfinity \n",
       "        62 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#final table with the frequency of all complaint types\n",
    "table(df$Complaint.Types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.059215,
     "end_time": "2020-11-21T21:15:57.711642",
     "exception": false,
     "start_time": "2020-11-21T21:15:57.652427",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Most of the complaints belong to Internet issues.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.058475,
     "end_time": "2020-11-21T21:15:57.828562",
     "exception": false,
     "start_time": "2020-11-21T21:15:57.770087",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Creating df$complaint_status to store status Open or Closed:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:57.973023Z",
     "iopub.status.busy": "2020-11-21T21:15:57.971652Z",
     "iopub.status.idle": "2020-11-21T21:15:57.988685Z",
     "shell.execute_reply": "2020-11-21T21:15:57.987511Z"
    },
    "papermill": {
     "duration": 0.079367,
     "end_time": "2020-11-21T21:15:57.988827",
     "exception": false,
     "start_time": "2020-11-21T21:15:57.909460",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#new categorical variable with value as Open and Closed\n",
    "open_complaints <- (df$Status == \"Open\" | df$Status == \"Pending\")\n",
    "closed_complaints <- (df$Status == \"Closed\" | df$Status == \"Solved\")\n",
    "df$complaint_status[open_complaints] <- \"Open\" \n",
    "df$complaint_status[closed_complaints] <- \"Closed\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.059168,
     "end_time": "2020-11-21T21:15:58.107078",
     "exception": false,
     "start_time": "2020-11-21T21:15:58.047910",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Deriving state wise status of complaints. It can be seen that few states are in upper-lower case mix in  State (e.g. District of Columbia and District Of Columbia both are present). To fix that issue I have created  state_lower by converting all state names in lowercase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:58.232254Z",
     "iopub.status.busy": "2020-11-21T21:15:58.230859Z",
     "iopub.status.idle": "2020-11-21T21:15:58.243415Z",
     "shell.execute_reply": "2020-11-21T21:15:58.242285Z"
    },
    "papermill": {
     "duration": 0.075935,
     "end_time": "2020-11-21T21:15:58.243563",
     "exception": false,
     "start_time": "2020-11-21T21:15:58.167628",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Statewise status of complains\n",
    "#few states are in upper-lower case mix (e.g. District of Columbia)\n",
    "df$state_lower <- tolower(df$State)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.059005,
     "end_time": "2020-11-21T21:15:58.362364",
     "exception": false,
     "start_time": "2020-11-21T21:15:58.303359",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Creating state wise status of complaints in a stacked bar chart:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:58.485683Z",
     "iopub.status.busy": "2020-11-21T21:15:58.484381Z",
     "iopub.status.idle": "2020-11-21T21:15:58.900531Z",
     "shell.execute_reply": "2020-11-21T21:15:58.899731Z"
    },
    "papermill": {
     "duration": 0.479144,
     "end_time": "2020-11-21T21:15:58.900686",
     "exception": false,
     "start_time": "2020-11-21T21:15:58.421542",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`summarise()` regrouping output by 'state_lower' (override with `.groups` argument)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "count_states <- summarise(group_by(df, state_lower, complaint_status), Count = n())\n",
    "ggplot(as.data.frame(count_states), aes(x = state_lower, y = Count))+\n",
    "  geom_col(aes(fill = complaint_status))+\n",
    "  theme(axis.text.x = element_text(angle = 90))+\n",
    "  labs(title = \"Complaint Status By States Stacked Bar Chart\", x = \"States\", y = \"No. of Complaints\", fill= \"Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.061851,
     "end_time": "2020-11-21T21:15:59.024529",
     "exception": false,
     "start_time": "2020-11-21T21:15:58.962678",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Identifying the state that has maximum number of complaints (Open + Closed):**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:59.161920Z",
     "iopub.status.busy": "2020-11-21T21:15:59.160262Z",
     "iopub.status.idle": "2020-11-21T21:15:59.201748Z",
     "shell.execute_reply": "2020-11-21T21:15:59.199834Z"
    },
    "papermill": {
     "duration": 0.114964,
     "end_time": "2020-11-21T21:15:59.201974",
     "exception": false,
     "start_time": "2020-11-21T21:15:59.087010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`summarise()` ungrouping output (override with `.groups` argument)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>state_lower</th><th scope=col>Count</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>georgia</td><td>289</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 2\n",
       "\\begin{tabular}{ll}\n",
       " state\\_lower & Count\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t georgia & 289\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 2\n",
       "\n",
       "| state_lower &lt;chr&gt; | Count &lt;int&gt; |\n",
       "|---|---|\n",
       "| georgia | 289 |\n",
       "\n"
      ],
      "text/plain": [
       "  state_lower Count\n",
       "1 georgia     289  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#State having the maximum complaints (total)\n",
    "max_comp <- arrange(summarise(group_by(df, state_lower), Count = n()), desc(Count))\n",
    "max_comp[1,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.064476,
     "end_time": "2020-11-21T21:15:59.341202",
     "exception": false,
     "start_time": "2020-11-21T21:15:59.276726",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "State having the maximum number of complaints (Open + Closed) is Georgia, having 289 complaints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.063114,
     "end_time": "2020-11-21T21:15:59.468689",
     "exception": false,
     "start_time": "2020-11-21T21:15:59.405575",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Now, Identifying the state that has maximum number of Open complaints:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:15:59.607319Z",
     "iopub.status.busy": "2020-11-21T21:15:59.606169Z",
     "iopub.status.idle": "2020-11-21T21:15:59.640838Z",
     "shell.execute_reply": "2020-11-21T21:15:59.639710Z"
    },
    "papermill": {
     "duration": 0.109354,
     "end_time": "2020-11-21T21:15:59.641097",
     "exception": false,
     "start_time": "2020-11-21T21:15:59.531743",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`summarise()` ungrouping output (override with `.groups` argument)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>state_lower</th><th scope=col>Count</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>georgia</td><td>81</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 2\n",
       "\\begin{tabular}{ll}\n",
       " state\\_lower & Count\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t georgia & 81\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 2\n",
       "\n",
       "| state_lower &lt;chr&gt; | Count &lt;int&gt; |\n",
       "|---|---|\n",
       "| georgia | 81 |\n",
       "\n"
      ],
      "text/plain": [
       "  state_lower Count\n",
       "1 georgia     81   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#State having the maximum Open complaints\n",
    "max_open_comp <- arrange(summarise(group_by(filter(df, complaint_status == \"Open\"), state_lower), Count = n()), desc(Count))\n",
    "max_open_comp[1,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.06437,
     "end_time": "2020-11-21T21:15:59.774010",
     "exception": false,
     "start_time": "2020-11-21T21:15:59.709640",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "State having the maximum number of Open complaints is Georgia, having 81 Open complaints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.064191,
     "end_time": "2020-11-21T21:15:59.902425",
     "exception": false,
     "start_time": "2020-11-21T21:15:59.838234",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Identifying the state having the highest percentage of unresolved complaints:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:16:00.035866Z",
     "iopub.status.busy": "2020-11-21T21:16:00.034589Z",
     "iopub.status.idle": "2020-11-21T21:16:00.064055Z",
     "shell.execute_reply": "2020-11-21T21:16:00.062857Z"
    },
    "papermill": {
     "duration": 0.097573,
     "end_time": "2020-11-21T21:16:00.064198",
     "exception": false,
     "start_time": "2020-11-21T21:15:59.966625",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>state_lower</th><th scope=col>Count.x</th><th scope=col>Count.y</th><th scope=col>% unresolved</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>kansas</td><td>2</td><td>1</td><td>50</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & state\\_lower & Count.x & Count.y & \\% unresolved\\\\\n",
       "  & <chr> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & kansas & 2 & 1 & 50\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 4\n",
       "\n",
       "| <!--/--> | state_lower &lt;chr&gt; | Count.x &lt;int&gt; | Count.y &lt;dbl&gt; | % unresolved &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | kansas | 2 | 1 | 50 |\n",
       "\n"
      ],
      "text/plain": [
       "  state_lower Count.x Count.y % unresolved\n",
       "1 kansas      2       1       50          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#State having the highest percentage of unresolved complaints\n",
    "count_by_states <- merge(x = max_comp, y = max_open_comp, by = \"state_lower\", all.x = TRUE)\n",
    "count_by_states$Count.y <- ifelse(is.na(count_by_states$Count.y),0,count_by_states$Count.y)\n",
    "count_by_states <- cbind(count_by_states, \"% unresolved\" = count_by_states$Count.y / count_by_states$Count.x * 100)\n",
    "count_by_states <- arrange(count_by_states, desc(count_by_states[\"% unresolved\"]))\n",
    "count_by_states[1,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.065573,
     "end_time": "2020-11-21T21:16:00.195388",
     "exception": false,
     "start_time": "2020-11-21T21:16:00.129815",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "State having the highest percentage of unresolved complaints is Kansas. It has 2 total complaints out of which 1 is unresolved. So, it’s 50%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:16:00.332337Z",
     "iopub.status.busy": "2020-11-21T21:16:00.331031Z",
     "iopub.status.idle": "2020-11-21T21:16:00.355873Z",
     "shell.execute_reply": "2020-11-21T21:16:00.354699Z"
    },
    "papermill": {
     "duration": 0.093922,
     "end_time": "2020-11-21T21:16:00.356039",
     "exception": false,
     "start_time": "2020-11-21T21:16:00.262117",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Internet'"
      ],
      "text/latex": [
       "'Internet'"
      ],
      "text/markdown": [
       "'Internet'"
      ],
      "text/plain": [
       "[1] \"Internet\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Closed</th><th scope=col>Open</th><th scope=col>Total</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Internet</th><td>1707</td><td>518</td><td>2225</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 3 of type dbl\n",
       "\\begin{tabular}{r|lll}\n",
       "  & Closed & Open & Total\\\\\n",
       "\\hline\n",
       "\tInternet & 1707 & 518 & 2225\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 3 of type dbl\n",
       "\n",
       "| <!--/--> | Closed | Open | Total |\n",
       "|---|---|---|---|\n",
       "| Internet | 1707 | 518 | 2225 |\n",
       "\n"
      ],
      "text/plain": [
       "         Closed Open Total\n",
       "Internet 1707   518  2225 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#percentage of complaints resolved till date, received through the Internet and customer care calls\n",
    "levels(as.factor(df$Received.Via))\n",
    "df2 <- table(df$Received.Via, df$complaint_status)\n",
    "df2 <- cbind(df2, Total = rowSums(df2))\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.066969,
     "end_time": "2020-11-21T21:16:00.490170",
     "exception": false,
     "start_time": "2020-11-21T21:16:00.423201",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Calculating and plotting total Closed and Open Complaints percentage:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:16:00.629590Z",
     "iopub.status.busy": "2020-11-21T21:16:00.628298Z",
     "iopub.status.idle": "2020-11-21T21:16:00.727988Z",
     "shell.execute_reply": "2020-11-21T21:16:00.727021Z"
    },
    "papermill": {
     "duration": 0.170801,
     "end_time": "2020-11-21T21:16:00.728142",
     "exception": false,
     "start_time": "2020-11-21T21:16:00.557341",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg\nAElEQVR4nOzdd3gU5d7G8d9seiVAQm+hQyghdDD0KqIgSrGjIuqxgIigqNjbkSPYXjuKhaYi\nAoIVpIMQkF6UXhI6pGfL8/6xYRNCSDaNSZ79fq5cV5Ldyew9u8/O3pmZnTWUUgIAAICyz2J2\nAAAAABQPih0AAIAmKHYAAACaoNgBAABogmIHAACgCYodAACAJih2AAAAmqDYAQAAaIJiBwAA\noAmKHQAAgCYodgAAAJqg2AEAAGiCYgcAAKAJih0AAIAmKHYAAACaoNgBAABogmIHAACgCYod\nAACAJih2AAAAmqDYAQAAaIJiBwAAoAmKHQAAgCYodgAAAJqg2AEAAGiCYgcAAKAJih0AAIAm\nKHYAAACaoNgBAABogmIHAACgCYodAACAJih2AAAAmqDYAQAAaIJiBwAAoAmKHQAAgCYodgAA\nAJqg2AEAAGiCYgcAAKAJih0AAIAmKHYAAACaoNh5nLSzPxm5sVi8K9VqGNt74MMvfnwwze6a\n/szuW13TrEnMKPY8J7f89Pxj93Rs3qhKxXJ+wRWbtOp4/dA7XvrohxMZjhxTJqy7zpXkQLo9\n17mVNer7t8ZGN44M8fUJCArp8PhfuU70Q1RErg/Z5ap1XHyVF0AK+7hYk/Z9PvW5wd3b1q1Z\nJcDXP6Ja7ehO/ca//F5cQmqJpi3TbKm7XHf19X+fdPOvdH2KlfS9UZoV8ZEq6bU6TKbgYVLP\nLMp3VARVjf3zdJpz+tO7bnFdvvpCejEmcdguvDGyh7dh5JohsHLb95cezT59/NoBrmv3p9mK\nMUneyntn/v/T6YOdxTvnA/PuyL7ITe5fnetk85qGu/l0rtrhp0LEKOICFuJxWfXRhHrBPrku\ngpdP+MjnZtsLkcMDWFN2uu6ogZtP5Dt9WXmKFU5J3xulWREfqWJZq5fcihFFxBY75CL5+Iob\nu71Qsreh0sf3aPzE9D9sSuV6fUrCXw/1jnpr46mSjWGqRRMWOn+weJe/4c57bmgXYW6eq2P+\n09d1vu/1f5OsuV5rt56a/tywFiNeL+3bi0o/nmLZcW/AY3ibHQBmajL6lxUvxzh/Tjp1NO63\nrx547H8JGXYROb31lZknnx4RERBU6fapU9s5p4n0K7YBs/aF3lOWH3P+bPGpcMujY/p2at+y\nYaWTh/bO/b9XPliwWUQctnNP9Rk26sRvwV65/5Nd1u1Oziw3EdGf/vD54CtNNmD1nlO2rP1E\niYdfiWz1P+fPnT/eMH9wHddVFu/QEglafA79+OiglzO3GRuGd487HhnSo3ObVvVO7dq0bs1v\nb78z86zNISLbZ03s16LLr092NDVs2cZTLDvujexKaK2O0sLsTYa42rLvim02Zl2Oaze93t51\nbZ/FB0sogy11XzU/L+etePlEfL75dI4JPrqjoSvG3auOOy/Ub1fsQ9WCnXOu3m2J+391/sAk\n1/3QddY/RY9x1XbFOqyno4N9nVNavAJfXLgvxwRnd8xuEJC5i9biHbqmWHf9a8D9nY9l6ylW\nOCV9b5RmpeGRYldsqcWuWFyi/q3DXT+f235B8jxK99hf8/5z28CGNasE+fpVqFS9XZ+hb3z1\npzt70A4tGn3s4qxiJi++s2WFHBPc9d7XAZbMf5qXv7cn15nsWfTO4Nhm4aH+5avWvabvLd9t\nPJFzCmVb9e3bN/bp2qxB7RB///BqdaLbdr7vmfd2nErLPtX2aR2cS+flU0FE4ld9fVPnFhUC\n/Q6m253vWjh7cWvZ6vubGIbR/89jeS+dPe34l288MSC2ZZWK5Xx8/MOr1uwyYMTrM35NzXZw\ntnPO7x5Lcv56dFk/wzAqt1qY95wLxP0YV1xA9+5A9x344c7NSZlHakdPXPL0gMgcE4Q1GfrL\nV5kj0GG7MGryZufPcU8NjY2NjY2N7TXwaVHpX77wn+i61QN9A6rUajT0/kkbT+Tyfgt3Buf+\nuaOds+3a7VoR+evr165t37hiiH/5qnU79xr02dL9bi2Ve/dSIW7rwj+/P3pb/9pVK/j6h0Q2\n6/Lkuz+rK+xGvFyxPMWyc2c4uez+48s7BvaObhwZ6h9QtXbDTt36PjF19klrLpO6vw4x695w\nc8F3vnOn8/Htee0YZb/w6aR7W9SuEuAbWC2y+d0T3zljc4jI39+9ObBzVMUQ/3KVa3foPmj6\n0n3Z51CIQZ4L90Zjrmt194dovitG9wcASoTZzRJXW95b7BKPTnNd2/mjXerK/xrOf/4mS26H\nIdfqOeZYej7Hvn/buapzYsMw1idm5DrNrnVrVq5cuXLlynVxp5yXZE/yw9sjcty6YfF5fOEh\n15/bM07c165yrmPey6/6p7vOuqbcNjVzI6XFu/zJjdPCLv4buj/Nluu7Fvoty+sI67Pb57SP\nCMj1diNa37rpQubC5jrnStEL8r7fnNzZYleUGM4FdP8OdH/jwSfRmQcRWrwCtydbc5/Into6\nJHOrXlDlu5yX/TEoswIGhg9578b6OfJ4+9f+9OIgcXJzcGZ/6H9/rk+OiQ3DctO0uLwfi8IN\nM3dua9+Pz7uGokurUS+7fs57G1XRn2LZH0o3h5NSSjkyXh7SLNcp/cpHzdx5NnsA99chptwb\nBVrwdWMyl9o3pN24rlVzTFy5w4RfXh6Y40LDMO74bLdrDgUa5Lk+UkV8zro/RPNaMRZkAKCE\nUOw8Tt7Fbsv/so5qui3uhLrCKuDA9/e7Lgxr0G7w8GG9OmXty4gc8lHeGe6tkrkL0j+su/vJ\nsyfxMQwR8Q4Izv7C4B/Ww/VqsH5i66zLw2u3iGlVNdTXdUm5uuNds822OgsdWjUo+8ImrPxj\nyZIlwV6ZryhNH/54yZIlG86kXSlhRtJmVykREZ+giOYtGgRle0Gq2OIBq0MppZxzvr5i5mtG\neIuXlyxZ8sfq/N/Zp9wodgWNkesCun8Hul/s2lxMFVL9kTwmm9O+inMyw/A6bbWrbK95hsUv\nM1KFSr6WrIfeN6TNv6kFHpyuh94wLF7OERUY4pVtRHn5VTucntcSFWKYuXNbKQk/hvt4ZV3l\nW66Cf84DofKuMkV/irkeSveHk1Lqn69ucF3uF1ajU89eHWMaup6kAeF90y5O6f7DZNa9UaAF\ndxU716McEpDLsWsWn+BLxm1wS9ccCjTIc32kivicdX+I5rHecH8AoORQ7DxO9mLX5IHfz110\ndN/OhR9Nch2JYvEK3pZsVbmuAhzpvcv7Oy+JvPHd1ItlatNX97qm/OxYUh4ZYstlrrlCajzu\nfvLsSQIies5dt8+mVNrpHQ9EZe1Y+eFUqnNiV8I6g967mNC+9KWYi6tO3/SL6xfX6kxEvHyr\njH7pw8V/rF63cmnKxeVy/1CSZQ82dc1qyLPfOG/Clnr42ZuzXrHu+SNrg18JHWNX0Bi5LqD7\nd6Dbxc7u2tsVHjU3jwVc85+s/MvOpatsr3ki4l+hw8wNB5VS1uSEdx64xnV5+9e3KFWwwZn9\noQ9rOGzx1iN2pTIuHHzh+lquyx/+J69tDIUbZvne1uc9a1ycg/9/3vstyeZQ9pTVM57MvtUq\n7ypT9KeY66Es0HB6s3555yXlG03OuLjsR5a/4prymQPnlSrYw2TWvVGgBc9e7OoNe/HAuXSl\n7OtmjnVdaBheEz9fmWpXttTjr16X9aD/dCZzlVWAQX6FR6qIz9mCPh1yXW+4OwBQkih2Hsed\n89iJSP0R3zqnv3wVkH137fenUrLP/LqLm6CiHlqbR4ZrLq5nQ2tOdD959iSTt2TtlTi17W7X\n5c8fdK41HO9MmzZ16tSpU6cuSMhK+Nd7Wdsjj2dkrvmyr85umZvzcH5VkGLXOTRzuSo2n5T9\ncnv6seZBme8JqNJ+puvyEip2BY2R2wIW4A4sTLFr9m0eC5j9NXLxmVR16Wve2NXxWZM6rCMu\nbmQNrvaAKuDgzP7Qzz2e7Joy+cTXrsv7rzp25aSFHGZ535bderrcxUek7s3fZb+9pQ9lVY28\nq0zRn2Kuh7JAw8k1pP0rdH9vzu8JF3e4fzd79qxZs2bNmrX8TJoqyMNk4r1RoAV3DVrD8N6b\nmvUsaBqYOWWFJu+6Ljy79z+u5J/EZw4G9we5yv2RKupztqBPh1xXjG4OAJQoip3HcafYVWx5\n68G0K27zP7Skd75zKBf5ah4Z7q6SuZ4KqDjQ/eTZk+xNzTpCK+n4R67LJ13672BKwj8LZ376\n4qRxt954bavGtY1suxUuf8U1DMtZay77Cdwsdtnfo9fn50M5rv3l2toXF/kG14UlUewKESOP\nBXTnDnR/V2zMxbfEhtQYl8dkP3at7prhkfRLdsV6+9fJcfzmltfbOq+yeAXZCzg4s/bCe4Vk\nn6ct9V/XxHkfUun+veT+bWUvPU9fOp6zD/W8q0zRn2LOh7Kgw2nxLZccHGbxCmzeuf/YZ99c\nuHxzkj3ryeX+w2TWvVHQBc92jF2b7FO62mGju1e6Lsz+FL682OU7yFWeT7pCP2cL+nTIdb3h\n5gBAieIENsgSULFmq+jo9j1vfmr8reGXHarsknwoOd9ZWZO35nFt3zohn8Uni0jamSX70+yR\n/l6XT7NjxbLjGXYR8fav27VzzrdPZj99vGHkEjX1xJrH7xn9waJtjmxvnQuo6J96+orv6DS8\nQsO8C3/+KnvaAdfPNeoE57i2fPNy8pOIiC1tn5Sk4opRiDswX3fXDonbflpEkuM/PpD+eh2/\nXB53EfuUvzNPEhtQ4drqvpc8uD6BUTke7LAWYc4fHPbkU1ZHIQencemaMLcRlavC3Ev53ZYt\nda/r5+igSz6fwze4tbin6E8xp4IOp96f/f6MuuPNOctT7UpEHPaUrasWb121+K0XJCCiyahn\n3p72cC8pyDrErHuj0M8jw/DLNYPF191Ble8gr+ST+6yK7Tlb2KeDuD0AUKIodh6t2Zh1W99q\nV9C/CqiWua/EMLwWLl6U64uzl2+VPObQZkyUDI8XEaWsD8zZt+SOBjkmUI7kAX16H0iziUil\nmLkJG3N/1bkS5UgZ1b7f1wcuiIhvSO3rhg4d0KNDVHT7empMRLNvr/x3RTorqZd/HdfPRw4m\nS8Py2a89v+OC8wdvv1pSkoolRmHvwHz0Gd9S7vpDRBy2C7e9vXnl+FxemI/++vCf59KdP9fo\nPz7HtdaU7Y5LP+L6/Pbzzh8sXiERPpbUIg9O95XQveSV7aHZlmIdIlnvyrSm7HBzJsX1FCvo\ncPLyq/XCN8smvb1n3nfzlyxZ/PMvK+JTbM6rUk/ufPuR3hfqHJw+sJb76xCz7g0Tn875DvJc\n/6qERmNBuTkArloeD2X2JkNcbXm/K/Zyl2+0P38w69PGfjydWogM1pQ9lX0zV+Y+gU1+j0/J\nMcGq1/u5bqLLx7uulMQpOf4T1+XOXbHnDz7numTKv+dcU8a9mNUkctlH5l0+17TuH2PX4eJu\nl/CWk7Nfbk8/3uriXshKrae7Li+hY+wKGuPyBSzQHej+rlhb+pH6F98qaPEOe2d5zsPXLuxb\n4Dp6ybD4LTydy3Hl49YmZP2Bw3rrxfswpPpYVcDBeaWH3paWdcquPHbFFsswu/y27OlH/C4e\njNjg9kvOgLNqQrRr4rx3PhbjU8z94WRN2f3bRedsDqWUw54Ut3T+cw8MCbj4WQ4RLb9RBXmY\nTLw3CvQ8cu2K9QvtnH1i167Y7J8EnfeuWMlvkKvcV8vPuS4p3HO2oE+Hy9cb7g8AlCiKnccp\nerFz2BJdnx8QOeQj11ufrMn/3tiyaf369evXr3/ty5vznu3yJzu4Zutbrumktz7+dfXfx47s\n27Dq18mjrvO5uKfV26/WlqSMKyVxurzYndl9p+uSMb9nfn7GiY3f1s12AoJCFLu2b2zJe6H+\nuK+xa/7DXpzrPGDPnn508tCst9Hd9csR1/QlVOwKGuPyBSzQHVigk+Dv/OxW18SGV8AND06a\nPnvh5p3bf5v/zSsTR1bxzdp20+7xxVlLlO01LyC889zNR5VSttRT7z6Y9YbBLu/uUAUcnEUs\ndsUyzHK9rbdisk74N+6TFekOpRzWDbOeq+Dj7vtAVfE9xdwfTsnxn7ku6f9/611JHPak4ZUC\nnZdHNJ9V0IfJrHujQM+j4i12eQ/yXB+poj9nC13sXOsN9wcAShTFzuMUvdgppXa8N8h1YcVm\nsUNvu31Aj47VgzO3tXgH1F+X34dBOewpD3XM/VyaLobhNXrGnryTqNyKXerpS94gEhHZvGWj\n2j6WS/a0us6Amm+xc60ZAyt3f/r1KXMOJV5poTIS41wvVyLiV65aTOum5bKVlYotH8j+9owS\nKnYFjXH5AhboDizopxtNvz//T4CtO+Cp1GwHkGd/zXMKrlQ9INsHegZU7OHK4/7gLGKxK5Zh\nluttndv7sV+2+XgHhFcKvuTwMnGjyhTXU6wgw8k+5OL7Nw3DCK/RILb3tV07xNTMdo7fAd/8\nU9CHyax7o0DPo+Itdk55DPLLH6miP2cL+nTIbcVYgAGAkkOx8zjFUuyUcnz4ny6Sm4Dw9vP3\nunWmIof19PMjOuV66nkR8Q6o/fQXl5yL3/1ip5T68MZ6OWZo8akw9n9ZH5j23t7MvRX5FrsP\ne9fIPp+83yZ5ZuvMNhX9c12iyu3ucG0McCq5z4otUIxcF9D9O7AQH1v5038fqJb7OyfE4h06\ndPwn6Ze+f871mhdQof99TXN+HpRvSNTcPeeyTe7u4CxisSvQvVTQ29r0yegcr8oiUrN31pl9\n8q0yqvieYu4Pp/P/zI25wpQiUu/aJ7Odn7YA6xBT7o0CLXgxFjt3Bnmuj1QRn7MFHaK5rjcK\nMgBQUih2HqeYip1SSm37+bNRt97YsWXDEF+/iOp123TuO/m9ucdS3Xppdzm6ft5TD97aqlFk\nxXKBviEVG7dsP+DGW55884u9Z3Nu8ytQsXPYk7+fNr5d8wZhAf6RzTrcct/4Zfsu2NIOdO3U\nsUOHDh06dOh984eZS5FfsbMmb51we89aVSr6ePkEhYQNy+8zwm0ph6e/+sSw6/s0b1AzwL9c\nwxbtBw0f+caXv12+Riu5YlegGLkuoPt3YOE+jzz97I4PXpt4XWx0rarh/t4+5SOqRbXp8ciz\n/1tzMJdTW2f7tKWbbKkHXhw9pF6VCr7efpVqNB7x0AvbLhsqyr3BWfRiV/RhlsdtHV83+97B\nXaqFl/P2DarZqP0jr866kLy/w0WP73H305mK/hRTBRlODuvphZ++ekPP2GYNI8sH+wWEhtdv\nGt1r0L1f/fL35a/p7q9Drv69UaAFL8Zi584gz/WRKuJztqBD9EorxgINAJQEQ7n9OcoAYIql\ng+v2+GG/iASG35R8cq7ZcYDixyBHcSnA+WkAAABQmlHsAAAANEGxAwAA0ATFDgAAQBO8eQIA\nAEATbLEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRB\nsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA\n0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUO\nAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEAT\nFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAA\nAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDs\nAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0\nQbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMA\nANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATF\nDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABA\nExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsA\nAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q\n7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAA\nNEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbED\nAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAE\nxQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAA\nQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7\nAAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABN\nUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAA\nADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGx\nAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQ\nBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4A\nAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMU\nOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAA\nTVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRBsQMAANAExQ4AAEATFDsAAABNUOwA\nAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA0ATFDgAAQBMUOwAAAE1Q7AAAADRB\nsQMAANAExQ4AAEATFDsAAABNUOwAAAA0QbEDAADQBMUOAABAExQ7AAAATVDsAAAANEGxAwAA\n0IS32QEAoBRJTEw8evToyZMnz12UkpKSlpaWmpqaY8qAgAB/f//AwMCwsLCwsLDy5ctHRERU\nr149ODjYlOQAIBQ7AJ7JarX+888/O3bs2LNnz/79+w8cOHBg//6jR4+mXFbgCiowIKBGjRq1\n69SJjIysU6dOw4YNmzZtWr9+fR8fn+IIDgB5MZRSZmcAgBKXlJT0119/xcXFbd68eVNc3J49\ne6w2m+vaYMOoo1QtkUoi1UUqiYRd/AoSCRDxv2yGaSKpIski50TOiZwVOSlyVOSEyCGR/YaR\nnG3t6uPt3ahRo1YxMa1atYqJiWndujUb9gCUBIodAG2dOHFi6dKlK1asWL1q1datW212u/Py\nuobRQqnGIk1EGovUFQkvgVs/KbJfZJfITpFdIlsMY9/F9a23l1fzFi06deoUGxvbvXv3SpUq\nlcDtA/BEFDsAWsnIyFi+fPlPP/30+2+/bd22zbmKq2oYnZTqKNJaJFokzKRs50U2iWwUWS2y\nxjCOKyUihmE0b9asZ69e/fv379q1q6+vr0npAOiAYgdAB+fPn1+wYMG8efN+/eWXxKQkEalm\nGD2U6iHSXaSO2fFytV9kmcgfIn8YxjGlRCQ4KKhP376DBg0aOHBgWJhZ/RNAGUaxA1CGJSUl\nzZs3b9asWb/9+muG1eplGB2VGiByrUgLs7MVyFaRRSKLRNYYhl0pXx+f3n36DBs27MYbbwwK\nCjI7HYAyg2IHoOxxOBx//PHHjBkzvv/uu+SUFB/D6KnUEJFBJXO03NV0SuQHkW9F/jAMq1LB\nQUGDb7zxjjvu6NGjh8XCmUcB5INiB6AsSUhImD59+icff/zvvn2GSCeR20VuFqlgdrBid0Zk\njshXIqtFlEjdyMhR9903cuTIypUrmx0NQOlFsQNQNmzYsGHatGlzZs/OsFqrWCx3Oxx3i9Qz\nO9VV8K/IZyLTLZbjDoevj8/NQ4eOGTOmTZs2ZucCUBpR7ACUakqpH3/88c0331y5cqWI9BR5\nUGSgiKed7dcqskDkfZHfRUTkms6dxz/xxMCBAw3DMDkZgNKEYgeglLLb7bNmzXrt1Ve3bd/u\nbxi3KvWoSHOzU5lui8g0ka8NI12pZlFRE598cvjw4V5eXmbnAlAqUOwAlDoOh2P27NnPTZ68\nZ+/eEMO4X6nHRKqYnapUOS7ylsgHhpGoVMMGDSY/99zw4cN5dwUAih2A0mXBggVPPfnktu3b\nQw1jrFKPipQ3O1KpdUbkbZGphnFeqWZRUS+/8sr1119vdigAZqLYASgtNm7cOH78+KVLlwYZ\nxsNKjdfxva4l4YzIf0XeMYxkpbp16/bmm2+2bt3a7FAAzEGxA2C+hISEiRMnzpgxw1DqHqWe\nZ8drwcWLTBb51DCUYdxxxx2vvfYaJ0YBPBDFDoCZbDbb+++/P/mZZ85duNBXZIpIlNmRyrTt\nIuNEfhYJCw194aWXHnjgAW9vb7NDAbh6KHYATLNp06Z777knbtOmOhbLWw7HILPzaGOeyFjD\nOKhUTKtWn3z6aatWrcxOBOAq4S1UAEyQmpo6YcKEdm3bbt28+WmRHbS6YjVYZIdST4ls3by5\nXdu2EydOTE1NNTsUgKuBLXYArrb169ffcfvtu/fsaWcYnyjFqelKzlaRe3OMjVQAACAASURB\nVA1jvVKNGjac8eWX7dq1MzsRgJLFFjsAV4/Van322Wc7d+p0cO/e/4qsptWVsOYiq5X6r8jB\nvXs7d+o0efJkq9VqdigAJYgtdgCukgMHDowYPnztunWtDGOGUs3MzuNRtoncYRiblGrfrt3M\nWbMiIyPNTgSgRLDFDsDV8O2337aKjl6/fv0EkbW0uquumchapSaIrF+/PiY6+ttvvzU7EYAS\nQbFDwSwf0cDIz09n00ro1n/rX9swjE3J+e9LOv33widHD42qX6tcoF94jfodu/d/9qPFGZdu\nnnZ/biVn1cjGJXqPlQZWq3XMmDE333yzX2LiYqVeE/E1O5Jn8hV5TWSxiG9S0s033zxmzBh2\nywL64fxGKJiKbXoPSsva2hK/dPHa8+mN+lzXJDBrLFX2cevzyM/vfzys7pTYz/csv7NB8YZc\n8PzQm1/4Nt2hAsJrt+3S3Xry6M6Vv6xdtuT9d25csW529qilmT39gLf/FfeXVe3w0+FlTfKe\n4Nia/rleZUs++Ofva45mhLbp1K1ptcBLrlO2kZEVNw2Zv3lKt0LmvlR8fPzQoUNXrFjRVWSW\nw8Fph03XV2SzwzFCZNq0aXFxcXPmzKlShYcF0EfZeIVD6RE17v1547J+Xdiq8sDNJ2766OuX\naoeaF+oSW96+4frnfgyu0efreR8NaVPbeWHaqe2v3n3dCwu+73Xd20f/eMzchG4yDN82bdpc\nfrk97eCmbSdDm4TmO0Gusz36y3973PT0nsQMEbH4VBj930XvP9rBde2/3wz/5nT9f1+NLZZF\n2LBhww0DBx6Ljx8r8garm1KjqshvIuNFpq5Y0SYmZv6CBXwEGaAPBRTBguhKIjLpwPlC/O25\nfeNEJPbzPe7/ya/9aolIXFLGlSZIv7Am3MfLO6De2rNpOa6yW0/2Ku8vIl8mJLs5t6tg5V2N\nRGTRmVS3/8I++ZoqgRF9DqXZCjFBRmJcHX/vukMmbz9yNvnkwQ/HdzEsvu/uy3z4bGkHmgf5\nDJ/9b8GXIxdz5swJDAgIMIxvRBRfpfLrG5EAwwj09587d26xPOgATMcxdihBDuuptx+7q0ub\nqPIBIfWbt7/pznHL9iU6r/q/BhXC6k4RkRV3NTQM473jyc7LE/9dNvGu/k3q1gjx8w+vVqdd\nrxGfLNrr/i3u+N9/TlntMc/Pax/ml+Mqi3f4lPF9mzVr9vOyhEIEdvpr3rvXx7apValcQFjl\n5m16vDLj9xwzWPjWuGu7tq1aLqBSzQZ9bn/8113ns1+dmrD2P7dc37JeleBKdXvdcOuHv+93\nf9Gc/n7nhhfXpry/ak5Nv9z3d+c9wam/nzmQZlsw4+mm1cMCw2vd98bSxv7qg2m7nNcuHX/d\nocp3fTW0bkFTXe6VV14ZNmxYufT0ZUqNKPrsUDJGiPypVLmMjKFDh7766qtmxwFQHMxulijb\n8thil5G0uW/9ciJSvWXnW+6+q0/nloZhePvX+uKf80qp7TOnv/VSLxGpf+cLH3zwwbZkq1Iq\n/fzq6GBfw/Bu3XfwPaNHDRvUPczbYlj8Xv3rhHOe+W5jm1gzVEQWnXZrA1iOueUdWCm1+f+G\ni4h/xcZDb7vnntuGNgzzE5GbPtjuvNZhu/BIn7oiUqFJh+F33TOwR3sfw/DyrTptbWb4M9s/\nqh/oIyJ1o68ZNmJQdL3yhsV3QHRFcXuLXdKRmaHelm5vbiz0BKd3PiQiU7aedv6afn59qLel\nw7vblVIpCfODvbym7jrrTpI82Gy2Bx98UERaGcZhs7dI8eXO12GRaMMQkQcffNBmu9KWYABl\nA8UORZJHsZszsLaI9H52jv3iJX/PfNLLMEJr3+f89fJdsesfby4iN8/Y6brk5IZXRaR6tyXO\nX/MtdjX9vL18qzjcC59jbvkGrh/g7RscvTc185Uv/dy6MG+Lf/k+zl83vdRJRFo9Oj3t4s0f\nWflpmLfFv3yPczaHUvabqwSJyOj/W555tT313VGZb0Nxs9g92yrCv3yv87YrLl++EyhH+mOt\nI3wCIx+Y+OIbL0xsXy0ooGL3fak2pdSrHSvXvu4zd2LkITU1dfDgwSLS2zAumN1X+HL/64JI\nLxERGTx4cGqq+wcGACh1KHYokisVO4ftXJi3JaDCtdZLO8b7bSqJyKwTKSq3Ynfw+xkffPBB\nQoarWSlb+hERCW86z/lrPsXOkW4Yhl9oRzfDZ59bvoEd9iQvwwgIH5SSlU7tWLd61ep1zp9j\ny/n5hXa8cGmpWjW+hYg8+e+5xCNviUh4i+eyX2tLP1w/wNvNYhe/6nERGbXkcKEnyLzRtCNT\nHhrcuGbFwPBa3YeM3XAiVSmVsGail2+lVefT842Rh6SkpF69eonIrSLpZjcVvgr6lS5yq4iI\n9OzZMykpqSgjAYCJeJsaSkTKyTnnbI7aHcd6G5dc3vOhhnLXia//PT8sIuDyv6o1+PbRIiKi\nbMn/7Ngat371zws+KcCtGr7lvIwk68mSCVzlla5VJyz7oUbjTiNH3jmgV5cOrRs3adfROZk1\naeOK8+nBVaPmfP5Z9j9PCvISkTUbTp8OXCgizZ+7I/u1Xr41XomqOHTDFY/5y6Js/7nx/4Kr\n3f1h3xqFnMB1o37VH3vn+8feyfanjuTRg95u98yfnUJ9RcSWcnjFH2sOpQS26dQjqkbgFWd0\nqfPnzw+49tpVq1ffL/IeZ8gsg3xFvhSJEJn6++/du3Vb8vPPFSpUMDsUgAKj2KFE2NMPikhI\ng5xn3AhtHCoiiYdSpEMuf+XIODrtqQmzf1m3dce+VIcKrx3Vrm0rkZ3u326vMP9vT/0Tl2SN\nCfa5/Fpr0obHnvzCr9w1b740rBCBx/28PvDpJ97+Yt6Up9ZMeUp8gqr1uH7Iky+/3DUyxJa6\nW0SSjn9y7725NNGUoympgakiUr5RzvlXrh8ibhS7k5vGf5eQ3Pebp4zCTpCHHf83eEl6u6MT\nY0Qk/s9pvQZN2H4uXUQs3uXueW3RR+M65zuHc+fO9e7Va8PGjRNEXit4AJQShshbIn4ir2/Y\n0K9v319+/TUsLMzsUAAKhv+rUSK8/GqKSNK/iTkuT/o3SUQCquayuU5EXu/V9rEpX/s07vHf\nGQv2nUg+sX/rgpnvF+h27+9eRUQmzT+Y67UJa1959913v/kjl87nTmAv3+oPvfH1noTEPRuW\nfvLWC9e1Cv1l1ru9o1pvSrJ6+VYXkaodF+a6YXzd2GZBkUEicm53zvmfPpDkznJ9ee/XXr6V\nPrqxTqEnuBJbyrbrHl965+wvK3hbbCk7u187Pil23JZDp1NOH/5kQrtPxnd/+9+sN/b++OOP\n48aNS0u75HMyzp8/37dPnw0bN06m1WnhNZHJIn9t2NCvb9/z58/n/wcASpWrvOsXmrniMXbW\n06HeFv/yvVLslxxz9lpMhIhMj09Wlx1jZ03ZJSLlIl/MPn36hTXi/jF2SiUd/czLMHxD2m5L\nvGwae+rddcuJyK1/Hr18bvkGTj3z00svvfT+D4eyX7vqqZYict2a40rZGwf6+Jfvk/0IPKXU\n0V8+e/PNNzckZiQd/1BEIqIvWTp7xsmWwb6S3zF2qWcWextGrX7fFnqCPHx3e4OKzZ5w/pyw\nYbCIbMh213UI9Wv64BrXr5MnTxaRrl27nj2b+ebZCxcudOzQQUSeNvsQMb6K9+tpERHp2KHD\nhQsXCjGuAJiFYociyeNdsd/0ryUivV/8wVWUts55UkRCatzl/NVZ7Dp/vMv5qzV5m4iE1Bjn\nmt5hu/Da4LoiUrHJTOcl7pxSeP6DLUQkIKLd9JVZJcyWevjN+9uISHD1m1zdK8fc8g6ccmKm\niARGDD1tzepun99cV0TG7D2rlFr6QFMR6fL8PNfpIlJPrm4b4usb3PKM1aGUuqVasIg89MnF\nnuSwTh+Tebr/vIvdjvc7isjt6+ILPcGVJB78wtfL94uDic5fz+x+RERe3nTK+WtG0t8RPl7t\n/rfVNb3D4ZgwYYKING3a9NChQ2lpaT179hSRiWa3EL5K4muiiIj07NkzLS3n6b4BlFoUOxRJ\nXuexS9zQKSJARGq36X7n6FEDurW2GIZvaJMZe885J0g8/IaIhNbt9+QzT644n66UerVHNRGp\n3773/Y8//diDd7eqFlipzbCoIB+LT4UbR45Rbn5WhCNt6sjMI/hCKkd26Tewe8eYaiE+IhJa\nr9uSY8muCXOexy6/wG/0ryki5eq1v/PeB0bdckOzOuEiUrXrROf5TWzph4fFRIhIlVbdbx/1\n4Mhbbqjq723xDp2yMrNvndnxYWVfLxFp1Lb7bXcO6xBVyTC8bnu8ab7F7sV6YYbhtSfFWugJ\nruSJZhXr3/ZdtrsuY3zbSt4BtUdPfOG/Lz7ZqWawf4UurtO7uEydOtVisVSrVq13794i8oDZ\n/YOvkvsaIyIiN9xwg9Va4NEFwBQUOxRJ3h8pln5u64sP3npNTJOwgMA6TVsPvv2JreeynVDD\nkfbM0E5hgT4hFSrPSEhWStlS973+8OCGtav4B1SI7tzn4ZdmptrV0d/ev7ln267971AF+RCw\nvX/MeGBE/3o1qwT6+kTUbNCpx7Vj//dt0qW7WS+fW96B7enHP3nhgZio+hVD/ALDKjeN7vT4\nm1+fyHZyFoc96Ytn7+vevkXFIP9KtRr2GPLQkq2nst/ima0L7xt2XfN6VX38Q+q37f/24r2n\ntz573XXXbbh8x/FFtrQDgV6WgIrXF3qCKzn882hv/zpbky95wbalHfnfw4Mb1woPDK/Z7cYx\n6+NTcv3br7/+2mKxiMgwEbvZ5YOvkvuyizg/OGT06NEFHWAATGEopQp7eB4AD/XGG29MmDCh\nrshOEV+zw6BEWUWuE/lF5PXXX3/iiSfMjgMgHxQ7AAUzZ86cESNGNFNqhVI5T98CHV0QiTWM\nbSIzZ80aOnSo2XEA5IViB6AANmzY0CU2tkJGxhqHo6bZYXDVHBbpYLGc9fVdsXJl69atzY4D\n4IoodgDclZCQ0LZNm5NHj/6pVDuzw+Aq2yRyjWGEVa7818aN1apVMzsOgNxxgmIAbrFarTfd\ndNPhI0c+ptV5pFYiHyl1LD5++LBhVqvV7DgAckexA+CWCRMmrFy5cpzIbWYngVluFRknsmLl\nSufpDAGUQuyKBZC/H3/8cdCgQR1E/lQql09kg8ewifQUWWEYc+fOHTJkiNlxAOREsQOQj337\n9sVER/snJ8c5HBxahWMiMRZLWlBQ3ObNdevWNTsOgEuwKxZAXmw222233ZaYlPQVrQ4iIlJN\n5CuHIzEx8dZbbrHZbGbHAXAJih2AvLzwwgtr1qwZp1Qvs5Og9Ogl8pjI2nXrXnrpJbOzALgE\nu2IBXNHatWuv6dy5hVJrleITJpBdhkgHw9hiGCtXrerQoYPZcQBkotgByF1aWlqr6OgDe/bE\nKdXE7DAohXaKxBhGZKNGcZs2+fv7mx0HgAi7YgFcybPPPrtr9+7naXW4giYizym1c9eu5557\nzuwsADKxxQ5ALjZu3Ni+Xbs2Sq1SysvsMCi17CKdDGOjYaxbv56PGgNKA4odgJzsdnv7du22\nbNoUp1Qzs8OglNsmEmMYLWNi1q5b5+XFfwGAydgVCyCnd999d2Nc3DhaHdzQTOQxpTZs3Pj+\n+++bnQUAW+wAXCo+Pr5RgwYVU1K2ORyBZodBmZAiEmWxnAkM3L13b5UqVcyOA3g0ttgBuMSk\nSZMuJCW9RauD2wJFpjocF5KSJk2aZHYWwNOxxQ5Alri4uLZt23ZzOH43OwnKnH4iv1osa9as\nadeundlZAM/FFjsAWcaOHWtR6m2zY6AselPEotTjjz9udhDAo1HsAGRatGjR8uXL71Eqyuwk\nKIuaidyj1IoVKxYuXGh2FsBzsSsWgIiIw+FoHROze8uWPUrVMDsMyqjjIg0MI7Jp081//82p\nTwBTsMUOgIjIzJkzN//99xhaHYqgqsijSm3bvn3mzJlmZwE8FFvsAIjdbm8WFXV8794DDkeY\n2WFQpp0TqWMYVRo02L5jBxvtgKuPLXYAZPbs2bt2736UVociCxN5RKnde/bMmTPH7CyAJ2KL\nHeDplFLNoqKO7N693+GoYHYYaOCMSKTFUqNRo23btxuGYXYcwLOwxQ7wdAsXLtyxc+cDtDoU\nkwoiDzocO3bu5O2xwNXHFjvA08XGxv61atV+paqanQTaSBCpYxhtr7lm+fLlZmcBPAtb7ACP\ntm7dupUrV95Kq0Oxqixyi1IrVqxYt26d2VkAz0KxAzzaO++8IyJjzY4B/TwmYlwcYACuGnbF\nAp7r5MmTNWvU6JSR8YfZSaClHiKrfHwOHT5cuXJls7MAnoItdoDn+uijj9IzMh40OwZ09R+R\nDKv1008/NTsI4EHYYgd4KKVUvbp10w8ePKiUt9lhoCWbSB3D8K1d+999+zjvCXB1sMUO8FBL\nly7df+DAXbQ6lBhvkTuU2n/gwLJly8zOAngKih3goaZPny4it5sdA3obKWJcHGwArgJ2xQKe\nKDExsUrlyjGpqSvMTgLtxYrEBQTEJySEhISYnQXQH1vsAE80f/78lNTU28yOAU9wq0hKaur8\n+fPNDgJ4BIod4IlmzpzpbRiDzY4BT3CTiI9hzJ492+wggEdgVyzgcc6ePVu5UqWeNttis5PA\nQ/QX+cPHJz4hoXz58mZnATTHFjvA4yxatMhqs91kdgx4jptEMqzWRYsWmR0E0B/FDvA4P/zw\ng0XkOrNjwHMMFPEyDA6zA64CdsUCniUtLS0iPDw6OZn3w+JqihXZHBR08tQpf39/s7MAOmOL\nHeBZVqxYkZScPNDsGPA0A0WSkpNXrOAfCqBkUewAz/Lzzz+LSD+zY8DT9BURkV9++cXkHIDu\n2BULeJbmzZqd2rHjmFJ8cieuJiVS3TDCo6K2bN1qdhZAZ2yxAzxIQkLC9h07+tDqcNUZIr2V\n2rZ9e0JCgtlZAJ1R7AAPsnz5cqVUN7NjwDN1FVFKcZgdUKIodoAHWb58uYh0MTsGPJNz4FHs\ngBJFsQM8yMoVK6obRj2zY8Az1RepbhjL//zT7CCAzih2gKdISkraunVrJ94vBfN0VGrr1q1J\nSUlmBwG0RbEDPMXGjRvtDkdbs2PAk7UVsTsccXFxZgcBtEWxAzzF+vXrRaSd2THgyZz/VziH\nIoCSQLEDPMXff/9tiLQyOwY8WWsRQ2TTpk1mBwG0RbEDPMXfmzdHGkao2THgyUJF6hjG1i1b\nzA4CaItiB3gEq9W6Z8+e5rxzAmZrrtSuXbsyMjLMDgLoiWIHeITdu3dnWK3NzI4BNBex2mx7\n9uwxOwigJ4od4BGcr6ONzY4BNBIRkb1795qcA9AUxQ7wCP/884+INDA7BuAchBQ7oIRQ7ACP\n4HwdrW92DMA5CJ3/aQAodhQ7wCMcOHAg1DAqmh0DCBcJNYz9+/ebHQTQE8UO8AiHDh6sZXYG\nwKmmyOFDh8xOAeiJYgd4hCNHjtTkXCcoHWoodfjwYbNTAHqi2AH6O3v2bEpqanWzYwBONURS\nUlPPnj1rdhBAQxQ7QH8nTpwQkcpmxwCcKomIyMmTJ03OAeiIYgfoz/kKGm52DMDJORQpdkBJ\noNgB+jt9+rSI8JZYlBIRIiJy6tQpk3MAOqLYAfo7d+6ciJQ3Owbg5ByKzmEJoHhR7AD9JSYm\nikiI2TEAp2AREUlKSjI5B6Ajih2gP+craLDZMQAnih1Qcih2gP5SUlJEJMjsGICTs9glJyeb\nnAPQEcUO0J/VahURX7NjAE7eIiJis9lMzgHoiGIH6M/5CuptdgzAiWIHlBxW9YD+nK+gXmbH\n0EBnkc1mZ9CA87Ptdu/ebXIOQEcUO0B/hmGYHUETW0RSvLwkOtrsIGVcRoZs3ar48GKgBFDs\nAP15eXmJiN3sGBoIE0kSkbVrxZuVZxEcPCh16jRu3NjsHICGOMYO0J+3t7eIcEBT0UWIiN0u\nx4+bHaSMcx70STkGSgDFDtCfj4+PiGSYHUMDNZzfjhwxN0aZZ7UKxQ4oGRQ7QH+BgYEiwknD\niq6u89vhw+bGKPOSkkQkKIhTKwLFj2IH6C84OFicB4ehaDIPCmOLXRElJYlISAifcgcUP4od\noD/nK2ii2TE0kPluWIpdESUmysX/NwAUL4odoL+wsDAROWt2DA20FBHDoNgV1blzIlKuXDmz\ncwAaotgB+gsPDxeRk2bH0ECAiIViV3QnTohIpUqVzM4BaIhiB+jPWexOmx1DDwEOhxw6ZHaK\nMu7UKRGJiIgwOwegIYodoD/nppETZsfQQzkRiY8XO+d7LoKTJ+Xi/xsAihfFDtBf+fLlg4OC\n2H1YLCoJ5ygusiNHAoOCnId+AiheFDvAI1SvXv0InxhbHDhHcTE4fLhmzZpmhwD0RLEDPEKN\nmjUPU+yKQ6TzG+coLgLj8OFaFDugZFDsAI8QGRl53uE4Y3YMDXCO4qI6fVolJtaqVcvsHICe\nKHaAR6hfv76I7DU7hgY4R3FR7dkjIg0bNjQ7B6Anih3gERo0aCAUu+IQLZyjuGj27hWKHVBi\nKHaAR2jUqJGI7DI7hgYCOUdxEe3ZIxf/0wBQ7Ch2gEdo2LChr4/PVrNj6IFzFBfJli0+vr4U\nO6CEUOwAj+Dj49OoUaNtvDG2OJQTkePHOUdx4RhbtjRp0sTX19fsIICeKHaAp2jeosV+pS6Y\nHUMDESJit0t8vNlByqDz59WhQy1btDA7B6Atih3gKWJiYpRInNkxNMA5igtv0yZRqgXFDigx\nFDvAU7Rt21ZE1psdQwOco7jw1q4Vkfbt25udA9AWxQ7wFDExMV4Wy19mx9AA5yguvPXrLV5e\nMTExZucAtEWxAzxFcHBw8+bN1/D+iSJr6fxGsSs4Y/365s2bBwUFmR0E0BbFDvAgsV26HFXq\nX7NjlHV8+EQh/fuvOno09pprzM4B6IxiB3iQ2NhYEVludoyyLljEYrFQ7Aps2TIR6dq1q9k5\nAJ1R7AAP0qVLF8MwlpodQwOco7gwli83DKNLly5m5wB0ZiilzM4A4Opp0bz5ye3bjynFoXZF\nUU3kuLe3pKeLhX+P3aOUUbNmk7Cw7du2mR0F0BmrJMCz9OnbN16pLWbHKOsqiYjNxjmKC2DL\nFnX06LX9+5udA9AcxQ7wLH379hWRn82OUdZVc37jMDv3LV4sIv369TM7B6A5ih3gWWJjY4OD\nghaYHaOsq+v8RrFz3+LFQcHB1/CWWKCEUewAz+Lv79+vf//VIuxELIpGzm98+ISbTpwwVq3q\n36+fn5+f2VEAzVHsAI9zww03OEQWmR2jTMv8rNOjR82NUWbMn6/s9sGDB5udA9AfxQ7wOAMG\nDPD18fnW7BhlWivnN7bYuemHH3x8fa+99lqzcwD6o9gBHqd8+fJ9+vb9zTBOmJ2k7ArlHMXu\nO33a+PXX3r16hYWFmR0F0B/FDvBEw4cPtyn1ndkxyjR/h4Mtdm6ZM0dZrbfccovZOQCPwAmK\nAU+UmJhYpXLlmNTUFWYnKbuqisRzjmJ3XHNNwKZNJxISgoODzY4C6I/1EeCJQkJCbhwyZKXI\nLrOTlF0RImKzSUKC2UFKt3/+kdWrBw8aRKsDrg6KHeChRo4cKSJfmh2j7OIcxW755BNR6u67\n7zY7B+ApKHaAh+revXtknTpfGIbN7CRlFOcozp/NZsyYUScysnv37mZHATwFxQ7wUIZh3Dtq\n1FGlfjA7SRnV2PmNYpeHH39Ux4+PuvdeC4chAlcLTzbAc40aNcrfz+89s2OUUc2d3yh2eXj7\nbR9fX/bDAlcTxQ7wXBEREUNuuulPke1mJymLYpzfOOPJlWzbJsuXjxg+vEqVKmZHATwIxQ7w\naI888ogSmWJ2jLKoHOcoztvUqaLUww8/bHYOwLNwHjvA03Xp0mXtypX7lKphdpIyJ0gkpU4d\n2b/f7CClz/HjRmRk57ZtV6zgVInAVcUWO8DTjR8/3qrUu2bHKItCReToUXE4zA5S+rz1lkpP\nnzhxotk5AI/DFjvA0ymlmkVFHdm9e7/DUcHsMGVLc5FtInL8uHAYWXbnzllq125Sq9bWLVsM\nwzA7DeBZ2GIHeDrDMCY9/fQFh2Oa2UnKHM5RnLupUx0XLkycMIFWB1x9bLEDIHa7vVlU1PG9\new84HGFmhylDHhD5QETmzZNBg8zOUmqcO2eJjKwbEbFzxw5vb2+z0wAehy12AMTLy+vpZ545\n73C8YXaSsqWR8xtb7LL7738d58698PzztDrAFBQ7ACIiI0aMiG7ZcqphUFLcl3mOYk5l53L8\nuDFtWlSzZsOGDTM7CuChKHYAREQsFsuLL72UqtRLZicpQ1o7v7HFzuWZZ1Ry8uuvvcZniAFm\n4Rg7AFm6dOmyZuXKzUpFmZ2krLBYLOqaa+TPP80OUgrs3Gm0aNGlc+dly5aZHQXwXPxTBSDL\ntGnTHIbxiNkxyhB/h4NdsZnGjDEcjilT+BwTwEwUOwBZWrVqdeedd/4hMt/sJGVF5jmK2fXx\n/ffyyy8jR45s3bq12VEAj8auWACXiI+Pb9SgQcWUlG0OR6DZYUq/ZiLbRSQ+XipXNjuLeVJT\nLVFRQadP79m9uwrnagZMxRY7AJeoUqXKiy+/vN/h4F0U7uAcxSIiL77o2L//pRdfpNUBpmOL\nHYCc7HZ7h/bt/46L28S7KPIzWuQjEfnhB7nhBrOzmGTrVqN16+ZNm2746y8fHx+z0wCeji12\nAHLy8vL64MMPHYZxt2HYzQ5TyjV2fvPYLXYOhzF6tEWpzz79lFYHlAYUOwC5aN269ePjx69X\n6k2zk5RyzZzfPLbYTZmi1qx5bOxY3jMBlBLsigWQu7S0tJhWrfbv3h2nVBOzw5RaZ0UqiMht\nt8mXX5qd5arbscNo3bphnTqb4uICAgLMTgNAhC12AK7E39//s+nTbRbLbYaRYXaYUqu8iGGx\neOKp7Gw24847LTbbjC++oNUBpQfFDsAVdejQYdLTT8cp9YzZSUozod3UGwAAHnVJREFUDz1H\n8aRJasOGSU891a5dO7OjAMjCrlgAebHZbF26dFm3du0vSvU0O0zpVEUkwddX0tLEMMzOcrX8\n/LNce23bNm1WrVzJeyaAUoViByAf+/bti4mO9k9OjnM4qpkdphSKEtkhIidOSESE2Vmuivh4\nS3R0cHr635s21alTx+w0AC7BrlgA+ahbt+6Mr746odRNhmE1O0wplFl2PWRvrNUqw4Y5EhI+\n/fhjWh1QClHsAOTv+uuvf/TRR9co9aTZSUqhSOc3Dznjyfjxsnz52LFjb7rpJrOjAMgFxQ6A\nW954443Y2NgpIl+bnaS0aeT85gnFbuZMmTbtmtjY119/3ewoAHJHsQPgFh8fn7lz59asUWOU\nYWwwO0yp0tz5Tfti99dfxj33VKtRY+6cObxhAii1KHYA3FW5cuV5P/wgfn43WCyecUCZW2Kc\n3/QudseOWW680U9k3nffValSxew0AK6IYgegAFq3bv3FjBnxIv0N45zZYUqJcO3PUXzhgjFg\ngDp69Ksvv8xx1rrlIxoY+fnpbFoJ5fqtf23DMDYl5/+Wngv7lj835q62TepWCPEPDK1Yv2n7\nu8c+v/JAUgkFA0zkbXYAAGXMzTffvG/fvokTJw4TWSjCPjkR8Xc4UnUtdunpEh2t9u9/9dVX\nhwwZkuPKim16D0pr5vo1funitefTG/W5rklg1otLZR8vd27n/P7Hw+pOif18z/I7GxRLcJcF\nr9w6/NlZKXaHl2+5pq06V7DHb/p74/Sd6794583bXp37xfh+xXtzheCwnfnixfGfLFy7d++/\nRoXIZi3a/ufZN25sk7VlNP301hcfm7Ro/da9+09WrF2veYcBk195um31wDzmaUs++Ofva45m\nhLbp1K1ptUunVLaRkRU3DZm/eUq3klkgmEoBQMGNHj1aRIaL2EWUx39VEhE/P3E4RCmtvhwO\nGT5cRCwWy9dff53vqFgQXUlEJh04X4gRdW7fOBGJ/XyP+3/ya79aIhKXlJHHNH8+30NE/Cu0\nfffHjSn2zAsdtuT137/VspyfiPR7fW0h0hYjW9qh6yNDRaRi1DXD7xo15NpuQV4Ww/AePnW9\nc4Lk+IV1A31EpGX3AXeNGjmgW5SIePlW/eZg4pXmeeTnNxqG+Dpf5S0+FR6Yuib7tf98NcQ3\nOOZwuq1kFwwmodgBKAybzXbzzTeLyP1ml6rS8NXU+RJ68qT5Vax4vx56SER69+5dq1YtwzAm\nT56c96gobcUu8fAnPhbDJ7DJihOpl1+bfOy3ugHehiVgTkJyIQIXl03PtxGReiM+SHNkXnJ2\nx4+1/b0tXiErzqcrpWZ0qy4it36zxfUnGz8ZKiKV2nyY6wwzEuPq+HvXHTJ5+5GzyScPfji+\ni2HxfXdf5oNiSzvQPMhn+Ox/S3apYB6KHYBCSktL69mzp4hMNLtXmf6V+WFrcXHmV7Fi/Jow\nQUR69OyZmpp67Nix6OhoEXnkkUfsdvuVhkTexc6ecXLa2DtjWzcN8w+u16zdkDseW/rvBedV\n79cvn31X0rvHkpyXX/hn6YQ7+zWOrB7s61exau22PYd/vDCr+eVb7JyVaPDcfVeaYM/0PiJS\n54YfnL/2DPMPbzov7dSGR2/pXa1iSHDlut0GDH17wfYcy7Hgf4/179KmSqh/RI36vW8b98vO\nc67r/hxe37krbPHUx9vUrx7oG1CjfrN7n/800eZQV3B/1WDD4pNjKTY9FyMivX86qJSqF+Dt\nF9rxkjvdnhrmbfENaZvrDI+tHCAi25OtrqmbBPo0e3Sd85dfH25Wru4oNtZpjGIHoPASExM7\ndewoIs+YXa3M/brH2Ud+/NH8NlZcX889JyKdOndOSsrsWGfPnu3atauI5LHdLo9il5G0uW/9\nciJSvWXnW+6+q0/nloZhePvX+uKf80qp7TOnv/VSLxGpf+cLH3zwwbZkq1Iq/fzq6GBfw/Bu\n3XfwPaNHDRvUPczbYlj8Xv3rhHOe+RQ7e0pNP28vn4hzVy5V9vRjQV4W3+Bo5xQ9w/zL1X6u\nY3n/htePfmf67A/efL5nrWDDsNz9yQ7n9A7bhUf61BWRCk06DL/rnoE92vsYhpdv1WlrMyM5\ni92vT8VW7zLq87mL//z5hzHXNxCRmLG/XilDh3p16kXdmuPCvV91EZHYGXuUI33ixImTX12U\n/VqHPTnCxysgfHCuMzy98yERmbL1tPPX9PPrQ70tHd7drpRKSZgf7OU1ddfZK4WBBih2wP+3\nd+dRUpV34oe/t3pnF2VzY1EJIi40CkrcADUaRYMaMpNEjSYu40RNcEMzejTLoMYkavIzMQtR\nJoafuERRY3TEXUkAQQXjLlFDQFRohG5sups7f7QiIiIodMHr85x7TndVv3Xr5XQpH27d+xaf\nyqJFiwYNHBgRFxW7roq4/bg57K6+uvhBtl62Cy6IiAG7715TU7Py73rp0qWjRo267bbbPurF\nsIawmzC8e0QceOGEFUeenhx/XkmWtet+UvPND78VO+WsnSPiy+OeWXHPG9PGRMRW+/+l+eaa\nw27pW3dGRLttv7fmF/BJ3dpExN/eXpbn+bAOlRHR40v/b8UBrYa6l77UtXVJebfn6hryPJ/x\nw8ER0f+M36942/Sfj/yuQ2mhcrOhzfnYHHab9TxlpfP5FlW3KS9rvfOap/EBy+tP36FDlhV+\nN3fJB3/QtGTRWy898cD3j+0XEUdcOeujHj5qQKeyVj3/Y/QPLvv+6EFbtq7afMjLSxvzPB+z\nV5fuh41dh5mwCRJ2wKe1cOHC3QcMiIhzix1Yxdruag67888vfpOtp6qrHjDgrbfeWtdXwkeF\n3fLGmg6lhaqOX2z44LGzq3fvHBH/f35dvrqwe+WWcb/61a9eX/b+m5CN9f+MiC36/qn55prD\nbvE/fxYRm/f5w5rnfNV2m0XEda/X5nk+rENllmUT3/zACXmv3HF4RHzhT7PzPN+nfUVFu73e\n/uAhwEfP3iUiznupJn8v7L7+6NyVB4zp2b5Qutmap7FCw5IXL/hyn4jo89VVC+zJS3ZvfqFl\nWckJv7h3DTtpfOefP/n2iD7bbN5qi22HHPXdafOX5nn++uTRJeWdH11Uv5YzYRNluRPg0+rQ\nocO9kyYd+sUvXvrYY7URV0VkxZ5SC3t3jeJNfcWT5cvjjDPiF7/Ya/Dgv9x1V7t27dbXjuve\nmFDTuLz7Xt8t/eArY9i3e8c35l//0qKvdKr68KO2HXHMyRERkTfWvvj3mdOnPHb37b9d+yct\nqdg6Iprq/7XmYfMamiKi83trspS3Gzx888qVB3Te8xsRE5/93UsNB7z18KL6Nt12mnDt2JUH\nLGldEhGTp70Vvdo333Pk59qvPKAiW6v/IPLGmglXXvy9i65+ubZx/29e9udfH7/KgM57fuO8\n7x3Wdeut/jXtxsvP/HrXLg/+6Ojeq91VScVWo35+y6ifr7Tz5bUnf+mqgRc8OLhdeUQ01r32\n8H2TX61rtfvgoTttvaZlU9jkCDtgPWjfvv3d99xzxBFH/GLSpIURYyPKiz2lltQ5IisU8k36\nwycaGuK442L8+KHDht12661t2rRZj/tuqn8lItrusGoptuvTLiIWv1oXe67mUcuXzbny/HNv\nuOdvM//+8tLl+Rbddxq4R/+IZ9bySSva79e2pFD7+timOPsjV9LL6/8wv65Q1nGvdu++YEsr\neq4ypKSie0TUzl7UuPT1iFgy97ff+tZq+rJuTt2K7zuUrvPi/7PvH3vCN7/zwOzF2ww68tox\nPzl2SI8Pj+m633/+937N335ry5ldR31176998V99W63V3+N//+WIv9QPnDO6OiLmPXjlAV86\n9+ma+ogolLb/5iV3/vrMz6/rhNlo+eQJYP1o3br1HXfcceSRR14fcWiWLS72fFpYxfLlm/AR\nu8WLY/jwGD/+6KOP/vOdd67fqouIkoptImLJS6u+KJa8tCQiqrqt5nBdRFx6wB6jfnJ9WZ+h\nPx53+8vza+fPnnn7+KvX/kkLZZ3O792hoe7Zs6bM/6gxcx86/dV3Gjff6aL2Je8eVGuoe3qV\nMcuWTI+Iyi5tSsq3iohue92x2ve//vbdfvFJPXDZ0dsP+9ZfF23389unv/rXm1euuiVzfjZi\nxIizbvrHKg/Z+wvdmhreuOLVt9dm/411sw476/7jbvifjqWFxrpnhnzx7CX7nPnUq2/VvfXa\nb88d+Nuzh1z10qJPPHk2Oi381i+QtsbGxlNPPTUidsuy14p96ltLbp0iorKy+GfIfYLt1Vez\nXXaJiJNPPrmx8VOtg/GR59g1vNWutFC52QF1TR84O+2S6k4R8ft5tfmHzrFrqHs2Itr3/MHK\n4+vfnhxrfY5dnufzHx8dEa06HfhCXcOHf1q/6Kl9O1ZGxA+fXtB8T/PFE1e9/IH5P3bubhGx\n//+8kOdNfVqVVW52UN0H13uZc8/Yyy+/fNriZfl759jdV/POygN+2qvDGs6xm33jiVmWbXPw\nd55ZvJo/SO3r10VE172uW+X+G4ZsFREXv/L2R+12ZTcfs8Pm/c5p/v71aSMiYtpKz7Vnu4q+\np07+iIey6RF2wPo3ZsyYLMu6FQpTit1bLbbt2Pxv5TffLH6ordM2bVphyy2zLLvkkks+/e99\nDVfF/vGQbSPiwB/cuqLsZk44LyLabv2N5pvNYff53zzbfLOhdlZEtN36zBXjlze+fcmIXhGx\n+Y7jm+9Zm0+eGHd834ho2+Pg8Q+/uFJUNj1160/6daiIiP7/edOKe5vDrmO/E198LwTnPfa7\nbuUlhbKOM5Ysy/P8/v/oGxH7XvynFf279I3H9mhbXt5m1wUN718Vu05hd3DHyrJWn5tT/1Gr\nAzYd3LEyK5Rd9fBrK+565YGrKgpZRbvBS5o+ciWXFRa/cl15Sfl1731MxYLnTo+IH814s/nm\nsiVPdiorGfjTmR+7HzYVwg7YIG666aZWVVVVWTa+2MnVMtvQ5rB74onit9rab3/4Q1ZVVVFZ\nOWHChPXyS1/TOnaLpw3uVBUR3XcfctzJJx66/4BClpW323HcC++uqLL4tcsiol2vg8+74Lzm\nT1wYM3TLiNh+0IGnnPVfo049of+WrTrv/pWdWpcVyjoeefx38rULu7xp6TWnHVCSZRFR0aHb\nHvsefNDeA7q2K4+IrFB22NnXrXyh7rAOleVtqndsXVbZ6XOHjTx2+JCBrUsKWaHypGve7Z7G\n+te+Ut0pIrr2H3LMiace/9UjulWWFkrb/eSRec0D1jXs3llwd0SUVmyz5+qMmvVWnudzJl1Y\nVciyrHTQsEOPPeHYQ/YfUJJlJWWdxjwwd7X7XMU5/Tbf/us3v397+bKz9+hcWtX95NHf//EP\nzhu8TZvKjvu+sNSKxekQdsCGMnXq1C27do2I70Y0FDu8NvR2QnPY3X578XNtbbbGxjjzzIjY\ncuutp0yZsr5+42v+5In6mpk/OPVre1fv2KGqVY++A0Ycc87MmpWW3lj+zgUjB3doVda2Y5dx\nr9fmed649OVLTxvRu3vXyqqOu33+oNN+OH5pUz7n3qu/PGyP/Q45Nl/LsMvzPM/nzbjj7JP+\nbdfttmnfqqyidYfuO/T/2qnn3T3rzVWGDetQ2abrtxb/4/5T/+3Qfj07t9qi+z4HH/Wbh+as\nPGZ505LrLjxpyKBdNm9d2Xnb3kOP+vZfZr6/n3UNu5qXRq3hXKlD//puLy54+i8nHrb/jj23\nrCpv06tv9eHHnDvlX3Uf+6fO8/y1u08urewxs/YD70Q3vvPPn542os+2W7TaYpv9j/zOlHlr\ntSs2FVme5+t6Wh7AWpo3b97IkSMffvjh/SLGR3Qr9nw2nMsizo2IX/4yTjml2HP5OHPnxr//\nezz44N57733TTTd16dKl2BPaWBywWdXfKr++eO5v1vN+86ZlyxrKKyo/fiR8aq6KBTagrl27\nTpo06fTTT38wYrdC4e5iz2fDefeSyI1/xZN77y3stlv20ENnnHHGpEmTVF1LyEpUHS1G2AEb\nVllZ2ZVXXnnjjTcua9v2kIhzI5YVe0obwoDmLxtz2C1bFueeG1/4Qptly26++eYrrriivPwz\ntdogfCYIO6AlHH300TOeeGLgwIGXRQzKslnFns961yUiKxQ23qXs/v73bM8947LLBg0cOOPx\nx0eMGFHsCQEbhLADWkiPHj0efuSRCy+8cFahsEeWXR7RVOwprV8b6RrFTU3x4x9nAwYUZs68\n6KKLHnn44V69ehV7ThupexcuXf8n2EHLcvEE0NKmTJly7DHHPPf88wOz7Ld5vnOx57O+dI54\no6oq6uo+fmiLmTUr++Y38ylTevfpM+7aawcNGlTsCQEbliN2QEsbOHDgjCeeOOecc6YXCgOy\n7L8iNqYU+uQ2j4ilS2PBgmJPJCIi6uri/POzAQMK06ePHj36yRkzVB18Fgg7oAiqqqouvfTS\nv02Z0m/XXX8U0TfLbi32lD69dxdz2Riun5g4MdtppxgzZrd+/f46efKYMWMqK12VCZ8Jwg4o\nmurq6ilTp1555ZU1bduOiDg4YtUPYN+k9Gj+UtzT7GbOjIMOiiOOaLNw4VVXXTV1ypTdd9+9\nmPMBWpawA4qptLT09NNPf+7554877rj/LRR2zbKTI+YVe1afzA7NX4p1xG7evDjllKx//8Kk\nSccff/zzzz572mmnlZSUFGcyQJEIO6D4unTpcu21106dOnWf/fb7dcT2WXZ+xMZxqto62KX5\nS8uH3cKFcd552XbbxTXX7PP5z0+dOnXs2LFdu3Zt6WkAGwFhB2wsqqur77///okTJ/baaacx\nEb0KhYsjFhZ7VmuvuvlLS4bdggVx0UVZr15xySX9tt9+4sSJDz74YHV1dctNANjICDtg4zJ8\n+PAnnnxy/PjxXbbb7qKI7ll2ziby5my3llyjeN68GD260KNHXHzxdp06XX/99U/MmDF8+PCW\neGpgI2YdO2Aj1djYeMMNN1wyZsysp5+uzLKv5fkZERv5oneVWVa/ww7x3HMb8Dlmzoyf/Sz7\n4x/z+vqd+vX73vnnjxw50rl0QDNhB2zU8jyfOHHi5Zdf/sgjj0TEsIhTI4ZHlBV7YqvVKeLN\nVq2itnb977qhIW67LX71q7jvvsjzvffZ5+yzzho+fHiWZev/uYBNlrADNg3Tpk274oorbpww\nYVlDQ7dC4fjly4+P2L7Ys1pFn4jnImLBgthss/W20xdfjGuvzcaOzefOLSsvP/qoo0aNGmUR\nE2C1hB2wKXn99dd///vf/+bXv3559uwsYq+IYyJGRnQs9sSaDYl4ICKeeip2/tRvGi9YEDfe\nGOPGxeTJkec9evY86cQTTzjhhC5dunz6eQKpEnbApmf58uX33XffuHHj/nTLLUtqa8uybGie\nHx3xpYgtijqxb0RcFxF//nMccsgn3MWbb8Ztt8WNN2b33Zc3NLRq3fqoI4889thjhw4dWii4\n3A34GMIO2ITV1tbecsstN9xww//ec8+yhoaSLNsrzw+NOLRIl1n8d8T3IuKaa+Kkk9btkbNm\nxZ13xh13ZJMn501N5RUVBx144MiRI0eMGNGmTZsNMlcgRcIOSEFNTc3tt99+66233nP33Utq\nayNiyywbmudDI/aP6NlS07g94vCIuOCC+P73P3707Nnx4IMxaVI2aVI+d25EtG3X7gsHHXT4\n4Ycffvjh7du339CzBdIj7ICk1NfXP/TQQ3fdddeke++dOWtW8//iumXZXnk+OGJARP+IDVdM\ncyK2jojjj4+xY1fz40WLYsaMePzxeOyxbPLk5pjLsqzfzjsfeMABhxxyyL777lteXr7BZgek\nT9gByZo/f/7999//yCOPPProozOfeqqxqSkisoheWbZznveJ2DGiT0TPiE7r70kLhUI+bFjc\nc0+88UbMnh3PPhvPPBPPPpvNnJm//HLkeUSUlJbuussugwcP3nvvvYcMGdK5c+f19/zAZ5qw\nAz4TlixZ8vjjj0+fPn369Okzpk9//vnnGxobV/y0TZb1zPNtIjpHbBXRKWKziA4RHSJaR1RG\nVH1oh+9ELI2ojah5b5sfMSdiXMTyioqsrCxfsmTF4NKyst69ew+oru7fv391dfWAAQOcOQds\nCMIO+CxqaGh44YUXnnnmmeeff3727NmzZ89+5R//mDNnTt3SpZ9yz2WlpZWtWu05aFCPHj16\n9uzZu3fvvn37br/99mVlG+eaykBShB3A+xYvXjxnzpw333xz4cKFNTU1NTU1dXV19fX1dXV1\nq4ysqqqqrKxs1apVh/d07tx5q622cigOKCJhBwCQCMtdAgAkQtgBACRC2AEAJELYAQAkQtgB\nACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELY\nAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC\n2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAk\nQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEA\nJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgB\nACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELY\nAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC\n2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAk\nQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEA\nJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgB\nACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELY\nAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC\n2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAk\nQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEA\nJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgB\nACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELY\nAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC\n2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAk\nQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEA\nJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgB\nACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELY\nAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC\n2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAk\nQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEA\nJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgB\nACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELY\nAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC\n2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAk\nQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEA\nJELYAQAkQtgBACTi/wCLHs+cnNvxvQAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart for Total Closed and Open Complaints\n",
    "slices <- c(1707, 517)\n",
    "names <- c(\"Total Closed\", \"Total Open\")\n",
    "slices_per <- round(slices/sum(slices)*100)\n",
    "slices_per <- as.character(slices_per)\n",
    "slices_names_per <- paste(names, slices_per,\"%\")\n",
    "pie(x=slices,labels = slices_names_per, col=rainbow(length(names)),\n",
    "    main=\"Pie Chart of Total Open and Closed Complaints\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.069933,
     "end_time": "2020-11-21T21:16:00.867324",
     "exception": false,
     "start_time": "2020-11-21T21:16:00.797391",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Calculating and plotting Closed and Open Complaints received via Customer Care Calls:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:16:01.013079Z",
     "iopub.status.busy": "2020-11-21T21:16:01.011685Z",
     "iopub.status.idle": "2020-11-21T21:16:01.104519Z",
     "shell.execute_reply": "2020-11-21T21:16:01.103646Z"
    },
    "papermill": {
     "duration": 0.165163,
     "end_time": "2020-11-21T21:16:01.104667",
     "exception": false,
     "start_time": "2020-11-21T21:16:00.939504",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nYQcAkMT/AfwWJMNYRYEyAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart for Closed and Open Complaints received via Customer Care Calls\n",
    "slices1 <- c(864, 255)\n",
    "names1 <- c(\"Closed\", \"Open\")\n",
    "slices_per1 <- round(slices1/sum(slices1)*100)\n",
    "slices_per1 <- as.character(slices_per1)\n",
    "slices_names_per1 <- paste(names1, slices_per1,\"%\")\n",
    "pie(x=slices1,labels = slices_names_per1, col=rainbow(length(names1)),\n",
    "    main=\"Closed and Open Complaints received via CC Calls\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.070437,
     "end_time": "2020-11-21T21:16:01.245792",
     "exception": false,
     "start_time": "2020-11-21T21:16:01.175355",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Calculating and plotting Closed and Open Complaints received via Internet:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:16:01.390883Z",
     "iopub.status.busy": "2020-11-21T21:16:01.389566Z",
     "iopub.status.idle": "2020-11-21T21:16:01.487411Z",
     "shell.execute_reply": "2020-11-21T21:16:01.486604Z"
    },
    "papermill": {
     "duration": 0.171475,
     "end_time": "2020-11-21T21:16:01.487553",
     "exception": false,
     "start_time": "2020-11-21T21:16:01.316078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nrkJggg=="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart for Closed and Open Complaints received via Internet\n",
    "slices2 <- c(843, 262)\n",
    "names2 <- c(\"Closed\", \"Open\")\n",
    "slices_per2 <- round(slices2/sum(slices2)*100)\n",
    "slices_per2 <- as.character(slices_per2)\n",
    "slices_names_per2 <- paste(names2, slices_per2,\"%\")\n",
    "pie(x=slices2,labels = slices_names_per2, col=rainbow(length(names2)),\n",
    "    main=\"Closed and Open Complaints received via Internet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.071764,
     "end_time": "2020-11-21T21:16:01.631868",
     "exception": false,
     "start_time": "2020-11-21T21:16:01.560104",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Calculating and plotting All Closed Complaints that are received via Internet and received via Customer Care Calls:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-21T21:16:01.780627Z",
     "iopub.status.busy": "2020-11-21T21:16:01.779452Z",
     "iopub.status.idle": "2020-11-21T21:16:01.871598Z",
     "shell.execute_reply": "2020-11-21T21:16:01.870912Z"
    },
    "papermill": {
     "duration": 0.167303,
     "end_time": "2020-11-21T21:16:01.871743",
     "exception": false,
     "start_time": "2020-11-21T21:16:01.704440",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\ngg=="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart for Total Closed Complaints received via Internet and Customer Care Call\n",
    "slices3 <- c(864, 843)\n",
    "names3 <- c(\"Customer Care Call\", \"Internet\")\n",
    "slices_per3 <- round(slices3/sum(slices3)*100)\n",
    "slices_per3 <- as.character(slices_per3)\n",
    "slices_names_per3 <- paste(names3, slices_per3,\"%\")\n",
    "pie(x=slices3,labels = slices_names_per3, col=rainbow(length(names3)),\n",
    "    main=\"Total Closed Complaints received via Internet and CC Calls\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.072897,
     "end_time": "2020-11-21T21:16:02.018106",
     "exception": false,
     "start_time": "2020-11-21T21:16:01.945209",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From the Pie chart above it can be seen that Total Resolved complaints is 77% and total Unresolved complaints is 23%.\n",
    "For Complaints, received via Customer Care Call, 77% are Closed and 23% are Open.\n",
    "For Complaints, received via the Internet 76% are Closed and 24% are Open.\n",
    "For all complaints which are Resolved/Closed, 51% of those were received via Customer Care Call and the rest of 49% were received via Internet.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.072995,
     "end_time": "2020-11-21T21:16:02.164563",
     "exception": false,
     "start_time": "2020-11-21T21:16:02.091568",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Insights:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.072966,
     "end_time": "2020-11-21T21:16:02.310831",
     "exception": false,
     "start_time": "2020-11-21T21:16:02.237865",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* According to the above analysis, it can be seen that complaints are higher between 12th June and 30th June. To be more precise on 23rd, 24th and 25th June there are sudden spikes of daily complaints. \n",
    "* Most of these complaints are related to Internet Service and Billing related issues. Few other major complaints are related to Data, Usage Caps, Speed Throttling and Charges related. \n",
    "* Highest amount of complaints has been received from Georgia. It also has the highest number of Open complaints.\n",
    "* For Complaints, received via Customer Care Call, 77% are Closed and 23% are Open.\n",
    "* For Complaints, received via the Internet 76% are Closed and 24% are Open. \n",
    "* It can be concluded that there is no discrepancy in resolving complaints between complaints logged via Customer Care call and logged via Internet. \n",
    "* Out of all complaints 77% are already closed and 23% complaints are still open."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.3"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 14.636605,
   "end_time": "2020-11-21T21:16:02.492116",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2020-11-21T21:15:47.855511",
   "version": "2.2.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}