From 144c8831c289f9db10f4bf9bd53fb12849d9de13 Mon Sep 17 00:00:00 2001
From: Shashnk <70314463+Sam09DS@users.noreply.github.com>
Date: Fri, 4 Dec 2020 20:45:18 +0530
Subject: [PATCH 1/8] Create Customer Service Requests Analysis.md

---
 .../Customer Service Requests Analysis.md     | 24 +++++++++++++++++++
 1 file changed, 24 insertions(+)
 create mode 100644 Intern-basics/Customer Service Requests Analysis.md

diff --git a/Intern-basics/Customer Service Requests Analysis.md b/Intern-basics/Customer Service Requests Analysis.md
new file mode 100644
index 0000000..eddba5a
--- /dev/null
+++ b/Intern-basics/Customer Service Requests Analysis.md	
@@ -0,0 +1,24 @@
+DESCRIPTION
+
+Background of Problem Statement :
+
+NYC 311's mission is to provide the public with quick and easy access to all New York City government services and information while offering the best customer service. Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. The agency responds to the request, addresses it, and then closes it.
+
+Problem Objective :
+
+Perform a service request data analysis of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.
+Domain: Customer Service
+
+Analysis Tasks to be performed:
+
+(Perform a service request data analysis of New York City 311 calls) 
+
+Import a 311 NYC service request.
+Read or convert the columns ‘Created Date’ and Closed Date’ to datetime datatype and create a new column ‘Request_Closing_Time’ as the time elapsed between request creation and request closing. (Hint: Explore the package/module datetime)
+Provide major insights/patterns that you can offer in a visual format (graphs or tables); at least 4 major conclusions that you can come up with after generic data mining.
+Order the complaint types based on the average ‘Request_Closing_Time’, grouping them for different locations.
+Perform a statistical test for the following:
+Please note: For the below statements you need to state the Null and Alternate and then provide a statistical test to accept or reject the Null Hypothesis along with the corresponding ‘p-value’.
+
+Whether the average response time across complaint types is similar or not (overall)
+Are the type of complaint or service requested and location related?

From 8bb2fd94a786c70b66a33c0ce49e24b55ae3d97f Mon Sep 17 00:00:00 2001
From: Shashnk <70314463+Sam09DS@users.noreply.github.com>
Date: Fri, 4 Dec 2020 20:49:47 +0530
Subject: [PATCH 2/8] Add files via upload

---
 .../Customer Service Requests Analysis.docx   |  Bin 0 -> 19169 bytes
 Intern-basics/NYC311.ipynb                    | 1000 +++++++++++++++++
 2 files changed, 1000 insertions(+)
 create mode 100644 Intern-basics/Customer Service Requests Analysis.docx
 create mode 100644 Intern-basics/NYC311.ipynb

diff --git a/Intern-basics/Customer Service Requests Analysis.docx b/Intern-basics/Customer Service Requests Analysis.docx
new file mode 100644
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diff --git a/Intern-basics/NYC311.ipynb b/Intern-basics/NYC311.ipynb
new file mode 100644
index 0000000..7dff746
--- /dev/null
+++ b/Intern-basics/NYC311.ipynb
@@ -0,0 +1,1000 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "### import libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import style\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (48,49) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
+     ]
+    }
+   ],
+   "source": [
+    "NYC311 = pd.read_csv ('311_Service_Requests_from_2010_to_Present.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unique Key</th>\n",
+       "      <th>Created Date</th>\n",
+       "      <th>Closed Date</th>\n",
+       "      <th>Agency</th>\n",
+       "      <th>Agency Name</th>\n",
+       "      <th>Complaint Type</th>\n",
+       "      <th>Descriptor</th>\n",
+       "      <th>Location Type</th>\n",
+       "      <th>Incident Zip</th>\n",
+       "      <th>Incident Address</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Bridge Highway Name</th>\n",
+       "      <th>Bridge Highway Direction</th>\n",
+       "      <th>Road Ramp</th>\n",
+       "      <th>Bridge Highway Segment</th>\n",
+       "      <th>Garage Lot Name</th>\n",
+       "      <th>Ferry Direction</th>\n",
+       "      <th>Ferry Terminal Name</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>32310363</td>\n",
+       "      <td>12/31/2015 11:59:45 PM</td>\n",
+       "      <td>01-01-16 0:55</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Noise - Street/Sidewalk</td>\n",
+       "      <td>Loud Music/Party</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>10034.0</td>\n",
+       "      <td>71 VERMILYEA AVENUE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.865682</td>\n",
+       "      <td>-73.923501</td>\n",
+       "      <td>(40.86568153633767, -73.92350095571744)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>32309934</td>\n",
+       "      <td>12/31/2015 11:59:44 PM</td>\n",
+       "      <td>01-01-16 1:26</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Blocked Driveway</td>\n",
+       "      <td>No Access</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>11105.0</td>\n",
+       "      <td>27-07 23 AVENUE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.775945</td>\n",
+       "      <td>-73.915094</td>\n",
+       "      <td>(40.775945312321085, -73.91509393898605)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32309159</td>\n",
+       "      <td>12/31/2015 11:59:29 PM</td>\n",
+       "      <td>01-01-16 4:51</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Blocked Driveway</td>\n",
+       "      <td>No Access</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>10458.0</td>\n",
+       "      <td>2897 VALENTINE AVENUE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.870325</td>\n",
+       "      <td>-73.888525</td>\n",
+       "      <td>(40.870324522111424, -73.88852464418646)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>32305098</td>\n",
+       "      <td>12/31/2015 11:57:46 PM</td>\n",
+       "      <td>01-01-16 7:43</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Illegal Parking</td>\n",
+       "      <td>Commercial Overnight Parking</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>10461.0</td>\n",
+       "      <td>2940 BAISLEY AVENUE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.835994</td>\n",
+       "      <td>-73.828379</td>\n",
+       "      <td>(40.83599404683083, -73.82837939584206)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>32306529</td>\n",
+       "      <td>12/31/2015 11:56:58 PM</td>\n",
+       "      <td>01-01-16 3:24</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Illegal Parking</td>\n",
+       "      <td>Blocked Sidewalk</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>11373.0</td>\n",
+       "      <td>87-14 57 ROAD</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.733060</td>\n",
+       "      <td>-73.874170</td>\n",
+       "      <td>(40.733059618956815, -73.87416975810375)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 53 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unique Key            Created Date    Closed Date Agency  \\\n",
+       "0    32310363  12/31/2015 11:59:45 PM  01-01-16 0:55   NYPD   \n",
+       "1    32309934  12/31/2015 11:59:44 PM  01-01-16 1:26   NYPD   \n",
+       "2    32309159  12/31/2015 11:59:29 PM  01-01-16 4:51   NYPD   \n",
+       "3    32305098  12/31/2015 11:57:46 PM  01-01-16 7:43   NYPD   \n",
+       "4    32306529  12/31/2015 11:56:58 PM  01-01-16 3:24   NYPD   \n",
+       "\n",
+       "                       Agency Name           Complaint Type  \\\n",
+       "0  New York City Police Department  Noise - Street/Sidewalk   \n",
+       "1  New York City Police Department         Blocked Driveway   \n",
+       "2  New York City Police Department         Blocked Driveway   \n",
+       "3  New York City Police Department          Illegal Parking   \n",
+       "4  New York City Police Department          Illegal Parking   \n",
+       "\n",
+       "                     Descriptor    Location Type  Incident Zip  \\\n",
+       "0              Loud Music/Party  Street/Sidewalk       10034.0   \n",
+       "1                     No Access  Street/Sidewalk       11105.0   \n",
+       "2                     No Access  Street/Sidewalk       10458.0   \n",
+       "3  Commercial Overnight Parking  Street/Sidewalk       10461.0   \n",
+       "4              Blocked Sidewalk  Street/Sidewalk       11373.0   \n",
+       "\n",
+       "        Incident Address  ... Bridge Highway Name Bridge Highway Direction  \\\n",
+       "0    71 VERMILYEA AVENUE  ...                 NaN                      NaN   \n",
+       "1        27-07 23 AVENUE  ...                 NaN                      NaN   \n",
+       "2  2897 VALENTINE AVENUE  ...                 NaN                      NaN   \n",
+       "3    2940 BAISLEY AVENUE  ...                 NaN                      NaN   \n",
+       "4          87-14 57 ROAD  ...                 NaN                      NaN   \n",
+       "\n",
+       "  Road Ramp Bridge Highway Segment Garage Lot Name Ferry Direction  \\\n",
+       "0       NaN                    NaN             NaN             NaN   \n",
+       "1       NaN                    NaN             NaN             NaN   \n",
+       "2       NaN                    NaN             NaN             NaN   \n",
+       "3       NaN                    NaN             NaN             NaN   \n",
+       "4       NaN                    NaN             NaN             NaN   \n",
+       "\n",
+       "  Ferry Terminal Name   Latitude  Longitude  \\\n",
+       "0                 NaN  40.865682 -73.923501   \n",
+       "1                 NaN  40.775945 -73.915094   \n",
+       "2                 NaN  40.870325 -73.888525   \n",
+       "3                 NaN  40.835994 -73.828379   \n",
+       "4                 NaN  40.733060 -73.874170   \n",
+       "\n",
+       "                                   Location  \n",
+       "0   (40.86568153633767, -73.92350095571744)  \n",
+       "1  (40.775945312321085, -73.91509393898605)  \n",
+       "2  (40.870324522111424, -73.88852464418646)  \n",
+       "3   (40.83599404683083, -73.82837939584206)  \n",
+       "4  (40.733059618956815, -73.87416975810375)  \n",
+       "\n",
+       "[5 rows x 53 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(300698, 53)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Unique Key', 'Created Date', 'Closed Date', 'Agency', 'Agency Name',\n",
+       "       'Complaint Type', 'Descriptor', 'Location Type', 'Incident Zip',\n",
+       "       'Incident Address', 'Street Name', 'Cross Street 1', 'Cross Street 2',\n",
+       "       'Intersection Street 1', 'Intersection Street 2', 'Address Type',\n",
+       "       'City', 'Landmark', 'Facility Type', 'Status', 'Due Date',\n",
+       "       'Resolution Description', 'Resolution Action Updated Date',\n",
+       "       'Community Board', 'Borough', 'X Coordinate (State Plane)',\n",
+       "       'Y Coordinate (State Plane)', 'Park Facility Name', 'Park Borough',\n",
+       "       'School Name', 'School Number', 'School Region', 'School Code',\n",
+       "       'School Phone Number', 'School Address', 'School City', 'School State',\n",
+       "       'School Zip', 'School Not Found', 'School or Citywide Complaint',\n",
+       "       'Vehicle Type', 'Taxi Company Borough', 'Taxi Pick Up Location',\n",
+       "       'Bridge Highway Name', 'Bridge Highway Direction', 'Road Ramp',\n",
+       "       'Bridge Highway Segment', 'Garage Lot Name', 'Ferry Direction',\n",
+       "       'Ferry Terminal Name', 'Latitude', 'Longitude', 'Location'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Noise - Street/Sidewalk', 'Blocked Driveway', 'Illegal Parking',\n",
+       "       'Derelict Vehicle', 'Noise - Commercial',\n",
+       "       'Noise - House of Worship', 'Posting Advertisement',\n",
+       "       'Noise - Vehicle', 'Animal Abuse', 'Vending', 'Traffic',\n",
+       "       'Drinking', 'Bike/Roller/Skate Chronic', 'Panhandling',\n",
+       "       'Noise - Park', 'Homeless Encampment', 'Urinating in Public',\n",
+       "       'Graffiti', 'Disorderly Youth', 'Illegal Fireworks',\n",
+       "       'Ferry Complaint', 'Agency Issues', 'Squeegee', 'Animal in a Park'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311['Complaint Type'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Loud Music/Party', 'No Access', 'Commercial Overnight Parking',\n",
+       "       'Blocked Sidewalk', 'Posted Parking Sign Violation',\n",
+       "       'Blocked Hydrant', 'With License Plate', 'Partial Access',\n",
+       "       'Unauthorized Bus Layover', 'Double Parked Blocking Vehicle',\n",
+       "       'Double Parked Blocking Traffic', 'Vehicle', 'Loud Talking',\n",
+       "       'Banging/Pounding', 'Car/Truck Music', 'Tortured',\n",
+       "       'In Prohibited Area', 'Congestion/Gridlock', 'Neglected',\n",
+       "       'Car/Truck Horn', 'In Public', 'Other (complaint details)', nan,\n",
+       "       'No Shelter', 'Truck Route Violation', 'Unlicensed',\n",
+       "       'Overnight Commercial Storage', 'Engine Idling',\n",
+       "       'After Hours - Licensed Est', 'Detached Trailer',\n",
+       "       'Underage - Licensed Est', 'Chronic Stoplight Violation',\n",
+       "       'Loud Television', 'Chained', 'Building', 'In Car',\n",
+       "       'Police Report Requested', 'Chronic Speeding',\n",
+       "       'Playing in Unsuitable Place', 'Drag Racing',\n",
+       "       'Police Report Not Requested', 'Nuisance/Truant', 'Homeless Issue',\n",
+       "       'Language Access Complaint', 'Disruptive Passenger',\n",
+       "       'Animal Waste'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311['Descriptor'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Complaint Type</th>\n",
+       "      <th>City</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>ARVERNE</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>ASTORIA</td>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>BAYSIDE</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>BELLEROSE</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>BREEZY POINT</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Complaint Type          City  count\n",
+       "0   Animal Abuse       ARVERNE     38\n",
+       "1   Animal Abuse       ASTORIA    125\n",
+       "2   Animal Abuse       BAYSIDE     37\n",
+       "3   Animal Abuse     BELLEROSE      7\n",
+       "4   Animal Abuse  BREEZY POINT      2"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Complaint_type_city = pd.DataFrame({'count':\n",
+    "                                   NYC311.groupby(['Complaint Type','City']).size()}).reset_index()\n",
+    "Complaint_type_city.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Borough      Complaint Type         Descriptor               \n",
+       "BRONX        Animal Abuse           Chained                      132\n",
+       "                                    In Car                        36\n",
+       "                                    Neglected                    673\n",
+       "                                    No Shelter                    71\n",
+       "                                    Other (complaint details)    311\n",
+       "                                                                ... \n",
+       "Unspecified  Noise - Vehicle        Engine Idling                 11\n",
+       "             Posting Advertisement  Vehicle                        1\n",
+       "             Traffic                Truck Route Violation          1\n",
+       "             Vending                In Prohibited Area             2\n",
+       "                                    Unlicensed                     5\n",
+       "Length: 288, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.groupby(['Borough','Complaint Type','Descriptor']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (48,49) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
+     ]
+    }
+   ],
+   "source": [
+    "NYC311_df = pd.read_csv(\"311_Service_Requests_from_2010_to_Present.csv\", parse_dates=[\"Created Date\", \"Closed Date\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NYC311_df['Request_Closing_Time'] = NYC311_df['Closed Date'] - NYC311_df['Created Date']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "NYC311_df['Status'].value_counts().plot(kind='bar',alpha=0.6,figsize=(7,7))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Complaint type Breakdown with bar plot to figure out majority of complaint types and top 10 complaints\n",
+    "NYC311_df['Complaint Type'].value_counts().head(10).plot(kind='barh',figsize=(8,8));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Borough      Complaint Type         Descriptor               \n",
+       "BRONX        Animal Abuse           Chained                      132\n",
+       "                                    In Car                        36\n",
+       "                                    Neglected                    673\n",
+       "                                    No Shelter                    71\n",
+       "                                    Other (complaint details)    311\n",
+       "                                                                ... \n",
+       "Unspecified  Noise - Vehicle        Engine Idling                 11\n",
+       "             Posting Advertisement  Vehicle                        1\n",
+       "             Traffic                Truck Route Violation          1\n",
+       "             Vending                In Prohibited Area             2\n",
+       "                                    Unlicensed                     5\n",
+       "Length: 288, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.groupby([\"Borough\",\"Complaint Type\",\"Descriptor\"]).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Complaint Type</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Blocked Driveway</td>\n",
+       "      <td>77044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Illegal Parking</td>\n",
+       "      <td>75361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Noise - Street/Sidewalk</td>\n",
+       "      <td>48612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Noise - Commercial</td>\n",
+       "      <td>35577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Derelict Vehicle</td>\n",
+       "      <td>17718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Noise - Vehicle</td>\n",
+       "      <td>17083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>7778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Traffic</td>\n",
+       "      <td>4498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Homeless Encampment</td>\n",
+       "      <td>4416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Noise - Park</td>\n",
+       "      <td>4042</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Complaint Type  count\n",
+       "0         Blocked Driveway  77044\n",
+       "1          Illegal Parking  75361\n",
+       "2  Noise - Street/Sidewalk  48612\n",
+       "3       Noise - Commercial  35577\n",
+       "4         Derelict Vehicle  17718\n",
+       "5          Noise - Vehicle  17083\n",
+       "6             Animal Abuse   7778\n",
+       "7                  Traffic   4498\n",
+       "8      Homeless Encampment   4416\n",
+       "9             Noise - Park   4042"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "majorcomplints=NYC311.dropna(subset=[\"Complaint Type\"])\n",
+    "majorcomplints=NYC311.groupby(\"Complaint Type\")\n",
+    "\n",
+    "sortedComplaintType = majorcomplints.size().sort_values(ascending = False)\n",
+    "sortedComplaintType = sortedComplaintType.to_frame('count').reset_index()\n",
+    "\n",
+    "sortedComplaintType\n",
+    "sortedComplaintType.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sortedComplaintType = sortedComplaintType.head()\n",
+    "plt.figure(figsize=(5,5))\n",
+    "plt.pie(sortedComplaintType['count'],labels=sortedComplaintType[\"Complaint Type\"], autopct=\"%1.1f%%\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#Group dataset by complaint type to display plot against city\n",
+    "groupedby_complainttype = NYC311_df.groupby('Complaint Type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(77044, 54)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grp_data = groupedby_complainttype.get_group('Blocked Driveway')\n",
+    "grp_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unique Key                             0\n",
+       "Created Date                           0\n",
+       "Closed Date                         2164\n",
+       "Agency                                 0\n",
+       "Agency Name                            0\n",
+       "Complaint Type                         0\n",
+       "Descriptor                          5914\n",
+       "Location Type                        131\n",
+       "Incident Zip                        2615\n",
+       "Incident Address                   44410\n",
+       "Street Name                        44410\n",
+       "Cross Street 1                     49279\n",
+       "Cross Street 2                     49779\n",
+       "Intersection Street 1             256840\n",
+       "Intersection Street 2             257336\n",
+       "Address Type                        2815\n",
+       "City                                2614\n",
+       "Landmark                          300349\n",
+       "Facility Type                       2171\n",
+       "Status                                 0\n",
+       "Due Date                               3\n",
+       "Resolution Description                 0\n",
+       "Resolution Action Updated Date      2187\n",
+       "Community Board                        0\n",
+       "Borough                                0\n",
+       "X Coordinate (State Plane)          3540\n",
+       "Y Coordinate (State Plane)          3540\n",
+       "Park Facility Name                     0\n",
+       "Park Borough                           0\n",
+       "School Name                            0\n",
+       "School Number                          0\n",
+       "School Region                          1\n",
+       "School Code                            1\n",
+       "School Phone Number                    0\n",
+       "School Address                         0\n",
+       "School City                            0\n",
+       "School State                           0\n",
+       "School Zip                             1\n",
+       "School Not Found                       0\n",
+       "School or Citywide Complaint      300698\n",
+       "Vehicle Type                      300698\n",
+       "Taxi Company Borough              300698\n",
+       "Taxi Pick Up Location             300698\n",
+       "Bridge Highway Name               300455\n",
+       "Bridge Highway Direction          300455\n",
+       "Road Ramp                         300485\n",
+       "Bridge Highway Segment            300485\n",
+       "Garage Lot Name                   300698\n",
+       "Ferry Direction                   300697\n",
+       "Ferry Terminal Name               300696\n",
+       "Latitude                            3540\n",
+       "Longitude                           3540\n",
+       "Location                            3540\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To get nan values in the entire dataset\n",
+    "NYC311.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fix blank values in City column\n",
+    "NYC311['City'].dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(298084,)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Shape after dropping nan values\n",
+    "NYC311['City'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "283"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#count of null values in grouped city column data\n",
+    "grp_data['City'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6245: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._update_inplace(new_data)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#fix those NAN with \"unknown city\" value instead\n",
+    "grp_data['City'].fillna('Unknown City', inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Scatter plot displaying all the cities that raised complaint of type 'Blocked Driveway'\n",
+    "plt.figure(figsize=(20, 15))\n",
+    "plt.scatter(grp_data['Complaint Type'],grp_data['City'])\n",
+    "plt.title('Plot showing list of cities that raised complaint of type Blocked Driveway')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 6f6565d8af35f73857d8cb5af34ff5c0d827264a Mon Sep 17 00:00:00 2001
From: Shashnk <70314463+Sam09DS@users.noreply.github.com>
Date: Fri, 4 Dec 2020 20:52:52 +0530
Subject: [PATCH 3/8] Add files via upload


From 5e0a0f98801010ef62b61c44c1d686b1ae88e5f4 Mon Sep 17 00:00:00 2001
From: Shashnk <70314463+Sam09DS@users.noreply.github.com>
Date: Fri, 4 Dec 2020 20:56:20 +0530
Subject: [PATCH 4/8] Add files via upload


From 113091019b788047e6c84953b6904655fe55b974 Mon Sep 17 00:00:00 2001
From: Shashnk <70314463+Sam09DS@users.noreply.github.com>
Date: Fri, 4 Dec 2020 21:04:27 +0530
Subject: [PATCH 5/8] Create Customer Service Requests Analysis.md

---
 .../Customer Service Requests Analysis.md     | 26 +++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 inter-basics/Customer Service Requests Analysis.md

diff --git a/inter-basics/Customer Service Requests Analysis.md b/inter-basics/Customer Service Requests Analysis.md
new file mode 100644
index 0000000..5d210b7
--- /dev/null
+++ b/inter-basics/Customer Service Requests Analysis.md	
@@ -0,0 +1,26 @@
+# Cutomer Service Request Analusis
+
+DESCRIPTION
+
+Background of Problem Statement :
+
+NYC 311's mission is to provide the public with quick and easy access to all New York City government services and information while offering the best customer service. Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. The agency responds to the request, addresses it, and then closes it.
+
+Problem Objective :
+
+Perform a service request data analysis of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.
+Domain: Customer Service
+
+Analysis Tasks to be performed:
+
+(Perform a service request data analysis of New York City 311 calls) 
+
+Import a 311 NYC service request.
+Read or convert the columns ‘Created Date’ and Closed Date’ to datetime datatype and create a new column ‘Request_Closing_Time’ as the time elapsed between request creation and request closing. (Hint: Explore the package/module datetime)
+Provide major insights/patterns that you can offer in a visual format (graphs or tables); at least 4 major conclusions that you can come up with after generic data mining.
+Order the complaint types based on the average ‘Request_Closing_Time’, grouping them for different locations.
+Perform a statistical test for the following:
+Please note: For the below statements you need to state the Null and Alternate and then provide a statistical test to accept or reject the Null Hypothesis along with the corresponding ‘p-value’.
+
+Whether the average response time across complaint types is similar or not (overall)
+Are the type of complaint or service requested and location related?

From 44d70f6b519a0fa9d3712ac2c1bdd15d2957f20b Mon Sep 17 00:00:00 2001
From: Shashnk <70314463+Sam09DS@users.noreply.github.com>
Date: Fri, 4 Dec 2020 21:06:08 +0530
Subject: [PATCH 6/8] Create Customer Service Requests .md

---
 Intern-basics/Customer Service Requests .md | 26 +++++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 Intern-basics/Customer Service Requests .md

diff --git a/Intern-basics/Customer Service Requests .md b/Intern-basics/Customer Service Requests .md
new file mode 100644
index 0000000..0ba7817
--- /dev/null
+++ b/Intern-basics/Customer Service Requests .md	
@@ -0,0 +1,26 @@
+# Customer Service Requests Analysis
+
+DESCRIPTION
+
+Background of Problem Statement :
+
+NYC 311's mission is to provide the public with quick and easy access to all New York City government services and information while offering the best customer service. Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. The agency responds to the request, addresses it, and then closes it.
+
+Problem Objective :
+
+Perform a service request data analysis of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.
+Domain: Customer Service
+
+Analysis Tasks to be performed:
+
+(Perform a service request data analysis of New York City 311 calls) 
+
+Import a 311 NYC service request.
+Read or convert the columns ‘Created Date’ and Closed Date’ to datetime datatype and create a new column ‘Request_Closing_Time’ as the time elapsed between request creation and request closing. (Hint: Explore the package/module datetime)
+Provide major insights/patterns that you can offer in a visual format (graphs or tables); at least 4 major conclusions that you can come up with after generic data mining.
+Order the complaint types based on the average ‘Request_Closing_Time’, grouping them for different locations.
+Perform a statistical test for the following:
+Please note: For the below statements you need to state the Null and Alternate and then provide a statistical test to accept or reject the Null Hypothesis along with the corresponding ‘p-value’.
+
+Whether the average response time across complaint types is similar or not (overall)
+Are the type of complaint or service requested and location related?

From 37648ff8ce325d247332462dcff77d23b4938ceb Mon Sep 17 00:00:00 2001
From: Shashnk <70314463+Sam09DS@users.noreply.github.com>
Date: Fri, 4 Dec 2020 21:07:44 +0530
Subject: [PATCH 7/8] Delete Customer Service Requests .md

---
 Intern-basics/Customer Service Requests .md | 26 ---------------------
 1 file changed, 26 deletions(-)
 delete mode 100644 Intern-basics/Customer Service Requests .md

diff --git a/Intern-basics/Customer Service Requests .md b/Intern-basics/Customer Service Requests .md
deleted file mode 100644
index 0ba7817..0000000
--- a/Intern-basics/Customer Service Requests .md	
+++ /dev/null
@@ -1,26 +0,0 @@
-# Customer Service Requests Analysis
-
-DESCRIPTION
-
-Background of Problem Statement :
-
-NYC 311's mission is to provide the public with quick and easy access to all New York City government services and information while offering the best customer service. Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. The agency responds to the request, addresses it, and then closes it.
-
-Problem Objective :
-
-Perform a service request data analysis of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.
-Domain: Customer Service
-
-Analysis Tasks to be performed:
-
-(Perform a service request data analysis of New York City 311 calls) 
-
-Import a 311 NYC service request.
-Read or convert the columns ‘Created Date’ and Closed Date’ to datetime datatype and create a new column ‘Request_Closing_Time’ as the time elapsed between request creation and request closing. (Hint: Explore the package/module datetime)
-Provide major insights/patterns that you can offer in a visual format (graphs or tables); at least 4 major conclusions that you can come up with after generic data mining.
-Order the complaint types based on the average ‘Request_Closing_Time’, grouping them for different locations.
-Perform a statistical test for the following:
-Please note: For the below statements you need to state the Null and Alternate and then provide a statistical test to accept or reject the Null Hypothesis along with the corresponding ‘p-value’.
-
-Whether the average response time across complaint types is similar or not (overall)
-Are the type of complaint or service requested and location related?

From 5b65afe75a52bc05bee7166891139a4e19c3864e Mon Sep 17 00:00:00 2001
From: Shashnk <70314463+Sam09DS@users.noreply.github.com>
Date: Fri, 4 Dec 2020 21:09:27 +0530
Subject: [PATCH 8/8] Delete Customer Service Requests Analysis.md

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-# Cutomer Service Request Analusis
-
-DESCRIPTION
-
-Background of Problem Statement :
-
-NYC 311's mission is to provide the public with quick and easy access to all New York City government services and information while offering the best customer service. Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. The agency responds to the request, addresses it, and then closes it.
-
-Problem Objective :
-
-Perform a service request data analysis of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.
-Domain: Customer Service
-
-Analysis Tasks to be performed:
-
-(Perform a service request data analysis of New York City 311 calls) 
-
-Import a 311 NYC service request.
-Read or convert the columns ‘Created Date’ and Closed Date’ to datetime datatype and create a new column ‘Request_Closing_Time’ as the time elapsed between request creation and request closing. (Hint: Explore the package/module datetime)
-Provide major insights/patterns that you can offer in a visual format (graphs or tables); at least 4 major conclusions that you can come up with after generic data mining.
-Order the complaint types based on the average ‘Request_Closing_Time’, grouping them for different locations.
-Perform a statistical test for the following:
-Please note: For the below statements you need to state the Null and Alternate and then provide a statistical test to accept or reject the Null Hypothesis along with the corresponding ‘p-value’.
-
-Whether the average response time across complaint types is similar or not (overall)
-Are the type of complaint or service requested and location related?