Project2_Group7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24c9d582",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from dmba import classificationSummary, gainsChart, plotDecisionTree, regressionSummary\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn import tree\n",
    "import graphviz\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import OrdinalEncoder, LabelEncoder\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "277b461b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>CRS_DEP_TIME</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>Flight Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1455</td>\n",
       "      <td>OH</td>\n",
       "      <td>1455</td>\n",
       "      <td>JFK</td>\n",
       "      <td>184</td>\n",
       "      <td>37987</td>\n",
       "      <td>5935</td>\n",
       "      <td>BWI</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N940CA</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1640</td>\n",
       "      <td>DH</td>\n",
       "      <td>1640</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>6155</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N405FJ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1245</td>\n",
       "      <td>DH</td>\n",
       "      <td>1245</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7208</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N695BR</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1715</td>\n",
       "      <td>DH</td>\n",
       "      <td>1709</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7215</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N662BR</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1039</td>\n",
       "      <td>DH</td>\n",
       "      <td>1035</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7792</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N698BR</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CRS_DEP_TIME CARRIER  DEP_TIME DEST  DISTANCE     FL_DATE  FL_NUM ORIGIN  \\\n",
       "0          1455      OH      1455  JFK       184       37987    5935    BWI   \n",
       "1          1640      DH      1640  JFK       213  2004-01-01    6155    DCA   \n",
       "2          1245      DH      1245  LGA       229  2004-01-01    7208    IAD   \n",
       "3          1715      DH      1709  LGA       229  2004-01-01    7215    IAD   \n",
       "4          1039      DH      1035  LGA       229  2004-01-01    7792    IAD   \n",
       "\n",
       "   Weather  DAY_WEEK  DAY_OF_MONTH TAIL_NUM Flight Status  \n",
       "0        0         4             1   N940CA        ontime  \n",
       "1        0         4             1   N405FJ        ontime  \n",
       "2        0         4             1   N695BR        ontime  \n",
       "3        0         4             1   N662BR        ontime  \n",
       "4        0         4             1   N698BR        ontime  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Read the dataset\n",
    "\n",
    "df_delay = pd.read_csv('FlightDelays.csv')\n",
    "df_delay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ad0f184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRS_DEP_TIME      int64\n",
       "CARRIER          object\n",
       "DEP_TIME          int64\n",
       "DEST             object\n",
       "DISTANCE          int64\n",
       "FL_DATE          object\n",
       "FL_NUM            int64\n",
       "ORIGIN           object\n",
       "Weather           int64\n",
       "DAY_WEEK          int64\n",
       "DAY_OF_MONTH      int64\n",
       "TAIL_NUM         object\n",
       "Flight Status    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking the data types\n",
    "\n",
    "df_delay.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8578f4ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRS_DEP_TIME     0\n",
       "CARRIER          0\n",
       "DEP_TIME         0\n",
       "DEST             0\n",
       "DISTANCE         0\n",
       "FL_DATE          0\n",
       "FL_NUM           0\n",
       "ORIGIN           0\n",
       "Weather          0\n",
       "DAY_WEEK         0\n",
       "DAY_OF_MONTH     0\n",
       "TAIL_NUM         0\n",
       "Flight Status    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for null values\n",
    "\n",
    "df_delay.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23902337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['OH', 'DH', 'DL', 'MQ', 'UA', 'US', 'RU', 'CO'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for unique Carrier\n",
    "\n",
    "df_delay['CARRIER'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77911ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['JFK', 'LGA', 'EWR'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for unique Destinations\n",
    "\n",
    "df_delay['DEST'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8274b473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BWI', 'DCA', 'IAD'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for unique Origin locations\n",
    "\n",
    "df_delay['ORIGIN'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82333779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for different weather conditions\n",
    "\n",
    "df_delay['Weather'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "98764cf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>Flight Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>1030</td>\n",
       "      <td>RU</td>\n",
       "      <td>1057</td>\n",
       "      <td>EWR</td>\n",
       "      <td>169</td>\n",
       "      <td>2004-01-18</td>\n",
       "      <td>2303</td>\n",
       "      <td>BWI</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>N17984</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>2120</td>\n",
       "      <td>DH</td>\n",
       "      <td>2122</td>\n",
       "      <td>JFK</td>\n",
       "      <td>228</td>\n",
       "      <td>2004-01-25</td>\n",
       "      <td>7814</td>\n",
       "      <td>IAD</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>N657BR</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814</th>\n",
       "      <td>1455</td>\n",
       "      <td>DL</td>\n",
       "      <td>1509</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-26</td>\n",
       "      <td>746</td>\n",
       "      <td>DCA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>N905DL</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1815</th>\n",
       "      <td>730</td>\n",
       "      <td>DL</td>\n",
       "      <td>741</td>\n",
       "      <td>LGA</td>\n",
       "      <td>214</td>\n",
       "      <td>2004-01-26</td>\n",
       "      <td>1742</td>\n",
       "      <td>DCA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>N225DL</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>1530</td>\n",
       "      <td>DL</td>\n",
       "      <td>1540</td>\n",
       "      <td>LGA</td>\n",
       "      <td>214</td>\n",
       "      <td>2004-01-26</td>\n",
       "      <td>1758</td>\n",
       "      <td>DCA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>N225DL</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRS_DEP_TIME CARRIER  DEP_TIME DEST  DISTANCE     FL_DATE  FL_NUM  \\\n",
       "1251          1030      RU      1057  EWR       169  2004-01-18    2303   \n",
       "1744          2120      DH      2122  JFK       228  2004-01-25    7814   \n",
       "1814          1455      DL      1509  JFK       213  2004-01-26     746   \n",
       "1815           730      DL       741  LGA       214  2004-01-26    1742   \n",
       "1822          1530      DL      1540  LGA       214  2004-01-26    1758   \n",
       "\n",
       "     ORIGIN  Weather  DAY_WEEK  DAY_OF_MONTH TAIL_NUM Flight Status  \n",
       "1251    BWI        1         7            18   N17984       delayed  \n",
       "1744    IAD        1         7            25   N657BR       delayed  \n",
       "1814    DCA        1         1            26   N905DL       delayed  \n",
       "1815    DCA        1         1            26   N225DL       delayed  \n",
       "1822    DCA        1         1            26   N225DL       delayed  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking to find out what weather conditions are delaying flights\n",
    "\n",
    "df_delay.loc[df_delay['Weather'] == 1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74f7f6d",
   "metadata": {},
   "source": [
    "Looking at the weather types and final flight status it looks like \n",
    "\n",
    "Weather = 1 = Bad \n",
    "\n",
    "Weather = 0 = Good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "93dfeb31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "549"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for unique Tail numbers on the aircraft\n",
    "\n",
    "df_delay['TAIL_NUM'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d5bc0527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for unique Flight numbers of the aircrafts\n",
    "\n",
    "df_delay['FL_NUM'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f27a61b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>Flight Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [CRS_DEP_TIME, CARRIER, DEP_TIME, DEST, DISTANCE, FL_DATE, FL_NUM, ORIGIN, Weather, DAY_WEEK, DAY_OF_MONTH, TAIL_NUM, Flight Status]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking if any flights scheduled after 11pm to see next day departures\n",
    "\n",
    "df_delay.loc[df_delay['CRS_DEP_TIME'] > 2300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ef2415a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>Flight Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2120</td>\n",
       "      <td>DH</td>\n",
       "      <td>2305</td>\n",
       "      <td>JFK</td>\n",
       "      <td>228</td>\n",
       "      <td>2004-01-02</td>\n",
       "      <td>7814</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>N657BR</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>2120</td>\n",
       "      <td>DH</td>\n",
       "      <td>2312</td>\n",
       "      <td>EWR</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-05</td>\n",
       "      <td>7304</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>N667BR</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2120</td>\n",
       "      <td>DH</td>\n",
       "      <td>2330</td>\n",
       "      <td>EWR</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-14</td>\n",
       "      <td>7304</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>N629BR</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CRS_DEP_TIME CARRIER  DEP_TIME DEST  DISTANCE     FL_DATE  FL_NUM ORIGIN  \\\n",
       "62           2120      DH      2305  JFK       228  2004-01-02    7814    IAD   \n",
       "291          2120      DH      2312  EWR       213  2004-01-05    7304    IAD   \n",
       "998          2120      DH      2330  EWR       213  2004-01-14    7304    IAD   \n",
       "\n",
       "     Weather  DAY_WEEK  DAY_OF_MONTH TAIL_NUM Flight Status  \n",
       "62         0         5             2   N657BR       delayed  \n",
       "291        0         1             5   N667BR       delayed  \n",
       "998        0         3            14   N629BR       delayed  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking if any flights departed after 11pm \n",
    "\n",
    "df_delay.loc[df_delay['DEP_TIME'] > 2300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8339cded",
   "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>CRS_DEP_TIME</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>Flight Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>2120</td>\n",
       "      <td>DH</td>\n",
       "      <td>10</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-19</td>\n",
       "      <td>7684</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>N627BR</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRS_DEP_TIME CARRIER  DEP_TIME DEST  DISTANCE     FL_DATE  FL_NUM  \\\n",
       "1270          2120      DH        10  LGA       229  2004-01-19    7684   \n",
       "\n",
       "     ORIGIN  Weather  DAY_WEEK  DAY_OF_MONTH TAIL_NUM Flight Status  \n",
       "1270    IAD        0         1            19   N627BR       delayed  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking if any flights after midnight to see next day departures\n",
    "\n",
    "df_delay.loc[(df_delay['DEP_TIME'] > 0) & (df_delay['DEP_TIME'] < 59)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9a720a55",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>Flight Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>556</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N717MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>552</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-02</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>N736MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>555</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-03</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>N712MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>554</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-04</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>N737MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>557</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-05</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>N707MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>558</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-06</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>N739MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>555</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-07</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>N705MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>552</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-08</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>N737MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>558</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>N801MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>557</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-10</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>N718MQ</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>552</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-11</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>N710MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>553</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-12</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>N717MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>553</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-13</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>N707MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>553</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-14</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>N843MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1038</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>553</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>N728MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>553</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-16</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>N710MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>554</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-17</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>N729MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>552</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-18</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>N731MQ</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>2120</td>\n",
       "      <td>DH</td>\n",
       "      <td>10</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-19</td>\n",
       "      <td>7684</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>N627BR</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>553</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-19</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>N736MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>548</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-20</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>N721MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>555</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-21</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>N709MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>553</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-22</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>N838MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>2120</td>\n",
       "      <td>DH</td>\n",
       "      <td>109</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-23</td>\n",
       "      <td>7684</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>N668BR</td>\n",
       "      <td>delayed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>553</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-23</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>N730MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>550</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-24</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>N703MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>600</td>\n",
       "      <td>MQ</td>\n",
       "      <td>559</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-29</td>\n",
       "      <td>4760</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>N701MQ</td>\n",
       "      <td>ontime</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRS_DEP_TIME CARRIER  DEP_TIME DEST  DISTANCE     FL_DATE  FL_NUM  \\\n",
       "18             600      MQ       556  JFK       213  2004-01-01    4760   \n",
       "72             600      MQ       552  JFK       213  2004-01-02    4760   \n",
       "133            600      MQ       555  JFK       213  2004-01-03    4760   \n",
       "187            600      MQ       554  JFK       213  2004-01-04    4760   \n",
       "256            600      MQ       557  JFK       213  2004-01-05    4760   \n",
       "335            600      MQ       558  JFK       213  2004-01-06    4760   \n",
       "419            600      MQ       555  JFK       213  2004-01-07    4760   \n",
       "504            600      MQ       552  JFK       213  2004-01-08    4760   \n",
       "589            600      MQ       558  JFK       213  2004-01-09    4760   \n",
       "662            600      MQ       557  JFK       213  2004-01-10    4760   \n",
       "718            600      MQ       552  JFK       213  2004-01-11    4760   \n",
       "790            600      MQ       553  JFK       213  2004-01-12    4760   \n",
       "874            600      MQ       553  JFK       213  2004-01-13    4760   \n",
       "959            600      MQ       553  JFK       213  2004-01-14    4760   \n",
       "1038           600      MQ       553  JFK       213  2004-01-15    4760   \n",
       "1111           600      MQ       553  JFK       213  2004-01-16    4760   \n",
       "1181           600      MQ       554  JFK       213  2004-01-17    4760   \n",
       "1232           600      MQ       552  JFK       213  2004-01-18    4760   \n",
       "1270          2120      DH        10  LGA       229  2004-01-19    7684   \n",
       "1292           600      MQ       553  JFK       213  2004-01-19    4760   \n",
       "1378           600      MQ       548  JFK       213  2004-01-20    4760   \n",
       "1462           600      MQ       555  JFK       213  2004-01-21    4760   \n",
       "1548           600      MQ       553  JFK       213  2004-01-22    4760   \n",
       "1606          2120      DH       109  LGA       229  2004-01-23    7684   \n",
       "1632           600      MQ       553  JFK       213  2004-01-23    4760   \n",
       "1704           600      MQ       550  JFK       213  2004-01-24    4760   \n",
       "2015           600      MQ       559  JFK       213  2004-01-29    4760   \n",
       "\n",
       "     ORIGIN  Weather  DAY_WEEK  DAY_OF_MONTH TAIL_NUM Flight Status  \n",
       "18      DCA        0         4             1   N717MQ        ontime  \n",
       "72      DCA        0         5             2   N736MQ        ontime  \n",
       "133     DCA        0         6             3   N712MQ        ontime  \n",
       "187     DCA        0         7             4   N737MQ        ontime  \n",
       "256     DCA        0         1             5   N707MQ        ontime  \n",
       "335     DCA        0         2             6   N739MQ        ontime  \n",
       "419     DCA        0         3             7   N705MQ        ontime  \n",
       "504     DCA        0         4             8   N737MQ        ontime  \n",
       "589     DCA        0         5             9   N801MQ        ontime  \n",
       "662     DCA        0         6            10   N718MQ       delayed  \n",
       "718     DCA        0         7            11   N710MQ        ontime  \n",
       "790     DCA        0         1            12   N717MQ        ontime  \n",
       "874     DCA        0         2            13   N707MQ        ontime  \n",
       "959     DCA        0         3            14   N843MQ        ontime  \n",
       "1038    DCA        0         4            15   N728MQ        ontime  \n",
       "1111    DCA        0         5            16   N710MQ        ontime  \n",
       "1181    DCA        0         6            17   N729MQ        ontime  \n",
       "1232    DCA        0         7            18   N731MQ       delayed  \n",
       "1270    IAD        0         1            19   N627BR       delayed  \n",
       "1292    DCA        0         1            19   N736MQ        ontime  \n",
       "1378    DCA        0         2            20   N721MQ        ontime  \n",
       "1462    DCA        0         3            21   N709MQ        ontime  \n",
       "1548    DCA        0         4            22   N838MQ        ontime  \n",
       "1606    IAD        0         5            23   N668BR       delayed  \n",
       "1632    DCA        0         5            23   N730MQ        ontime  \n",
       "1704    DCA        0         6            24   N703MQ        ontime  \n",
       "2015    DCA        0         4            29   N701MQ        ontime  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking if any flights departed early morning to see next day departures\n",
    "## 2 exist\n",
    "\n",
    "df_delay.loc[(df_delay['DEP_TIME'] > 0) & (df_delay['DEP_TIME'] < 600)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a263214f",
   "metadata": {},
   "source": [
    "2 flights that were scheduled at 2120 departed the next day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a15f535",
   "metadata": {},
   "source": [
    "#### Creating a new \"Delayed_Departure\" to capture if the departure was delayed or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a87cec26",
   "metadata": {},
   "outputs": [],
   "source": [
    "## New column with departure delayed or not \n",
    "## Ontime Departure = 1 & Delayed Departure = 0 \n",
    "\n",
    "df_delay['Delayed_Departure'] = np.where(df_delay['CRS_DEP_TIME'] - df_delay['DEP_TIME'] >=0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "961bde17",
   "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>CRS_DEP_TIME</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>Flight Status</th>\n",
       "      <th>Delayed_Departure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1455</td>\n",
       "      <td>OH</td>\n",
       "      <td>1455</td>\n",
       "      <td>JFK</td>\n",
       "      <td>184</td>\n",
       "      <td>37987</td>\n",
       "      <td>5935</td>\n",
       "      <td>BWI</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N940CA</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1640</td>\n",
       "      <td>DH</td>\n",
       "      <td>1640</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>6155</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N405FJ</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1245</td>\n",
       "      <td>DH</td>\n",
       "      <td>1245</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7208</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N695BR</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1715</td>\n",
       "      <td>DH</td>\n",
       "      <td>1709</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7215</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N662BR</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1039</td>\n",
       "      <td>DH</td>\n",
       "      <td>1035</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7792</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>N698BR</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CRS_DEP_TIME CARRIER  DEP_TIME DEST  DISTANCE     FL_DATE  FL_NUM ORIGIN  \\\n",
       "0          1455      OH      1455  JFK       184       37987    5935    BWI   \n",
       "1          1640      DH      1640  JFK       213  2004-01-01    6155    DCA   \n",
       "2          1245      DH      1245  LGA       229  2004-01-01    7208    IAD   \n",
       "3          1715      DH      1709  LGA       229  2004-01-01    7215    IAD   \n",
       "4          1039      DH      1035  LGA       229  2004-01-01    7792    IAD   \n",
       "\n",
       "   Weather  DAY_WEEK  DAY_OF_MONTH TAIL_NUM Flight Status  Delayed_Departure  \n",
       "0        0         4             1   N940CA        ontime                  1  \n",
       "1        0         4             1   N405FJ        ontime                  1  \n",
       "2        0         4             1   N695BR        ontime                  1  \n",
       "3        0         4             1   N662BR        ontime                  1  \n",
       "4        0         4             1   N698BR        ontime                  1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_delay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "30bf7bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Two flights exist that had night departures and took flight after midnight changing them to \"delayed\"\n",
    "\n",
    "df_delay.at[1270,'Delayed_Departure'] = 0\n",
    "df_delay.at[1606,'Delayed_Departure'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "00ccc734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRS_DEP_TIME               2120\n",
       "CARRIER                      DH\n",
       "DEP_TIME                     10\n",
       "DEST                        LGA\n",
       "DISTANCE                    229\n",
       "FL_DATE              2004-01-19\n",
       "FL_NUM                     7684\n",
       "ORIGIN                      IAD\n",
       "Weather                       0\n",
       "DAY_WEEK                      1\n",
       "DAY_OF_MONTH                 19\n",
       "TAIL_NUM                 N627BR\n",
       "Flight Status           delayed\n",
       "Delayed_Departure             0\n",
       "Name: 1270, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_delay.iloc[1270]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21484f40",
   "metadata": {},
   "source": [
    "#### Starting the week from Sunday as 1 to Saturday as 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "74029cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Change day of the week to name so that it can be encoded easily with column names\n",
    "\n",
    "## days = {1:\"Monday\", 2:\"Tuesday\", 3:\"Wednesday\", 4:\"Thursday\", 5:\"Friday\", 6:\"Saturday\", 7:\"Sunday\"}\n",
    "\n",
    "## df_delay.replace({\"DAY_WEEK\": days},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37c52b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Starting the week from Sunday as 1 to Saturday as 7\n",
    "\n",
    "days = {1:5,2:6,3:7,4:1,5:2,6:3,7:4}\n",
    "\n",
    "df_delay.replace({'DAY_WEEK': days},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "517b42c8",
   "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>CRS_DEP_TIME</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>Flight Status</th>\n",
       "      <th>Delayed_Departure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1455</td>\n",
       "      <td>OH</td>\n",
       "      <td>1455</td>\n",
       "      <td>JFK</td>\n",
       "      <td>184</td>\n",
       "      <td>37987</td>\n",
       "      <td>5935</td>\n",
       "      <td>BWI</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N940CA</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1640</td>\n",
       "      <td>DH</td>\n",
       "      <td>1640</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>6155</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N405FJ</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1245</td>\n",
       "      <td>DH</td>\n",
       "      <td>1245</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7208</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N695BR</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1715</td>\n",
       "      <td>DH</td>\n",
       "      <td>1709</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7215</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N662BR</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1039</td>\n",
       "      <td>DH</td>\n",
       "      <td>1035</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7792</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N698BR</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CRS_DEP_TIME CARRIER  DEP_TIME DEST  DISTANCE     FL_DATE  FL_NUM ORIGIN  \\\n",
       "0          1455      OH      1455  JFK       184       37987    5935    BWI   \n",
       "1          1640      DH      1640  JFK       213  2004-01-01    6155    DCA   \n",
       "2          1245      DH      1245  LGA       229  2004-01-01    7208    IAD   \n",
       "3          1715      DH      1709  LGA       229  2004-01-01    7215    IAD   \n",
       "4          1039      DH      1035  LGA       229  2004-01-01    7792    IAD   \n",
       "\n",
       "   Weather  DAY_WEEK  DAY_OF_MONTH TAIL_NUM Flight Status  Delayed_Departure  \n",
       "0        0         1             1   N940CA        ontime                  1  \n",
       "1        0         1             1   N405FJ        ontime                  1  \n",
       "2        0         1             1   N695BR        ontime                  1  \n",
       "3        0         1             1   N662BR        ontime                  1  \n",
       "4        0         1             1   N698BR        ontime                  1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_delay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "156d6731",
   "metadata": {},
   "outputs": [],
   "source": [
    "##flightstatus = {\"ontime\":1,\"delayed\":0}\n",
    "\n",
    "##df_delay.replace({\"Flight Status\":flightstatus},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0b93d6ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "##df_delay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4a019a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping unnecessary columns\n",
    "\n",
    "## df_delay.drop(['CRS_DEP_TIME','DEP_TIME','FL_NUM','FL_DATE','TAIL_NUM'],axis=1,inplace=True)\n",
    "\n",
    "\n",
    "## df_delay.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c70d383d",
   "metadata": {},
   "source": [
    "#### Changing the scheduled departure time to just hours "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03ca100c",
   "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",
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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>CRS_DEP_TIME</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>Flight Status</th>\n",
       "      <th>Delayed_Departure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>OH</td>\n",
       "      <td>1455</td>\n",
       "      <td>JFK</td>\n",
       "      <td>184</td>\n",
       "      <td>37987</td>\n",
       "      <td>5935</td>\n",
       "      <td>BWI</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N940CA</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>DH</td>\n",
       "      <td>1640</td>\n",
       "      <td>JFK</td>\n",
       "      <td>213</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>6155</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N405FJ</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>DH</td>\n",
       "      <td>1245</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7208</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N695BR</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>DH</td>\n",
       "      <td>1709</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7215</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N662BR</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>DH</td>\n",
       "      <td>1035</td>\n",
       "      <td>LGA</td>\n",
       "      <td>229</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>7792</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N698BR</td>\n",
       "      <td>ontime</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CRS_DEP_TIME CARRIER  DEP_TIME DEST  DISTANCE     FL_DATE  FL_NUM ORIGIN  \\\n",
       "0            15      OH      1455  JFK       184       37987    5935    BWI   \n",
       "1            16      DH      1640  JFK       213  2004-01-01    6155    DCA   \n",
       "2            12      DH      1245  LGA       229  2004-01-01    7208    IAD   \n",
       "3            17      DH      1709  LGA       229  2004-01-01    7215    IAD   \n",
       "4            10      DH      1035  LGA       229  2004-01-01    7792    IAD   \n",
       "\n",
       "   Weather  DAY_WEEK  DAY_OF_MONTH TAIL_NUM Flight Status  Delayed_Departure  \n",
       "0        0         1             1   N940CA        ontime                  1  \n",
       "1        0         1             1   N405FJ        ontime                  1  \n",
       "2        0         1             1   N695BR        ontime                  1  \n",
       "3        0         1             1   N662BR        ontime                  1  \n",
       "4        0         1             1   N698BR        ontime                  1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_delay.CRS_DEP_TIME = [round(t / 100) for t in df_delay.CRS_DEP_TIME]\n",
    "\n",
    "df_delay.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788931b7",
   "metadata": {},
   "source": [
    "#### Converting a few columns to category datatype for better classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ade3c8f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_delay.CRS_DEP_TIME = df_delay.CRS_DEP_TIME.astype('category')\n",
    "df_delay.Weather = df_delay.Weather.astype('category')\n",
    "df_delay.DAY_WEEK = df_delay.DAY_WEEK.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8fae118b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_delay['Flight Status'] = df_delay['Flight Status'].replace({'ontime':1,'delayed':0})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "663bb6b8",
   "metadata": {},
   "source": [
    "#### Creating the final dataset with the required columns\n",
    "\n",
    "Dropping real departure time as our priority to predict on is scheduled departure time and also that we've created a column to see if the departure was delayed or not\n",
    "\n",
    "Distance is dropped as all the columns in the dataset are categories.\n",
    "\n",
    "Flight date as the day of the week and day number columns already exist and this data set is only of one month\n",
    "\n",
    "There are too many Flight numbers and tail numbers, and creating dummies for them is not useful for the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2bb76d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = df_delay[['CRS_DEP_TIME','Delayed_Departure','CARRIER','DEST','ORIGIN','Weather','DAY_WEEK','Flight Status']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2cfac67c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>Delayed_Departure</th>\n",
       "      <th>CARRIER</th>\n",
       "      <th>DEST</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>Weather</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>Flight Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>OH</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BWI</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>DH</td>\n",
       "      <td>JFK</td>\n",
       "      <td>DCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>DH</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>DH</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>DH</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CRS_DEP_TIME  Delayed_Departure CARRIER DEST ORIGIN Weather DAY_WEEK  \\\n",
       "0           15                  1      OH  JFK    BWI       0        1   \n",
       "1           16                  1      DH  JFK    DCA       0        1   \n",
       "2           12                  1      DH  LGA    IAD       0        1   \n",
       "3           17                  1      DH  LGA    IAD       0        1   \n",
       "4           10                  1      DH  LGA    IAD       0        1   \n",
       "\n",
       "   Flight Status  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e8fd33",
   "metadata": {},
   "source": [
    "#### Creating the Training data set file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "71b21c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv('FlightDelaysTrainingData.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b09291",
   "metadata": {},
   "source": [
    "#### Creating the DataExploration file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "19a3a2f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv('FlightDelaysDataExploration.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b0982484",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Creating the Training data set file\n",
    "\n",
    "final_df.to_csv('FlightDelaysTrainingData.csv',index=False)\n",
    "\n",
    "#### Creating the DataExploration file\n",
    "\n",
    "final_df.to_csv('FlightDelaysDataExploration.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df5c11f",
   "metadata": {},
   "source": [
    "### Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7ee1ed5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Weather</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flight Status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>396</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>1773</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Weather           0   1\n",
       "Flight Status          \n",
       "0               396  32\n",
       "1              1773   0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.pivot_table('DAY_WEEK', index='Flight Status', columns='Weather',aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b7dbf367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ORIGIN</th>\n",
       "      <th>BWI</th>\n",
       "      <th>DCA</th>\n",
       "      <th>IAD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>24</td>\n",
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       "      <td>111</td>\n",
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       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>246</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>137</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>152</td>\n",
       "      <td>82</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>196</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>198</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19</td>\n",
       "      <td>204</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ORIGIN    BWI  DCA  IAD\n",
       "DAY_WEEK               \n",
       "1          24  237  111\n",
       "2          25  246  120\n",
       "3          24  137   89\n",
       "4          19  152   82\n",
       "5          17  196   95\n",
       "6          17  198   92\n",
       "7          19  204   97"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.pivot_table('CARRIER', index='DAY_WEEK', columns='ORIGIN',aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3a5c6cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>DEST</th>\n",
       "      <th>EWR</th>\n",
       "      <th>JFK</th>\n",
       "      <th>LGA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>113</td>\n",
       "      <td>63</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122</td>\n",
       "      <td>64</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86</td>\n",
       "      <td>59</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>55</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>90</td>\n",
       "      <td>47</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>86</td>\n",
       "      <td>48</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>96</td>\n",
       "      <td>50</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DEST      EWR  JFK  LGA\n",
       "DAY_WEEK               \n",
       "1         113   63  196\n",
       "2         122   64  205\n",
       "3          86   59  105\n",
       "4          72   55  126\n",
       "5          90   47  171\n",
       "6          86   48  173\n",
       "7          96   50  174"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.pivot_table('CARRIER', index='DAY_WEEK', columns='DEST',aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6eb404fc",
   "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>Flight Status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARRIER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>26</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DH</th>\n",
       "      <td>137</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>47</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQ</th>\n",
       "      <td>80</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>94</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>35</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Flight Status    0    1\n",
       "CARRIER                \n",
       "CO              26   68\n",
       "DH             137  414\n",
       "DL              47  341\n",
       "MQ              80  215\n",
       "OH               4   26\n",
       "RU              94  314\n",
       "UA               5   26\n",
       "US              35  369"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot = final_df.pivot_table('DAY_WEEK', index='CARRIER', columns='Flight Status',aggfunc='count')\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b70b4955",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Flight Status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Average Delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARRIER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>26</td>\n",
       "      <td>68</td>\n",
       "      <td>0.276596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DH</th>\n",
       "      <td>137</td>\n",
       "      <td>414</td>\n",
       "      <td>0.248639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>47</td>\n",
       "      <td>341</td>\n",
       "      <td>0.121134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQ</th>\n",
       "      <td>80</td>\n",
       "      <td>215</td>\n",
       "      <td>0.271186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>94</td>\n",
       "      <td>314</td>\n",
       "      <td>0.230392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>35</td>\n",
       "      <td>369</td>\n",
       "      <td>0.086634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Flight Status    0    1  Average Delay\n",
       "CARRIER                               \n",
       "CO              26   68       0.276596\n",
       "DH             137  414       0.248639\n",
       "DL              47  341       0.121134\n",
       "MQ              80  215       0.271186\n",
       "OH               4   26       0.133333\n",
       "RU              94  314       0.230392\n",
       "UA               5   26       0.161290\n",
       "US              35  369       0.086634"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot['Average Delay'] = (pivot[0]/(pivot[0] + pivot[1]))\n",
    "pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41858eb0",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fbed200d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Defining independent and depended columns\n",
    "\n",
    "X = pd.get_dummies(final_df[['CRS_DEP_TIME','Delayed_Departure','CARRIER','DEST','ORIGIN','Weather','DAY_WEEK']])\n",
    "y = final_df['Flight Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cd2292b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the dataset to train and test data\n",
    "\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.33, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "46ae24d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train (1474, 40) (1474,)\n",
      "Test (727, 40) (727,)\n"
     ]
    }
   ],
   "source": [
    "# summarize\n",
    "print('Train', X_train.shape, y_train.shape)\n",
    "print('Test', X_valid.shape, y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced494b8",
   "metadata": {},
   "source": [
    "### Chi-Squared Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c7eda77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectKBest(k=&#x27;all&#x27;, score_func=&lt;function chi2 at 0x000001B6A64FA8B0&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=&#x27;all&#x27;, score_func=&lt;function chi2 at 0x000001B6A64FA8B0&gt;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectKBest(k='all', score_func=<function chi2 at 0x000001B6A64FA8B0>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = SelectKBest(score_func=chi2, k='all')\n",
    "fs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "03aad895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Delayed_Departure', 'CRS_DEP_TIME_6', 'CRS_DEP_TIME_7',\n",
       "       'CRS_DEP_TIME_8', 'CRS_DEP_TIME_9', 'CRS_DEP_TIME_10',\n",
       "       'CRS_DEP_TIME_11', 'CRS_DEP_TIME_12', 'CRS_DEP_TIME_13',\n",
       "       'CRS_DEP_TIME_14', 'CRS_DEP_TIME_15', 'CRS_DEP_TIME_16',\n",
       "       'CRS_DEP_TIME_17', 'CRS_DEP_TIME_18', 'CRS_DEP_TIME_19',\n",
       "       'CRS_DEP_TIME_20', 'CRS_DEP_TIME_21', 'CARRIER_CO', 'CARRIER_DH',\n",
       "       'CARRIER_DL', 'CARRIER_MQ', 'CARRIER_OH', 'CARRIER_RU', 'CARRIER_UA',\n",
       "       'CARRIER_US', 'DEST_EWR', 'DEST_JFK', 'DEST_LGA', 'ORIGIN_BWI',\n",
       "       'ORIGIN_DCA', 'ORIGIN_IAD', 'Weather_0', 'Weather_1', 'DAY_WEEK_1',\n",
       "       'DAY_WEEK_2', 'DAY_WEEK_3', 'DAY_WEEK_4', 'DAY_WEEK_5', 'DAY_WEEK_6',\n",
       "       'DAY_WEEK_7'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d3c1001c",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f422c75c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 0: 117.410494\n",
      "Feature 1: 1.446246\n",
      "Feature 2: 0.000318\n",
      "Feature 3: 0.023694\n",
      "Feature 4: 4.485986\n",
      "Feature 5: 1.522559\n",
      "Feature 6: 3.706472\n",
      "Feature 7: 1.446246\n",
      "Feature 8: 3.314081\n",
      "Feature 9: 0.515174\n",
      "Feature 10: 13.143050\n",
      "Feature 11: 0.170562\n",
      "Feature 12: 2.449676\n",
      "Feature 13: 1.269442\n",
      "Feature 14: 3.897926\n",
      "Feature 15: 0.088776\n",
      "Feature 16: 1.722903\n",
      "Feature 17: 2.068920\n",
      "Feature 18: 6.670314\n",
      "Feature 19: 8.163570\n",
      "Feature 20: 11.325131\n",
      "Feature 21: 0.215363\n",
      "Feature 22: 0.843531\n",
      "Feature 23: 0.358938\n",
      "Feature 24: 23.877073\n",
      "Feature 25: 3.051534\n",
      "Feature 26: 2.504756\n",
      "Feature 27: 4.779395\n",
      "Feature 28: 5.079684\n",
      "Feature 29: 7.524986\n",
      "Feature 30: 8.550548\n",
      "Feature 31: 2.056898\n",
      "Feature 32: 110.234483\n",
      "Feature 33: 1.053229\n",
      "Feature 34: 0.062635\n",
      "Feature 35: 8.101739\n",
      "Feature 36: 5.249380\n",
      "Feature 37: 6.730063\n",
      "Feature 38: 1.103105\n",
      "Feature 39: 2.296288\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the scores for the features\n",
    "for i in range(len(fs.scores_)):\n",
    "    print('Feature %d: %f' % (i, fs.scores_[i]))\n",
    "\n",
    "# plot the scores\n",
    "plt.bar([i for i in range(len(fs.scores_))], fs.scores_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6256cfcb",
   "metadata": {},
   "source": [
    "The features with most importance to the final data are Delayed Departure and Weather conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34af595",
   "metadata": {},
   "source": [
    "## Naive Bayes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c49f79c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB(alpha=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=0.01)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB(alpha=0.01)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delays_nb = MultinomialNB(alpha=0.01)\n",
    "delays_nb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "de377751",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = delays_nb.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cfe57d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "50d01e8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score: 0.8418\n"
     ]
    }
   ],
   "source": [
    "print('Model accuracy score: {0:0.4f}'. format(accuracy_score(y_valid, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6ae98dea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Checking for overfitting\n",
    "\n",
    "\n",
    "y_pred_train = delays_nb.predict(X_train)\n",
    "\n",
    "y_pred_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7bb9577c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training-set accuracy score: 0.8372\n"
     ]
    }
   ],
   "source": [
    "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "60784aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.8372\n",
      "Test set score: 0.8418\n"
     ]
    }
   ],
   "source": [
    "# print the scores on training and test set\n",
    "\n",
    "print('Training set score: {:.4f}'.format(delays_nb.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(delays_nb.score(X_valid, y_valid)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "20a3f887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 43  95]\n",
      " [ 20 569]]\n"
     ]
    }
   ],
   "source": [
    "confusion_matrix = confusion_matrix(y_valid, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "012cd1ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [\"delayed\", \"ontime\"],)\n",
    "\n",
    "cm.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991dcebf",
   "metadata": {},
   "source": [
    "### CART Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c7e4f38c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=7)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=7)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classTree = DecisionTreeClassifier(max_depth=7)\n",
    "classTree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7125c958",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- feature_0 <= 0.50\n",
      "|   |--- feature_19 <= 0.50\n",
      "|   |   |--- feature_32 <= 0.50\n",
      "|   |   |   |--- feature_26 <= 0.50\n",
      "|   |   |   |   |--- feature_35 <= 0.50\n",
      "|   |   |   |   |   |--- feature_24 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_7 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- feature_7 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_24 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_33 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_33 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- feature_35 >  0.50\n",
      "|   |   |   |   |   |--- feature_3 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_4 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_4 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_3 >  0.50\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- feature_26 >  0.50\n",
      "|   |   |   |   |--- feature_34 <= 0.50\n",
      "|   |   |   |   |   |--- feature_3 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_3 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_35 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_35 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- feature_34 >  0.50\n",
      "|   |   |   |   |   |--- feature_18 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_21 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- feature_21 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- feature_18 >  0.50\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |--- feature_32 >  0.50\n",
      "|   |   |   |--- class: 0\n",
      "|   |--- feature_19 >  0.50\n",
      "|   |   |--- feature_26 <= 0.50\n",
      "|   |   |   |--- feature_31 <= 0.50\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |   |   |--- feature_31 >  0.50\n",
      "|   |   |   |   |--- feature_38 <= 0.50\n",
      "|   |   |   |   |   |--- feature_35 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_37 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_37 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_35 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_15 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- feature_15 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- feature_38 >  0.50\n",
      "|   |   |   |   |   |--- feature_11 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_13 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_13 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- feature_11 >  0.50\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |--- feature_26 >  0.50\n",
      "|   |   |   |--- feature_35 <= 0.50\n",
      "|   |   |   |   |--- feature_31 <= 0.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- feature_31 >  0.50\n",
      "|   |   |   |   |   |--- feature_36 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_39 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_39 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_36 >  0.50\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- feature_35 >  0.50\n",
      "|   |   |   |   |--- class: 0\n",
      "|--- feature_0 >  0.50\n",
      "|   |--- feature_15 <= 0.50\n",
      "|   |   |--- feature_2 <= 0.50\n",
      "|   |   |   |--- feature_36 <= 0.50\n",
      "|   |   |   |   |--- feature_10 <= 0.50\n",
      "|   |   |   |   |   |--- feature_3 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_14 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_14 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_3 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- feature_10 >  0.50\n",
      "|   |   |   |   |   |--- feature_39 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_22 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_22 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_39 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_24 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_24 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- feature_36 >  0.50\n",
      "|   |   |   |   |--- feature_1 <= 0.50\n",
      "|   |   |   |   |   |--- feature_16 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_22 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_22 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_16 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_25 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_25 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- feature_1 >  0.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |--- feature_2 >  0.50\n",
      "|   |   |   |--- feature_20 <= 0.50\n",
      "|   |   |   |   |--- feature_30 <= 0.50\n",
      "|   |   |   |   |   |--- feature_37 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_35 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_35 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_37 >  0.50\n",
      "|   |   |   |   |   |   |--- feature_27 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_27 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- feature_30 >  0.50\n",
      "|   |   |   |   |   |--- feature_34 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_35 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_35 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_34 >  0.50\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- feature_20 >  0.50\n",
      "|   |   |   |   |--- feature_33 <= 0.50\n",
      "|   |   |   |   |   |--- feature_34 <= 0.50\n",
      "|   |   |   |   |   |   |--- feature_39 <= 0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- feature_39 >  0.50\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- feature_34 >  0.50\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- feature_33 >  0.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |--- feature_15 >  0.50\n",
      "|   |   |--- feature_39 <= 0.50\n",
      "|   |   |   |--- feature_38 <= 0.50\n",
      "|   |   |   |   |--- feature_33 <= 0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- feature_33 >  0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- feature_38 >  0.50\n",
      "|   |   |   |   |--- feature_19 <= 0.50\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- feature_19 >  0.50\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |--- feature_39 >  0.50\n",
      "|   |   |   |--- feature_19 <= 0.50\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |   |   |--- feature_19 >  0.50\n",
      "|   |   |   |   |--- class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "text_representation = tree.export_text(classTree)\n",
    "print(text_representation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0258fe13",
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"646.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.468</text>\n",
       "<text text-anchor=\"middle\" x=\"646.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 225</text>\n",
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       "<text text-anchor=\"middle\" x=\"1156.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_2 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1156.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.486</text>\n",
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       "<!-- 3&#45;&gt;17 -->\n",
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       "<text text-anchor=\"middle\" x=\"384.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CARRIER_US &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"384.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"middle\" x=\"384.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 207</text>\n",
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       "<text text-anchor=\"middle\" x=\"646.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CRS_DEP_TIME_8 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"646.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.278</text>\n",
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       "<text text-anchor=\"middle\" x=\"184.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CRS_DEP_TIME_12 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"184.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.412</text>\n",
       "<text text-anchor=\"middle\" x=\"184.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 186</text>\n",
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       "<!-- 5&#45;&gt;6 -->\n",
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       "<text text-anchor=\"middle\" x=\"384.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_1 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"384.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.408</text>\n",
       "<text text-anchor=\"middle\" x=\"384.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 21</text>\n",
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       "<title>7</title>\n",
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       "<text text-anchor=\"middle\" x=\"58.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.394</text>\n",
       "<text text-anchor=\"middle\" x=\"58.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 174</text>\n",
       "<text text-anchor=\"middle\" x=\"58.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [127, 47]</text>\n",
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       "<!-- 6&#45;&gt;7 -->\n",
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       "<title>6&#45;&gt;7</title>\n",
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       "<title>8</title>\n",
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       "<text text-anchor=\"middle\" x=\"184.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.486</text>\n",
       "<text text-anchor=\"middle\" x=\"184.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 12</text>\n",
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       "<!-- 6&#45;&gt;8 -->\n",
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       "<title>6&#45;&gt;8</title>\n",
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       "<text text-anchor=\"middle\" x=\"302.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.142</text>\n",
       "<text text-anchor=\"middle\" x=\"302.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 13</text>\n",
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       "<text text-anchor=\"middle\" x=\"419.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n",
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       "<!-- 9&#45;&gt;11 -->\n",
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       "<text text-anchor=\"middle\" x=\"568.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CRS_DEP_TIME_9 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"568.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.208</text>\n",
       "<text text-anchor=\"middle\" x=\"568.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 17</text>\n",
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       "<!-- 12&#45;&gt;13 -->\n",
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       "<title>12&#45;&gt;13</title>\n",
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       "<title>12&#45;&gt;16</title>\n",
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       "<text text-anchor=\"middle\" x=\"1070.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_3 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1070.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"middle\" x=\"1070.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n",
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       "<text text-anchor=\"middle\" x=\"774.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.497</text>\n",
       "<text text-anchor=\"middle\" x=\"774.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\n",
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       "<text text-anchor=\"middle\" x=\"895.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.408</text>\n",
       "<text text-anchor=\"middle\" x=\"895.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7</text>\n",
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       "<text text-anchor=\"middle\" x=\"1008.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1008.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n",
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       "<text text-anchor=\"middle\" x=\"1121.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
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       "<text text-anchor=\"middle\" x=\"1241.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CARRIER_OH &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1241.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.48</text>\n",
       "<text text-anchor=\"middle\" x=\"1241.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"1241.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 2]</text>\n",
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       "<text text-anchor=\"middle\" x=\"1384.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1384.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11</text>\n",
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       "<text text-anchor=\"middle\" x=\"1234.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1234.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
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       "<text text-anchor=\"middle\" x=\"1347.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1347.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "<!-- 26&#45;&gt;28 -->\n",
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       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Weather_0 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.281</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 77</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [13, 64]</text>\n",
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       "<!-- 31&#45;&gt;32 -->\n",
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       "<text text-anchor=\"middle\" x=\"2082.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_3 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2082.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.475</text>\n",
       "<text text-anchor=\"middle\" x=\"2082.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18</text>\n",
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       "<!-- 31&#45;&gt;47 -->\n",
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       "<text text-anchor=\"middle\" x=\"1783.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1783.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "<!-- 32&#45;&gt;33 -->\n",
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       "<title>34</title>\n",
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       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_6 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.266</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 76</text>\n",
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       "<text text-anchor=\"middle\" x=\"1719.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_3 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1719.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.233</text>\n",
       "<text text-anchor=\"middle\" x=\"1719.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 67</text>\n",
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       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\n",
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       "<text text-anchor=\"middle\" x=\"1533.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_5 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1533.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.2</text>\n",
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       "<text text-anchor=\"middle\" x=\"1719.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n",
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       "<title>37</title>\n",
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       "<text text-anchor=\"middle\" x=\"1464.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 50</text>\n",
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       "<text text-anchor=\"middle\" x=\"1582.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 12</text>\n",
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       "<text text-anchor=\"middle\" x=\"1697.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"middle\" x=\"1697.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
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       "<text text-anchor=\"middle\" x=\"1810.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
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       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CRS_DEP_TIME_18 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.245</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 6]</text>\n",
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       "<text text-anchor=\"middle\" x=\"2082.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
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       "<!-- 42&#45;&gt;46 -->\n",
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       "<title>42&#45;&gt;46</title>\n",
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       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"1923.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 5]</text>\n",
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       "<!-- 43&#45;&gt;44 -->\n",
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       "<text text-anchor=\"middle\" x=\"2036.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2036.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
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       "<text text-anchor=\"middle\" x=\"2082.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Weather_0 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2082.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2082.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 14</text>\n",
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       "<text text-anchor=\"middle\" x=\"2210.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
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       "<text text-anchor=\"middle\" x=\"2082.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"2082.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "<text text-anchor=\"middle\" x=\"2222.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 13</text>\n",
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       "<text text-anchor=\"middle\" x=\"2152.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11</text>\n",
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       "<text text-anchor=\"middle\" x=\"4859.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 28</text>\n",
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       "<!-- 56&#45;&gt;104 -->\n",
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       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_4 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.101</text>\n",
       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 955</text>\n",
       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [51, 904]</text>\n",
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       "<text text-anchor=\"middle\" x=\"4162.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CARRIER_MQ &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"4162.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.233</text>\n",
       "<text text-anchor=\"middle\" x=\"4162.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 67</text>\n",
       "<text text-anchor=\"middle\" x=\"4162.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [9, 58]</text>\n",
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       "<!-- 57&#45;&gt;83 -->\n",
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       "<text text-anchor=\"middle\" x=\"3054.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CRS_DEP_TIME_15 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"3054.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.09</text>\n",
       "<text text-anchor=\"middle\" x=\"3054.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 849</text>\n",
       "<text text-anchor=\"middle\" x=\"3054.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [40, 809]</text>\n",
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       "<!-- 58&#45;&gt;59 -->\n",
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       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CRS_DEP_TIME_6 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.186</text>\n",
       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 106</text>\n",
       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [11, 95]</text>\n",
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       "<text text-anchor=\"middle\" x=\"2732.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CRS_DEP_TIME_8 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2732.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.079</text>\n",
       "<text text-anchor=\"middle\" x=\"2732.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 751</text>\n",
       "<text text-anchor=\"middle\" x=\"2732.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [31, 720]</text>\n",
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       "<!-- 59&#45;&gt;60 -->\n",
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       "<title>59&#45;&gt;60</title>\n",
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       "<title>67</title>\n",
       "<polygon fill=\"#4da7e8\" stroke=\"black\" points=\"3129,-261 2980,-261 2980,-193 3129,-193 3129,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"3054.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_7 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"3054.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.167</text>\n",
       "<text text-anchor=\"middle\" x=\"3054.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 98</text>\n",
       "<text text-anchor=\"middle\" x=\"3054.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [9, 89]</text>\n",
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       "<!-- 59&#45;&gt;67 -->\n",
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       "<title>59&#45;&gt;67</title>\n",
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       "<title>61</title>\n",
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       "<text text-anchor=\"middle\" x=\"2521.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CRS_DEP_TIME_19 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2521.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.07</text>\n",
       "<text text-anchor=\"middle\" x=\"2521.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 686</text>\n",
       "<text text-anchor=\"middle\" x=\"2521.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [25, 661]</text>\n",
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       "<!-- 60&#45;&gt;61 -->\n",
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       "<title>60&#45;&gt;61</title>\n",
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       "<!-- 64 -->\n",
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       "<title>64</title>\n",
       "<polygon fill=\"#4da7e8\" stroke=\"black\" points=\"2808,-157 2657,-157 2657,-89 2808,-89 2808,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"2732.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CARRIER_MQ &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2732.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.168</text>\n",
       "<text text-anchor=\"middle\" x=\"2732.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 65</text>\n",
       "<text text-anchor=\"middle\" x=\"2732.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6, 59]</text>\n",
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       "<!-- 60&#45;&gt;64 -->\n",
       "<g id=\"edge64\" class=\"edge\">\n",
       "<title>60&#45;&gt;64</title>\n",
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       "<text text-anchor=\"middle\" x=\"2392.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.061</text>\n",
       "<text text-anchor=\"middle\" x=\"2392.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 636</text>\n",
       "<text text-anchor=\"middle\" x=\"2392.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [20, 616]</text>\n",
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       "<!-- 61&#45;&gt;62 -->\n",
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       "<!-- 63 -->\n",
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       "<polygon fill=\"#4fa8e8\" stroke=\"black\" points=\"2571.5,-53 2469.5,-53 2469.5,0 2571.5,0 2571.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"2520.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.18</text>\n",
       "<text text-anchor=\"middle\" x=\"2520.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 50</text>\n",
       "<text text-anchor=\"middle\" x=\"2520.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5, 45]</text>\n",
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       "<!-- 61&#45;&gt;63 -->\n",
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       "<title>61&#45;&gt;63</title>\n",
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       "<title>65</title>\n",
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       "<text text-anchor=\"middle\" x=\"2640.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.1</text>\n",
       "<text text-anchor=\"middle\" x=\"2640.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 57</text>\n",
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       "<title>64&#45;&gt;65</title>\n",
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       "<text text-anchor=\"middle\" x=\"2757.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.469</text>\n",
       "<text text-anchor=\"middle\" x=\"2757.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n",
       "<text text-anchor=\"middle\" x=\"2757.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 5]</text>\n",
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       "<title>64&#45;&gt;66</title>\n",
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       "<title>68</title>\n",
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       "<text text-anchor=\"middle\" x=\"2972.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CARRIER_RU &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"2972.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.074</text>\n",
       "<text text-anchor=\"middle\" x=\"2972.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 78</text>\n",
       "<text text-anchor=\"middle\" x=\"2972.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 75]</text>\n",
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       "<title>67&#45;&gt;68</title>\n",
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       "<text text-anchor=\"middle\" x=\"3137.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CARRIER_US &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"3137.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.42</text>\n",
       "<text text-anchor=\"middle\" x=\"3137.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 20</text>\n",
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       "<text text-anchor=\"middle\" x=\"3114.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.346</text>\n",
       "<text text-anchor=\"middle\" x=\"3114.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18</text>\n",
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       "<text text-anchor=\"middle\" x=\"3525.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.174</text>\n",
       "<text text-anchor=\"middle\" x=\"3525.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 104</text>\n",
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       "<text text-anchor=\"middle\" x=\"3684.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
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       "<text text-anchor=\"middle\" x=\"3446.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">CARRIER_RU &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"3446.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.153</text>\n",
       "<text text-anchor=\"middle\" x=\"3446.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 96</text>\n",
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       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DEST_EWR &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"middle\" x=\"3605.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n",
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       "<!-- 75&#45;&gt;79 -->\n",
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       "<text text-anchor=\"middle\" x=\"3348.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 82</text>\n",
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       "<text text-anchor=\"middle\" x=\"3468.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.337</text>\n",
       "<text text-anchor=\"middle\" x=\"3468.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 14</text>\n",
       "<text text-anchor=\"middle\" x=\"3468.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 11]</text>\n",
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       "<!-- 76&#45;&gt;78 -->\n",
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       "<text text-anchor=\"middle\" x=\"3584.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.278</text>\n",
       "<text text-anchor=\"middle\" x=\"3584.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n",
       "<text text-anchor=\"middle\" x=\"3584.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 5]</text>\n",
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       "<!-- 79&#45;&gt;80 -->\n",
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       "<text text-anchor=\"middle\" x=\"3697.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"3697.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"middle\" x=\"3697.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 1]</text>\n",
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       "<!-- 79&#45;&gt;81 -->\n",
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       "<title>84</title>\n",
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       "<text text-anchor=\"middle\" x=\"4162.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">ORIGIN_IAD &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"4162.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.183</text>\n",
       "<text text-anchor=\"middle\" x=\"4162.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 59</text>\n",
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       "<!-- 83&#45;&gt;84 -->\n",
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       "<text text-anchor=\"middle\" x=\"4468.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_1 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"4468.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.469</text>\n",
       "<text text-anchor=\"middle\" x=\"4468.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n",
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       "<!-- 83&#45;&gt;97 -->\n",
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       "<title>83&#45;&gt;97</title>\n",
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       "<text text-anchor=\"middle\" x=\"4078.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_5 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"4078.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.12</text>\n",
       "<text text-anchor=\"middle\" x=\"4078.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 47</text>\n",
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       "<!-- 84&#45;&gt;85 -->\n",
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       "<text text-anchor=\"middle\" x=\"4245.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_2 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"4245.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"middle\" x=\"4245.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 12</text>\n",
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       "<text text-anchor=\"middle\" x=\"3924.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.091</text>\n",
       "<text text-anchor=\"middle\" x=\"3924.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 42</text>\n",
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       "<text text-anchor=\"middle\" x=\"4082.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n",
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       "<text text-anchor=\"middle\" x=\"3931.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n",
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       "<text text-anchor=\"middle\" x=\"4044.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
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       "<text text-anchor=\"middle\" x=\"4243.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.18</text>\n",
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       "<text text-anchor=\"middle\" x=\"4270.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7</text>\n",
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       "<text text-anchor=\"middle\" x=\"4383.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"middle\" x=\"4383.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
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       "<text text-anchor=\"middle\" x=\"4468.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_2 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"4468.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.408</text>\n",
       "<text text-anchor=\"middle\" x=\"4468.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7</text>\n",
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       "<text text-anchor=\"middle\" x=\"4608.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "<text text-anchor=\"middle\" x=\"4523.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"middle\" x=\"4523.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n",
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       "<text text-anchor=\"middle\" x=\"4516.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
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       "<text text-anchor=\"middle\" x=\"4629.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
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       "<text text-anchor=\"middle\" x=\"4859.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">DAY_WEEK_6 &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"4859.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.227</text>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x1b6ab1c4ac0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = tree.export_graphviz(classTree, \n",
    "                                out_file=None, \n",
    "                                feature_names=X.columns, \n",
    "                                filled=True)\n",
    "\n",
    "# Draw graph\n",
    "graph = graphviz.Source(dot_data, format=\"png\") \n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "16748534",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Predicting the model on test data\n",
    "\n",
    "y_pred = classTree.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "082dca64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8239339752407153\n"
     ]
    }
   ],
   "source": [
    "## Accuracy of the test data\n",
    "\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "917a692d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.8942\n",
      "Test set score: 0.8239\n"
     ]
    }
   ],
   "source": [
    "# print the scores on training and test set\n",
    "\n",
    "print('Training set score: {:.4f}'.format(classTree.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(classTree.score(X_valid, y_valid)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3f6d69a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "38531003",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 84  54]\n",
      " [ 74 515]]\n"
     ]
    }
   ],
   "source": [
    "confusion_matrix1 = confusion_matrix(y_valid, y_pred)\n",
    "print(confusion_matrix1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1c34f93f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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aWlVWVjoqKpI0ZcoUTZo0SYsWLZKfn59eeOEFR8vp2LFjys/PV2pqqqZMmeI4p7a2VlFRUZKkPXv26KKLLlJoaKjj+Pe/hjO57777NH36dMfr0tJSJSTQfvCkoGB7vZsa2e02Nfuubvju39tox7+dPzsPP7NN7/69jda9Gn+eogQ8JyDQroTOVdr57zCtf7mFtr3vvO4qc9UBvfNyC639S7SXIoQnGA9c9WO+Oz8yMtIpUTmd/Px83XPPPVq7dq2Cg8+81slmc47JGFNvX704GjDmh7yWqBjz47fJs9vt8vPzU15envz8/JyOhYeffhHkrFmz9Pbbb+vxxx9X586dFRISol/96leqrq52jBk5cqSCgoKUm5uroKAgVVVV6frrr3e8p3Sy/ZOcnOw096kYzhb3mQQFBZ0xc4Vn/PufLXVj6kEdKwjW4f3hurB7mX75m8Nau6atJKmsJFBlJYFO59TV2lT8VaCOHg7zRsiAS6Y8+IU2r41U0dEANW9Zq3FpRQqNqNO6l6JVVuyvsmLnH+m1tTYVFwXoyH4W1jZl5/vpyXl5eSoqKlLfvn0d++rq6vTPf/5TTz/9tPbu3SvpZNWkTZv/rQEsKipyVFni4uJUXV2t4uJip6pKUVGRBgwY4FLsXktUOnfurICAAG3evFnt25+8x0VxcbH27dungQMHKikpSXV1dSoqKtIVVzSsJP/+++9r4sSJ+uUvfynpZHvp0KFDTmP8/f01YcIEZWVlKSgoSDfeeKOjOhIbG6u2bdvqwIEDGj9+/Gnfo2fPnlq5cqUqKysVEhIiSdq8efO5fAvgYc/O766b79ivO3//H0W1qNY3x4L0j7+106rnOp39ZKAJaNmmRvc9c1iR0XUq+dpP/9kWprQRXVR0NPDsJwMNNHjwYH366adO+2699VZ1795d9957rzp16qS4uDitW7dOSUlJkk5e/LJx40bNnz9f0smLVAICArRu3TqNHTtWklRQUKCdO3dqwYIFLsXjtUQlPDxcqampmjVrlmJiYhQbG6v09HQ1+65O37VrV40fP1633HKLFi5cqKSkJH311Vd69913lZiYqGuuuabenJ07d9Yrr7yikSNHymaz6YEHHnBUSb5v8uTJ6tGjhyTpgw8+cDqWkZGhadOmKTIyUsOHD1dVVZW2bt2q4uJiTZ8+XePGjVN6erpSU1N1//3369ChQ3r88ccb4TsEV1Ue99dzj3fTc493a/A5rEtBUzJvqmt3UGZdyk/D+b4zbUREhHr37u20LywsTDExMY79aWlpyszMVJcuXdSlSxdlZmYqNDRU48aNkyRFRUUpNTVVM2bMUExMjKKjozVz5kwlJibWW5x7Nl69beFjjz2m8vJyjRo1ShEREZoxY4ZKSkocx7OysvTII49oxowZOnr0qGJiYpSSknLaJEWSFi9erEmTJmnAgAFq2bKl7r333tNeXdOlSxcNGDBAX3/9db0Wz+TJkxUaGqrHHntMs2fPVlhYmBITEx03ugkPD9frr7+u22+/XUlJSerZs6fmz5/vaB8BAOBJ57v10xCzZ89WZWWl7rjjDhUXFys5OVlr165VRESEY8zixYvl7++vsWPHqrKyUoMHD1Z2dna95RxnYzPnuuiiCTPGqHv37rrtttucFrieT6WlpYqKitLg1pPl34yyLX6a6r4s8nYIQKOoNTXaoFdVUlJy1sWp5+rU74nRaycpIMy93xM1FdV69ao/NWq8jcXnHgRRVFSklStX6ujRo7r11lu9HQ4AAD/K15/143OJSmxsrFq2bKnnnnuu3vXdAABYjRVbP+eTzyUqPtjpAgCgyfK5RAUAgKaEigoAALAsX09UvPpQQgAAgB9DRQUAAAvz9YoKiQoAABZm5P7lxU35MhISFQAALMzXKyqsUQEAAJZFRQUAAAvz9YoKiQoAABbm64kKrR8AAGBZVFQAALAwX6+okKgAAGBhxthk3Ew03D3fm2j9AAAAy6KiAgCAhdllc/uGb+6e700kKgAAWJivr1Gh9QMAACyLigoAABbm64tpSVQAALAwX2/9kKgAAGBhvl5RYY0KAACwLCoqAABYmPFA66cpV1RIVAAAsDAjyRj352iqaP0AAADLoqICAICF2WWTjTvTAgAAK+KqHwAAAIuiogIAgIXZjU02bvgGAACsyBgPXPXThC/7ofUDAAAsi4oKAAAW5uuLaUlUAACwMBIVAABgWb6+mJY1KgAAwLKoqAAAYGG+ftUPiQoAABZ2MlFxd42Kh4LxAlo/AADAsqioAABgYVz1AwAALMt8t7k7R1NF6wcAAFgWFRUAACyM1g8AALAuH+/9kKgAAGBlHqioqAlXVFijAgAALIuKCgAAFsadaQEAgGX5+mJaWj8AAMCyqKgAAGBlxub+YtgmXFEhUQEAwMJ8fY0KrR8AAGBZVFQAALAybvgGAACsytev+mlQovLkk082eMJp06adczAAAADf16BEZfHixQ2azGazkagAAOBpTbh1464GJSoHDx5s7DgAAMBp+Hrr55yv+qmurtbevXtVW1vryXgAAMD3GQ9tTZTLicrx48eVmpqq0NBQ9erVS59//rmkk2tTHn30UY8HCAAAfJfLicp9992njz/+WBs2bFBwcLBj/5AhQ/SXv/zFo8EBAACbh7amyeXLk9esWaO//OUvuuyyy2Sz/e8L79mzp/bv3+/R4AAA8Hk+fh8Vlysqx44dU+vWrevtr6iocEpcAAAA3OVyotK/f3/9/e9/d7w+lZwsX75cKSkpnosMAAD4/GJal1s/8+bN09VXX63du3ertrZWTzzxhHbt2qV//etf2rhxY2PECACA7/Lxpye7XFEZMGCAPvjgAx0/flwXXnih1q5dq9jYWP3rX/9S3759GyNGAADgo87pWT+JiYnKycnxdCwAAOAHjDm5uTtHU3VOiUpdXZ1yc3O1Z88e2Ww29ejRQ6NHj5a/P884BADAo3z8qh+XM4udO3dq9OjRKiwsVLdu3SRJ+/btU6tWrfTaa68pMTHR40ECAADf5PIalcmTJ6tXr146cuSItm3bpm3btik/P199+vTRb3/728aIEQAA33VqMa27WxPlcqLy8ccfa968eWrRooVjX4sWLTR37lzt2LHDk7EBAODzbMYzW0MtXbpUffr0UWRkpCIjI5WSkqJ//OMfjuPGGGVkZCg+Pl4hISEaNGiQdu3a5TRHVVWV7r77brVs2VJhYWEaNWqUjhw5ck5fv8uJSrdu3fTll1/W219UVKTOnTufUxAAAOAMzvN9VNq1a6dHH31UW7du1datW/WLX/xCo0ePdiQjCxYs0KJFi/T0009ry5YtiouL09ChQ1VWVuaYIy0tTbm5uVq9erU2bdqk8vJyjRgxQnV1dS5/+Q1KVEpLSx1bZmampk2bpr/97W86cuSIjhw5or/97W9KS0vT/PnzXQ4AAACcH9//fV5aWqqqqqp6Y0aOHKlrrrlGXbt2VdeuXTV37lyFh4dr8+bNMsZoyZIlSk9P15gxY9S7d2/l5OTo+PHjWrVqlSSppKREK1as0MKFCzVkyBAlJSXp+eef16effqr169e7HHODFtM2b97c6fb4xhiNHTvWsc98d93TyJEjzylbAgAAZ+DBG74lJCQ47X7ooYeUkZFxxtPq6ur017/+VRUVFUpJSdHBgwdVWFioq666yjEmKChIAwcO1IcffqjbbrtNeXl5qqmpcRoTHx+v3r1768MPP9SwYcNcCr1Bicp7773n0qQAAMBDPHh5cn5+viIjIx27g4KCTjv8008/VUpKik6cOKHw8HDl5uaqZ8+e+vDDDyVJsbGxTuNjY2N1+PBhSVJhYaECAwOd1rKeGlNYWOhy6A1KVAYOHOjyxAAAwFpOLZA9m27dumnHjh369ttv9fLLL2vChAlOj8n54UOIjTFnfTBxQ8aczjnfoe348eP6/PPPVV1d7bS/T58+5zolAAD4IS/c8C0wMNBxgUy/fv20ZcsWPfHEE7r33nslnayatGnTxjG+qKjIUWWJi4tTdXW1iouLnaoqRUVFGjBggMuhu3zVz7FjxzRixAhFRESoV69eSkpKctoAAIAHWeDpycYYVVVVqWPHjoqLi9O6descx6qrq7Vx40ZHEtK3b18FBAQ4jSkoKNDOnTvPKVFxuaKSlpam4uJibd68WVdeeaVyc3P15Zdf6pFHHtHChQtdDgAAAFjH73//ew0fPlwJCQkqKyvT6tWrtWHDBr311luy2WxKS0tTZmamunTpoi5duigzM1OhoaEaN26cJCkqKkqpqamaMWOGYmJiFB0drZkzZyoxMVFDhgxxOR6XE5V3331Xr776qvr3769mzZqpQ4cOGjp0qCIjIzVv3jxde+21LgcBAADOwINX/TTEl19+qZtvvlkFBQWKiopSnz599NZbb2no0KGSpNmzZ6uyslJ33HGHiouLlZycrLVr1yoiIsIxx+LFi+Xv76+xY8eqsrJSgwcPVnZ2tvz8/FwO3eVEpaKiQq1bt5YkRUdH69ixY+ratasSExO1bds2lwMAAABn5uqdZc80R0OtWLHix+ey2ZSRkfGjlzUHBwfrqaee0lNPPdXwNz6Dc7oz7d69eyVJF198sZYtW6ajR4/q2WefdVpYAwAA4K5zWqNSUFAg6eSNYoYNG6YXXnhBgYGBys7O9nR8AAD4Ni9c9WMlLicq48ePd/w7KSlJhw4d0n/+8x+1b99eLVu29GhwAADAt53zfVROCQ0N1SWXXOKJWAAAwA/Y5IE1Kh6JxDsalKhMnz69wRMuWrTonIMBAAD4vgYlKtu3b2/QZOdya1yfV13lfqoMWNTbX+zwdghAoygts6tF1/P0Zuf58mSr4aGEAABYmY8vpnX58mQAAIDzxe3FtAAAoBH5eEWFRAUAAAs733emtRpaPwAAwLKoqAAAYGU+3vo5p4rKypUrdfnllys+Pl6HDx+WJC1ZskSvvvqqR4MDAMDnGQ9tTZTLicrSpUs1ffp0XXPNNfr2229VV1cnSWrevLmWLFni6fgAAIAPczlReeqpp7R8+XKlp6fLz8/Psb9fv3769NNPPRocAAC+7tRiWne3psrlNSoHDx5UUlJSvf1BQUGqqKjwSFAAAOA7Pn5nWpcrKh07dtSOHTvq7f/HP/6hnj17eiImAABwio+vUXG5ojJr1izdeeedOnHihIwx+uijj/Tiiy9q3rx5+uMf/9gYMQIAAB/lcqJy6623qra2VrNnz9bx48c1btw4tW3bVk888YRuvPHGxogRAACf5es3fDun+6hMmTJFU6ZM0VdffSW73a7WrVt7Oi4AACD5/H1U3LrhW8uWLT0VBwAAQD0uJyodO3aUzXbm1cMHDhxwKyAAAPA9nri82JcqKmlpaU6va2pqtH37dr311luaNWuWp+ICAAASrR9XT7jnnntOu////u//tHXrVrcDAgAAOMVjT08ePny4Xn75ZU9NBwAAJO6j4qmJ/va3vyk6OtpT0wEAAHF5ssuJSlJSktNiWmOMCgsLdezYMT3zzDMeDQ4AAPg2lxOV6667zul1s2bN1KpVKw0aNEjdu3f3VFwAAACuJSq1tbW64IILNGzYMMXFxTVWTAAA4BQfv+rHpcW0/v7+mjp1qqqqqhorHgAA8D2n1qi4uzVVLl/1k5ycrO3btzdGLAAAAE5cXqNyxx13aMaMGTpy5Ij69u2rsLAwp+N9+vTxWHAAAEBNunXjrgYnKpMmTdKSJUt0ww03SJKmTZvmOGaz2WSMkc1mU11dneejBADAV/n4GpUGJyo5OTl69NFHdfDgwcaMBwAAwKHBiYoxJ9OxDh06NFowAADAGTd8c8GPPTUZAAA0Alo/Dde1a9ezJivffPONWwEBAACc4lKiMmfOHEVFRTVWLAAA4Ado/bjgxhtvVOvWrRsrFgAA8EM+3vpp8A3fWJ8CAADON5ev+gEAAOeRj1dUGpyo2O32xowDAACcBmtUAACAdfl4RcXlhxICAACcL1RUAACwMh+vqJCoAABgYb6+RoXWDwAAsCwqKgAAWBmtHwAAYFW0fgAAACyKigoAAFZG6wcAAFiWjycqtH4AAIBlUVEBAMDCbN9t7s7RVJGoAABgZT7e+iFRAQDAwrg8GQAAwKKoqAAAYGW0fgAAgKU14UTDXbR+AACAZVFRAQDAwnx9MS2JCgAAVubja1Ro/QAAAMuiogIAgIXR+gEAANZF6wcAAMCaqKgAAGBhtH4AAIB1+Xjrh0QFAAAr8/FEhTUqAADAYd68eerfv78iIiLUunVrXXfdddq7d6/TGGOMMjIyFB8fr5CQEA0aNEi7du1yGlNVVaW7775bLVu2VFhYmEaNGqUjR464HA+JCgAAFnZqjYq7W0Nt3LhRd955pzZv3qx169aptrZWV111lSoqKhxjFixYoEWLFunpp5/Wli1bFBcXp6FDh6qsrMwxJi0tTbm5uVq9erU2bdqk8vJyjRgxQnV1dS59/bR+AACwsvPc+nnrrbecXmdlZal169bKy8vTz3/+cxljtGTJEqWnp2vMmDGSpJycHMXGxmrVqlW67bbbVFJSohUrVmjlypUaMmSIJOn5559XQkKC1q9fr2HDhjU4HioqAAD4iNLSUqetqqrqrOeUlJRIkqKjoyVJBw8eVGFhoa666irHmKCgIA0cOFAffvihJCkvL081NTVOY+Lj49W7d2/HmIYiUQEAwMJsxnhkk6SEhARFRUU5tnnz5v3oextjNH36dP3sZz9T7969JUmFhYWSpNjYWKexsbGxjmOFhYUKDAxUixYtzjimoWj9AABgZR5s/eTn5ysyMtKxOygo6EdPu+uuu/TJJ59o06ZN9Y7ZbDbntzCm3r56YTRgzA9RUQEAwEdERkY6bT+WqNx999167bXX9N5776ldu3aO/XFxcZJUrzJSVFTkqLLExcWpurpaxcXFZxzTUCQqAABY2Pm+6scYo7vuukuvvPKK3n33XXXs2NHpeMeOHRUXF6d169Y59lVXV2vjxo0aMGCAJKlv374KCAhwGlNQUKCdO3c6xjQUrR8AAKzsPF/1c+edd2rVqlV69dVXFRER4aicREVFKSQkRDabTWlpacrMzFSXLl3UpUsXZWZmKjQ0VOPGjXOMTU1N1YwZMxQTE6Po6GjNnDlTiYmJjquAGopEBQAAOCxdulSSNGjQIKf9WVlZmjhxoiRp9uzZqqys1B133KHi4mIlJydr7dq1ioiIcIxfvHix/P39NXbsWFVWVmrw4MHKzs6Wn5+fS/HYjDFN+Ma6TVdpaamioqI0uPnN8rcFejscoFG8uXujt0MAGkVpmV0tuh5QSUmJ0+JUj77Hd78nLrlprvwCg92aq676hLa9mN6o8TYWKioAAFiZjz/rh0QFAAALc3Ux7JnmaKq46gcAAFgWFRUAAKyM1g8AALCypty6cRetHwAAYFlUVAAAsDJjTm7uztFEkagAAGBhXPUDAABgUVRUAACwMq76AQAAVmWzn9zcnaOpovUDAAAsi4oKfjKy1v1bsW2r6u1/Y1UbPfNIF6d9d2Xs0zVjC7VsXie9urLd+QoRcMnKx+P0/KI4p30tWtVo9ce7JEmb3ozSmytj9N9PQlVa7K9n1u7Vhb0rncbPur6zPvlXuNO+gaOK9ftnDzdu8PAcWj++xWazKTc3V9ddd523Q4GH3TM2Sd9/eniHLhXKXPGp3n+7ldO4lMFfqVufMn31JU+thvV16FapR/+y3/G6md//fuOcON5MPftX6IoR32rJrPZnnGP4+K90y6xCx+ug4CbcB/BBvn7Vz082UcnIyNCaNWu0Y8cOp/0FBQVq0aKFd4JCoyotdk48fj05X198HqxPt0Q59sW0rtLU9M90/28TNWfpzvMdIuAyPz8punXtaY8N+VWxJKkw/8eT7qAQc8Y50ARwHxXfEhcXd/ZBaPL8A+y6cuSXys1pJ8kmSbLZjGY++h+9/KcEff5ZmHcDBBro6MFA3ZTUSwGBdnVPOq5b7ytQmw7VLs3x3ist9O7LLdS8VY36X1mm38woVGg4VRU0DZZdTFtVVaVp06apdevWCg4O1s9+9jNt2bJFkrRhwwbZbDa988476tevn0JDQzVgwADt3btXkpSdna05c+bo448/ls1mk81mU3Z2tqSTrZ81a9ZIkg4dOiSbzaaXXnpJV1xxhUJCQtS/f3/t27dPW7ZsUb9+/RQeHq6rr75ax44dc4ovKytLPXr0UHBwsLp3765nnnnmrF9PaWmp04bGkzL4a4VH1Gp9bqxj368n56uuzqZXn4/3YmRAw3W/pEKznvxcmav2K+2xfBUfC9D/G9VFpd/4nf3k71w55hv97plDeuzlzzQ+7UttejNKf0jt2IhRw9NOtX7c3ZoqyyYqs2fP1ssvv6ycnBxt27ZNnTt31rBhw/TNN984xqSnp2vhwoXaunWr/P39NWnSJEnSDTfcoBkzZqhXr14qKChQQUGBbrjhhjO+10MPPaT7779f27Ztk7+/v2666SbNnj1bTzzxhN5//33t379fDz74oGP88uXLlZ6errlz52rPnj3KzMzUAw88oJycnDO+x7x58xQVFeXYEhISPPBdwplcNaZQW9+P1jfHgiRJnXuWadTNR7Xo9910qsICWF3/X5TpimtL1LHHCV3y83I9vPKAJGndX6MbPMc147/RJT8v1wXdT2jQdd/qgeWHtP39CP33k5DGChueZjy0NVGWbP1UVFRo6dKlys7O1vDhwyWdTA7WrVunFStWqH///pKkuXPnauDAgZKk3/3ud7r22mt14sQJhYSEKDw8XP7+/g1q9cycOVPDhg2TJN1zzz266aab9M477+jyyy+XJKWmpjoqMpL08MMPa+HChRozZowkqWPHjtq9e7eWLVumCRMmnPY97rvvPk2fPt3xurS0lGSlkbSOP6GLU4o1956ejn29+paoeXSNct75t2Ofn780efYBXXfLUd06NNkboQIuCQ6164LuJ3T0YNA5z9E5sVL+AXYdPRikLn0qz34C4GWWTFT279+vmpoaR6IgSQEBAbr00ku1Z88eR6LSp08fx/E2bdpIkoqKitS+/ZlXv5/O9+eJjT3ZKkhMTHTaV1RUJEk6duyY8vPzlZqaqilTpjjG1NbWKirqf4s2fygoKEhBQef+wwUNN/SXhSr5JlAfbYxx7Hv3tVjt+JfzIuqHl3+qd1+L1brvtYcAK6uusin/syD1Ti4/5zkO7w1WbU0zxcTWeDAyNCau+rEg893qZJvNVm//9/cFBAQ4/n1qv93u+gKx083zw32n5j31v8uXL1dysvNf4X5+De8bo3HYbEZDf/ml1q+Jlb3uf5+VspIAlZUEOI2tq7Wp+KsAHT0Uer7DBBrkuTnxuuyqErVuW6Nvv/LXqiWxOl7mp6FjT7bAS4v9dOxooL7+8uSP8vz9J/8YatG6RtGta/XFoUC9+0oLXTq4VJHRdfp8X5Cem9NWnXsfV8/+FV77uuAirvqxns6dOyswMFCbNm3SuHHjJEk1NTXaunWr0tLSGjRHYGCg6urqPB5bbGys2rZtqwMHDmj8+PEenx/uuTilWK3jq7TuFaokaPq+KgjQvDsuUOk3foqKqVX3S45ryRv7FNvuZDVk89ooLfx//6sgz5t6gSTpN9MLdfPMQvkHGO3YFKE1K1rpREUztYyvUfLgUo2fXij+rkJTYclEJSwsTFOnTtWsWbMUHR2t9u3ba8GCBTp+/LhSU1P18ccfn3WOCy64QAcPHtSOHTvUrl07RUREeKz1kpGRoWnTpikyMlLDhw9XVVWVtm7dquLiYqd1KDj/tn8YrWt6/rxBY1mXAqs7291jr7rhG111wzdnPN66bY0ef+UzT4eF84zWj0U9+uijstvtuvnmm1VWVqZ+/frp7bffbvDN2q6//nq98soruvLKK/Xtt98qKytLEydO9EhskydPVmhoqB577DHNnj1bYWFhSkxMbHC1BwCABvPxW+jbjGnCjasmrLS0VFFRURrc/Gb527iVO36a3ty90dshAI2itMyuFl0PqKSkRJGRkY3zHt/9nki5+g/yDwh2a67amhP611sPNmq8jcWyFRUAAEDrh0QFAAArs5uTm7tzNFEkKgAAWJmPr1Gx7C30AQAAqKgAAGBhNnlgjYpHIvEOEhUAAKzMx+9MS+sHAABYFhUVAAAsjMuTAQCAdXHVDwAAgDVRUQEAwMJsxsjm5mJYd8/3JhIVAACszP7d5u4cTRStHwAAYFlUVAAAsDBaPwAAwLp8/KofEhUAAKyMO9MCAABYExUVAAAsjDvTAgAA66L1AwAAYE1UVAAAsDCb/eTm7hxNFYkKAABWRusHAADAmqioAABgZdzwDQAAWJWv30Kf1g8AALAsKioAAFiZjy+mJVEBAMDKjCR3Ly9uunkKiQoAAFbGGhUAAACLoqICAICVGXlgjYpHIvEKEhUAAKzMxxfT0voBAACWRUUFAAArs0uyeWCOJopEBQAAC+OqHwAAAIuiogIAgJX5+GJaEhUAAKzMxxMVWj8AAMCyqKgAAGBlPl5RIVEBAMDKuDwZAABYFZcnAwAAWBQVFQAArMzH16hQUQEAwMrsxjObC/75z39q5MiRio+Pl81m05o1a5yOG2OUkZGh+Ph4hYSEaNCgQdq1a5fTmKqqKt19991q2bKlwsLCNGrUKB05csTlL59EBQAAOKmoqNBFF12kp59++rTHFyxYoEWLFunpp5/Wli1bFBcXp6FDh6qsrMwxJi0tTbm5uVq9erU2bdqk8vJyjRgxQnV1dS7FQusHAAAr80LrZ/jw4Ro+fPgZpjJasmSJ0tPTNWbMGElSTk6OYmNjtWrVKt12220qKSnRihUrtHLlSg0ZMkSS9PzzzyshIUHr16/XsGHDGhwLFRUAACzN/C9ZOddNJxOV0tJSp62qqsrlaA4ePKjCwkJdddVVjn1BQUEaOHCgPvzwQ0lSXl6eampqnMbEx8erd+/ejjENRaICAICPSEhIUFRUlGObN2+ey3MUFhZKkmJjY532x8bGOo4VFhYqMDBQLVq0OOOYhqL1AwCAlXmw9ZOfn6/IyEjH7qCgoHOe0mZzvgudMabevvphnH3MD1FRAQDAyjx41U9kZKTTdi6JSlxcnCTVq4wUFRU5qixxcXGqrq5WcXHxGcc0FIkKAABosI4dOyouLk7r1q1z7KuurtbGjRs1YMAASVLfvn0VEBDgNKagoEA7d+50jGkoWj8AAFiZsZ/c3J3DBeXl5frss88crw8ePKgdO3YoOjpa7du3V1pamjIzM9WlSxd16dJFmZmZCg0N1bhx4yRJUVFRSk1N1YwZMxQTE6Po6GjNnDlTiYmJjquAGopEBQAAK/PC5clbt27VlVde6Xg9ffp0SdKECROUnZ2t2bNnq7KyUnfccYeKi4uVnJystWvXKiIiwnHO4sWL5e/vr7Fjx6qyslKDBw9Wdna2/Pz8XIrFZkwTvq9uE1ZaWqqoqCgNbn6z/G2B3g4HaBRv7t7o7RCARlFaZleLrgdUUlLitDjVo+/x3e+JIW1vl3+zc1/0Kkm19iqtP/pso8bbWFijAgAALIvWDwAAVubjDyUkUQEAwMqMPJCoeCQSr6D1AwAALIuKCgAAVkbrBwAAWJbdLsnN+6jY3Tzfi2j9AAAAy6KiAgCAldH6AQAAluXjiQqtHwAAYFlUVAAAsDK7kds3QrE33YoKiQoAABZmjF3Gzacnu3u+N5GoAABgZca4XxFhjQoAAIDnUVEBAMDKjAfWqDThigqJCgAAVma3SzY315g04TUqtH4AAIBlUVEBAMDKaP0AAACrMna7jJutn6Z8eTKtHwAAYFlUVAAAsDJaPwAAwLLsRrL5bqJC6wcAAFgWFRUAAKzMGEnu3kel6VZUSFQAALAwYzcybrZ+DIkKAABoFMYu9ysqXJ4MAADgcVRUAACwMFo/AADAuny89UOi4iWnsttaU+3lSIDGU1rWdH84Aj+mtPzkZ/t8VCpqVeP2/d5qVeOZYLyARMVLysrKJEkbS/7i5UiAxtOiq7cjABpXWVmZoqKiGmXuwMBAxcXFaVPhmx6ZLy4uToGBgR6Z63yymabcuGrC7Ha7vvjiC0VERMhms3k7nJ+80tJSJSQkKD8/X5GRkd4OB/A4PuPnlzFGZWVlio+PV7NmjXddyokTJ1Rd7ZnKe2BgoIKDgz0y1/lERcVLmjVrpnbt2nk7DJ8TGRnJD3H8pPEZP38aq5LyfcHBwU0yufAkLk8GAACWRaICAAAsi0QFPiEoKEgPPfSQgoKCvB0K0Cj4jOOnisW0AADAsqioAAAAyyJRAQAAlkWiAgAALItEBZY0aNAgpaWlNWhsdna2mjdv3qjxNMShQ4dks9m0Y8cOb4cCH2Sz2bRmzRpvhwF4HIkKADQhGRkZuvjii+vtLygo0PDhw89/QEAj4860APATEBcX5+0QgEZBRQVeV1FRoVtuuUXh4eFq06aNFi5c6HS8urpas2fPVtu2bRUWFqbk5GRt2LDhjPPt379fo0ePVmxsrMLDw9W/f3+tX7/ecfwPf/iDEhMT653Xt29fPfjgg47XWVlZ6tGjh4KDg9W9e3c988wzTuM/+ugjJSUlKTg4WP369dP27dvP8TsAX1JVVaVp06apdevWCg4O1s9+9jNt2bJFkrRhwwbZbDa988476tevn0JDQzVgwADt3btX0sk255w5c/Txxx/LZrPJZrMpOztbknPr51Qb8qWXXtIVV1yhkJAQ9e/fX/v27dOWLVvUr18/hYeH6+qrr9axY8ec4jvb5x447wzgZVOnTjXt2rUza9euNZ988okZMWKECQ8PN/fcc48xxphx48aZAQMGmH/+85/ms88+M4899pgJCgoy+/btM8YYk5WVZaKiohzz7dixwzz77LPmk08+Mfv27TPp6ekmODjYHD582BhjTH5+vmnWrJn56KOPHOd8/PHHxmazmf379xtjjHnuuedMmzZtzMsvv2wOHDhgXn75ZRMdHW2ys7ONMcaUl5ebVq1amRtuuMHs3LnTvP7666ZTp05Gktm+fXvjf9PQZE2bNs3Ex8ebN9980+zatctMmDDBtGjRwnz99dfmvffeM5JMcnKy2bBhg9m1a5e54oorzIABA4wxxhw/ftzMmDHD9OrVyxQUFJiCggJz/PhxY4wxkkxubq4xxpiDBw8aSaZ79+7mrbfeMrt37zaXXXaZueSSS8ygQYPMpk2bzLZt20znzp3N7bff7ojtbJ97wBtIVOBVZWVlJjAw0Kxevdqx7+uvvzYhISHmnnvuMZ999pmx2Wzm6NGjTucNHjzY3HfffcaY+onK6fTs2dM89dRTjtfDhw83U6dOdbxOS0szgwYNcrxOSEgwq1atcprj4YcfNikpKcYYY5YtW2aio6NNRUWF4/jSpUtJVPCjysvLTUBAgHnhhRcc+6qrq018fLxZsGCBI1FZv3694/jf//53I8lUVlYaY4x56KGHzEUXXVRv7tMlKn/84x8dx1988UUjybzzzjuOffPmzTPdunVzvD7b5x7wBtaowKv279+v6upqpaSkOPZFR0erW7dukqRt27bJGKOuXbs6nVdVVaWYmJjTzllRUaE5c+bojTfe0BdffKHa2lpVVlbq888/d4yZMmWKJk2apEWLFsnPz08vvPCCo+V07Ngx5efnKzU1VVOmTHGcU1tb63ha6p49e3TRRRcpNDTUcfz7XwNwOvv371dNTY0uv/xyx76AgABdeuml2rNnj/r37y9J6tOnj+N4mzZtJElFRUVq3769S+/3/XliY2MlyantGRsbq6KiIkkN+9wD3kCiAq8yZ3mCg91ul5+fn/Ly8uTn5+d0LDw8/LTnzJo1S2+//bYef/xxde7cWSEhIfrVr36l6upqx5iRI0cqKChIubm5CgoKUlVVla6//nrHe0rS8uXLlZyc7DT3qRjOFjdwOqc+Nzabrd7+7+8LCAhw/PvU/lOfS1ecbp4f7js1b0M+94A3kKjAqzp37qyAgABt3rzZ8ddicXGx9u3bp4EDByopKUl1dXUqKirSFVdc0aA533//fU2cOFG//OUvJUnl5eU6dOiQ0xh/f39NmDBBWVlZCgoK0o033uiojsTGxqpt27Y6cOCAxo8ff9r36Nmzp1auXKnKykqFhIRIkjZv3nwu3wL4kM6dOyswMFCbNm3SuHHjJEk1NTXaunVrg+8bFBgYqLq6Oo/H1pDPPeANJCrwqvDwcKWmpmrWrFmKiYlRbGys0tPT1azZyQvSunbtqvHjx+uWW27RwoULlZSUpK+++krvvvuuEhMTdc0119Sbs3PnznrllVc0cuRI2Ww2PfDAA6f9a3Ty5Mnq0aOHJOmDDz5wOpaRkaFp06YpMjJSw4cPV1VVlbZu3ari4mJNnz5d48aNU3p6ulJTU3X//ffr0KFDevzxxxvhO4SfkrCwME2dOlWzZs1SdHS02rdvrwULFuj48eNKTU3Vxx9/fNY5LrjgAh08eFA7duxQu3btFBER4bEnJp/tcw94A4kKvO6xxx5TeXm5Ro0apYiICM2YMUMlJSWO41lZWXrkkUc0Y8YMHT16VDExMUpJSTltkiJJixcv1qRJkzRgwAC1bNlS9957r0pLS+uN69KliwYMGKCvv/66Xql78uTJCg0N1WOPPabZs2crLCxMiYmJjr96w8PD9frrr+v2229XUlKSevbsqfnz5zvaR8CZPProo7Lb7br55ptVVlamfv366e2331aLFi0adP7111+vV155RVdeeaW+/fZbZWVlaeLEiR6J7Wyfe8AbbIZmO3yUMUbdu3fXbbfdxl+LAGBRVFTgk4qKirRy5UodPXpUt956q7fDAQCcAYkKfFJsbKxatmyp5557rsEldwDA+UeiAp9ExxMAmgae9QMAACyLRAUAAFgWiQoAALAsEhUAAGBZJCoAAMCySFQAH5aRkaGLL77Y8XrixIm67rrrznschw4dks1m044dO8445oILLtCSJUsaPGd2draaN2/udmw2m01r1qxxex4A54ZEBbCYiRMnymazyWazKSAgQJ06ddLMmTNVUVHR6O/9xBNPKDs7u0FjG5JcAIC7uI8KYEFXX321srKyVFNTo/fff1+TJ09WRUWFli5dWm9sTU2NAgICPPK+UVFRHpkHADyFigpgQUFBQYqLi1NCQoLGjRun8ePHO9oPp9o1f/rTn9SpUycFBQXJGKOSkhL99re/VevWrRUZGalf/OIX9Z7G++ijjyo2NlYRERFKTU3ViRMnnI7/sPVjt9s1f/58de7cWUFBQWrfvr3mzp0rSerYsaMkKSkpSTabTYMGDXKcl5WVpR49eig4OFjdu3fXM8884/Q+H330kZKSkhQcHKx+/fpp+/btLn+PFi1apMTERIWFhSkhIUF33HGHysvL641bs2aNunbtquDgYA0dOlT5+flOx19//XX17dtXwcHB6tSpk+bMmaPa2lqX4wHQOEhUgCYgJCRENTU1jtefffaZXnrpJb388suO1su1116rwsJCvfnmm8rLy9Mll1yiwYMH65tvvpEkvfTSS3rooYc0d+5cbd26VW3atKmXQPzQfffdp/nz5+uBBx7Q7t27tWrVKsXGxko6mWxI0vr161VQUKBXXnlFkrR8+XKlp6dr7ty52rNnjzIzM/XAAw8oJydHklRRUaERI0aoW7duysvLU0ZGhmbOnOny96RZs2Z68skntXPnTuXk5Ojdd9/V7NmzncYcP35cc+fOVU5Ojj744AOVlpbqxhtvdBx/++239Zvf/EbTpk3T7t27tWzZMmVnZzuSMQAWYABYyoQJE8zo0aMdr//973+bmJgYM3bsWGOMMQ899JAJCAgwRUVFjjHvvPOOiYyMNCdOnHCa68ILLzTLli0zxhiTkpJibr/9dqfjycnJ5qKLLjrte5eWlpqgoCCzfPny08Z58OBBI8ls377daX9CQoJZtWqV076HH37YpKSkGGOMWbZsmYmOjjYVFRWO40uXLj3tXN/XoUMHs3jx4jMef+mll0xMTIzjdVZWlpFkNm/e7Ni3Z88eI8n8+9//NsYYc8UVV5jMzEyneVauXGnatGnjeC3J5ObmnvF9ATQu1qgAFvTGG28oPDxctbW1qqmp0ejRo/XUU085jnfo0EGtWrVyvM7Ly1N5ebliYmKc5qmsrNT+/fslSXv27NHtt9/udDwlJUXvvffeaWPYs2ePqqqqNHjw4AbHfezYMeXn5ys1NVVTpkxx7K+trXWsf9mzZ48uuugihYaGOsXhqvfee0+ZmZnavXu3SktLVVtbqxMnTqiiokJhYWGSJH9/f/Xr189xTvfu3dW8eXPt2bNHl156qfLy8rRlyxanCkpdXZ1OnDih48ePO8UIwDtIVAALuvLKK7V06VIFBAQoPj6+3mLZU7+IT7Hb7WrTpo02bNhQb65zvUQ3JCTE5XPsdrukk+2f5ORkp2N+fn6SPPNAyMOHD+uaa67R7bffrocffljR0dHatGmTUlNTnVpk0snLi3/o1D673a45c+ZozJgx9cYEBwe7HScA95GoABYUFhamzp07N3j8JZdcosLCQvn7++uCCy447ZgePXpo8+bNuuWWWxz7Nm/efMY5u3TpopCQEL3zzjuaPHlyveOBgYGSTlYgTomNjVXbtm114MABjR8//rTz9uzZUytXrlRlZaUjGfqxOE5n69atqq2t1cKFC9Ws2cmldi+99FK9cbW1tdq6dasuvfRSSdLevXv17bffqnv37pJOft/27t3r0vcawPlFogL8BAwZMkQpKSm67rrrNH/+fHXr1k1ffPGF3nzzTV133XXq16+f7rnnHk2YMEH9+vXTz372M73wwgvatWuXOnXqdNo5g4ODde+992r27NkKDAzU5ZdfrmPHjmnXrl1KTU1V69atFRISorfeekvt2rVTcHCwoqKilJGRoWnTpikyMlLDhw9XVVWVtm7dquLiYk2fPl3jxo1Tenq6UlNTdf/99+vQoUN6/PHHXfp6L7zwQtXW1uqpp57SyJEj9cEHH+jZZ5+tNy4gIEB33323nnzySQUEBOiuu+7SZZdd5khcHnzwQY0YMUIJCQn69a9/rWbNmumTTz7Rp59+qkceecT1/yMAeBxX/QA/ATabTW+++aZ+/vOfa9KkSeratatuvPFGHTp0yHGVzg033KAHH3xQ9957r/r27avDhw9r6tSpPzrvAw88oBkzZujBBx9Ujx49dMMNN6ioqEjSyfUfTz75pJYtW6b4+HiNHj1akjR58mT98Y9/VHZ2thITEzVw4EBlZ2c7LmcODw/X66+/rt27dyspKUnp6emaP3++S1/vxRdfrEWLFmn+/Pnq3bu3XnjhBc2bN6/euNDQUN17770aN26cUlJSFBISotWrVzuODxs2TG+88YbWrVun/v3767LLLtOiRYvUoUMHl+IB0HhsxhMNYwAAgEZARQUAAFgWiQoAALAsEhUAAGBZJCoAAMCySFQAAIBlkagAAADLIlEBAACWRaICAAAsi0QFAABYFokKAACwLBIVAABgWf8foGYOt86ekJMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix1, display_labels = [\"delayed\", \"ontime\"])\n",
    "\n",
    "cm_display.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e15871",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bfe636e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "33d01664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "aa54eaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Training Set is 86.63500678426051\n"
     ]
    }
   ],
   "source": [
    "train_acc = model.score(X_train, y_train)\n",
    "print(\"The Accuracy for Training Set is {}\".format(train_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b9baf3b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "00fef69e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Test Set is 85.14442916093536\n"
     ]
    }
   ],
   "source": [
    "test_acc = accuracy_score(y_valid, y_pred)\n",
    "print(\"The Accuracy for Test Set is {}\".format(test_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bc0c04aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.55      0.58       138\n",
      "           1       0.90      0.92      0.91       589\n",
      "\n",
      "    accuracy                           0.85       727\n",
      "   macro avg       0.76      0.74      0.75       727\n",
      "weighted avg       0.85      0.85      0.85       727\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "aa23554a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 76  62]\n",
      " [ 46 543]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix2 = confusion_matrix(y_valid, y_pred)\n",
    "print(confusion_matrix2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5ec4d7d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix2, display_labels = [\"delayed\", \"ontime\"])\n",
    "\n",
    "cm_display.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b0e7b4",
   "metadata": {},
   "source": [
    "### Creating a Test Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c6ff47a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = X.sample(n = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8d63a10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "naives_pred = delays_nb.predict(test_data)\n",
    "cart_pred = classTree.predict(test_data)\n",
    "log_pred = model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "09e6ea42",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Naives_Prediction'] = naives_pred\n",
    "test_data['CART_Prediction'] = cart_pred\n",
    "test_data['Logistic_Prediction'] = log_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "dc7aaee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.to_csv('FlightDelaysTestingData.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}