flight_delay_anchal_bhalla.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> <center>Flight Delays Analysis and Predictive Model Solution by Anchal Bhalla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"https://ei.marketwatch.com/Multimedia/2019/02/25/Photos/ZQ/MW-HE536_airpla_20190225131547_ZQ.jpg?uuid=5fa81d1a-3929-11e9-9462-ac162d7bc1f7\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "pd.set_option('display.max_columns', None)\n",
    "import datetime, warnings, scipy\n",
    "warnings.filterwarnings(\"ignore\") \n",
    "\n",
    "from sklearn import metrics, linear_model\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, plot_confusion_matrix\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, RepeatedStratifiedKFold\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier\n",
    "from scipy.optimize import curve_fit\n",
    "from sklearn.svm import SVC\n",
    "from random import sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bringing the dataset to the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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>Airline</th>\n",
       "      <th>Flight_Number</th>\n",
       "      <th>Plane_ID</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>Origin_Airport</th>\n",
       "      <th>Destination_Airport</th>\n",
       "      <th>Flight_Distance</th>\n",
       "      <th>Scheduled_Departure_Time</th>\n",
       "      <th>Actual_Departure_Time</th>\n",
       "      <th>Departure_Delay_Minutes</th>\n",
       "      <th>Departure_Taxi</th>\n",
       "      <th>Departure_WheelsOff</th>\n",
       "      <th>Scheduled_Arrival_Time</th>\n",
       "      <th>Actual_Arrival_Time</th>\n",
       "      <th>Arrival_Delay_Minutes</th>\n",
       "      <th>Arrival_Taxi</th>\n",
       "      <th>Arrival_WheelsOn</th>\n",
       "      <th>Delay_Reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TR</td>\n",
       "      <td>4135</td>\n",
       "      <td>GJLOAB</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>IYF</td>\n",
       "      <td>HIX</td>\n",
       "      <td>678.0</td>\n",
       "      <td>1</td>\n",
       "      <td>103.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>127</td>\n",
       "      <td>219.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>LateAircraft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TR</td>\n",
       "      <td>3776</td>\n",
       "      <td>GDISCI</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HEN</td>\n",
       "      <td>HIX</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>616</td>\n",
       "      <td>624.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RO</td>\n",
       "      <td>4754</td>\n",
       "      <td>GHHWLL</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZN</td>\n",
       "      <td>413.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>242</td>\n",
       "      <td>227.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XM</td>\n",
       "      <td>5604</td>\n",
       "      <td>GBGWFC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZU</td>\n",
       "      <td>130.0</td>\n",
       "      <td>10</td>\n",
       "      <td>44.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>111</td>\n",
       "      <td>155.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>Weather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XM</td>\n",
       "      <td>5612</td>\n",
       "      <td>GBISBC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>LKF</td>\n",
       "      <td>258.0</td>\n",
       "      <td>10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>132</td>\n",
       "      <td>358.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>Carrier</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Airline  Flight_Number Plane_ID  FlightDate Origin_Airport  \\\n",
       "0      TR           4135   GJLOAB  2019-12-01            IYF   \n",
       "1      TR           3776   GDISCI  2019-12-01            HEN   \n",
       "2      RO           4754   GHHWLL  2019-12-01            HIX   \n",
       "3      XM           5604   GBGWFC  2019-12-01            HIX   \n",
       "4      XM           5612   GBISBC  2019-12-01            HIX   \n",
       "\n",
       "  Destination_Airport  Flight_Distance  Scheduled_Departure_Time  \\\n",
       "0                 HIX            678.0                         1   \n",
       "1                 HIX           1700.0                        10   \n",
       "2                 IZN            413.0                        10   \n",
       "3                 IZU            130.0                        10   \n",
       "4                 LKF            258.0                        10   \n",
       "\n",
       "   Actual_Departure_Time  Departure_Delay_Minutes  Departure_Taxi  \\\n",
       "0                  103.0                     62.0            15.0   \n",
       "1                   25.0                     15.0            21.0   \n",
       "2                   10.0                      0.0            15.0   \n",
       "3                   44.0                     34.0            34.0   \n",
       "4                  234.0                    144.0            26.0   \n",
       "\n",
       "   Departure_WheelsOff  Scheduled_Arrival_Time  Actual_Arrival_Time  \\\n",
       "0                118.0                     127                219.0   \n",
       "1                 46.0                     616                624.0   \n",
       "2                 25.0                     242                227.0   \n",
       "3                118.0                     111                155.0   \n",
       "4                300.0                     132                358.0   \n",
       "\n",
       "   Arrival_Delay_Minutes  Arrival_Taxi  Arrival_WheelsOn  Delay_Reason  \n",
       "0                   52.0           7.0             212.0  LateAircraft  \n",
       "1                    8.0          27.0             557.0           NaN  \n",
       "2                    0.0           5.0             222.0           NaN  \n",
       "3                   44.0           4.0             151.0       Weather  \n",
       "4                  146.0           6.0             352.0       Carrier  "
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import os, types\n",
    "import pandas as pd\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share the notebook.\n",
    "\n",
    "if os.environ.get('RUNTIME_ENV_LOCATION_TYPE') == 'external':\n",
    "    endpoint_c5f62ca4e5164bc2aba7c3a9d128c2c9 = 'https://s3-api.us-geo.objectstorage.softlayer.net'\n",
    "else:\n",
    "    endpoint_c5f62ca4e5164bc2aba7c3a9d128c2c9 = 'https://s3-api.us-geo.objectstorage.service.networklayer.com'\n",
    "\n",
    "client_c5f62ca4e5164bc2aba7c3a9d128c2c9 = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id='lrBn3OPuGSMo84U4i9q_QVpHz_epnyakRORbWXcM-W0e',\n",
    "    ibm_auth_endpoint=\"https://iam.cloud.ibm.com/oidc/token\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url=endpoint_c5f62ca4e5164bc2aba7c3a9d128c2c9)\n",
    "\n",
    "body = client_c5f62ca4e5164bc2aba7c3a9d128c2c9.get_object(Bucket='bain-donotdelete-pr-7gkirh4unfosyn',Key='Flight_on_time_HIX.csv')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
    "\n",
    "df_data_1 = pd.read_csv(body)\n",
    "df_data_1.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking null values in the final dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = df_data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airline                         0\n",
      "Flight_Number                   0\n",
      "Plane_ID                        0\n",
      "FlightDate                      0\n",
      "Origin_Airport                  0\n",
      "Destination_Airport             0\n",
      "Flight_Distance                 0\n",
      "Scheduled_Departure_Time        0\n",
      "Actual_Departure_Time           0\n",
      "Departure_Delay_Minutes         0\n",
      "Departure_Taxi                  0\n",
      "Departure_WheelsOff             0\n",
      "Scheduled_Arrival_Time          0\n",
      "Actual_Arrival_Time             0\n",
      "Arrival_Delay_Minutes           0\n",
      "Arrival_Taxi                    0\n",
      "Arrival_WheelsOn                0\n",
      "Delay_Reason                51588\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing = final_dataset.isnull().sum() \n",
    "print(missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will replace all the missing Delay Reason with 'None' becasue in that case there was no delay caused at all "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Flight_Number</th>\n",
       "      <th>Plane_ID</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>Origin_Airport</th>\n",
       "      <th>Destination_Airport</th>\n",
       "      <th>Flight_Distance</th>\n",
       "      <th>Scheduled_Departure_Time</th>\n",
       "      <th>Actual_Departure_Time</th>\n",
       "      <th>Departure_Delay_Minutes</th>\n",
       "      <th>Departure_Taxi</th>\n",
       "      <th>Departure_WheelsOff</th>\n",
       "      <th>Scheduled_Arrival_Time</th>\n",
       "      <th>Actual_Arrival_Time</th>\n",
       "      <th>Arrival_Delay_Minutes</th>\n",
       "      <th>Arrival_Taxi</th>\n",
       "      <th>Arrival_WheelsOn</th>\n",
       "      <th>Delay_Reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TR</td>\n",
       "      <td>4135</td>\n",
       "      <td>GJLOAB</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>IYF</td>\n",
       "      <td>HIX</td>\n",
       "      <td>678.0</td>\n",
       "      <td>1</td>\n",
       "      <td>103.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>127</td>\n",
       "      <td>219.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>LateAircraft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TR</td>\n",
       "      <td>3776</td>\n",
       "      <td>GDISCI</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HEN</td>\n",
       "      <td>HIX</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>616</td>\n",
       "      <td>624.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RO</td>\n",
       "      <td>4754</td>\n",
       "      <td>GHHWLL</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZN</td>\n",
       "      <td>413.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>242</td>\n",
       "      <td>227.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XM</td>\n",
       "      <td>5604</td>\n",
       "      <td>GBGWFC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZU</td>\n",
       "      <td>130.0</td>\n",
       "      <td>10</td>\n",
       "      <td>44.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>111</td>\n",
       "      <td>155.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>Weather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XM</td>\n",
       "      <td>5612</td>\n",
       "      <td>GBISBC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>LKF</td>\n",
       "      <td>258.0</td>\n",
       "      <td>10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>132</td>\n",
       "      <td>358.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>Carrier</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Airline  Flight_Number Plane_ID  FlightDate Origin_Airport  \\\n",
       "0      TR           4135   GJLOAB  2019-12-01            IYF   \n",
       "1      TR           3776   GDISCI  2019-12-01            HEN   \n",
       "2      RO           4754   GHHWLL  2019-12-01            HIX   \n",
       "3      XM           5604   GBGWFC  2019-12-01            HIX   \n",
       "4      XM           5612   GBISBC  2019-12-01            HIX   \n",
       "\n",
       "  Destination_Airport  Flight_Distance  Scheduled_Departure_Time  \\\n",
       "0                 HIX            678.0                         1   \n",
       "1                 HIX           1700.0                        10   \n",
       "2                 IZN            413.0                        10   \n",
       "3                 IZU            130.0                        10   \n",
       "4                 LKF            258.0                        10   \n",
       "\n",
       "   Actual_Departure_Time  Departure_Delay_Minutes  Departure_Taxi  \\\n",
       "0                  103.0                     62.0            15.0   \n",
       "1                   25.0                     15.0            21.0   \n",
       "2                   10.0                      0.0            15.0   \n",
       "3                   44.0                     34.0            34.0   \n",
       "4                  234.0                    144.0            26.0   \n",
       "\n",
       "   Departure_WheelsOff  Scheduled_Arrival_Time  Actual_Arrival_Time  \\\n",
       "0                118.0                     127                219.0   \n",
       "1                 46.0                     616                624.0   \n",
       "2                 25.0                     242                227.0   \n",
       "3                118.0                     111                155.0   \n",
       "4                300.0                     132                358.0   \n",
       "\n",
       "   Arrival_Delay_Minutes  Arrival_Taxi  Arrival_WheelsOn  Delay_Reason  \n",
       "0                   52.0           7.0             212.0  LateAircraft  \n",
       "1                    8.0          27.0             557.0          None  \n",
       "2                    0.0           5.0             222.0          None  \n",
       "3                   44.0           4.0             151.0       Weather  \n",
       "4                  146.0           6.0             352.0       Carrier  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset['Delay_Reason'] = final_dataset['Delay_Reason'].fillna('None') \n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to integer values to int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['Flight_Distance'],final_dataset['Scheduled_Departure_Time'],final_dataset['Actual_Departure_Time'],final_dataset['Departure_Delay_Minutes'],final_dataset['Departure_Taxi'],final_dataset['Departure_WheelsOff'],final_dataset['Scheduled_Arrival_Time'],final_dataset['Actual_Arrival_Time'],final_dataset['Arrival_Delay_Minutes'],final_dataset['Arrival_Taxi'],final_dataset['Arrival_WheelsOn'] = final_dataset['Flight_Distance'].astype(int),final_dataset['Scheduled_Departure_Time'].astype(int),final_dataset['Actual_Departure_Time'].astype(int),final_dataset['Departure_Delay_Minutes'].astype(int),final_dataset['Departure_Taxi'].astype(int),final_dataset['Departure_WheelsOff'].astype(int),final_dataset['Scheduled_Arrival_Time'].astype(int),final_dataset['Actual_Arrival_Time'].astype(int),final_dataset['Arrival_Delay_Minutes'].astype(int),final_dataset['Arrival_Taxi'].astype(int),final_dataset['Arrival_WheelsOn'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the Date Coumn I will be splitting the date into day and month. This will be used for day, month and weekday analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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>Airline</th>\n",
       "      <th>Flight_Number</th>\n",
       "      <th>Plane_ID</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>Origin_Airport</th>\n",
       "      <th>Destination_Airport</th>\n",
       "      <th>Flight_Distance</th>\n",
       "      <th>Scheduled_Departure_Time</th>\n",
       "      <th>Actual_Departure_Time</th>\n",
       "      <th>Departure_Delay_Minutes</th>\n",
       "      <th>Departure_Taxi</th>\n",
       "      <th>Departure_WheelsOff</th>\n",
       "      <th>Scheduled_Arrival_Time</th>\n",
       "      <th>Actual_Arrival_Time</th>\n",
       "      <th>Arrival_Delay_Minutes</th>\n",
       "      <th>Arrival_Taxi</th>\n",
       "      <th>Arrival_WheelsOn</th>\n",
       "      <th>Delay_Reason</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TR</td>\n",
       "      <td>4135</td>\n",
       "      <td>GJLOAB</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>IYF</td>\n",
       "      <td>HIX</td>\n",
       "      <td>678</td>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>118</td>\n",
       "      <td>127</td>\n",
       "      <td>219</td>\n",
       "      <td>52</td>\n",
       "      <td>7</td>\n",
       "      <td>212</td>\n",
       "      <td>LateAircraft</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TR</td>\n",
       "      <td>3776</td>\n",
       "      <td>GDISCI</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HEN</td>\n",
       "      <td>HIX</td>\n",
       "      <td>1700</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>616</td>\n",
       "      <td>624</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>557</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RO</td>\n",
       "      <td>4754</td>\n",
       "      <td>GHHWLL</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZN</td>\n",
       "      <td>413</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>242</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>222</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XM</td>\n",
       "      <td>5604</td>\n",
       "      <td>GBGWFC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZU</td>\n",
       "      <td>130</td>\n",
       "      <td>10</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>118</td>\n",
       "      <td>111</td>\n",
       "      <td>155</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>Weather</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XM</td>\n",
       "      <td>5612</td>\n",
       "      <td>GBISBC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>LKF</td>\n",
       "      <td>258</td>\n",
       "      <td>10</td>\n",
       "      <td>234</td>\n",
       "      <td>144</td>\n",
       "      <td>26</td>\n",
       "      <td>300</td>\n",
       "      <td>132</td>\n",
       "      <td>358</td>\n",
       "      <td>146</td>\n",
       "      <td>6</td>\n",
       "      <td>352</td>\n",
       "      <td>Carrier</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Airline  Flight_Number Plane_ID  FlightDate Origin_Airport  \\\n",
       "0      TR           4135   GJLOAB  2019-12-01            IYF   \n",
       "1      TR           3776   GDISCI  2019-12-01            HEN   \n",
       "2      RO           4754   GHHWLL  2019-12-01            HIX   \n",
       "3      XM           5604   GBGWFC  2019-12-01            HIX   \n",
       "4      XM           5612   GBISBC  2019-12-01            HIX   \n",
       "\n",
       "  Destination_Airport  Flight_Distance  Scheduled_Departure_Time  \\\n",
       "0                 HIX              678                         1   \n",
       "1                 HIX             1700                        10   \n",
       "2                 IZN              413                        10   \n",
       "3                 IZU              130                        10   \n",
       "4                 LKF              258                        10   \n",
       "\n",
       "   Actual_Departure_Time  Departure_Delay_Minutes  Departure_Taxi  \\\n",
       "0                    103                       62              15   \n",
       "1                     25                       15              21   \n",
       "2                     10                        0              15   \n",
       "3                     44                       34              34   \n",
       "4                    234                      144              26   \n",
       "\n",
       "   Departure_WheelsOff  Scheduled_Arrival_Time  Actual_Arrival_Time  \\\n",
       "0                  118                     127                  219   \n",
       "1                   46                     616                  624   \n",
       "2                   25                     242                  227   \n",
       "3                  118                     111                  155   \n",
       "4                  300                     132                  358   \n",
       "\n",
       "   Arrival_Delay_Minutes  Arrival_Taxi  Arrival_WheelsOn  Delay_Reason  Day  \\\n",
       "0                     52             7               212  LateAircraft    1   \n",
       "1                      8            27               557          None    1   \n",
       "2                      0             5               222          None    1   \n",
       "3                     44             4               151       Weather    1   \n",
       "4                    146             6               352       Carrier    1   \n",
       "\n",
       "   Month  \n",
       "0     12  \n",
       "1     12  \n",
       "2     12  \n",
       "3     12  \n",
       "4     12  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset['Day'] = pd.DatetimeIndex(final_dataset['FlightDate']).day\n",
    "final_dataset['Month'] = pd.DatetimeIndex(final_dataset['FlightDate']).month \n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Visual Purposes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "import calendar\n",
    "final_dataset['MONTH_AB'] = final_dataset['Month'].apply(lambda x: calendar.month_abbr[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Airline</th>\n",
       "      <th>Flight_Number</th>\n",
       "      <th>Plane_ID</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>Origin_Airport</th>\n",
       "      <th>Destination_Airport</th>\n",
       "      <th>Flight_Distance</th>\n",
       "      <th>Scheduled_Departure_Time</th>\n",
       "      <th>Actual_Departure_Time</th>\n",
       "      <th>Departure_Delay_Minutes</th>\n",
       "      <th>Departure_Taxi</th>\n",
       "      <th>Departure_WheelsOff</th>\n",
       "      <th>Scheduled_Arrival_Time</th>\n",
       "      <th>Actual_Arrival_Time</th>\n",
       "      <th>Arrival_Delay_Minutes</th>\n",
       "      <th>Arrival_Taxi</th>\n",
       "      <th>Arrival_WheelsOn</th>\n",
       "      <th>Delay_Reason</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>MONTH_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TR</td>\n",
       "      <td>4135</td>\n",
       "      <td>GJLOAB</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>IYF</td>\n",
       "      <td>HIX</td>\n",
       "      <td>678</td>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>118</td>\n",
       "      <td>127</td>\n",
       "      <td>219</td>\n",
       "      <td>52</td>\n",
       "      <td>7</td>\n",
       "      <td>212</td>\n",
       "      <td>LateAircraft</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TR</td>\n",
       "      <td>3776</td>\n",
       "      <td>GDISCI</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HEN</td>\n",
       "      <td>HIX</td>\n",
       "      <td>1700</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>616</td>\n",
       "      <td>624</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>557</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RO</td>\n",
       "      <td>4754</td>\n",
       "      <td>GHHWLL</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZN</td>\n",
       "      <td>413</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>242</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>222</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XM</td>\n",
       "      <td>5604</td>\n",
       "      <td>GBGWFC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZU</td>\n",
       "      <td>130</td>\n",
       "      <td>10</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>118</td>\n",
       "      <td>111</td>\n",
       "      <td>155</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>Weather</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XM</td>\n",
       "      <td>5612</td>\n",
       "      <td>GBISBC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>LKF</td>\n",
       "      <td>258</td>\n",
       "      <td>10</td>\n",
       "      <td>234</td>\n",
       "      <td>144</td>\n",
       "      <td>26</td>\n",
       "      <td>300</td>\n",
       "      <td>132</td>\n",
       "      <td>358</td>\n",
       "      <td>146</td>\n",
       "      <td>6</td>\n",
       "      <td>352</td>\n",
       "      <td>Carrier</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Airline  Flight_Number Plane_ID  FlightDate Origin_Airport  \\\n",
       "0      TR           4135   GJLOAB  2019-12-01            IYF   \n",
       "1      TR           3776   GDISCI  2019-12-01            HEN   \n",
       "2      RO           4754   GHHWLL  2019-12-01            HIX   \n",
       "3      XM           5604   GBGWFC  2019-12-01            HIX   \n",
       "4      XM           5612   GBISBC  2019-12-01            HIX   \n",
       "\n",
       "  Destination_Airport  Flight_Distance  Scheduled_Departure_Time  \\\n",
       "0                 HIX              678                         1   \n",
       "1                 HIX             1700                        10   \n",
       "2                 IZN              413                        10   \n",
       "3                 IZU              130                        10   \n",
       "4                 LKF              258                        10   \n",
       "\n",
       "   Actual_Departure_Time  Departure_Delay_Minutes  Departure_Taxi  \\\n",
       "0                    103                       62              15   \n",
       "1                     25                       15              21   \n",
       "2                     10                        0              15   \n",
       "3                     44                       34              34   \n",
       "4                    234                      144              26   \n",
       "\n",
       "   Departure_WheelsOff  Scheduled_Arrival_Time  Actual_Arrival_Time  \\\n",
       "0                  118                     127                  219   \n",
       "1                   46                     616                  624   \n",
       "2                   25                     242                  227   \n",
       "3                  118                     111                  155   \n",
       "4                  300                     132                  358   \n",
       "\n",
       "   Arrival_Delay_Minutes  Arrival_Taxi  Arrival_WheelsOn  Delay_Reason  Day  \\\n",
       "0                     52             7               212  LateAircraft    1   \n",
       "1                      8            27               557          None    1   \n",
       "2                      0             5               222          None    1   \n",
       "3                     44             4               151       Weather    1   \n",
       "4                    146             6               352       Carrier    1   \n",
       "\n",
       "   Month MONTH_AB  \n",
       "0     12      Dec  \n",
       "1     12      Dec  \n",
       "2     12      Dec  \n",
       "3     12      Dec  \n",
       "4     12      Dec  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Months present in dataframe: [12]\n",
      "Months present in dataframe: ['Dec']\n"
     ]
    }
   ],
   "source": [
    "print('Months present in dataframe:', final_dataset.Month.unique())\n",
    "print('Months present in dataframe:', final_dataset.MONTH_AB.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assinging a number to the weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['FlightDate'] = pd.to_datetime(final_dataset['FlightDate'])\n",
    "final_dataset['WEEKDAY'] = final_dataset['FlightDate'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WEEKDAY GLOSSARY\n",
    "\n",
    "0 = Monday\n",
    "1 = Tuesday\n",
    "2 = Weednesday\n",
    "3 = Thursday\n",
    "4 = Friday\n",
    "5 = Saturday\n",
    "6 = Sunday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since this is a binary classification problem, we will need to create a label column indicating whether a delay has occurred or not. 0 means no delay and 1 means yes delay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Airline</th>\n",
       "      <th>Flight_Number</th>\n",
       "      <th>Plane_ID</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>Origin_Airport</th>\n",
       "      <th>Destination_Airport</th>\n",
       "      <th>Flight_Distance</th>\n",
       "      <th>Scheduled_Departure_Time</th>\n",
       "      <th>Actual_Departure_Time</th>\n",
       "      <th>Departure_Delay_Minutes</th>\n",
       "      <th>Departure_Taxi</th>\n",
       "      <th>Departure_WheelsOff</th>\n",
       "      <th>Scheduled_Arrival_Time</th>\n",
       "      <th>Actual_Arrival_Time</th>\n",
       "      <th>Arrival_Delay_Minutes</th>\n",
       "      <th>Arrival_Taxi</th>\n",
       "      <th>Arrival_WheelsOn</th>\n",
       "      <th>Delay_Reason</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>MONTH_AB</th>\n",
       "      <th>WEEKDAY</th>\n",
       "      <th>IsDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TR</td>\n",
       "      <td>4135</td>\n",
       "      <td>GJLOAB</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>IYF</td>\n",
       "      <td>HIX</td>\n",
       "      <td>678</td>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>118</td>\n",
       "      <td>127</td>\n",
       "      <td>219</td>\n",
       "      <td>52</td>\n",
       "      <td>7</td>\n",
       "      <td>212</td>\n",
       "      <td>LateAircraft</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TR</td>\n",
       "      <td>3776</td>\n",
       "      <td>GDISCI</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HEN</td>\n",
       "      <td>HIX</td>\n",
       "      <td>1700</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>616</td>\n",
       "      <td>624</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>557</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RO</td>\n",
       "      <td>4754</td>\n",
       "      <td>GHHWLL</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZN</td>\n",
       "      <td>413</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>242</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>222</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XM</td>\n",
       "      <td>5604</td>\n",
       "      <td>GBGWFC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>IZU</td>\n",
       "      <td>130</td>\n",
       "      <td>10</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>118</td>\n",
       "      <td>111</td>\n",
       "      <td>155</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>Weather</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XM</td>\n",
       "      <td>5612</td>\n",
       "      <td>GBISBC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>HIX</td>\n",
       "      <td>LKF</td>\n",
       "      <td>258</td>\n",
       "      <td>10</td>\n",
       "      <td>234</td>\n",
       "      <td>144</td>\n",
       "      <td>26</td>\n",
       "      <td>300</td>\n",
       "      <td>132</td>\n",
       "      <td>358</td>\n",
       "      <td>146</td>\n",
       "      <td>6</td>\n",
       "      <td>352</td>\n",
       "      <td>Carrier</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Airline  Flight_Number Plane_ID FlightDate Origin_Airport  \\\n",
       "0      TR           4135   GJLOAB 2019-12-01            IYF   \n",
       "1      TR           3776   GDISCI 2019-12-01            HEN   \n",
       "2      RO           4754   GHHWLL 2019-12-01            HIX   \n",
       "3      XM           5604   GBGWFC 2019-12-01            HIX   \n",
       "4      XM           5612   GBISBC 2019-12-01            HIX   \n",
       "\n",
       "  Destination_Airport  Flight_Distance  Scheduled_Departure_Time  \\\n",
       "0                 HIX              678                         1   \n",
       "1                 HIX             1700                        10   \n",
       "2                 IZN              413                        10   \n",
       "3                 IZU              130                        10   \n",
       "4                 LKF              258                        10   \n",
       "\n",
       "   Actual_Departure_Time  Departure_Delay_Minutes  Departure_Taxi  \\\n",
       "0                    103                       62              15   \n",
       "1                     25                       15              21   \n",
       "2                     10                        0              15   \n",
       "3                     44                       34              34   \n",
       "4                    234                      144              26   \n",
       "\n",
       "   Departure_WheelsOff  Scheduled_Arrival_Time  Actual_Arrival_Time  \\\n",
       "0                  118                     127                  219   \n",
       "1                   46                     616                  624   \n",
       "2                   25                     242                  227   \n",
       "3                  118                     111                  155   \n",
       "4                  300                     132                  358   \n",
       "\n",
       "   Arrival_Delay_Minutes  Arrival_Taxi  Arrival_WheelsOn  Delay_Reason  Day  \\\n",
       "0                     52             7               212  LateAircraft    1   \n",
       "1                      8            27               557          None    1   \n",
       "2                      0             5               222          None    1   \n",
       "3                     44             4               151       Weather    1   \n",
       "4                    146             6               352       Carrier    1   \n",
       "\n",
       "   Month MONTH_AB  WEEKDAY  IsDelay  \n",
       "0     12      Dec        6        1  \n",
       "1     12      Dec        6        0  \n",
       "2     12      Dec        6        0  \n",
       "3     12      Dec        6        1  \n",
       "4     12      Dec        6        1  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status = []\n",
    "\n",
    "for value in final_dataset['Arrival_Delay_Minutes']:\n",
    "    if value >= 15:\n",
    "        status.append(1) #delayed\n",
    "    else:\n",
    "        status.append(0) #no delay\n",
    "final_dataset['IsDelay'] = status\n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.785576\n",
       "1    0.214424\n",
       "Name: IsDelay, dtype: float64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.IsDelay.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>The above calculation shows there that 78% of the flights were not delayed in the Month of december and 21% of them were delayed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.set(font_scale=1.6)\n",
    "axis = sns.countplot(x=final_dataset['Origin_Airport'], data=final_dataset, order=final_dataset['Origin_Airport'].value_counts().iloc[0:18].index, orient=\"v\")\n",
    "axis.set_xticklabels(axis.get_xticklabels(), rotation=90, ha='right')\n",
    "plt.title('Total Flights departing from Airport', fontsize=24)\n",
    "plt.xlabel('Airport', fontsize=18)\n",
    "plt.ylabel('Number of flights', fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>From the above chart we can see HIX is the Airport from where most of the flights are departing from in the month of December"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 20 Destinations where Flights are landing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HIX    32806\n",
       "EXU     1000\n",
       "ERR      672\n",
       "TKF      640\n",
       "WTU      640\n",
       "UFM      626\n",
       "WWQ      615\n",
       "FJJ      598\n",
       "WKQ      537\n",
       "LWI      533\n",
       "IYR      478\n",
       "VDB      475\n",
       "WVH      474\n",
       "VMA      474\n",
       "BEX      468\n",
       "FTI      449\n",
       "XNL      448\n",
       "UEU      446\n",
       "BRB      437\n",
       "LVU      429\n",
       "Name: Destination_Airport, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.Destination_Airport.value_counts().iloc[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_dataset.Destination_Airport.value_counts().iloc[:20].plot(kind='bar', figsize=(15,8), rot=0) \n",
    "plt.xlabel(\"Airport\", labelpad=14)\n",
    "plt.ylabel(\"No. of Flights\", labelpad=14)\n",
    "plt.title(\"Top 20 Airport Destinations\", y=1.02);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>HIX is the top destination airport followed by EXU. This is the reason why most of the delays are happening in this particular airport, since it's the most busiest place."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing airlines causing most delays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sJO/MsPvkJz/Z4D8PPvhgku1/ad32eO6557JixYq0bt06vXv3brHrzpkzJzU1Ndm0aVMGDBjQoC+9e/fOSy+9lLfffjv33HNPi9W0pepertW3b98dXEnT6p7Xupeo7YwqKipy1FFH5Wtf+1qSlO2FZv369UuSPPXUU2U537vV/ePPn//850bf9erq6iTJH//4x21ek3zXXXfNgQcemJ/+9Kdp165dvvvd7zYZ+jeXkSNH5iMf+UhuueWWrV4uBQDY+VkyAgDYafXs2TOHHXZYnnjiidx22235+te/3uR+L7/8ciZPnpwkOeOMM7bpWlOmTMmGDRvSu3fv7L///k3u8/DDD2f+/Pl5/vnn07Nnz226zvb4l3/5lyTJMccck6qqqha7bt1MyWOOOSadOnVqtH3Tpk259957c/fdd+crX/nKdi3bUU6vvPJKffBXF/J90Jxwwgm5/vrr89vf/jaPP/54jjjiiB1d0jZr165dWc+3atWqJO88X+X01ltvZdq0aWnVqlWGDRvW5PO6YsWKzJs3L3ffffc2/yNT8s5vOXzlK1/JD37wg0yYMCHXXHPN9pS+xfbYY4+MHDky48aNq/97AwAoDoEwALBT+9a3vpUzzzwzP/3pT9O1a9d8/vOfb7C9pqYmF110UV577bWcdtpp9bOKt1Zd6DlmzJiSs0lvvPHG/PjHP87kyZNLhtPNYe3atbnxxhszbdq07LHHHrn88stb7NorVqzIY489lg4dOmT8+PEl1yN97rnnsnDhwsyfPz9HHnlki9VXyuLFi3PFFVdk5cqVGThwYI499tgdXVKT2rdvn6uuuiqjR4/OpZdemnHjxmXQoEGN9lu3bl3efvvtHVDh/5g7d27Wr1+fY445ptE62q+//nr9+rmHHHLIdl9r2bJleeihh5Kkfn3hcnnwwQfz2muvZcCAAbnuuuua3Gft2rU5+uij88ADD+Sqq65K+/btt/l6w4cPz8SJEzNlypR8+ctfzj777LPN59oan//85/Ozn/0skyZNql9+BwAoBoEwALBT++QnP5mf/OQn+drXvpbvfve7+fnPf57DDjss7dq1y3PPPZdHH30069atS3V1db773e9u0zWeeOKJPPPMM9lvv/02u7TA5z73ufzkJz/JPffck0suuSStW//P/9RauHBhRo8e3eRxn/70p7f45VQTJ07MHnvskdra2qxduzbPPPNMfv3rX+eNN97IPvvskxtuuCF/+7d/2+SxCxYsyIQJE5rc1r59+5x77rmNxq+//vrsvvvuTR4zatSo3HPPPdm4cWNOPfXUzb6c6vTTT8/ChQszadKk7QqE6+6/Kf37928UDr7wwgv19/z2229n5cqVefrpp/P73/8+yTszg8eOHbvN9WypUj/7JPmHf/iH7LbbbiW3Dxs2LG+99VauueaajBw5Mh//+Mdz8MEHp1OnTnn99ddTU1OTefPm5Y033ihL2Lqt/vznP2fcuHHp0KFDPvOZz2SfffZJZWVlVqxYkUcffTSrV69Ov3798sUvfnGrzjtlypQsWLAgyTs/wxdeeCGzZ8/OG2+8kWOOOSbHHXdcWe+jbtmX008/veQ+VVVVOeGEE3Lvvfdm+vTp+cIXvrDN19ttt91y/vnnZ9y4cfnRj36UH/7wh4322Z7np5Q2bdrksssuy6WXXpoXXnhhs/tu7fcOAPhgEwgDADu9ww8/PL/85S/z7//+75kzZ06mTp2aN998Mx07dsyAAQNyxhln5Kijjtrm829JQJS887Kmv/u7v8t//ud/Zs6cORk8eHD9tr/85S/5y1/+0uRxq1ev3uJAuG6WZevWrdOuXbt07do1xx13XI499tgMGjQobdq0KXnsggUL6oO19+revXuTgXDd2shNGT58eP26wJ/73Oc2W/cJJ5yQa6+9Ng8//HBeeeWVJpeW2BJ199+Uiy++uMlA+Kabbkryzrqte+yxR/bee++cd955OfXUU0su/1FuU6ZMKbntqquuet9A78wzz8zAgQNz5513Zv78+Zk5c2Zef/31tG3bNt27d091dXVOOeWU9O/fv9ylb7FTTz01VVVVmT9/fhYvXlz/DxXt27fPpz71qZxwwgk5/fTTN/uMNuXdvauoqEj79u2z//77Z8iQIfnc5z5X1iVInn322SxYsCCdOnXKMcccs9l9Tz/99Nx777256667tisQTpKzzjort912W+6///5ccMEF+dSnPtVg+/Y+P6WceOKJmThxYn77299udr+t/d4BAB9sFbW1tbU7uggAAAAAAJpfqx1dAAAAAAAALUMgDAAAAABQEAJhAAAAAICCEAgDAAAAABSEQBgAAAAAoCBa7+gCdpSVK1/Ppk21O7qMzerUqSqvvLJ2R5fxoaGf5aWf5aWf5aWf5aWf5aWf5aWf5aWf5aen5aWf5aWf5aWf5aWf5aWf5bUz9LNVq4rsuWe7JrcVNhDetKn2Ax8IJ9kpatyZ6Gd56Wd56Wd56Wd56Wd56Wd56Wd56Wf56Wl56Wd56Wd56Wd56Wd56Wd57cz9tGQEAAAAAEBBCIQBAAAAAApCIAwAAAAAUBACYQAAAACAghAIAwAAAAAUhEAYAAAAAKAgBMIAAAAAAAUhEAYAAAAAKAiBMAAAAABAQQiEAQAAAAAKQiAMAAAAAFAQAmEAAAAAgIIQCAMAAAAAFIRAGAAAAACgIATCAAAAAAAFIRAGAAAAACgIgTAAAAAAQEEIhAEAAAAACkIgDAAAAABQEAJhAAAAAICCEAgDAAAAABSEQBgAAAAAoCAEwgAAAAAABSEQBgAAAAAoCIEwAAAAAEBBCIQBAAAAAAqi9Y4u4MOi/R67pe2u5W9n587ty3q+9W++nTWr15X1nAAAAADAzkEgXCZtd22d6sun7egy3tcvfjAka3Z0EQAAAADADmHJCAAAAACAghAIAwAAAAAURIsGwsuXL8/w4cNz4okn5uSTT87EiROTJBMmTMjRRx+dIUOGZMiQIXn00Ufrj7n55pszePDgfPazn81jjz1WP/7000+nuro6gwcPzjXXXJPa2tqWvBUAAAAAgJ1Oi64hXFlZmdGjR6d3795Zu3Zt/v7v/z5HHnlkkuTcc8/Nl770pQb7L126NDNmzMiMGTNSU1OTESNG5MEHH0xlZWXGjBmTsWPH5sADD8z555+fuXPnZuDAgS15OwAAAAAAO5UWnSHcpUuX9O7dO0lSVVWVXr16paampuT+s2fPzsknn5w2bdqkZ8+e2XvvvfPUU0/lxRdfzNq1a3PQQQeloqIiQ4cOzezZs1vqNgAAAAAAdkotOkP43ZYtW5ZFixalX79++c1vfpOf/exnmTp1avr06ZPRo0enQ4cOqampSb9+/eqP6dq1a2pqatK6det069atfrxbt26bDZab0qlTVdnuZWfTuXP7HV3CDlPke28O+lle+lle+lle+lle+lle+lle+ll+elpe+lle+lle+lle+lle+lleO3M/d0gg/Prrr2fUqFG56qqrUlVVlbPOOisXXXRRKioqMn78+Hzve9/LuHHjmlwXuKKiouT41njllbXZtKl86w7vTA/BSy+t2dEl7BCdO7cv7L03B/0sL/0sL/0sL/0sL/0sL/0sL/0sPz0tL/0sL/0sL/0sL/0sL/0sr52hn61aVZScENuiS0YkyYYNGzJq1KhUV1fn+OOPT5LstddeqaysTKtWrXL66afnd7/7XZJ3Zv6uWLGi/tiampp06dKl0fiKFSvSpUuXlr0RAAAAAICdTIsGwrW1tfnWt76VXr16ZcSIEfXjL774Yv2fZ82alX333TdJMmjQoMyYMSNvvfVWnn/++Tz77LPp27dvunTpknbt2uXJJ59MbW1tpk6dmmOPPbYlbwUAAAAAYKfToktGLFy4MNOmTct+++2XIUOGJEkuu+yy3HfffVm8eHGSpHv37hk7dmySZN99982JJ56Yk046KZWVlbn66qtTWVmZJBkzZkyuvPLKrF+/PgMGDMiAAQNa8lYAAAAAAHY6LRoIH3LIIVmyZEmj8YEDB5Y8ZuTIkRk5cmSj8QMOOCD33XdfWesDAAAAAPgwa/E1hAEAAAAA2DEEwgAAAAAABSEQBgAAAAAoiBZdQxi2VPs9dkvbXcv/eHbu3L6s51v/5ttZs3pdWc8JAAAAAM1FIMwHUttdW6f68mk7uoz39YsfDMmaHV0EAAAAAGwhS0YAAAAAABSEQBgAAAAAoCAEwgAAAAAABSEQBgAAAAAoCIEwAAAAAEBBCIQBAAAAAApCIAwAAAAAUBACYQAAAACAghAIAwAAAAAUhEAYAAAAAKAgBMIAAAAAAAUhEAYAAAAAKAiBMAAAAABAQQiEAQAAAAAKQiAMAAAAAFAQAmEAAAAAgIIQCAMAAAAAFIRAGAAAAACgIATCAAAAAAAFIRAGAAAAACgIgTAAAAAAQEEIhAEAAAAACkIgDAAAAABQEAJhAAAAAICCEAgDAAAAABSEQBgAAAAAoCAEwgAAAAAABSEQBgAAAAAoCIEwAAAAAEBBCIQBAAAAAApCIAwAAAAAUBACYQAAAACAghAIAwAAAAAUhEAYAAAAAKAgBMIAAAAAAAUhEAYAAAAAKAiBMAAAAABAQQiEAQAAAAAKQiAMAAAAAFAQAmEAAAAAgIIQCAMAAAAAFIRAGAAAAACgIATCAAAAAAAFIRAGAAAAACgIgTAAAAAAQEEIhAEAAAAACkIgDAAAAABQEAJhAAAAAICCEAgDAAAAABSEQBgAAAAAoCAEwgAAAAAABSEQBgAAAAAoCIEwAAAAAEBBCIQBAAAAAApCIAwAAAAAUBACYQAAAACAghAIAwAAAAAUhEAYAAAAAKAgBMIAAAAAAAUhEAYAAAAAKAiBMAAAAABAQQiEAQAAAAAKQiAMAAAAAFAQAmEAAAAAgIIQCAMAAAAAFIRAGAAAAACgIATCAAAAAAAFIRAGAAAAACgIgTAAAAAAQEEIhAEAAAAACkIgDAAAAABQEAJhAAAAAICCEAgDAAAAABSEQBgAAAAAoCAEwgAAAAAABSEQBgAAAAAoCIEwAAAAAEBBCIQBAAAAAApCIAwAAAAAUBCtd3QBQPNrv8duabtr+b/unTu3L+v51r/5dtasXlfWcwIAAADwPwTCUABtd22d6sun7egy3tcvfjAka3Z0EQAAAAAfYpaMAAAAAAAoCIEwAAAAAEBBCIQBAAAAAApCIAwAAAAAUBACYQAAAACAghAIAwAAAAAUhEAYAAAAAKAgBMIAAAAAAAUhEAYAAAAAKAiBMAAAAABAQbTe0QUA7Gza77Fb2u5a/r8+O3duX9bzrX/z7axZva6s5wQAAAB2bgJhgK3UdtfWqb582o4u43394gdDsmZHFwEAAAB8oFgyAgAAAACgIATCAAAAAAAFIRAGAAAAACgIgTAAAAAAQEEIhAEAAAAACkIgDAAAAABQEAJhAAAAAICCEAgDAAAAABSEQBgAAAAAoCBaNBBevnx5hg8fnhNPPDEnn3xyJk6cmCR57bXXMmLEiBx//PEZMWJEVq1aVX/MzTffnMGDB+ezn/1sHnvssfrxp59+OtXV1Rk8eHCuueaa1NbWtuStAAAAAADsdFo0EK6srMzo0aPzwAMPZNKkSfn5z3+epUuX5pZbbskRRxyRmTNn5ogjjsgtt9ySJFm6dGlmzJiRGTNm5NZbb813v/vdbNy4MUkyZsyYjB07NjNnzsyzzz6buXPntuStAAAAAADsdFo0EO7SpUt69+6dJKmqqkqvXr1SU1OT2bNnZ+jQoUmSoUOHZtasWUmS2bNn5+STT06bNm3Ss2fP7L333nnqqafy4osvZu3atTnooINSUVGRoUOHZvbs2S15KwAAAAAAO53WO+rCy5Yty6JFi9KvX7+88sor6dKlS5J3QuNXX301SVJTU5N+/frVH9O1a9fU1NSkdevW6datW/14t27dUlNTs1XX79Spqgx3sXPq3Ln9ji7hQ0U/y0s/y6vI/SzyvTcH/Swv/Swv/Swv/Sw/PS0v/Swv/Swv/Swv/Swv/SyvnbmfOyQQfv311zNq1KhcddVVqaoqHcw2tS5wRUVFyfGt8cora7NpU/nWHd6ZHoKXXlqzo0t4X/pZXvpZXvr5wfRBSMoAACAASURBVNe5c/vC3ntz0M/y0s/y0s/y0s/y09Py0s/y0s/y0s/y0s/y0s/y2hn62apVRckJsS26ZESSbNiwIaNGjUp1dXWOP/74JEmnTp3y4osvJklefPHFdOzYMck7M39XrFhRf2xNTU26dOnSaHzFihX1M4wBAAAAAGhaiwbCtbW1+da3vpVevXplxIgR9eODBg3K1KlTkyRTp07NscceWz8+Y8aMvPXWW3n++efz7LPPpm/fvunSpUvatWuXJ598MrW1tQ2OAQAAAACgaS26ZMTChQszbdq07LfffhkyZEiS5LLLLssFF1yQSy+9NJMnT87f/M3fZPz48UmSfffdNyeeeGJOOumkVFZW5uqrr05lZWWSZMyYMbnyyiuzfv36DBgwIAMGDGjJWwEAAAAA2Om0aCB8yCGHZMmSJU1umzhxYpPjI0eOzMiRIxuNH3DAAbnvvvvKWh8AAAAAwIdZi68hDAAAAADAjiEQBgAAAAAoCIEwAAAAAEBBCIQBAAAAAApCIAwAAAAAUBACYQAAAACAghAIAwAAAAAUhEAYAAAAAKAgBMIAAAAAAAUhEAYAAAAAKAiBMAAAAABAQQiEAQAAAAAKQiAMAAAAAFAQAmEAAAAAgIIQCAMAAAAAFIRAGAAAAACgIATCAAAAAAAFIRAGAAAAACgIgTAAAAAAQEEIhAEAAAAACkIgDAAAAABQEAJhAAAAAICCEAgDAAAAABSEQBgAAAAAoCAEwgAAAAAABSEQBgAAAAAoCIEwAAAAAEBBCIQBAAAAAApCIAwAAAAAUBACYQAAAACAghAIAwAAAAAUhEAYAAAAAKAgBMIAAAAAAAUhEAYAAAAAKAiBMAAAAABAQQiEAQAAAAAKQiAMAAAAAFAQAmEAAAAAgIIQCAMAAAAAFIRAGAAAAACgIATCAAAAAAAFIRAGAAAAACgIgTAAAAAAQEEIhAEAAAAACkIgDAAAAABQEAJhAAAAAICCEAgDAAAAABSEQBgAAAAAoCAEwgAAAAAABSEQBgAAAAAoCIEwAAAAAEBBCIQBAAAAAApCIAwAAAAAUBACYQAAAACAghAIAwAAAAAUhEAYAAAAAKAgBMIAAAAAAAUhEAYAAAAAKAiBMAAAAABAQQiEAQAAAAAKQiAMAAAAAFAQAmEAAAAAgIIQCAMAAAAAFIRAGAAAAACgIATCAAAAAAAFIRAGAAAAACiI1tty0NKlS7N06dJ07tw5n/nMZ8pdEwAAAAAAzaBkIPyLX/wic+fOzQ033NBg/Oqrr87dd99d//mggw7Krbfemt133735qgQAAAAAYLuVXDJi2rRpqaioaDD2wAMP5K677srhhx+ef/mXf8lll12W3/3ud7nllluavVAAAAAAALZPyRnC//3f/52vf/3rDcbuu+++7L777pkwYUKqqqoyaNCgrFmzJrNmzcqll17a7MUCAAAAALDtSs4QXrlyZbp3795g7Iknnkj//v1TVVVVP3booYdm2bJlzVchAAAAAABlUTIQ7tChQ1atWlX/ecmSJVm7dm369evXYL+2bds2WloCAAAAAIAPnpKB8Cc/+clMmzat/vMDDzyQioqKHH300Q32e+aZZ9K5c+fmqxAAAAAAgLIouYbwBRdckHPOOSd///d/n06dOmXevHk55JBD0qdPnwb73X///endu3ezFwoAAAAAwPYpOUO4f//+mTBhQnbbbbcsX748Q4cOzfjx4xvss2LFirzyyis5/vjjm71QAAAAAAC2T8kZwn/9618zcODAHHfccSUP7tatW37xi180S2EAAAAAAJRXyRnCxx57bBYtWtSStQAAAAAA0IxKBsK1tbUtWQcAAAAAAM2sZCAMAAAAAMCHi0AYAAAAAKAgSr5ULklGjhyZXXbZ5X1PUlFRkTlz5pStKAAAAAAAym+zgXCfPn3SsWPHlqoFAAAAAIBmtNlA+Ktf/Wr69u3bUrUAAAAAANCMrCEMAAAAAFAQAmEAAAAAgIIoGQgfeuihadeuXUvWAgAAAABAMyq5hvAdd9zRknUAAAAAANDMSgbCV1555RafpKKiItddd11ZCgIAAAAAoHmUDIT/8z//MxUVFQ3GXnzxxXTs2DGtWzc87L37AQAAAADwwVMyEJ47d26Dz2+//Xb69OmTW265Jb179272wgAAAAAAKK+SL5V7L7OAAQAAAAB2blscCAMAAAAAsHMTCAMAAAAAFIRAGAAAAACgIEq+VO7xxx9v8Lm2tjYVFRX53e9+l9WrVzfa/4gjjih/dQAAAAAAlE3JQHjEiBGpqKhIbW1tg/ExY8bU/7lue0VFRRYtWtRsRQIAAAAAsP1KBsK33357S9YBAAAAAEAzKxkI9+/fvyXrAAAAAACgmXmpHAAAAABAQZScIfzmm2/mjjvuyH777ZcBAwYkSTZt2pThw4c32K+qqioTJkxImzZtmrdSAAAAAAC2S8kZwvfcc08mTJiQXr161Y/V1tZm4cKFWbduXVq1apVWrVrlt7/9baZMmdIixQIAAAAAsO1KzhB+6KGHMmTIkPTo0aPRtn/8x39M7969kyQTJkzIgw8+mDPPPLP5qgQAAAAAYLuVnCG8ePHiHH300e97gr59+2bx4sVlLQoAAAAAgPIrGQivWbMmHTt2bDBWWVmZH/7wh/nYxz5WP1ZVVZXVq1c3X4UAAAAAAJRFySUjqqqq8uqrrzYaP+mkkxp8fvXVV1NVVVX+ygAAAAAAKKuSM4Q//elPZ86cOe97gjlz5mT//fcva1EAAAAAAJRfyUB42LBhmT59embPnl3y4FmzZmX69Ok57bTTtuhiV155ZY444oiccsop9WMTJkzI0UcfnSFDhmTIkCF59NFH67fdfPPNGTx4cD772c/mscceqx9/+umnU11dncGDB+eaa65JbW3tFl0fAAAAAKDISi4ZUV1dnZkzZ+aSSy7JoEGDMnDgwHz0ox9Nkvz1r3/NI488kjlz5mTQoEGprq7eoouddtpp+eIXv5hvfvObDcbPPffcfOlLX2owtnTp0syYMSMzZsxITU1NRowYkQcffDCVlZUZM2ZMxo4dmwMPPDDnn39+5s6dm4EDB27tvQMAAAAAFErJQDhJbrzxxvyf//N/MnHixMyaNSsVFRVJktra2lRVVeXCCy/MV7/61S2+2KGHHpply5Zt0b6zZ8/OySefnDZt2qRnz57Ze++989RTT6V79+5Zu3ZtDjrooCTJ0KFDM3v2bIEwAAAAAMD72GwgXFlZmUsuuSRf+cpX8vTTT2fFihVJkm7duqVPnz5p06ZNWYr42c9+lqlTp6ZPnz4ZPXp0OnTokJqamvTr169+n65du6ampiatW7dOt27d6se7deuWmpqarb5mp07FfRFe587td3QJHyr6WV76WV5F7meR77056Gd56Wd56Wd56Wf56Wl56Wd56Wd56Wd56Wd56Wd57cz93GwgXKdNmzY5+OCDm6WAs846KxdddFEqKioyfvz4fO9738u4ceOaXBe4oqKi5PjWeuWVtdm0qXxrD+9MD8FLL63Z0SW8L/0sL/0sL/384OvcuX1h77056Gd56Wd56Wd56Wf56Wl56Wd56Wd56Wd56Wd56Wd57Qz9bNWqouSE2JKB8PPPP79VF+nZs+fWVfX/7bXXXvV/Pv3003PhhRcmeWfmb92M5CSpqalJly5dGo2vWLEiXbp02aZrAwAAAAAUSclAePDgwVs183bRokXbVMCLL75YH+jOmjUr++67b5Jk0KBBufzyyzNixIjU1NTk2WefTd++fVNZWZl27drlySefTL9+/TJ16tQMHz58m64NAAAAAFAkJQPhcePGlf1il112WRYsWJCVK1dmwIABueSSS7JgwYIsXrw4SdK9e/eMHTs2SbLvvvvmxBNPzEknnZTKyspcffXVqaysTJKMGTMmV155ZdavX58BAwZkwIABZa8VAAAAAODDpmQgPGzYsLJf7Ic//GGjsdNPP73k/iNHjszIkSMbjR9wwAG57777ylobAAAAAMCHXavtPcGsWbOa7YVzAAAAAACUz3YHwhs3bsy6devKUQsAAAAAAM1ouwNhAAAAAAB2DgJhAAAAAICCEAgDAAAAABRE61IbHn/88S06wR//+MeyFQMAAAAAQPMpGQiPGDEiFRUVqa2tLXlw3faKiopmKQ4AAAAAgPIpGQjffvvtLVkHAAAAAADNrGQg3L9//5asAwAAAACAZlbypXLPP/98NmzY0JK1AAAAAADQjEoGwscff3wWLVpU/7m2tjZXXHFFXnjhhRYpDAAAAACA8ioZCL/3ZXKbNm3K9OnT89prrzV7UQAAAAAAlF/JQBgAAAAAgA8XgTAAAAAAQEG03tzGTZs2ZdOmTUmSjRs3Nhp7t1atZMsAAAAAAB9kmw2EzzrrrEZjZ5xxRqOxioqK/OEPfyhfVQAAAAAAlF3JQPjiiy9uyToAAAAAAGhmAmEAAAAAgIKw8C8AAAAAQEEIhAEAAAAACkIgDAAAAABQEAJhAAAAAICCEAgDAAAAABSEQBgAAAAAoCBKBsJf+MIX8qc//anB2OOPP5433nij2YsCAAAAAKD8SgbCCxcuzOuvv17/eePGjTnvvPPyzDPPtEhhAAAAAACU11YtGVFbW9tcdQAAAAAA0MysIQwAAAAAUBBbHQhXVFQ0Rx0AAAAAADSz1pvbOGHChOy5555J/me5iPHjx6dDhw4N9quoqMj3v//9ZioRAAAAAIByKBkIf/SjH82f/vSnRmP//d//3Whfs4YBAAAAAD74SgbCDz/8cEvWAQAAAABAM/NSOQAAAACAgtjsGsJ1nnrqqfzqV7/KihUrkiTdunXL4Ycfnr59+zZrcQAAAAAAlM9mA+GamppcccUVWbBgQf1L5epUVFTk0EMPzfXXX59u3bo1a5EAAAAAAGy/koHw6tWrM3z48KxatSqXX355Bg0alB49eiRJli1bltmzZ+fWW2/N2WefncmTJ2ePPfZosaIBAAAAANh6JQPhW265Ja+//nruvffedO/evcG2Xr16pVevXjnhhBPyv/7X/8ott9ySb3zjG81eLAAAAAAA267kS+UeeuihnH/++Y3C4Hfr2bNnzj///Dz00EPNUhwAAAAAAOVTMhBevnx5+vTp874n6N27d5YvX17WogAAAAAAKL+SgfBuu+2W11577X1PsGrVquy2225lLQoAAAAAgPIrGQj37ds306dPf98TTJs2LQcccEBZiwIAAAAAoPxKBsLnnHNOZs6cme9///t56623Gm1/66238v3vfz+zZs3Kueee25w1AgAAAABQBq1LbTjqqKNy6aWXZvz48Zk6dWr+7u/+Lj169EiSLFu2LPPnz89rr72WSy65JEcddVSLFQwAAAAAwLYpGQgnyYUXXpgDDzwwt956a2bPnp3169cnSdq2bZtDDjkkX/rSl3LEEUe0SKEAAAAAAGyfzQbCSXL44Yfn8MMPz8aNG/Paa6+ltrY2e+65ZyorK1uiPgAAAAAAyuR9A+E6lZWV6dSpU3PWAgAAAABAMyr5UjkAAAAAAD5cBMIAAAAAAAUhEAYAAAAAKAiBMAAAAABAQQiEAQAAAAAKomQgfPHFF+e5555rMPZv//ZvefXVVxuMLVmyJNXV1c1THQAAAAAAZVMyEJ41a1ZWrVpV/3njxo25/vrrs3z58gb7rV+/PkuXLm2+CgEAAAAAKIutWjKitra2ueoAAAAAAKCZWUMYAAAAAKAgBMIAAAAAAAWx1YFwRUVFc9QBAAAAAEAza725jSNHjswuu+zSYOyCCy5I69b/c9iGDRuapzIAAAAAAMqqZCA8bNiwlqwDAAAAAIBmVjIQHjduXEvWAQAAAABAMyvLS+VefvnlcpwGAAAAAIBmtNk1hN9PTU1NfvrTn2by5Ml58skny1UTAAXSfo/d0nbX7fqvoyZ17ty+rOdb/+bbWbN6XVnPCQAAAC1ts/8P/Kmnnsq9996b5cuX52Mf+1jOOeec9OjRIytXrsxNN92Uu+++O2+//XZOPPHElqoXgA+Ztru2TvXl03Z0Ge/rFz8YkjU7uggAAADYTiUD4YcffjijRo3Kxo0b07Fjx8ybNy/3339/fvSjH+VrX/taXn755QwePDijRo3Kvvvu25I1AwAAAACwDUoGwj/96U/ziU98IjfffHO6du2atWvX5qqrrsp5552Xdu3aZeLEiTnssMNaslYAAAAAALZDyZfKLVmyJBdeeGG6du2aJKmqqsr//t//O2+++Wa+8Y1vCIMBAAAAAHYyJQPhN954Ix/96EcbjNV9/sQnPtG8VQEAAAAAUHYlA+HNad26/G+DBwAAAACgeW022b366qvTrl27RuPf+ta3svvuu9d/rqioyL//+7+XvzoAAAAAAMqmZCB86KGHbtU4AAAAAAAfbCUD4TvuuKMl6wAAAAAAoJlt0xrCAAAAAADsfLbp7XCPPfZYlixZko4dO+b4449PVVVVuesCAAAAAKDMSgbCb731Vq6//vr88pe/zIYNG3LyySdn9OjRGTVqVB555JH6/SZMmJD/+I//SNeuXVuiXgAAAAAAtlHJQPjWW2/NnXfemZNOOint2rXL9OnTs2LFijz99NO58cYbs++++2bx4sW57rrr8uMf/zhjxoxpwbIBAADg/7V35+FR1ff+wD8hEQIICFwCFilWpWBVFoHWFRQXZBNwrVqvUgVFoNcFK27UpV6ldcFWKKJWsFqtIiaEoFis4kZbW7RUS69LpVKEqCCyKAmE/P7oY37SJEBgyGQ4r9fz5Hky55w5854vCTnznjPfAwDUVLWFcGFhYYwcOTJGjx4dERG9e/eOkSNHxo033hgnnXRSRETsv//+8emnn8ZDDz1UO2kBAAAAANhh1V5U7sMPP4yePXtW3P7y+29+85tbbNepU6dYvnz5LooHAAAAAECqVFsIl5SURMOGDStuf/l9/fr1t9hujz32iE2bNu2ieAAAAAAApEq1hXB1srKydkUOAAAAAAB2sWrnEI6IGD9+fDRu3HiLZddee200atSo4vb69et3TTIAAAAAAFKq2kL4q/MHb21ZkyZNokePHqlNBQAAAABAylVbCP/qV7+qzRwAAAAAAOxiNZ5DGAAAAACAzFTtGcKbN2+u0Y7q1dMtAwAAAADUZdUWwgcddNB27yQrKyv+9re/pSQQAAAAAAC7RrWFcHl5eey5555xwgknRNu2bWszEwAAAAAAu0C1hfCoUaOisLAw8vPzo0ePHjFkyJA46aSTonHjxrWZDwAAAACAFKm2EB4zZkyMGTMm/vSnP8WsWbNiwoQJ8eMf/zj69OkTQ4cOjSOPPDKysrJqMysAsA1NmjaM3AbV/nnfYa1aNUnp/jaUbIq1a75I6T4BAADYtm2+YuzRo0f06NEjrrvuupg3b17MmjUrLr744mjevHlccsklcdZZZ9VGTgBgO+Q2yIlBVxSkO8Y2Fd4xONamOwQAAEAC1dveDevXrx/9+/ePW265Jc4///xYuXJlvPLKK7syGwAAAAAAKbRdnyktKSmJefPmRX5+fixYsCBatWoVF1xwQZx66qm7Oh8AAAAAACmy1UL497//fRQUFMTcuXOjvLw8TjzxxLjvvvvisMMOM38wAAAAAECGqbYQPvbYY+Ojjz6K73znO/GjH/0o+vbtG7m5ubWZDQAAAACAFKq2EF6+fHk0bNgw/vGPf8Rdd90Vd911V7U7ycrKiueff36XBAQAAAAAIDWqLYSHDh1amzkAAAAAANjFqi2Eb7311trMAQAAAADALlYv3QEAAAAAAKgd1Z4hPGPGjOrvlJMTLVu2jK5du0aTJk12STAAAAAAAFKr2kL4uuuu2+adc3NzY8SIEXHJJZekNBQAAAAAAKlXbSH83HPPVXunzZs3R3FxccydOzfuueeeaNOmTZxyyim7JCAAAAAAAKlRbSHctm3brd6xXbt20aNHjygtLY1f//rXCmEAAAAAgDpupy8qd8wxx8R7772XiiwAAAAAAOxCO10IZ2dnx+bNm1ORBQAAAACAXWinC+E//OEP0b59+1RkAQAAAABgF6p2DuGtnfVbVlYWH330UTz77LMxffr0GDt27C4JBwAAAABA6lRbCH/rW9+KrKysrd65vLw8Tj/99Dj//PNTnQsAAAAAgBSrthAeNWpUtYVwdnZ2tGzZMr797W/Hvvvuu90PdvXVV8cLL7wQLVu2jNmzZ0dExOrVq+Oyyy6LZcuWRdu2bWPixInRrFmziIi49957Y8aMGVGvXr247rrr4uijj46IiDfffDOuvvrq2LBhQ/Tu3TuuvfbabZbXAAAAAABJV20hPGbMmJQ/2CmnnBLf+9734qqrrqpYNnXq1Dj88MNjxIgRMXXq1Jg6dWpceeWV8e6770ZRUVEUFRVFcXFxDBs2LObOnRvZ2dlxww03xE033RRdu3aN4cOHx4svvhi9e/dOeV4AAAAAgN3JTl9UriZ69uxZcfbvl5577rkYMmRIREQMGTIk5s2bV7F8wIABUb9+/WjXrl20b98+Fi1aFB999FGsW7cuunXrFllZWTFkyJB47rnnavNpAAAAAABkpGrPEL7nnntqtKPRo0fvUICVK1dGXl5eRETk5eXFqlWrIiKiuLg4unTpUrFd69ato7i4OHJycqJNmzYVy9u0aRPFxcU1ftyWLffcoby7g1atmqQ7wm7FeKaW8Uwt45laxjO1kjyeSX7uu4LxTC3jmXrGNLWMZ2oZz9QynqllPFPLeKZWJo/nThXCX523d0cL4eqUl5dX+XjVLa+plSvXxebNlfe1ozLph+Djj9emO8I2Gc/UMp6pZTxTy3imlvGs+1q1apLY574rGM/UMp6pZ0xTy3imlvFMLeOZWsYztYxnamXCeNarl1XtCbHVFsJvvfXWVne6ePHimDhxYrz88svRvn37HQ7XsmXL+OijjyIvLy8++uijaNGiRUT8+8zfFStWVGxXXFwceXl5lZavWLGi4gxjAAAAAACqV+0cwtnZ2VV+LV26NK688so444wz4r333oubb7455syZs8MB+vTpE/n5+RERkZ+fH8cdd1zF8qKioigtLY2lS5fGkiVLonPnzpGXlxeNGzeON954I8rLy7e4DwAAAAAA1av2DOH/tHz58rjnnnuioKAgmjZtGj/84Q/j7LPPjvr162/3g11++eXxxz/+MT799NPo1atXjBkzJkaMGBGXXnppzJgxI/bee++4++67IyKiQ4cO0a9fv+jfv39kZ2fH+PHjIzs7OyIibrjhhrj66qtjw4YN0atXr+jVq1cNnzYAAAAAQPJssxBeuXJl/OIXv4jHH388GjRoEJdcckmcf/750ahRoxo/2J133lnl8unTp1e5fOTIkTFy5MhKyw855JCYPXt2jR8fAAAAACDJqi2E165dG/fdd1/86le/ioiI//7v/47hw4dHs2bNai0cAAAAAACpU20hfNxxx8XatWvjyCOPjJEjR0ZeXl6sWbMm1qxZU+X27dq122UhAQAAAADYedUWwl8Wvy+//HK88sor29zR4sWLU5cKAAAAAICUq7YQvvXWW2szBwAAAAAAu1i1hfDQoUNrMwcAAAAAALtYvXQHAAAAAACgdiiEAQAAAAASQiEMAAAAAJAQCmEAAAAAgIRQCAMAAAAAJIRCGAAAAAAgIRTCAAAAAAAJoRAGAAAAAEgIhTAAAAAAQEIohAEAAAAAEkIhDAAAAACQEAphAAAAAICEUAgDAAAAACSEQhgAAAAAICEUwgAAAAAACaEQBgAAAABICIUwAAAAAEBCKIQBAAAAABJCIQwAAAAAkBAKYQAAAACAhFAIAwAAAAAkhEIYAAAAACAhFMIAAAAAAAmhEAYAAAAASAiFMAAAAABAQiiEAQAAAAASQiEMAAAAAJAQCmEAAAAAgIRQCAMAAAAAJIRCGAAAAAAgIRTCAAAAAAAJoRAGAAAAAEgIhTAAAAAAQEIohAEAAAAAEkIhDAAAAACQEAphAAAAAICEUAgDAAAAACSEQhgAAAAAICEUwgAAAAAACaEQBgAAAABICIUwAAAAAEBCKIQBAAAAABJCIQwAAAAAkBAKYQAAAACAhFAIAwAAAAAkhEIYAAAAACAhFMIAAAAAAAmhEAYAAAAASAiFMAAAAABAQiiEAQAAAAASQiEMAAAAAJAQCmEAAAAAgIRQCAMAAAAAJIRCGAAAAAAgIRTCAAAAAAAJoRAGAAAAAEgIhTAAAAAAQEIohAEAAAAAEkIhDAAAAACQEAphAAAAAICEUAgDAAAAACSEQhgAAAAAICEUwgAAAAAACaEQBgAAAABICIUwAAAAAEBCKIQBAAAAABJCIQwAAAAAkBAKYQAAAACAhFAIAwAAAAAkhEIYAAAAACAhFMIAAAAAAAmRk+4AAAB1VZOmDSO3QeoPl1q1apLS/W0o2RRr13yR0n0CAAC7J4UwAEA1chvkxKArCtIdY5sK7xgca9MdAgAAyAimjAAAAAAASAiFMAAAAABAQiiEAQAAAAASQiEMAAAAAJAQCmEAAAAAgIRQCAMAAAAAJIRCGAAAAAAgIRTCAAAAAAAJoRAGAAAAAEgIhTAAAAAAQEIohAEAAAAAEkIhDAAAAACQEAphAAAAAICEUAgDAAAAACSEQhgAAAAAICEUwgAAAAAACaEQBgAAAABICIUwAAAAAEBCKIQBAAAAABJCIQwAAAAAkBAKYQAAAACAhFAIAwAAAAAkhEIYAAAAACAhFMIAAAAAAAmhEAYAAAAASAiFMAAAAABAQiiEAQAAAAASQiEMAAAAAJAQOekO8KU+ffpE48aNo169epGdnR0zZ86M1atXx2WXXRbLli2Ltm3bxsSJE6NZs2YREXHvvffGjBkzol69enHdddfF0UcfneZnAAAAAABQt9WpM4SnT58eBQUFMXPmzIiImDp1ahx++OHx7LPPxuGHHx5T55B7bgAAIABJREFUp06NiIh33303ioqKoqioKO6///648cYbo6ysLJ3RAQAAAADqvDpVCP+n5557LoYMGRIREUOGDIl58+ZVLB8wYEDUr18/2rVrF+3bt49FixalMyoAAAAAQJ1XZ6aMiIi44IILIisrK84888w488wzY+XKlZGXlxcREXl5ebFq1aqIiCguLo4uXbpU3K9169ZRXFxco8dq2XLP1AXPMK1aNUl3hN2K8Uwt45laxjO1jGdqGc/USvJ4Jvm57wrGM/WMaWoZz9QynqllPFPLeKaW8UytTB7POlMIP/roo9G6detYuXJlDBs2LPbbb79qty0vL6+0LCsrq0aPt3Lluti8ufJ+dlQm/RB8/PHadEfYJuOZWsYztYxnahnP1DKeqWU8675WrZok9rnvCsYz9YxpahnP1DKeqWU8U8t4ppbxTK1MGM969bKqPSG2zkwZ0bp164iIaNmyZZxwwgmxaNGiaNmyZXz00UcREfHRRx9FixYtIiKiTZs2sWLFior7FhcXV5xJDAAAAABA1epEIfz555/HunXrKr5/5ZVXokOHDtGnT5/Iz8+PiIj8/Pw47rjjIiKiT58+UVRUFKWlpbF06dJYsmRJdO7cOW35AQAAAAAyQZ2YMmLlypUxatSoiIgoKyuLgQMHRq9eveKQQw6JSy+9NGbMmBF777133H333RER0aFDh+jXr1/0798/srOzY/z48ZGdnZ3OpwAAAAAAUOfViUK4Xbt2MWvWrErLmzdvHtOnT6/yPiNHjoyRI0fu6mgAAAAAALuNOlEIAwCw+2vStGHkNkj94WeqL/63oWRTrF3zRUr3CQAAdYVCGACAWpHbICcGXVGQ7hjbVHjH4Kjb14wGAIAdVycuKgcAAAAAwK6nEAYAAAAASAiFMAAAAABAQiiEAQAAAAASQiEMAAAAAJAQCmEAAAAAgIRQCAMAAAAAJIRCGAAAAAAgIRTCAAAAAAAJkZPuAAAAQM01adowchuk/nC+VasmKd3fhpJNsXbNFyndJwAAO04hDAAAGSi3QU4MuqIg3TG2qfCOwbE23SEAAKhgyggAAAAAgIRQCAMAAAAAJIRCGAAAAAAgIRTCAAAAAAAJoRAGAAAAAEgIhTAAAAAAQEIohAEAAAAAEkIhDAAAAACQEAphAAAAAICEUAgDAAAAACRETroDAAAA1AVNmjaM3Aapf4nUqlWTlO5vQ8mmWLvmi5TuEwBIDoUwAABAROQ2yIlBVxSkO8Y2Fd4xONamOwQAkLFMGQEAAAAAkBAKYQAAAACAhFAIAwAAAAAkhEIYAAAAACAhFMIAAAAAAAmhEAYAAAAASAiFMAAAAABAQiiEAQAAAAASQiEMAAAAAJAQCmEAAAAAgIRQCAMAAAAAJIRCGAAAAAAgIRTCAAAAAAAJoRAGAAAAAEgIhTAAAAAAQEIohAEAAAAAEkIhDAAAAACQEAphAAAAAICEUAgDAAAAACSEQhgAAAAAICEUwgAAAAAACaEQBgAAAABICIUwAAAAAEBCKIQBAAAAABJCIQwAAAAAkBAKYQAAAACAhFAIAwAAAAAkhEIYAAAAACAhctIdAAAAgN1Pk6YNI7dB6l9ytmrVJKX721CyKdau+SKl+wSAukwhDAAAQMrlNsiJQVcUpDvGNhXeMTjWpjsEANQiU0YAAAAAACSEQhgAAAAAICEUwgAAAAAACaEQBgAAAABICIUwAAAAAEBCKIQBAAAAABJCIQwAAAAAkBAKYQAAAACAhFAIAwAAAAAkhEIYAAAAACAhFMIAAAAAAAmhEAYAAAAASAiFMAAAAABAQiiEAQAAAAASQiEMAAAAAJAQCmEAAAAAgIRQCAMAAAAAJIRCGAAAAAAgIRTCAAAAAAAJoRAGAAAAAEiInHQHAAAAALauSdOGkdsg9S/hW7VqktL9bSjZFGvXfJHSfQKQWgphAAAAqONyG+TEoCsK0h1jmwrvGBxr0x0CgK0yZQQAAAAAQEI4QxgAAABIFFNwAEmmEAYAAAASxRQcQJKZMgIAAAAAICEUwgAAAAAACaEQBgAAAABICIUwAAAAAEBCKIQBAAAAABJCIQwAAAAAkBAKYQAAAACAhFAIAwAAAAAkhEIYAAAAACAhFMIAAAAAAAmhEAYAAAAASIicdAcAAAAAIHM1adowchukvmJq1apJSve3oWRTrF3zRUr3CZlIIQwAAADADsttkBODrihId4xtKrxjcKxNdwioA0wZAQAAAACQEAphAAAAAICEUAgDAAAAACSEOYQBAAAAoI5wkT52NYUwAAAAANQRLtLHrmbKCAAAAACAhFAIAwAAAAAkhEIYAAAAACAhFMIAAAAAAAmhEAYAAAAASAiFMAAAAABAQiiEAQAAAAASQiEMAAAAAJAQOekOsDNefPHFuOWWW2Lz5s1x+umnx4gRI9IdCQAAAACoI5o0bRi5DVJfgbZq1SSl+9tQsinWrvkipfusTsYWwmVlZXHTTTfFgw8+GK1bt47TTjst+vTpEwcccEC6owEAAAAAdUBug5wYdEVBumNsU+Edg2NtLT1Wxk4ZsWjRomjfvn20a9cu6tevHwMGDIjnnnsu3bEAAAAAAOqsjD1DuLi4ONq0aVNxu3Xr1rFo0aLtvn+9elkpz5TXvGHK97kr7IrnvisYz9QynqllPFPLeKaW8Uwt45laxjO1jGfqGdPUMp6pZTxTy3imlvFMLeOZWkkcz63tK6u8vLw8ZY9Ui55++ul4+eWX45ZbbomIiPz8/PjrX/8a119/fZqTAQAAAADUTRk7ZUSbNm1ixYoVFbeLi4sjLy8vjYkAAAAAAOq2jC2EDznkkFiyZEksXbo0SktLo6ioKPr06ZPuWAAAAAAAdVbGziGck5MT48ePjwsvvDDKysri1FNPjQ4dOqQ7FgAAAABAnZWxcwgDAAAAAFAzGTtlBAAAAAAANaMQBgAAAABICIUwAAAAAEBCKIQBAAAAABJCIQwAAAAAkBAKYQAAAACAhFAIA9QBZWVlMWvWrHTHACBhSkpK4u2334533nknSkpK0h0HAEiBjRs3pjsCdVxOugOwdcuXL4+ioqK48MIL0x0lozz77LNbXX/iiSfWUpLdR1lZWXz22WfRokWLiIgoLS2Np556KqZNmxZPP/10mtNljnXr1sUjjzwSxcXF0adPnzjyyCPj4Ycfjl/+8pfRqVOnOPnkk9MdkYR78MEHt7p+2LBhtZRk9/DjH/94q+uvu+66Wkqy+1m/fn1kZWVFo0aN0h0lI23atCnuvPPOePLJJ6Nt27axefPmWLFiRZxyyilx2WWXxR577JHuiBln1apVsWrVqjjggAO2WP7OO+9Ey5YtK46hoLZ17do16tWr+lyw+vXrx9e//vW47LLL4vDDD6/lZJmrrKwsNmzYEI0bN46IiDfeeKOigDvwwANjzz33TGe8jNOtW7fIysqK8vLyyMrK2mKdn9Ed06tXrzjuuONiwIABcdhhh1UaV7bfpk2bIidn96tPd79ntBtYtWpVPPPMM1FUVBTFxcVxwgknpDtSxnn++ecrvv/d734Xffr02WK9QrhmioqKYvz48dGwYcPYd999Y/To0fHDH/4wDjnkkLj99tvTHS+jXHnlldGsWbPo2rVrPPHEE/HAAw/Exo0bY/LkyXHggQemO15GOvfcc6s9wMnKyorp06fXcqLMtn79+orvH3vssfjud7+bxjSZ77HHHosOHTpEv379Ii8vL8rLy9MdKeM98sgjcd9998UXX3wR5eXl0bhx47jwwgvjnHPOSXe0jPKTn/wk1q9fH88991xFcbFu3bqYMGFCTJgwwZsVO+Dmm2+Os846q9LyFStWxJQpU+KOO+5IQ6rMtWrVqvj1r38dTZs2jVNPPTV+8pOfxJ///Odo165djBs3Ltq3b5/uiBlj3333jfz8/CrXlZWVxTvvvBNjx46N2bNn13KyzHX77bdHixYtYvjw4RERcfnll8c3v/nNKCkpiW9961tx5ZVXpjlhZunevXv86Ec/inbt2lVa52d0x8yZMyfmzp0bkydPjquuuir69u0bAwcOjC5duqQ7WsY5/fTT46mnnoqIf/+tv/7669OcKDWyyr0yqRPWrVsX8+bNi8LCwliyZEmccMIJMWfOnHjxxRfTHS3jDRkypNoDILbPwIEDY9KkSdG+fft466234swzz4y77rrLmxU7YNCgQVFYWBgR/z64Oeyww+L55593FsFOePPNNyst+8tf/hL3339/tGjRIp588sk0pNo9+P9z53366afxzDPPxJw5cyInJyf69+8fJ554YjRr1izd0TLS5MmT4/XXX4/x48dXvGhcunRp3HLLLdG5c+e45JJL0pwwc5x44okxd+7cSm+olZWVRb9+/bb5aSsqGzBgQBQVFVW5buDAgYqMGvr+978fBx98cKxfvz4WLFgQp5xyShx77LHx5z//OQoLC+NXv/pVuiNmjKFDh1aUGdXxJnDNDBkyJGbMmFFx1uCXx0zl5eVx9tlnx6OPPprmhJnl6aefjokTJ8bQoUPjggsuqPJTKn5Gd1xxcXHF8ejKlStjwIABcdlll6U7Vsb46mui7fn/NFM4Q7iOOOKII6Jz585x6aWXRvfu3SMrKyt++9vfpjvWbsFHI3beHnvsUXEWxkEHHRT77LOPMngHffWjJtnZ2bHPPvsog3fSwQcfXPH9H//4x5g8eXKUlpbGDTfcEL17905jsszn/8+d17x58zjrrLPirLPOiuLi4pg9e3YMGDAgxo4dG0OGDEl3vIxTUFAQs2bNigYNGlQsa9euXUycODEGDx6sEK6BrKysKn/Hs7Oz/e7voE2bNlW7zlyONffJJ5/E5ZdfHuXl5XHsscdWTKG3//77xyOPPJLmdJll5cqVW50SatiwYYq2Gtq8efMWx/Vjx46NiH//3/r555+nK1bG6tevX/Tu3TsmTZoUp556agwePHiLaU78jO6c1q1bx2mnnRbNmjWLBx98MJ544gmFcA3srsdFCuE64vLLL4+ioqK44YYbYuDAgdG/f/90R4IK/3kQ+fnnn29x25yi22/x4sVx6KGHRkREeXl5lJSUxKGHHloxX9bChQvTnDAzvfTSSzF58uRo0KBBXHzxxXHYYYelOxJs4a233orZs2fHq6++Gr169drijQxq5qtl8Jdyc3N324P1XWX//feP/Pz8Sm9MFBQUxDe+8Y00pcps7du3j/nz51d6M3L+/PlVfgyarcvOzo6If78Qb968+RbrqpsPl6pt3rx5iymh2HkbN26MdevWVZzYcdRRR0VExNq1a12gcwfl5OREw4YNo7S0NNavX+/3PAVKSkrid7/7XRQVFcXChQvj6KOPjssvv7zi55Xt849//CMGDRoUEREffPBBxfdf+vITwJnGlBF1zNKlS2P27NkxZ86cWLJkSfzgBz+I448/3oF5DV188cUV3//pT3+KHj16bLF+ypQptR0po91zzz1bXT969OhaSpL5fAQ/9U499dT49NNP44ILLoiuXbtWWn/QQQelIVXm+uoBzgcffBBf//rXt1ifqQc86fKzn/0sXnjhhdhvv/1iwIABcfTRR++WF6WoLeedd15cfPHFlS4qs2DBgpg8ebKPkNfA8uXL4wc/+EHk5ubGQQcdFFlZWfHXv/41NmzYEJMmTYrWrVunO2LGWbJkSVx00UXRrVu3ir89b775ZrzxxhsxZcoUx/M11KNHj4pj+P88nv/zn/8cr732WrqiZZzd6SPOdcWDDz4Yr776atx4443xta99LSIili1bFjfccEMcfvjh8f3vfz/NCTPLiy++GLfddlv06dMnRo0aFQ0bNkx3pIx3xRVXxKuvvho9e/aM/v37x7HHHlvxpvq0adPi/PPPT2/ADDJixIi46KKLonXr1lWegNC2bds0pNp5CuE64p///Gd88skn0b1794plf//73+N///d/47XXXovFixenMV3m+eMf/7jV9d/+9rdrKQlsyQF56p177rlb3P7PP9IPPfRQbcbJeI8++mgcffTRVR7szJkzp+LiKWyfTp06Rbt27SI3N7fK9Qr2mnnnnXfikksuie7du29RYi5cuDAmT54cHTp0SHfEjPHl36MFCxbEu+++G+Xl5dGhQwdXcN8J06ZNi4MPPjj+8Y9/xPvvvx8R/z4Te9CgQVWe2c7WVXU8/+XfpvLycsfzNeCEhF3jscceiylTpsQXX3wRERGNGjWK4cOHx9lnn53mZJnn7LPPjhtvvNHf8RR66qmn4oQTTqhyesJjjjkmXnjhhdoPlaGmT58ec+bMiY8//jj69esXAwcO3C0uCK8QriMuuuiiuOyyy6JTp05bLF+0aFFMmjQp7r333jQly0wffvhhxTu17LytnSGclZUVo0aNqsU0ma1Xr15bnWLD9Bs1t2jRomjTpk3k5eVFxL8PfubOnRv77LNPjB49Ovbaa680J8wsBx54YPTs2TN++tOfVjpD0BsaNffaa69V+/do+fLllT7BwraVlJREYWFhRYl5wAEHxMknn6xwqyEFUepNmDAhXn/99XjvvfeiU6dO0a1btzj00EOja9eu/hbtgHnz5kVxcXGcc845ERFx2mmnxapVqyIrKyvGjh0b/fr1S3PCzLF69Wo/gyn25fR55eXlsWHDhti8eXPss88+0b17d1PEUOf17t075s+fn+4YGWfZsmVRVFQUc+bMiZKSkhgwYEAMGDAgYz8B5DOLdcSyZcsqlcEREZ07d44PP/wwDYky26hRoypKizFjxsTPf/7zNCfKbI0aNaq07PPPP48nn3wyVq9erRCuAXO4pd6PfvSjioPy1157Le644464/vrrY/HixTF+/Pj42c9+luaEmaVjx44xcODAOOOMM2LcuHFbvOD2HnLNjRs3Lr773e/GsGHDKqaK+OSTT+K2226L999/P5588sk0J8w8DRo0iNNOO22LZWVlZTFr1qw4+eST05Qq86xatWqbF5miZq666qqIiCgtLY0333wzXn/99XjyySfjuuuui6ZNm8acOXPSnDCz3H///XHXXXdV3N64cWM8+eST8cUXX8TVV1+tEK4BZXDqVXU8/+abb8aUKVNizJgxMWDAgDSkgu3jugs7pm3btjFixIgYMWJE/O1vf4trrrkmJk2alLGf6FcI1xFbm3h+w4YNtZhk9/DV0mLp0qVpTLJ7+OocWOvWrYuHHnooZs6cGf379zc/Vg21atXKnMspVlZWVvFCZ86cOXHmmWdG3759o2/fvjF48OA0p8s8WVlZccYZZ0TPnj1j7Nix8eKLL8b48eOjYcOGDh53wMyZM+OOO+6IoUOHxjXXXBNvv/12TJs2LS688ML4yU9+ku54GWfdunXxyCOPRHFxcRx33HFxxBFHxCOPPBIPPPBAdOrUSSFcA96g3HVKSkpi3bp1sXbt2li7dm3k5eVFx44d0x0r42zcuDH23nvvitvdu3eP5s2bR/PmzSs+og/pUt3x/OrVq2PYsGEKYdKuW7duVR67f3lhc2pu48aN8dJLL0VRUVH8/ve/j549e2b0a3uFcB1xyCGHxOOPPx5nnHHGFsufeOIJF0TaAV/9j0+BkRqrV6+OBx98MAoLCys+Nt6sWbN0x8o4zrBMvc2bN8emTZsiJycnFixYEDfffHPFurKysjQmy2zf+MY34je/+U1MnDgxhgwZorzcQc2aNYubbroppk+fHsOGDYu8vLx4/PHHo02bNumOlpGuvPLKaNasWXTt2jUef/zxuP/++2Pjxo0xefLk3WIut9rkDcrUu/766+Odd96Jxo0bR5cuXaJbt24xbNgwx0s7aM2aNVvcHj9+fMX3q1atqu04sF322msvx/vUCa+//nq6I+w2XnnllZg9e3bMnz8/OnfuHP3794+bb765yk9SZxKFcB1xzTXXxOjRo6OwsHCLqxJv3Lhxq/O3UrW///3vceihh1a8+3XooYdGxL/LuKysrFi4cGGaE2aWCRMmxG9/+9s444wzorCwMBo3bpzuSBlr2rRp6Y6w2xkwYEB873vfi+bNm0dubm7FnKz//Oc/q7yIAlv31RcxOTk5MXbs2Dj66KPj8ssv9wJ8B6xZsyZuv/32+Mtf/hL3339/zJ8/P4YPHx7XXHONi3ftgH/961/xi1/8IiIiTj/99DjssMPi+eef97u+AxQWqffhhx9GaWlp7LvvvtG6deto06ZNNG3aNN2xMlbnzp2rPGHmsccei86dO6cpFWzdggUL/N7DbmbKlCkxaNCguOqqq3arKXhcVK6O+f3vfx/vvPNOREQccMABXixSJ3Tq1Cnq168f2dnZW5xxrWCnrnjjjTfi448/jiOPPLLindr3338/Pv/8c5+yqKF58+bF8ccfX2n5Z599Fr/5zW9ixIgRaUiVuY477rg4++yz47zzzquYQ3jx4sVx4403xte+9rW4884705wws/znhQ1d6HDHucjUrlFeXh7vvPNOvP766/H666/H22+/HXvttVd07do1fvCDH6Q7XkZZuXJljBo1KvbYY4+Kv+VvvfVWlJaWxqRJk+K//uu/0pyQJBs0aFClZZ999lnk5eXFhAkTYv/9909DKoDtpxAGANhFVqxYUe30EFWd+cbWHXjggdGwYcOI+P9z4OXm5nqDkjpnxYoVsXDhwli4cGG88MILsXr16vjTn/6U7lgZacGCBfHuu+9GhBNmqDuWLVu2xe2srKzYa6+9Mv4j5EByKIQBAAB20kMPPRSvv/56LFy4MHJycuLQQw+Nrl27Rvfu3eOb3/xm1KtXL90RAQAiwhzCAAAAO23ZsmXRt2/fuPrqqyMvLy/dcQAAquUMYQAAAACAhPC5JQAAAACAhFAIAwAAAAAkhEIYAIC0ufbaa6Njx45x6623Vrl+5syZ0bFjx/jnP/9ZsWzcuHHRsWPHiq8uXbrE0KFDY8aMGZXu//Of/zw6duwYmzZtqjbDuHHjolevXlss69OnT3Ts2DHuvvvuStvfdddd0bFjxy2WnXvuuVtk+urXLbfcUm32r35dcskl1Wb86lj06NEjPvvssy3Wbdq0KTp27Bg///nPq7zv5MmTo2PHjjF69Ogq1//hD3+oyPHyyy9XWv+vf/0rOnXqFB07downnniiUqaqvnr06LHV5wMAQHq4qBwAAGmxYcOGeOaZZyIiorCwMK688srIydm+w9MWLVrEL37xi4iI+OSTT+Khhx6Ka6+9Nvbcc8846aSTUpZx2rRpce6550aLFi22uW3Hjh3jpptuqrS8VatWW9z+avavatas2XZlWrt2bdx3330xduzY7do+IqKgoCAiIl544YX49NNPo3nz5lVu17hx4ygoKIijjjpqi+X5+fnRqFGjWL9+fZX3u/vuu6NNmzZbLMvOzt7ufAAA1B6FMAAAafHb3/421q1bF71794758+fHSy+9FMcee+x23XePPfaIrl27Vtw+/PDD45hjjoknnngiZYXwt7/97XjjjTdi6tSpMW7cuG1u37hx4y0yVec/s9fUUUcdFQ8//HCcd955lcrmqixcuDCWLFlSMc5FRUXxve99r8ptTzzxxJg7d258/vnn0ahRo4rls2bNir59+8bMmTOrvN+BBx4Y7du337EnBABArTJlBAAAaZGfnx/NmjWL2267LXJzcyM/P3+H99W4cePYd999Y/ny5SnL17p16zjrrLPi17/+dRQXF6dsvztr5MiRERExZcqU7do+Pz8/srOz4+abb4699957q+N8wgknRETEs88+W7Fs4cKF8cEHH8TJJ5+8E6kBAKgrFMIAANS64uLiePXVV6Nfv37RokWLOP744+N3v/tdpblxt1dZWVksX748vv71r6c058UXXxzZ2dkxadKk7dp+06ZNlb7Ky8t3eLuqtGrVKs4555z4zW9+E8uWLdvqtiUlJfH000/HEUccEa1bt46TTz45/vrXv8Z7771X5fYNGzaMvn37xqxZsyqW5efnx6GHHhrt2rWr9nHKysoqPZ/Nmzdv1/MBAKB2KYQBAKh1BQUFsXnz5hgyZEhERAwZMiRKS0tjzpw5272PL4vH4uLiuOWWW2LdunUVZ8+mSosWLeK8886LmTNnxgcffLDVbRcuXBgHHXRQpa+5c+dusV1xcXGV2/3yl7/c7lzDhw+P3NzcuOeee7a63XPPPRdr1qzZYpwjIp566qlq7zN48OBYsGBBFBcXR2lpaTzzzDMV96tOv379Kj2fVP9bAACQGuYQBgCg1hUUFMS+++4b3bp1i4iII444IvLy8qKgoCDOOuusbd7/y1L1S1lZWXHnnXdGly5dUp71ggsuiEcffTR+9rOfxe23317tdp06dYof//jHlZb/51nLLVu2jHvvvbfSdnvvvfd2Z9prr71i2LBhMWnSpBg+fHi1Z0Y/9dRTseeee8bxxx8fERH77bdfdOnSJWbNmhWXX3551KtX+fyQww47LNq0aROFhYWxzz77xIYNG6Jfv35bPXt70qRJ0bp16y2WNW3adLufDwAAtUchDABArVq0aFG8++67MXz48FizZk3F8hNPPDEefvjheP/99+Mb3/jGVvfxZalaXl4eH3zwQUycODGuueaa6NixY+y///4pzdukSZO44IIL4q677oqLLrqo2u0aNWoUhxxyyDb3l5OTs13bbcv5558fDz/8cLVF9ccffxyvvPJK9OvXL0pLS6O0tDQi/j3OP/3pT2PBggVx5JFHVrpfVlZWDBo0KAoKCqJt27bRp0+faNKkyVYL4Q4dOrioHABAhjBlBAAAterLi5rdd9990bNnz4qvhx9+eIv1W/Nlqdq5c+cYOHBgTJ06NTZt2hQTJkzYJZnPPffcaNmyZUycOHGX7H9HNG7cOC666KJ45plnYvHixZXWz5o1K8rKymL27NlbjPNPf/rTiNj2tBFvv/12zJ8/f5vTRQAAkFmcIQwAQK0pLS2NoqKi6NKlS1xxxRUqvk6hAAACWElEQVSV1t96660xa9asuPTSSyMrK2u797vffvvFOeecE9OmTYtFixZF586dUxk7GjZsGCNHjoybbropNmzYkNJ974yzzz47HnzwwSqL6vz8/Gjbtm3ceuutldbdd999MW/evFi3bl3sueeeldbvv//+cc4558SqVaviqKOO2iXZAQBID4UwAAC15oUXXojVq1fHuHHj4jvf+U6l9WeeeWbccMMN8Yc//CEOO+ywGu17xIgR8fjjj8fkyZNjypQpW6x79tlnK82X26pVq+jevft27//000+PBx54IF5++eUq169fvz7eeOONSsubNm0a++23X8XtjRs3Vrldbm5udOrUabvzRETUr18/Ro0aFddff/0Wy9966614++23Y8yYMVWOc0lJSbz00ksxd+7cOPXUU6vc9/jx47c7x+LFi+PTTz+ttPzggw+OnBwvOQAA6hJHZwAA1JqnnnoqGjduHCeddFKV6wcOHBi33XZb5Ofn17gQbtmyZZx77rlx7733xt/+9rf41re+VbHusssuq7T9McccU+XF3apTv379GDNmTIwbN67K9f/3f/8XZ5555jYfZ9WqVVVu16FDh5g9e/Z25/nSKaecEg888EAsWbKkYtlTTz0VWVlZMXTo0Crvc9RRR8Xee+8d+fn51RbCNfE///M/VS5fsGBBtGjRYqf3DwBA6mSVl5eXpzsEAAAAAAC7novKAQAAAAAkhEIYAAAAACAhFMIAAAAAAAmhEAYAAAAASAiFMAAAAABAQiiEAQAAAAASQiEMAAAAAJAQCmEAAAAAgIT4fwhE42BIjFfnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "final_dataset.groupby('Airline').IsDelay.sum().sort_values(ascending=False).plot.bar()\n",
    "plt.title('TOTAL DELAYED FLIGHTS BY AIRLINE', fontsize=20)\n",
    "plt.xlabel('AIRLINEE NAME', fontsize=16)\n",
    "plt.ylabel('NUMBRE OF FLIGHTS', fontsize=16)\n",
    "plt.rc('xtick',labelsize=10)\n",
    "plt.rc('ytick',labelsize=10) \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> From the chart above it can be seen that the top 4 Airlines contributing to the delays being caused in CA, FH, NR and TR. The Company would need to check with these particular airlines why this is happening"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing Departure delay VS Arrival Delay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis will show us which of the two is the most contributing factor to the actual arrival delay happening for the Airlines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "mpl.rcParams['hatch.linewidth'] = 1.8  \n",
    "\n",
    "fig = plt.figure(1, figsize=(15,9))\n",
    "ax = sns.barplot(x=\"Departure_Delay_Minutes\", y=\"Airline\", data=final_dataset, color=\"lightskyblue\", ci=None)\n",
    "ax = sns.barplot(x=\"Arrival_Delay_Minutes\", y=\"Airline\", data=final_dataset, color=\"r\", hatch = '///', alpha = 0.0, ci=None)\n",
    "ax.yaxis.label.set_visible(False)\n",
    "plt.title('Departure vs Arrival Delay')\n",
    "plt.xlabel('Mean delay (min)', fontsize=14, labelpad=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>According to graph seen, for most of the airlines the departure delay is only minimal compared to the arrival delay. This could mean that either the airlines are not considering the extra time being elapsed and are not contributing to the customer satisfaction by hurrying up by adjusting flight speeds. It could also mean they have their time calculations all wrong. \n",
    "\n",
    "For some of the Airlines like VF, UG, TO, YJ even if there is a departure delay, they make sure to cover it up causing the minimal possible delay. These Airlines are very careful of the customer satisfaction they are delivering. LV seems to be the best Airline to be delivering that quality needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the correlations between the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Flight_Number</th>\n",
       "      <th>Flight_Distance</th>\n",
       "      <th>Scheduled_Departure_Time</th>\n",
       "      <th>Actual_Departure_Time</th>\n",
       "      <th>Departure_Delay_Minutes</th>\n",
       "      <th>Departure_Taxi</th>\n",
       "      <th>Departure_WheelsOff</th>\n",
       "      <th>Scheduled_Arrival_Time</th>\n",
       "      <th>Actual_Arrival_Time</th>\n",
       "      <th>Arrival_Delay_Minutes</th>\n",
       "      <th>Arrival_Taxi</th>\n",
       "      <th>Arrival_WheelsOn</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>IsDelay</th>\n",
       "      <th>WEEKDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Flight_Number</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.512545</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.055729</td>\n",
       "      <td>0.081297</td>\n",
       "      <td>0.030830</td>\n",
       "      <td>-0.017812</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>0.005714</td>\n",
       "      <td>-0.016714</td>\n",
       "      <td>-0.003833</td>\n",
       "      <td>0.009137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.056486</td>\n",
       "      <td>0.007751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flight_Distance</th>\n",
       "      <td>-0.512545</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015895</td>\n",
       "      <td>-0.028663</td>\n",
       "      <td>-0.021415</td>\n",
       "      <td>-0.041562</td>\n",
       "      <td>-0.048036</td>\n",
       "      <td>0.037238</td>\n",
       "      <td>0.018088</td>\n",
       "      <td>0.006020</td>\n",
       "      <td>0.012688</td>\n",
       "      <td>0.022817</td>\n",
       "      <td>0.051595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.022372</td>\n",
       "      <td>0.006063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scheduled_Departure_Time</th>\n",
       "      <td>0.000286</td>\n",
       "      <td>-0.015895</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.959753</td>\n",
       "      <td>0.036856</td>\n",
       "      <td>-0.009524</td>\n",
       "      <td>0.931480</td>\n",
       "      <td>0.723984</td>\n",
       "      <td>0.663731</td>\n",
       "      <td>0.001050</td>\n",
       "      <td>-0.036259</td>\n",
       "      <td>0.699490</td>\n",
       "      <td>-0.002883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.060051</td>\n",
       "      <td>0.013826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual_Departure_Time</th>\n",
       "      <td>0.015596</td>\n",
       "      <td>-0.028663</td>\n",
       "      <td>0.959753</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074546</td>\n",
       "      <td>-0.001605</td>\n",
       "      <td>0.969686</td>\n",
       "      <td>0.723896</td>\n",
       "      <td>0.686122</td>\n",
       "      <td>0.007946</td>\n",
       "      <td>-0.031694</td>\n",
       "      <td>0.722680</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.124682</td>\n",
       "      <td>-0.000276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Departure_Delay_Minutes</th>\n",
       "      <td>0.055729</td>\n",
       "      <td>-0.021415</td>\n",
       "      <td>0.036856</td>\n",
       "      <td>0.074546</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059158</td>\n",
       "      <td>0.069034</td>\n",
       "      <td>0.026681</td>\n",
       "      <td>0.007967</td>\n",
       "      <td>0.033673</td>\n",
       "      <td>0.005059</td>\n",
       "      <td>0.012293</td>\n",
       "      <td>0.031998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.467583</td>\n",
       "      <td>-0.062337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Departure_Taxi</th>\n",
       "      <td>0.081297</td>\n",
       "      <td>-0.041562</td>\n",
       "      <td>-0.009524</td>\n",
       "      <td>-0.001605</td>\n",
       "      <td>0.059158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028055</td>\n",
       "      <td>0.012408</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.001582</td>\n",
       "      <td>-0.089872</td>\n",
       "      <td>0.010296</td>\n",
       "      <td>-0.003764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.330395</td>\n",
       "      <td>-0.083749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Departure_WheelsOff</th>\n",
       "      <td>0.030830</td>\n",
       "      <td>-0.048036</td>\n",
       "      <td>0.931480</td>\n",
       "      <td>0.969686</td>\n",
       "      <td>0.069034</td>\n",
       "      <td>0.028055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.735600</td>\n",
       "      <td>0.707543</td>\n",
       "      <td>0.008095</td>\n",
       "      <td>-0.033220</td>\n",
       "      <td>0.744575</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.130143</td>\n",
       "      <td>-0.002465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scheduled_Arrival_Time</th>\n",
       "      <td>-0.017812</td>\n",
       "      <td>0.037238</td>\n",
       "      <td>0.723984</td>\n",
       "      <td>0.723896</td>\n",
       "      <td>0.026681</td>\n",
       "      <td>0.012408</td>\n",
       "      <td>0.735600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.855642</td>\n",
       "      <td>0.002524</td>\n",
       "      <td>-0.023712</td>\n",
       "      <td>0.850222</td>\n",
       "      <td>-0.000281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.053338</td>\n",
       "      <td>0.004393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual_Arrival_Time</th>\n",
       "      <td>0.001246</td>\n",
       "      <td>0.018088</td>\n",
       "      <td>0.663731</td>\n",
       "      <td>0.686122</td>\n",
       "      <td>0.007967</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.707543</td>\n",
       "      <td>0.855642</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008845</td>\n",
       "      <td>-0.001442</td>\n",
       "      <td>0.958663</td>\n",
       "      <td>0.004744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.055971</td>\n",
       "      <td>-0.006027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Delay_Minutes</th>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.006020</td>\n",
       "      <td>0.001050</td>\n",
       "      <td>0.007946</td>\n",
       "      <td>0.033673</td>\n",
       "      <td>0.001582</td>\n",
       "      <td>0.008095</td>\n",
       "      <td>0.002524</td>\n",
       "      <td>0.008845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>0.008534</td>\n",
       "      <td>0.007322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032212</td>\n",
       "      <td>-0.007922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Taxi</th>\n",
       "      <td>-0.016714</td>\n",
       "      <td>0.012688</td>\n",
       "      <td>-0.036259</td>\n",
       "      <td>-0.031694</td>\n",
       "      <td>0.005059</td>\n",
       "      <td>-0.089872</td>\n",
       "      <td>-0.033220</td>\n",
       "      <td>-0.023712</td>\n",
       "      <td>-0.001442</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020665</td>\n",
       "      <td>-0.003780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.125984</td>\n",
       "      <td>-0.000527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_WheelsOn</th>\n",
       "      <td>-0.003833</td>\n",
       "      <td>0.022817</td>\n",
       "      <td>0.699490</td>\n",
       "      <td>0.722680</td>\n",
       "      <td>0.012293</td>\n",
       "      <td>0.010296</td>\n",
       "      <td>0.744575</td>\n",
       "      <td>0.850222</td>\n",
       "      <td>0.958663</td>\n",
       "      <td>0.008534</td>\n",
       "      <td>-0.020665</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.059363</td>\n",
       "      <td>-0.005803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>0.009137</td>\n",
       "      <td>0.051595</td>\n",
       "      <td>-0.002883</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>0.031998</td>\n",
       "      <td>-0.003764</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>-0.000281</td>\n",
       "      <td>0.004744</td>\n",
       "      <td>0.007322</td>\n",
       "      <td>-0.003780</td>\n",
       "      <td>0.005981</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014461</td>\n",
       "      <td>0.012898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsDelay</th>\n",
       "      <td>0.056486</td>\n",
       "      <td>-0.022372</td>\n",
       "      <td>0.060051</td>\n",
       "      <td>0.124682</td>\n",
       "      <td>0.467583</td>\n",
       "      <td>0.330395</td>\n",
       "      <td>0.130143</td>\n",
       "      <td>0.053338</td>\n",
       "      <td>0.055971</td>\n",
       "      <td>0.032212</td>\n",
       "      <td>0.125984</td>\n",
       "      <td>0.059363</td>\n",
       "      <td>0.014461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEEKDAY</th>\n",
       "      <td>0.007751</td>\n",
       "      <td>0.006063</td>\n",
       "      <td>0.013826</td>\n",
       "      <td>-0.000276</td>\n",
       "      <td>-0.062337</td>\n",
       "      <td>-0.083749</td>\n",
       "      <td>-0.002465</td>\n",
       "      <td>0.004393</td>\n",
       "      <td>-0.006027</td>\n",
       "      <td>-0.007922</td>\n",
       "      <td>-0.000527</td>\n",
       "      <td>-0.005803</td>\n",
       "      <td>0.012898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.117098</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Flight_Number  Flight_Distance  \\\n",
       "Flight_Number                  1.000000        -0.512545   \n",
       "Flight_Distance               -0.512545         1.000000   \n",
       "Scheduled_Departure_Time       0.000286        -0.015895   \n",
       "Actual_Departure_Time          0.015596        -0.028663   \n",
       "Departure_Delay_Minutes        0.055729        -0.021415   \n",
       "Departure_Taxi                 0.081297        -0.041562   \n",
       "Departure_WheelsOff            0.030830        -0.048036   \n",
       "Scheduled_Arrival_Time        -0.017812         0.037238   \n",
       "Actual_Arrival_Time            0.001246         0.018088   \n",
       "Arrival_Delay_Minutes          0.005714         0.006020   \n",
       "Arrival_Taxi                  -0.016714         0.012688   \n",
       "Arrival_WheelsOn              -0.003833         0.022817   \n",
       "Day                            0.009137         0.051595   \n",
       "Month                               NaN              NaN   \n",
       "IsDelay                        0.056486        -0.022372   \n",
       "WEEKDAY                        0.007751         0.006063   \n",
       "\n",
       "                          Scheduled_Departure_Time  Actual_Departure_Time  \\\n",
       "Flight_Number                             0.000286               0.015596   \n",
       "Flight_Distance                          -0.015895              -0.028663   \n",
       "Scheduled_Departure_Time                  1.000000               0.959753   \n",
       "Actual_Departure_Time                     0.959753               1.000000   \n",
       "Departure_Delay_Minutes                   0.036856               0.074546   \n",
       "Departure_Taxi                           -0.009524              -0.001605   \n",
       "Departure_WheelsOff                       0.931480               0.969686   \n",
       "Scheduled_Arrival_Time                    0.723984               0.723896   \n",
       "Actual_Arrival_Time                       0.663731               0.686122   \n",
       "Arrival_Delay_Minutes                     0.001050               0.007946   \n",
       "Arrival_Taxi                             -0.036259              -0.031694   \n",
       "Arrival_WheelsOn                          0.699490               0.722680   \n",
       "Day                                      -0.002883               0.003611   \n",
       "Month                                          NaN                    NaN   \n",
       "IsDelay                                   0.060051               0.124682   \n",
       "WEEKDAY                                   0.013826              -0.000276   \n",
       "\n",
       "                          Departure_Delay_Minutes  Departure_Taxi  \\\n",
       "Flight_Number                            0.055729        0.081297   \n",
       "Flight_Distance                         -0.021415       -0.041562   \n",
       "Scheduled_Departure_Time                 0.036856       -0.009524   \n",
       "Actual_Departure_Time                    0.074546       -0.001605   \n",
       "Departure_Delay_Minutes                  1.000000        0.059158   \n",
       "Departure_Taxi                           0.059158        1.000000   \n",
       "Departure_WheelsOff                      0.069034        0.028055   \n",
       "Scheduled_Arrival_Time                   0.026681        0.012408   \n",
       "Actual_Arrival_Time                      0.007967        0.004789   \n",
       "Arrival_Delay_Minutes                    0.033673        0.001582   \n",
       "Arrival_Taxi                             0.005059       -0.089872   \n",
       "Arrival_WheelsOn                         0.012293        0.010296   \n",
       "Day                                      0.031998       -0.003764   \n",
       "Month                                         NaN             NaN   \n",
       "IsDelay                                  0.467583        0.330395   \n",
       "WEEKDAY                                 -0.062337       -0.083749   \n",
       "\n",
       "                          Departure_WheelsOff  Scheduled_Arrival_Time  \\\n",
       "Flight_Number                        0.030830               -0.017812   \n",
       "Flight_Distance                     -0.048036                0.037238   \n",
       "Scheduled_Departure_Time             0.931480                0.723984   \n",
       "Actual_Departure_Time                0.969686                0.723896   \n",
       "Departure_Delay_Minutes              0.069034                0.026681   \n",
       "Departure_Taxi                       0.028055                0.012408   \n",
       "Departure_WheelsOff                  1.000000                0.735600   \n",
       "Scheduled_Arrival_Time               0.735600                1.000000   \n",
       "Actual_Arrival_Time                  0.707543                0.855642   \n",
       "Arrival_Delay_Minutes                0.008095                0.002524   \n",
       "Arrival_Taxi                        -0.033220               -0.023712   \n",
       "Arrival_WheelsOn                     0.744575                0.850222   \n",
       "Day                                  0.000406               -0.000281   \n",
       "Month                                     NaN                     NaN   \n",
       "IsDelay                              0.130143                0.053338   \n",
       "WEEKDAY                             -0.002465                0.004393   \n",
       "\n",
       "                          Actual_Arrival_Time  Arrival_Delay_Minutes  \\\n",
       "Flight_Number                        0.001246               0.005714   \n",
       "Flight_Distance                      0.018088               0.006020   \n",
       "Scheduled_Departure_Time             0.663731               0.001050   \n",
       "Actual_Departure_Time                0.686122               0.007946   \n",
       "Departure_Delay_Minutes              0.007967               0.033673   \n",
       "Departure_Taxi                       0.004789               0.001582   \n",
       "Departure_WheelsOff                  0.707543               0.008095   \n",
       "Scheduled_Arrival_Time               0.855642               0.002524   \n",
       "Actual_Arrival_Time                  1.000000               0.008845   \n",
       "Arrival_Delay_Minutes                0.008845               1.000000   \n",
       "Arrival_Taxi                        -0.001442               0.010945   \n",
       "Arrival_WheelsOn                     0.958663               0.008534   \n",
       "Day                                  0.004744               0.007322   \n",
       "Month                                     NaN                    NaN   \n",
       "IsDelay                              0.055971               0.032212   \n",
       "WEEKDAY                             -0.006027              -0.007922   \n",
       "\n",
       "                          Arrival_Taxi  Arrival_WheelsOn       Day  Month  \\\n",
       "Flight_Number                -0.016714         -0.003833  0.009137    NaN   \n",
       "Flight_Distance               0.012688          0.022817  0.051595    NaN   \n",
       "Scheduled_Departure_Time     -0.036259          0.699490 -0.002883    NaN   \n",
       "Actual_Departure_Time        -0.031694          0.722680  0.003611    NaN   \n",
       "Departure_Delay_Minutes       0.005059          0.012293  0.031998    NaN   \n",
       "Departure_Taxi               -0.089872          0.010296 -0.003764    NaN   \n",
       "Departure_WheelsOff          -0.033220          0.744575  0.000406    NaN   \n",
       "Scheduled_Arrival_Time       -0.023712          0.850222 -0.000281    NaN   \n",
       "Actual_Arrival_Time          -0.001442          0.958663  0.004744    NaN   \n",
       "Arrival_Delay_Minutes         0.010945          0.008534  0.007322    NaN   \n",
       "Arrival_Taxi                  1.000000         -0.020665 -0.003780    NaN   \n",
       "Arrival_WheelsOn             -0.020665          1.000000  0.005981    NaN   \n",
       "Day                          -0.003780          0.005981  1.000000    NaN   \n",
       "Month                              NaN               NaN       NaN    NaN   \n",
       "IsDelay                       0.125984          0.059363  0.014461    NaN   \n",
       "WEEKDAY                      -0.000527         -0.005803  0.012898    NaN   \n",
       "\n",
       "                           IsDelay   WEEKDAY  \n",
       "Flight_Number             0.056486  0.007751  \n",
       "Flight_Distance          -0.022372  0.006063  \n",
       "Scheduled_Departure_Time  0.060051  0.013826  \n",
       "Actual_Departure_Time     0.124682 -0.000276  \n",
       "Departure_Delay_Minutes   0.467583 -0.062337  \n",
       "Departure_Taxi            0.330395 -0.083749  \n",
       "Departure_WheelsOff       0.130143 -0.002465  \n",
       "Scheduled_Arrival_Time    0.053338  0.004393  \n",
       "Actual_Arrival_Time       0.055971 -0.006027  \n",
       "Arrival_Delay_Minutes     0.032212 -0.007922  \n",
       "Arrival_Taxi              0.125984 -0.000527  \n",
       "Arrival_WheelsOn          0.059363 -0.005803  \n",
       "Day                       0.014461  0.012898  \n",
       "Month                          NaN       NaN  \n",
       "IsDelay                   1.000000 -0.117098  \n",
       "WEEKDAY                  -0.117098  1.000000  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1e/Dq1vf16dNHixcvVkREhGrWzJl6YvDgwZozZ46WLVtmFMDasmWL4uLiNHHiRFlbm39KqLgbqcrIypL3bU8cejk66Wox85BfSUmW921PJHs5OCk9M1NxxUwvki3p8JVo1XPzyNt2OSW50PoOZ+Ji9Uj9kp+4tyRbzpzR4ejovNd2uf+n3k5OupKc/yRcVUdHXU0u/sm4q8nJ8nI2LlNPJ6dCT+ZJ0lvduqlngwYavmSJopMKL1psY2Wluf37q5abm0YuW1bhR19JZVdPr6QkKz0rS1kF7gjPxF2Tj7OLbK2slJ67LlZlEnczpeiydCj8ZOe9+FOzQE1q2VEjNi7Ribgr9/15lsra1VayMigjwbh9ZVxPk41b4b4r60aGUs8nKuJimCK+DsvZmJ0tZUtHxm1T/amt5NI0fzrCrJuZig+NUbVB9cv0OCxBfFqKMrIy5Wln/ESsh52LrqWVvDB7ZGpOX38mKUaedq76U8MeRgGsbGUrPOWaJOlUYpTqOXvr2foPV9oAVlm272xJFxJzRrocj7ushm6eer5Fx0rxw2xeH3N7n3G3fYxjKa6FLkernruH0fb0rCxduJ5TtkevxKilTzWNbdFGf92x6R6PyHy4rjSNEutkMSMtrqSkFF8nC0w3nS3pQu6oteNXr6ihh6eebxtUKID1dufu6lm3gYat+l7RySWfiysSzpP3zpzt++L1BD25ZokcbWzlYmenKynJmtezvy4lFj9ts6WKSy26fXveZfv2zG3f8SVMJ19Qcx9feTk5a82T+b952FhZKbBmLY1qGSD/f88xuieqSLaGndHhiPwgqJ1Nzs+3Xi5OupKUX6aezk66mlz8Wop3440+XdW9cQON/GqpYhIrzzkSKFbFPD08kAhgASbWoEEDNW7cWGvXrlV0dLR69uyptm3bqlmzZvcc3Ll97atz584pISFBPj4+hUZ5SdK1azk/bJ09e7bQe7e7dOmSPvvsM+3evVsxMTFKuy0wcPny5bwAVtWqVdW7d2+tXbtW586dU7169STlrKNlMBj0+OOP39PxmVp6VpaOXYlRSO262ng+f1qlkJp1tel80dMsHYyJUg+/BkbbOteuq6NXYpRRwg/+TT19dPJa/g/a+6MjVN/deP2Yeu4eiqhAC24np6crOT7eaNvlpCSF+Pnp+JWcY7W1slJQrVp6f2fhhY1vORgVpRA/Py04cCBvW2c/Px2IjDRK91b37urdsKFGLF2q8OuFy+lW8Kquu7tGLFtW6hsaS1dW9XRfdIQGNnpIBuVfj9Vz91BMclKlDF5JuWUZG62QGnW18WL+mkMhNepq08VTJex5Z+ObBWpyq2CN3rRUR2Oj77xDBWZlYyXHuq5K+v2a3Np6521POn5NVVp7F07vYKPG7wQabYvdGqGkP+LkN6m57Lwdjd6L33tZ2enZcg+uVjYHYEEysjN14nqkAr0aavvl/FESgV4NtePyXYyaMEi2ViVfrhsMhjumqcjy2nf1Itr3pftr37czGAx5D21UdHl9TK262niuQB9Tqwyuhbx8dDK25OC+QaqwZct1pWnkl6OfcZ2s7adN54opx+hI9ah7WznW8btjORZV32Z07q4+9Rtq+A9LFV4By68knCfvnTnb9y2pGelKzUhXFTt7daldV//YveMej8Z80rOydOxyTvv+6WyBcqzjp01niy7HA9GR6lGviPZ9ueRyLOjXSxfU55uFRtve79lXZ+Ou6dP9oRU2eCVJyWnpSk4zDmZeTkxWp3p++iM6/568vV9Nzd6y676/b3rfburl31DPLF6m8PjKdY4EUPFV3rtdwExsbGy0aNEizZs3Tz/99JPee+89SZKHh4dGjhx5T6OUruQGDapWzbmBjc8NLpw6dUqnThV/U5Kamlri5164cEFPPPGEkpKSFBQUpG7dusnFxUVWVlYKDQ1VaGhooYDWsGHDtHbtWi1btkyvvfaaIiMj9euvvyo4OFi1alnOgrOfH9mnD7v305HL0ToQE6mnm7ZUDVdXfXP8sCTptcDO8nV20dRt/5MkfX38sEY1D9AbHbvquz+OqI1vDQ1r0kIvbl6b95kvte2ogzFROpcQJ1c7Oz3boo2aenrrzV2b89J8cWS/Vgx6Ss8HBGndmZNq5VNNTz3UStN2/FS+BWBiCw4e1MTAQJ2Pj9f5uDhNDApSakaG1pw4kZdmdt++ik5K0uxdORfQCw8c0HdPPqnx7dtr8+nT6tmwoYLr1DGaInBG9+4a0KSJJqxZo6S0tLz1sRLT0nQzI0PWBoPm9e+v5r6+em7VKlkZDHlpEm7cqPABmbKop1//flijm7fRW526a9Gxg6rr5qHnA4K08NiBIvNQWXz++1592Lm/jlyN1oErkXq6cSvVcK6ib04ekiS91qaLfJ1cNXXXurx9mlbNGd3qZGOrqvaOalrVR2mZmTqdkPO0+4TmgXo5oLNe2vGjwpMS8p7OTU5PU0rG3U0HW1F4966tS/OPy7Guq5wauunaz5FKj70pz245i0NHLTuj9PibqvOnpjJYGeRQy8Vof+sqtjLYWhXaLuVMH1iljZdsXGzL5VjM7dvzuzSj5RP6IyFCR+MvanDt9qrm4KaVF0MlSc837i0f+yp6++hySdLjdTooOjVeF5Jz+vxWHn4aWbezll7Mn/pzdP2H9UdChMJTYmVrZaNO3o3Vr0aAZh1fXf4HWI4+P75XH4b015HYu2jfHgXat4Ojmnr4KC0rv30/37yDjsRG60JinOysrNW1VgMNadBMb+yu2P11QZ8f3qcPe/TTkSvROhBdoI/5PbePCcrtY7bm9jG/5/YxwV313fEjalOtiGuhdrnXQvG3XQvtzL8WejUoRNsvnlNUUqKcbe30WMMm6lCjtkavW1G+BWBCXFeaxueH9uvDno/oyJWYoutkh5CcctyyQZL09bHDGtUiQG90eljfHT+aUycfaqEXf8pv68+3CdSRyzG6cD0+py371dMQ/6Z64+f86abf6dJDAxs30Z/Wr1Zyelre+q7Xb6bpZmZGOZZA2eE8ee/M1b671Korg0E6Ex+num7u+muHh3U2Pk7LTh4r3wIwkc8P7deHvR7R0cs57fupZi1Vw8VV3x7LKcdXO4aomouLpm7Kad/fHDusUS0D9P9CHtb3v+e276Yt9NLG/Dpqa2WlhlU9c/9trWouLnrIy1sp6em6kLuW2O1r2aWmpyvuRmqlWeOuoK9CD2hCSHudvxanC9fiNSEkUDfSM7T2WP49+ayBfRSTmKQPt/4iKacMG3jnlKGdtbV8XV3UxNdbKWlpuhiXEyB765Hu6t/cX88vWaPkm2nycs69J795UzczKtfUjHcjOUW6GJH/OjxK+uOU5FZFqlFxZ/oEKiQCWEAZqFq1qt58801Nnz5dZ8+e1e7du7V48WLNnTtXtra2mjBhQqk/KysrS/v25Szg3qJFC0k50wRKUp8+ffTxxx/fcz4XLlyohIQEffDBBxowYIDRe2+++aZCQ0ML7RMUFKR69epp9erVmjJlilasWKGsrCw98cQT95yPsrD2zEm5OzjqpXYd5e3krLBrVzVm/UpFJOU8TeTj7KyarvlrsIQnJmjM+hWaHtxNzzRvrcvJyZrxy1ZtOJcfIKxib6+/P9xb3k5OSkxL0/GrMXpyzfc6fDl/VMaRK9GasHG1XgvqrJfadtSlxATN/HWrVp/6o/wOvgz8d+9eOdjYaEb37nJzcNCh6Gg9u2KFkgus6Vbd1dXoKbcDUVF6ad06vdypk6YEB+tifLxeXLfOaHrCka1bS5K+GzbM6Pte27BBK44fVzVXV/Vq2FCStG7UKKM0Ty9dqj3hhdc2qEjKop5GJSdq1Lplmh7cTRueGK3o5CQtOHpAnx4q3J4rk7XnT8jd3lEvte4kb0dnhcVd1ZjNyxSRnFuWTi6q6WK87tL6AWPy/t3Sq7oGNWim8KQEhSz/VJL0TJM2sre20afdBhvtN+fQLs059EsZH5F5uAf5KiM5XTFrzisj4aYcarqo7pSWsvPKGU2VkXBT6bF3PwryZnSKUk4lqN4rrU2dZYu1Ofqo3GydNK5hd3nZu+pMYoym7F+k6Bs5D6F42bvK19E9L72VDJrUuI9qOHooMztL4Smx+iRso1Zeym+7jtZ2eq3pAPk4uOlmZrouJF/Rm0eWanP00XI/vvKU175b5bbv+Ksas6VA+3Z0UU3nO7Tv+rnte0VO+3a0tdU7HXqpupOrbmRm6EzCNU3ZuVZrz59QZZHXx7TtKG/n3D5mXYE+xsnZ6LwYnpigMetWaHqnAn3Mrq3acLbAtZDdbddCV2L05GrjayEvR2f9q3s/eTs7KzEtTSdir2j0uhXaFX6h/A7exLiuNI21p0/K3cFBL7XrkFMnY2M15seVikhMlJRbJ43K8brGrF2p6SFd9UyL3HLcaVwnHW1t9c7DPVTdxUU3MjJ0Ji5OUzb/T2tPn8xL80yLnL5nyeAnjfLzypYNWn7i97I85HLDefLemat9u9rb67XAzqrm4qKEGzf0v3OnNDt0Z6lHH1madadOysPBQS8G5rfvsQXbt7OzahTsc65f19g1K/VG5656pmVrXU5K1owdW7XhTH45+ji7aP1T+feA49u01/g27bU7/JKeWvXgrdE0/9d9srex0VuP9JCbo70OR0Rr7DcrlZxW4J68ivE9uY+ri1aPz59icVxwO40Lbqc95y9p1OKch6iebtdKkvT1aON78tdXb9SqIxV0vUUT+P2kNPrP+ct1zPok59+D+mbrH9PMlSvgwWTIvn1uMgB3JTw8XD169FBISIi++OKLYtNFRUWpa9euat26tZbkjkBZuXKlpk2bpqlTp2r8+PFF7rdq1Sq9/vrraty4sdasWSODwaCMjAwFBQXJzc1NGzdulK3tnZ9mnzt3rubNm6evvvpKQUFBkqRx48Zp165d2r9/f15QTMoJmg0cOFBhYWFG6W/58ssvNWvWLH300UeaNWuWUlNTtWPHjrte36vup7PvKj2KZp1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      "text/plain": [
       "<Figure size 2000x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coor_data = final_dataset.drop(['Month'], axis = 1) \n",
    "plt.figure(figsize=(20,14))\n",
    "ax = sns.heatmap(coor_data.corr(), cmap='viridis', center=0, annot=True)\n",
    "bottom, top = ax.get_ylim()\n",
    "plt.text(0,-0.6, \"Correlation Map\", fontsize = 30, color='Black', fontstyle='normal')\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "plt.yticks(rotation=0, fontsize=14)\n",
    "plt.xticks(rotation=90, fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<b>From the above chart it is observered that: \n",
    "\n",
    "There is a strong corelation between the Departure Taxi and the Arrival Taxi: meaning the times of departure wheels out and arrival wheels out are linked. Which is also trival considering the in-flight schedule being followed from time of taking off to landing.\n",
    "<br>\n",
    "The flight distance does not corelate strongly with the arrival delay: Means that the distance is not playing a major role in the flight being delayed. We can not say this though: Distance doesn't cause delay, because of the relationship and causation rule. \n",
    "<br>\n",
    "\n",
    "Departure Taxi and IsDelay have a corelation as well: There is some corelation here as well, which means that the company has to assess when the departure wheels are coming out because it seems to be causing some delay in this case. \n",
    "<br>\n",
    "\n",
    "IsDelay and Departure Delay Minutes have a corelationship: This means that a delay in the departure is contributing to the flight delay issue here. \n",
    "<br>\n",
    "\n",
    "Arrival Taxi and Departure Delay Minutes: This is trival since these 2 events are very different from each. \n",
    "<br>\n",
    "\n",
    "Also a strong correlation between isDelay and Departure delay than isDelay and Arrival Delay: meaning a late departure is a major contributing factor in flights being delayed than late arrivals (time spent in between the flights).\n",
    "<br>\n",
    "\n",
    "Other aspects are very trival like: strong relationship between arrival wheels on and departure time, departure wheels off and scheduled arrival time, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Spread out of data - Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_dataset.hist(figsize  = [15, 15],bins=12) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some observations: \n",
    "\n",
    "Fewer flights on the 25th December\n",
    "<br>\n",
    "Most flights on weekdays -Sunday and Monday\n",
    "<br>\n",
    "Most data for on time flights than delays\n",
    "<br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Checking out when most of the flights are delayed in the week\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "fig = plt.figure(1, figsize=(15,9))\n",
    "ax = sns.barplot(x=\"WEEKDAY\", y=\"IsDelay\", data=final_dataset, palette=\"Blues\", ci=None, estimator=sum)\n",
    "plt.title('No. of delays in the week ', fontsize=18)\n",
    "plt.xlabel('Day of the week', fontsize=14, labelpad=10);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Most of the delays as seen in the chart above are occuring on Monday followed by Tuesday. The least delays occur on Friday & Saturday which is actually the weekend. This can be analysed further if customer and country behaviour is present in the dataset. But a recommendation here to Compass Airlines would be to be strict on these days and find more by looking at customer, location and airplane schedule history."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysing what reason causing the most delay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "fig = plt.figure(1, figsize=(15,9))\n",
    "ax = sns.barplot(x=\"Delay_Reason\", y=\"IsDelay\", data=final_dataset, palette=\"Blues\", ci=None, estimator=sum)\n",
    "plt.title('No. of delays in the week ', fontsize=18)\n",
    "plt.xlabel('Day of the week', fontsize=14, labelpad=10);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>From the above chart it is clear that because the Aircraft is late from it's previous flight or other factors involved, that is the main reason why the entire flight ends up being delayed. Weather is barely playing a role in the flight being late in this month (December) of flights. This is also one of the reasons why I did not choose to include the weather data at this point. But I would want to use the weather data later, and if I would have had more time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_data = final_dataset\n",
    "status = []\n",
    "\n",
    "for value in filtered_data['Departure_Delay_Minutes']:\n",
    "    if int(value) >= 0.5:\n",
    "        status.append(1) #yes there is a delay\n",
    "    else:\n",
    "        status.append(0) #there is no delay\n",
    "filtered_data['Dep_Delay'] = status\n",
    "filtered_data.head() \n",
    "\n",
    "\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "fig = plt.figure(1, figsize=(15,9))\n",
    "ax = sns.barplot(x=\"Dep_Delay\", y=\"IsDelay\", data=filtered_data, palette=\"Blues\", ci=None, estimator=sum)\n",
    "plt.title('No. of delays in the week ', fontsize=18)\n",
    "plt.xlabel('Day of the week', fontsize=14, labelpad=10);\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> From the above chart it can be seen that departure delay along with the aircraft being late is causing the most delay to the flights. These are the key points that Compass Airlines needs to start working on immediately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this part, I would like to go with Random Forests becasue the approach is simple, efficient and will give the highest accuracy, especially considering  the amount of data and the kind of data I have"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding Delay_Reason for model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Flight_Number</th>\n",
       "      <th>Plane_ID</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>Origin_Airport</th>\n",
       "      <th>Destination_Airport</th>\n",
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       "      <th>Scheduled_Departure_Time</th>\n",
       "      <th>Actual_Departure_Time</th>\n",
       "      <th>Departure_Delay_Minutes</th>\n",
       "      <th>Departure_Taxi</th>\n",
       "      <th>Departure_WheelsOff</th>\n",
       "      <th>Scheduled_Arrival_Time</th>\n",
       "      <th>Actual_Arrival_Time</th>\n",
       "      <th>Arrival_Delay_Minutes</th>\n",
       "      <th>Arrival_Taxi</th>\n",
       "      <th>Arrival_WheelsOn</th>\n",
       "      <th>Delay_Reason</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>MONTH_AB</th>\n",
       "      <th>IsDelay</th>\n",
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       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>4135</td>\n",
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       "      <td>54</td>\n",
       "      <td>43</td>\n",
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       "      <td>62</td>\n",
       "      <td>15</td>\n",
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       "      <td>127</td>\n",
       "      <td>219</td>\n",
       "      <td>52</td>\n",
       "      <td>7</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3776</td>\n",
       "      <td>GDISCI</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>1700</td>\n",
       "      <td>10</td>\n",
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       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
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       "      <td>8</td>\n",
       "      <td>27</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>4754</td>\n",
       "      <td>GHHWLL</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>43</td>\n",
       "      <td>56</td>\n",
       "      <td>413</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>242</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>222</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>5604</td>\n",
       "      <td>GBGWFC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>43</td>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>10</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>118</td>\n",
       "      <td>111</td>\n",
       "      <td>155</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>5612</td>\n",
       "      <td>GBISBC</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>43</td>\n",
       "      <td>74</td>\n",
       "      <td>258</td>\n",
       "      <td>10</td>\n",
       "      <td>234</td>\n",
       "      <td>144</td>\n",
       "      <td>26</td>\n",
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       "      <td>132</td>\n",
       "      <td>358</td>\n",
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       "      <td>6</td>\n",
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      "text/plain": [
       "   Airline  Flight_Number Plane_ID FlightDate  Origin_Airport  \\\n",
       "0        9           4135   GJLOAB 2019-12-01              54   \n",
       "1        9           3776   GDISCI 2019-12-01              42   \n",
       "2        5           4754   GHHWLL 2019-12-01              43   \n",
       "3       13           5604   GBGWFC 2019-12-01              43   \n",
       "4       13           5612   GBISBC 2019-12-01              43   \n",
       "\n",
       "   Destination_Airport  Flight_Distance  Scheduled_Departure_Time  \\\n",
       "0                   43              678                         1   \n",
       "1                   43             1700                        10   \n",
       "2                   56              413                        10   \n",
       "3                   57              130                        10   \n",
       "4                   74              258                        10   \n",
       "\n",
       "   Actual_Departure_Time  Departure_Delay_Minutes  Departure_Taxi  \\\n",
       "0                    103                       62              15   \n",
       "1                     25                       15              21   \n",
       "2                     10                        0              15   \n",
       "3                     44                       34              34   \n",
       "4                    234                      144              26   \n",
       "\n",
       "   Departure_WheelsOff  Scheduled_Arrival_Time  Actual_Arrival_Time  \\\n",
       "0                  118                     127                  219   \n",
       "1                   46                     616                  624   \n",
       "2                   25                     242                  227   \n",
       "3                  118                     111                  155   \n",
       "4                  300                     132                  358   \n",
       "\n",
       "   Arrival_Delay_Minutes  Arrival_Taxi  Arrival_WheelsOn  Delay_Reason  Day  \\\n",
       "0                     52             7               212             1    1   \n",
       "1                      8            27               557             3    1   \n",
       "2                      0             5               222             3    1   \n",
       "3                     44             4               151             5    1   \n",
       "4                    146             6               352             0    1   \n",
       "\n",
       "   Month MONTH_AB  IsDelay  WEEKDAY  \n",
       "0     12      Dec        1        6  \n",
       "1     12      Dec        0        6  \n",
       "2     12      Dec        0        6  \n",
       "3     12      Dec        1        6  \n",
       "4     12      Dec        1        6  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dataset = final_dataset \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Performing label encoding on the delay_reason column to convert it into numerical form for building the model\n",
    "le = LabelEncoder()\n",
    "model_dataset['Delay_Reason'] = le.fit_transform(model_dataset['Delay_Reason'])\n",
    "model_dataset['Airline'] = le.fit_transform(model_dataset['Airline']) \n",
    "model_dataset['Origin_Airport'] = le.fit_transform(model_dataset['Origin_Airport']) \n",
    "model_dataset['Destination_Airport'] = le.fit_transform(model_dataset['Destination_Airport']) \n",
    "model_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Dropping unnecessary columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first 4 columns mentioned below are dropped because they will not contribute anything to the model, the next features are deleted because these values are occuring in the future once the delay/no delay has already occured, so it won't be needed as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "      <th>Scheduled_Departure_Time</th>\n",
       "      <th>Actual_Departure_Time</th>\n",
       "      <th>Departure_Delay_Minutes</th>\n",
       "      <th>Departure_Taxi</th>\n",
       "      <th>Departure_WheelsOff</th>\n",
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       "      <th>Day</th>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
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       "      <td>413</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>242</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>10</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>118</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>74</td>\n",
       "      <td>258</td>\n",
       "      <td>10</td>\n",
       "      <td>234</td>\n",
       "      <td>144</td>\n",
       "      <td>26</td>\n",
       "      <td>300</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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      ],
      "text/plain": [
       "   Airline  Origin_Airport  Destination_Airport  Flight_Distance  \\\n",
       "0        9              54                   43              678   \n",
       "1        9              42                   43             1700   \n",
       "2        5              43                   56              413   \n",
       "3       13              43                   57              130   \n",
       "4       13              43                   74              258   \n",
       "\n",
       "   Scheduled_Departure_Time  Actual_Departure_Time  Departure_Delay_Minutes  \\\n",
       "0                         1                    103                       62   \n",
       "1                        10                     25                       15   \n",
       "2                        10                     10                        0   \n",
       "3                        10                     44                       34   \n",
       "4                        10                    234                      144   \n",
       "\n",
       "   Departure_Taxi  Departure_WheelsOff  Scheduled_Arrival_Time  Day  IsDelay  \\\n",
       "0              15                  118                     127    1        1   \n",
       "1              21                   46                     616    1        0   \n",
       "2              15                   25                     242    1        0   \n",
       "3              34                  118                     111    1        1   \n",
       "4              26                  300                     132    1        1   \n",
       "\n",
       "   WEEKDAY  \n",
       "0        6  \n",
       "1        6  \n",
       "2        6  \n",
       "3        6  \n",
       "4        6  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dataset = model_dataset.drop(['Flight_Number'], axis = 1) \n",
    "model_dataset = model_dataset.drop(['Plane_ID'], axis = 1) \n",
    "model_dataset = model_dataset.drop(['FlightDate'], axis = 1)\n",
    "model_dataset = model_dataset.drop(['MONTH_AB'], axis = 1) \n",
    "model_dataset = model_dataset.drop(['Month'], axis = 1)\n",
    "model_dataset = model_dataset.drop(['Actual_Arrival_Time'], axis = 1) \n",
    "model_dataset = model_dataset.drop(['Arrival_Delay_Minutes'], axis = 1) \n",
    "model_dataset = model_dataset.drop(['Arrival_Taxi'], axis = 1) \n",
    "model_dataset = model_dataset.drop(['Arrival_WheelsOn'], axis = 1) \n",
    "model_dataset = model_dataset.drop(['Delay_Reason'], axis = 1)\n",
    "model_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this data is obviously not balanced, but this code is just for workflow purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Legend:\n",
      "0    51588\n",
      "1    14081\n",
      "Name: IsDelay, dtype: int64\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "case_count = model_dataset['IsDelay'].value_counts() # 'data' is our input which will be any of the 3 dataframes created\n",
    "print('Legend:')\n",
    "print(case_count)\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.barplot(x=case_count.index, y=case_count.values)\n",
    "plt.rcParams[\"figure.facecolor\"] = \"lightblue\"\n",
    "plt.title('Data Distribution', fontsize=16)\n",
    "plt.xlabel('Flight Status', fontsize=12)\n",
    "plt.ylabel('Number of Flights', fontsize=12)\n",
    "plt.xticks(range(len(case_count.index)), ['ON TIME(0)', 'DELAYED(1)'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.785576\n",
       "1    0.214424\n",
       "Name: IsDelay, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dataset.IsDelay.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So I will keep the the dataset in the same balance and use Bootstrat Class Weighting while creating a random forest to avoid the imbalanced class problem for this problem set "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labeling & Spliting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create features (X) and labels (y)\n",
    "y = model_dataset['IsDelay']\n",
    "X = model_dataset.drop(['IsDelay'], axis=1)\n",
    "\n",
    "# Perform the split\n",
    "X_test, X_train, y_test, y_train = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Random Forest Model - Bootstrat Class Weighting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below code will take into account that the data is imbalanced. Read below description of class weighting in random forest: \n",
    "\n",
    "Given that each decision tree is constructed from a bootstrap sample (e.g. random selection with replacement), the class distribution in the data sample will be different for each tree.\n",
    "\n",
    "As such, it might be interesting to change the class weighting based on the class distribution in each bootstrap sample, instead of the entire training dataset.\n",
    "\n",
    "This can be achieved by setting the class_weight argument to the value ‘balanced_subsample‘."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced_subsample', max_depth=5)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_bcw = RandomForestClassifier(n_estimators=100, max_depth=5, class_weight='balanced_subsample')\n",
    "forest_bcw.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9184431721281521"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now to check the accuracy score\n",
    "forest_bcw.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9141134190168727"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And for the test set:\n",
    "forest_bcw.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test set predictions\n",
    "pred_rfbcw = forest_bcw.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix\n",
      "----------------\n"
     ]
    },
    {
     "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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35599</td>\n",
       "      <td>3000</td>\n",
       "      <td>38599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1230</td>\n",
       "      <td>9422</td>\n",
       "      <td>10652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>36829</td>\n",
       "      <td>12422</td>\n",
       "      <td>49251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted      0      1    All\n",
       "True                          \n",
       "0          35599   3000  38599\n",
       "1           1230   9422  10652\n",
       "All        36829  12422  49251"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion matrix and classfication report\n",
    "print('\\nConfusion Matrix')\n",
    "print('----------------')\n",
    "pd.crosstab(y_test, pred_rfbcw, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Alternative confusion matrix\n",
    "plot_confusion_matrix(forest_bcw, X, y, values_format='.3g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>From the above confusion matrix it can be seen that model built has a higher false negative rate than false positive this means that if an error happens then customers will be informed more about \"yes, a delay will occur\" when it won't happen than \"no, a delay will happen\" when it will happen. This is a actually a better case scenario for this problem since we need increase the NPS along with the trust of the customers. If in case an error happens and we tell customers a delay won't happen when it will, this will set a negative image for Compass Airlines. But if we say a delay will happen when in reality it doesn't, this will increase the satisfaction and the trust of the customer, increasing the airline's quality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00201514, 0.0014634 , 0.00131225, 0.00378302, 0.024807  ,\n",
       "       0.01975702, 0.68595586, 0.1663709 , 0.02638929, 0.00983334,\n",
       "       0.0319497 , 0.02636307])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_bcw.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_feature_importances(model):\n",
    "    n_features = X_train.shape[1]\n",
    "    plt.figure(figsize=(10, 20))\n",
    "    plt.barh(range(n_features), model.feature_importances_, color='k', align='center')\n",
    "    plt.yticks(np.arange(n_features), X_train.columns.values)\n",
    "    plt.xlabel('Feature importance')\n",
    "    plt.ylabel('Features')\n",
    "    plt.rcParams[\"figure.facecolor\"] = \"lightblue\"\n",
    "    plt.grid(True)\n",
    "plot_feature_importances(forest_bcw)\n",
    "plt.rcParams[\"figure.facecolor\"] = \"lightblue\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>From the above chart we can see which features are contributing the most to the decision - Delay or No Delay. We can see here that delay in the departure is a main feature contributing to it and we also saw in before in one of that analysis that a 'late aircraft' is the one causing the most delay. My first recommendation to Compass airline would be is to ensure their aircrafts are arriving on time, their scheduled is fixed so that the flight delays don't keep occuring.\n",
    "\n",
    "Another reason we can see apart of the departure and the departure taxi is the day of the week, you can see that it is also causing an effect on the flight being delayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classificiation Report\n",
      "---------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.92      0.94     38599\n",
      "           1       0.76      0.88      0.82     10652\n",
      "\n",
      "    accuracy                           0.91     49251\n",
      "   macro avg       0.86      0.90      0.88     49251\n",
      "weighted avg       0.92      0.91      0.92     49251\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Classificiation Report')\n",
    "print('---------------------')\n",
    "print(classification_report(y_test, pred_rfbcw))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>I have already explained my views on the model in regards to the confusion matrix above which applies here as well. From this report we can see that the model provides a very high precision and recall which in return makes the f1-score very high too. With this being said the model is extremely accurate and can also classify cases that are difficult to assess as well. (please refer to my explaination for the confusion matrix part as well)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy for RandomForest Classifier for the predicting flight delay is: 91.41%\n"
     ]
    }
   ],
   "source": [
    "print(\"Testing Accuracy for RandomForest Classifier for the predicting flight delay is: {:.4}%\".format(accuracy_score(y_test, pred_rfbcw) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score:  0.9034032531083513\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "false_positive_rate1, true_positive_rate1, threshold1 = roc_curve(y_test, pred_rfbcw)\n",
    "print('roc_auc_score: ', roc_auc_score(y_test, pred_rfbcw)) \n",
    "\n",
    "plt.subplots(1, figsize=(10,10))\n",
    "plt.title('Receiver Operating Characteristic - Random Forest Model')\n",
    "plt.plot(false_positive_rate1, true_positive_rate1)\n",
    "plt.plot([0, 1], ls=\"--\")\n",
    "plt.plot([0, 0], [1, 0] , c=\".7\"), plt.plot([1, 1] , c=\".7\")\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><h3>With more time in hand: </h3>  \n",
    "   I want to more detailed analysis of the data and find more insights more this. I would want to use Tableau to do more deeper analysis on the reasons and connect with some geographical or other information to do more analysis. <br>\n",
    "   I would want to analyse the same data in regards to the weather as well, because that plays a part here too. Because of lack of time, I skipped that analysis for now.\n",
    "    <br>\n",
    "    I also wanted to perform hyperparameter optimization as well on the model to achieve a better result. But I have have skipped on that because even without it, my model is performing really well. The real assesment of the data would be when other month's data would be presented to it. <br> \n",
    "    I also wanted to showcase fairness and explainability of the model using Lime to showcase for each particular decision made by the model, which features are playing a major role here."
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}