ML_Model_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Dependencies\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/mlenv/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3457: DtypeWarning: Columns (11,13,78,85,86,93) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "import_df = pd.read_csv('Resources/Flights_2022_1.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making new binary column that tracks delay for both arr and dep\n",
    "\n",
    "arr_dep_delayed = (import_df['ArrDelayMinutes']>0) | (import_df['DepDelayMinutes']>0)\n",
    "\n",
    "import_df['Delayed'] = np.where(arr_dep_delayed, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates an integer coded target column for the Machine learning model \n",
    "\n",
    "on_time = (import_df['Delayed']==0) & (import_df['Cancelled']==0)\n",
    "delayed = import_df['Delayed']==1\n",
    "cancelled = import_df['Cancelled']==1\n",
    "\n",
    "import_df['Target'] = np.where(on_time, '0', np.where(delayed,'1', np.where(cancelled,'2','')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Year',\n",
       " 'Quarter',\n",
       " 'Month',\n",
       " 'DayofMonth',\n",
       " 'DayOfWeek',\n",
       " 'FlightDate',\n",
       " 'Marketing_Airline_Network',\n",
       " 'Operated_or_Branded_Code_Share_Partners',\n",
       " 'DOT_ID_Marketing_Airline',\n",
       " 'IATA_Code_Marketing_Airline',\n",
       " 'Flight_Number_Marketing_Airline',\n",
       " 'Originally_Scheduled_Code_Share_Airline',\n",
       " 'DOT_ID_Originally_Scheduled_Code_Share_Airline',\n",
       " 'IATA_Code_Originally_Scheduled_Code_Share_Airline',\n",
       " 'Flight_Num_Originally_Scheduled_Code_Share_Airline',\n",
       " 'Operating_Airline ',\n",
       " 'DOT_ID_Operating_Airline',\n",
       " 'IATA_Code_Operating_Airline',\n",
       " 'Tail_Number',\n",
       " 'Flight_Number_Operating_Airline',\n",
       " 'OriginAirportID',\n",
       " 'OriginAirportSeqID',\n",
       " 'OriginCityMarketID',\n",
       " 'Origin',\n",
       " 'OriginCityName',\n",
       " 'OriginState',\n",
       " 'OriginStateFips',\n",
       " 'OriginStateName',\n",
       " 'OriginWac',\n",
       " 'DestAirportID',\n",
       " 'DestAirportSeqID',\n",
       " 'DestCityMarketID',\n",
       " 'Dest',\n",
       " 'DestCityName',\n",
       " 'DestState',\n",
       " 'DestStateFips',\n",
       " 'DestStateName',\n",
       " 'DestWac',\n",
       " 'CRSDepTime',\n",
       " 'DepTime',\n",
       " 'DepDelay',\n",
       " 'DepDelayMinutes',\n",
       " 'DepDel15',\n",
       " 'DepartureDelayGroups',\n",
       " 'DepTimeBlk',\n",
       " 'TaxiOut',\n",
       " 'WheelsOff',\n",
       " 'WheelsOn',\n",
       " 'TaxiIn',\n",
       " 'CRSArrTime',\n",
       " 'ArrTime',\n",
       " 'ArrDelay',\n",
       " 'ArrDelayMinutes',\n",
       " 'ArrDel15',\n",
       " 'ArrivalDelayGroups',\n",
       " 'ArrTimeBlk',\n",
       " 'Cancelled',\n",
       " 'CancellationCode',\n",
       " 'Diverted',\n",
       " 'CRSElapsedTime',\n",
       " 'ActualElapsedTime',\n",
       " 'AirTime',\n",
       " 'Flights',\n",
       " 'Distance',\n",
       " 'DistanceGroup',\n",
       " 'CarrierDelay',\n",
       " 'WeatherDelay',\n",
       " 'NASDelay',\n",
       " 'SecurityDelay',\n",
       " 'LateAircraftDelay',\n",
       " 'FirstDepTime',\n",
       " 'TotalAddGTime',\n",
       " 'LongestAddGTime',\n",
       " 'DivAirportLandings',\n",
       " 'DivReachedDest',\n",
       " 'DivActualElapsedTime',\n",
       " 'DivArrDelay',\n",
       " 'DivDistance',\n",
       " 'Div1Airport',\n",
       " 'Div1AirportID',\n",
       " 'Div1AirportSeqID',\n",
       " 'Div1WheelsOn',\n",
       " 'Div1TotalGTime',\n",
       " 'Div1LongestGTime',\n",
       " 'Div1WheelsOff',\n",
       " 'Div1TailNum',\n",
       " 'Div2Airport',\n",
       " 'Div2AirportID',\n",
       " 'Div2AirportSeqID',\n",
       " 'Div2WheelsOn',\n",
       " 'Div2TotalGTime',\n",
       " 'Div2LongestGTime',\n",
       " 'Div2WheelsOff',\n",
       " 'Div2TailNum',\n",
       " 'Div3Airport',\n",
       " 'Div3AirportID',\n",
       " 'Div3AirportSeqID',\n",
       " 'Div3WheelsOn',\n",
       " 'Div3TotalGTime',\n",
       " 'Div3LongestGTime',\n",
       " 'Div3WheelsOff',\n",
       " 'Div3TailNum',\n",
       " 'Div4Airport',\n",
       " 'Div4AirportID',\n",
       " 'Div4AirportSeqID',\n",
       " 'Div4WheelsOn',\n",
       " 'Div4TotalGTime',\n",
       " 'Div4LongestGTime',\n",
       " 'Div4WheelsOff',\n",
       " 'Div4TailNum',\n",
       " 'Div5Airport',\n",
       " 'Div5AirportID',\n",
       " 'Div5AirportSeqID',\n",
       " 'Div5WheelsOn',\n",
       " 'Div5TotalGTime',\n",
       " 'Div5LongestGTime',\n",
       " 'Div5WheelsOff',\n",
       " 'Div5TailNum',\n",
       " 'Duplicate',\n",
       " 'Unnamed: 119',\n",
       " 'Delayed',\n",
       " 'Target']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in import_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= import_df[['Month', 'DayofMonth', 'DayOfWeek', 'Operating_Airline ', 'Origin',\n",
    "       'Dest', 'Diverted', 'CRSArrTime', 'CRSDepTime', 'ArrTime', 'DepTime',\n",
    "       'Target', 'OriginCityName', 'DestCityName']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=import_df[[\n",
    "    'CarrierDelay',\n",
    "    'WeatherDelay',\n",
    "    'NASDelay',\n",
    "    'SecurityDelay',\n",
    "    'LateAircraftDelay',\n",
    "    'Month', 'DayofMonth', 'DayOfWeek', 'Operating_Airline ', 'Origin',\n",
    "       'Dest', 'Diverted', 'CRSArrTime', 'CRSDepTime', 'ArrTime', 'DepTime',\n",
    "       'Target', 'OriginCityName', 'DestCityName','CancellationCode'\n",
    "    ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/mlenv/lib/python3.7/site-packages/pandas/core/frame.py:3641: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n",
      "/Applications/anaconda3/envs/mlenv/lib/python3.7/site-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# Get City/State into seperate columns\n",
    "\n",
    "df[['Origin_City','Origin_State']] = df['OriginCityName'].str.split(', ',expand=True)\n",
    "df[['Destination_City','Destination_State']] = df['DestCityName'].str.split(', ',expand=True)\n",
    "\n",
    "# Remove old column\n",
    "df.drop(columns=['OriginCityName','DestCityName'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CarrierDelay</th>\n",
       "      <th>WeatherDelay</th>\n",
       "      <th>NASDelay</th>\n",
       "      <th>SecurityDelay</th>\n",
       "      <th>LateAircraftDelay</th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Operating_Airline</th>\n",
       "      <th>Origin</th>\n",
       "      <th>...</th>\n",
       "      <th>CRSArrTime</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>ArrTime</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>Target</th>\n",
       "      <th>CancellationCode</th>\n",
       "      <th>Origin_City</th>\n",
       "      <th>Origin_State</th>\n",
       "      <th>Destination_City</th>\n",
       "      <th>Destination_State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>A</td>\n",
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       "      <td>FL</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>ATL</td>\n",
       "      <td>...</td>\n",
       "      <td>1821</td>\n",
       "      <td>1631</td>\n",
       "      <td>1820.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>FL</td>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>FLL</td>\n",
       "      <td>...</td>\n",
       "      <td>2127</td>\n",
       "      <td>1931</td>\n",
       "      <td>2115.0</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>FL</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>FLL</td>\n",
       "      <td>...</td>\n",
       "      <td>1227</td>\n",
       "      <td>1024</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>FL</td>\n",
       "      <td>Raleigh/Durham</td>\n",
       "      <td>NC</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>ATL</td>\n",
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       "      <td>1142</td>\n",
       "      <td>1117</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>Jackson/Vicksburg</td>\n",
       "      <td>MS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>563732</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>YV</td>\n",
       "      <td>IAH</td>\n",
       "      <td>...</td>\n",
       "      <td>1414</td>\n",
       "      <td>1240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563733</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>YV</td>\n",
       "      <td>OKC</td>\n",
       "      <td>...</td>\n",
       "      <td>1836</td>\n",
       "      <td>1702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>OK</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563734</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>YV</td>\n",
       "      <td>ATL</td>\n",
       "      <td>...</td>\n",
       "      <td>1602</td>\n",
       "      <td>1415</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563735</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>YV</td>\n",
       "      <td>IAD</td>\n",
       "      <td>...</td>\n",
       "      <td>1318</td>\n",
       "      <td>1115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563736</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>YV</td>\n",
       "      <td>IAH</td>\n",
       "      <td>...</td>\n",
       "      <td>930</td>\n",
       "      <td>730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>563737 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CarrierDelay  WeatherDelay  NASDelay  SecurityDelay  \\\n",
       "0                NaN           NaN       NaN            NaN   \n",
       "1                NaN           NaN       NaN            NaN   \n",
       "2                NaN           NaN       NaN            NaN   \n",
       "3                NaN           NaN       NaN            NaN   \n",
       "4                NaN           NaN       NaN            NaN   \n",
       "...              ...           ...       ...            ...   \n",
       "563732           NaN           NaN       NaN            NaN   \n",
       "563733           NaN           NaN       NaN            NaN   \n",
       "563734           NaN           NaN       NaN            NaN   \n",
       "563735           NaN           NaN       NaN            NaN   \n",
       "563736           NaN           NaN       NaN            NaN   \n",
       "\n",
       "        LateAircraftDelay  Month  DayofMonth  DayOfWeek Operating_Airline   \\\n",
       "0                     NaN      1           6          4                 DL   \n",
       "1                     NaN      1           6          4                 DL   \n",
       "2                     NaN      1           6          4                 DL   \n",
       "3                     NaN      1           6          4                 DL   \n",
       "4                     NaN      1           6          4                 DL   \n",
       "...                   ...    ...         ...        ...                ...   \n",
       "563732                NaN      1          24          1                 YV   \n",
       "563733                NaN      1          24          1                 YV   \n",
       "563734                NaN      1          24          1                 YV   \n",
       "563735                NaN      1          24          1                 YV   \n",
       "563736                NaN      1          24          1                 YV   \n",
       "\n",
       "       Origin  ... CRSArrTime  CRSDepTime  ArrTime  DepTime  Target  \\\n",
       "0         FLL  ...       1419        1126      NaN      NaN       2   \n",
       "1         ATL  ...       1821        1631   1820.0   1627.0       0   \n",
       "2         FLL  ...       2127        1931   2115.0   1929.0       0   \n",
       "3         FLL  ...       1227        1024   1212.0   1019.0       0   \n",
       "4         ATL  ...       1142        1117   1131.0   1113.0       0   \n",
       "...       ...  ...        ...         ...      ...      ...     ...   \n",
       "563732    IAH  ...       1414        1240      NaN      NaN       2   \n",
       "563733    OKC  ...       1836        1702      NaN      NaN       2   \n",
       "563734    ATL  ...       1602        1415      NaN      NaN       2   \n",
       "563735    IAD  ...       1318        1115      NaN      NaN       2   \n",
       "563736    IAH  ...        930         730      NaN      NaN       2   \n",
       "\n",
       "        CancellationCode      Origin_City Origin_State   Destination_City  \\\n",
       "0                      A  Fort Lauderdale           FL           New York   \n",
       "1                    NaN          Atlanta           GA    Fort Lauderdale   \n",
       "2                    NaN  Fort Lauderdale           FL            Atlanta   \n",
       "3                    NaN  Fort Lauderdale           FL     Raleigh/Durham   \n",
       "4                    NaN          Atlanta           GA  Jackson/Vicksburg   \n",
       "...                  ...              ...          ...                ...   \n",
       "563732                 D          Houston           TX      Oklahoma City   \n",
       "563733                 D    Oklahoma City           OK            Houston   \n",
       "563734                 D          Atlanta           GA         Washington   \n",
       "563735                 D       Washington           DC            Atlanta   \n",
       "563736                 D          Houston           TX          Nashville   \n",
       "\n",
       "       Destination_State  \n",
       "0                     NY  \n",
       "1                     FL  \n",
       "2                     GA  \n",
       "3                     NC  \n",
       "4                     MS  \n",
       "...                  ...  \n",
       "563732                OK  \n",
       "563733                TX  \n",
       "563734                DC  \n",
       "563735                GA  \n",
       "563736                TN  \n",
       "\n",
       "[563737 rows x 22 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/mlenv/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Applications/anaconda3/envs/mlenv/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# Fill NA values in Arr and Dep Time columns\n",
    "\n",
    "df['ArrTime'] = df['ArrTime'].fillna(0)\n",
    "df['DepTime'] = df['DepTime'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/mlenv/lib/python3.7/site-packages/pandas/core/frame.py:3641: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "df[['CarrierDelay',\n",
    "    'WeatherDelay',\n",
    "    'NASDelay',\n",
    "    'SecurityDelay',\n",
    "    'LateAircraftDelay']] = df[['CarrierDelay',\n",
    "                                'WeatherDelay',\n",
    "                                'NASDelay',\n",
    "                                'SecurityDelay',\n",
    "                                'LateAircraftDelay']].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of columns that need encoding\n",
    "\n",
    "encode_list = [\n",
    "    'Operating_Airline ',\n",
    "    'Origin',\n",
    "    'Dest',\n",
    "    'Origin_City',\n",
    "    'Origin_State',\n",
    "    'Destination_City',\n",
    "    'Destination_State'\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new df to perform transformations\n",
    "encoder_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create function to output encoding dictionaries \n",
    "\n",
    "def encoder (MasterDF, ColName, Dictionary):\n",
    "    uni_row_vals =[MasterDF[ColName].unique()][0]\n",
    "    for x in range(len(uni_row_vals)):\n",
    "        Dictionary[uni_row_vals[x]] = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Integer encode Origin and Destination airports\n",
    "\n",
    "loc_dict = {}\n",
    "encoder(encoder_df,'Origin',loc_dict)\n",
    "encoder_df['Origin']=encoder_df['Origin'].apply(lambda x: loc_dict[x])\n",
    "encoder_df['Dest']=encoder_df['Dest'].apply(lambda x: loc_dict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Integer encode Origin and Destination Cities\n",
    "\n",
    "city_dict = {}\n",
    "encoder(encoder_df,'Origin_City',city_dict)\n",
    "encoder_df['Origin_City']=encoder_df['Origin_City'].apply(lambda x: city_dict[x])\n",
    "encoder_df['Destination_City']=encoder_df['Destination_City'].apply(lambda x: city_dict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Integer encode Origin and Destination States\n",
    "\n",
    "state_dict = {}\n",
    "encoder(encoder_df,'Origin_State',state_dict)\n",
    "encoder_df['Origin_State']=encoder_df['Origin_State'].apply(lambda x: state_dict[x])\n",
    "encoder_df['Destination_State']=encoder_df['Destination_State'].apply(lambda x: state_dict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Integer encode operating airlines\n",
    "airline_dict = {}\n",
    "encoder(encoder_df,'Operating_Airline ',airline_dict)\n",
    "encoder_df['Operating_Airline ']=encoder_df['Operating_Airline '].apply(lambda x: airline_dict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that runs a Random Forest Model and outputs stats on model\n",
    "\n",
    "def rfmodel_complete (MasterDF,ColumnList,estimators=200,randomstate=52):\n",
    "\n",
    "    X = MasterDF[ColumnList]\n",
    "    y = MasterDF['Target']\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=randomstate)\n",
    "\n",
    "    # scaler = StandardScaler()\n",
    "\n",
    "    # X_Scaler = scaler.fit(X_train)\n",
    "\n",
    "\n",
    "    # X_train_scaled = X_Scaler.transform(X_train)\n",
    "    # X_test_scaled = X_Scaler.transform(X_test)\n",
    "\n",
    "    rf_model = RandomForestClassifier(n_estimators=estimators,random_state=randomstate)\n",
    "\n",
    "    rf_model = rf_model.fit(X_train,y_train)\n",
    "\n",
    "    rf_predictions = rf_model.predict(X_test)\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, rf_predictions)\n",
    "\n",
    "    # Create a DataFrame from the confusion matrix.\n",
    "    cm_df = pd.DataFrame(\n",
    "        cm, index=[\"On Time\", \"Delayed\",\"Cancelled\"], columns=[\"On Time (Pred)\", \"Delayed (Pred)\", \"Cancelled (Pred)\"])\n",
    "\n",
    "    acc_score = accuracy_score(y_test, rf_predictions)\n",
    "\n",
    "    # Create a classification report\n",
    "    class_report = classification_report(y_test, rf_predictions)\n",
    "\n",
    "    # Calculate feature importance in the Random Forest model.\n",
    "    importances = rf_model.feature_importances_\n",
    "\n",
    "    importances_dict = sorted(zip(importances, X.columns), reverse=True)\n",
    "\n",
    "    return (# Displaying results\n",
    "        print(\"\\nConfusion Matrix\\n\"),\n",
    "        \n",
    "        print(cm_df),\n",
    "        \n",
    "        print(f\"\\nAccuracy Score : {acc_score}\\n\"),\n",
    "        \n",
    "        print(\"\\nClassification Report\\n\"),\n",
    "\n",
    "        print(class_report),\n",
    "        \n",
    "        \"Feature Importances\",\n",
    "        \n",
    "        importances_dict\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix\n",
      "\n",
      "           On Time (Pred)  Delayed (Pred)  Cancelled (Pred)\n",
      "On Time             69141            5304                 0\n",
      "Delayed             13487           43988                 6\n",
      "Cancelled               4              21              8984\n",
      "\n",
      "Accuracy Score : 0.8664490722673573\n",
      "\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.93      0.88     74445\n",
      "           1       0.89      0.77      0.82     57481\n",
      "           2       1.00      1.00      1.00      9009\n",
      "\n",
      "    accuracy                           0.87    140935\n",
      "   macro avg       0.91      0.90      0.90    140935\n",
      "weighted avg       0.87      0.87      0.86    140935\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " 'Feature Importances',\n",
       " [(0.2234912678065637, 'ArrTime'),\n",
       "  (0.22147199286780633, 'DepTime'),\n",
       "  (0.11390584792576786, 'CRSArrTime'),\n",
       "  (0.09877279345024242, 'CRSDepTime'),\n",
       "  (0.08202005392984286, 'DayofMonth'),\n",
       "  (0.03603828944698542, 'Dest'),\n",
       "  (0.03525867819244737, 'Destination_City'),\n",
       "  (0.03359556543030884, 'Origin'),\n",
       "  (0.03323345107201007, 'Origin_City'),\n",
       "  (0.030587495475490698, 'Destination_State'),\n",
       "  (0.030383797803586274, 'DayOfWeek'),\n",
       "  (0.030318228223006297, 'Operating_Airline '),\n",
       "  (0.03005004723085815, 'Origin_State'),\n",
       "  (0.0008724911450837472, 'Diverted'),\n",
       "  (0.0, 'Month')])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First run with all features\n",
    "\n",
    "\n",
    "feature_list_1 = [\n",
    "    'Month',\n",
    "    'DayofMonth',\n",
    "    'DayOfWeek',\n",
    "    'Operating_Airline ',\n",
    "    'Origin',\n",
    "    'Dest',\n",
    "    'Diverted',\n",
    "    'CRSArrTime',\n",
    "    'CRSDepTime',\n",
    "    'ArrTime',\n",
    "    'DepTime',\n",
    "    'Origin_City',\n",
    "    'Origin_State',\n",
    "    'Destination_City',\n",
    "    'Destination_State'\n",
    "    ]\n",
    "\n",
    "rfmodel_complete(encoder_df,feature_list_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix\n",
      "\n",
      "           On Time (Pred)  Delayed (Pred)  Cancelled (Pred)\n",
      "On Time             52292           20983              1170\n",
      "Delayed             25700           29515              2266\n",
      "Cancelled            2304            4104              2601\n",
      "\n",
      "Accuracy Score : 0.5989143931599673\n",
      "\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.70      0.68     74445\n",
      "           1       0.54      0.51      0.53     57481\n",
      "           2       0.43      0.29      0.35      9009\n",
      "\n",
      "    accuracy                           0.60    140935\n",
      "   macro avg       0.54      0.50      0.52    140935\n",
      "weighted avg       0.59      0.60      0.59    140935\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " 'Feature Importances',\n",
       " [(0.19804401152793322, 'DayofMonth'),\n",
       "  (0.12388178712527272, 'Dest'),\n",
       "  (0.11965773316191546, 'Destination_City'),\n",
       "  (0.11695521991350682, 'Operating_Airline '),\n",
       "  (0.1021699572421629, 'Origin'),\n",
       "  (0.09902705708544723, 'Origin_City'),\n",
       "  (0.09392361442911198, 'Destination_State'),\n",
       "  (0.0790339698224714, 'Origin_State'),\n",
       "  (0.06539737513110658, 'DayOfWeek'),\n",
       "  (0.0019092745610717575, 'Diverted'),\n",
       "  (0.0, 'Month')])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Second run with removal of CRSArrTime, CRSDepTime, ArrTime, and DepTime as those weighted the model heavily\n",
    "\n",
    "feature_list_2 = [\n",
    "    'Month',\n",
    "    'DayofMonth',\n",
    "    'DayOfWeek',\n",
    "    'Operating_Airline ',\n",
    "    'Origin',\n",
    "    'Dest',\n",
    "    'Diverted',\n",
    "    'Origin_City',\n",
    "    'Origin_State',\n",
    "    'Destination_City',\n",
    "    'Destination_State'\n",
    "    ]\n",
    "\n",
    "rfmodel_complete(encoder_df,feature_list_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix\n",
      "\n",
      "           On Time (Pred)  Delayed (Pred)  Cancelled (Pred)\n",
      "On Time             57727           16343               375\n",
      "Delayed             25647           31016               818\n",
      "Cancelled            2499            3939              2571\n",
      "\n",
      "Accuracy Score : 0.6479157058218328\n",
      "\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.78      0.72     74445\n",
      "           1       0.60      0.54      0.57     57481\n",
      "           2       0.68      0.29      0.40      9009\n",
      "\n",
      "    accuracy                           0.65    140935\n",
      "   macro avg       0.65      0.53      0.56    140935\n",
      "weighted avg       0.65      0.65      0.64    140935\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " 'Feature Importances',\n",
       " [(0.17491138207964516, 'CRSArrTime'),\n",
       "  (0.16700836105264705, 'CRSDepTime'),\n",
       "  (0.15251587818769682, 'DayofMonth'),\n",
       "  (0.11115981450789747, 'Destination_City'),\n",
       "  (0.10562617054130811, 'Origin_City'),\n",
       "  (0.08350912233193347, 'Destination_State'),\n",
       "  (0.08060567352101397, 'Origin_State'),\n",
       "  (0.06928909940402478, 'Operating_Airline '),\n",
       "  (0.054522184557902, 'DayOfWeek'),\n",
       "  (0.0008523138159312633, 'Diverted')])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reintroduce CRS ARR and Dep time (planned arrival and departure time)\n",
    "\n",
    "feature_list_3 =[\n",
    "       'DayofMonth',\n",
    "       'DayOfWeek',\n",
    "       'Operating_Airline ',\n",
    "       'Diverted',\n",
    "       'CRSArrTime',\n",
    "       'CRSDepTime',\n",
    "       'Origin_City',\n",
    "       'Origin_State',\n",
    "       'Destination_City',\n",
    "       'Destination_State'\n",
    "       ]\n",
    "\n",
    "rfmodel_complete(encoder_df,feature_list_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix\n",
      "\n",
      "           On Time (Pred)  Delayed (Pred)  Cancelled (Pred)\n",
      "On Time             62352           12093                 0\n",
      "Delayed             23420           34052                 9\n",
      "Cancelled               0              28              8981\n",
      "\n",
      "Accuracy Score : 0.7477560577571221\n",
      "\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.84      0.78     74445\n",
      "           1       0.74      0.59      0.66     57481\n",
      "           2       1.00      1.00      1.00      9009\n",
      "\n",
      "    accuracy                           0.75    140935\n",
      "   macro avg       0.82      0.81      0.81    140935\n",
      "weighted avg       0.75      0.75      0.74    140935\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " 'Feature Importances',\n",
       " [(0.2659434620405917, 'DepTime'),\n",
       "  (0.24711678374795726, 'ArrTime'),\n",
       "  (0.09019876314567792, 'DayofMonth'),\n",
       "  (0.08980220867479816, 'Destination_City'),\n",
       "  (0.08331673839756659, 'Origin_City'),\n",
       "  (0.067367975420559, 'Destination_State'),\n",
       "  (0.06399005440807322, 'Origin_State'),\n",
       "  (0.058995341798787224, 'Operating_Airline '),\n",
       "  (0.03244755361679545, 'DayOfWeek'),\n",
       "  (0.0008211187491933908, 'Diverted')])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model with Actual Arrival and Departure Times\n",
    "\n",
    "feature_list_4 = [\n",
    "        'DayofMonth',\n",
    "        'DayOfWeek',\n",
    "        'Operating_Airline ',\n",
    "        'Diverted',\n",
    "        'ArrTime',\n",
    "        'DepTime',\n",
    "        'Origin_City',\n",
    "        'Origin_State',\n",
    "        'Destination_City',\n",
    "        'Destination_State'\n",
    "        ]\n",
    "\n",
    "rfmodel_complete(encoder_df,feature_list_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month                   int64\n",
       "DayofMonth              int64\n",
       "DayOfWeek               int64\n",
       "Operating_Airline      object\n",
       "Origin                 object\n",
       "Dest                   object\n",
       "Diverted              float64\n",
       "CRSArrTime              int64\n",
       "CRSDepTime              int64\n",
       "ArrTime               float64\n",
       "DepTime               float64\n",
       "Origin_City            object\n",
       "Origin_State           object\n",
       "Destination_City       object\n",
       "Destination_State      object\n",
       "CarrierDelay          float64\n",
       "WeatherDelay          float64\n",
       "NASDelay              float64\n",
       "SecurityDelay         float64\n",
       "LateAircraftDelay     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl5 = [\n",
    "    'Month',\n",
    "    'DayofMonth',\n",
    "    'DayOfWeek',\n",
    "    'Operating_Airline ',\n",
    "    'Origin',\n",
    "    'Dest',\n",
    "    'Diverted',\n",
    "    'CRSArrTime',\n",
    "    'CRSDepTime',\n",
    "    'ArrTime',\n",
    "    'DepTime',\n",
    "    'Origin_City',\n",
    "    'Origin_State',\n",
    "    'Destination_City',\n",
    "    'Destination_State',\n",
    "    'CarrierDelay',\n",
    "    'WeatherDelay',\n",
    "    'NASDelay',\n",
    "    'SecurityDelay',\n",
    "    'LateAircraftDelay'\n",
    "]\n",
    "\n",
    "\n",
    "df[fl5].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month                   int64\n",
       "DayofMonth              int64\n",
       "DayOfWeek               int64\n",
       "Operating_Airline      object\n",
       "Origin                 object\n",
       "Dest                   object\n",
       "Diverted              float64\n",
       "CRSArrTime              int64\n",
       "CRSDepTime              int64\n",
       "ArrTime               float64\n",
       "DepTime               float64\n",
       "Origin_City            object\n",
       "Origin_State           object\n",
       "Destination_City       object\n",
       "Destination_State      object\n",
       "CarrierDelay          float64\n",
       "WeatherDelay          float64\n",
       "NASDelay              float64\n",
       "SecurityDelay         float64\n",
       "LateAircraftDelay     float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fl5 = [\n",
    "    'Month',\n",
    "    'DayofMonth',\n",
    "    'DayOfWeek',\n",
    "    'Operating_Airline ',\n",
    "    'Origin',\n",
    "    'Dest',\n",
    "    'Diverted',\n",
    "    'CRSArrTime',\n",
    "    'CRSDepTime',\n",
    "    'ArrTime',\n",
    "    'DepTime',\n",
    "    'Origin_City',\n",
    "    'Origin_State',\n",
    "    'Destination_City',\n",
    "    'Destination_State',\n",
    "    'CarrierDelay',\n",
    "    'WeatherDelay',\n",
    "    'NASDelay',\n",
    "    'SecurityDelay',\n",
    "    'LateAircraftDelay'\n",
    "]\n",
    "\n",
    "\n",
    "df[fl5].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix\n",
      "\n",
      "           On Time (Pred)  Delayed (Pred)  Cancelled (Pred)\n",
      "On Time             70760            3685                 0\n",
      "Delayed             17630           39842                 9\n",
      "Cancelled               1              23              8985\n",
      "\n",
      "Accuracy Score : 0.8485259162025047\n",
      "\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.95      0.87     74445\n",
      "           1       0.91      0.69      0.79     57481\n",
      "           2       1.00      1.00      1.00      9009\n",
      "\n",
      "    accuracy                           0.85    140935\n",
      "   macro avg       0.90      0.88      0.89    140935\n",
      "weighted avg       0.86      0.85      0.84    140935\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " 'Feature Importances',\n",
       " [(0.17694192842261805, 'ArrTime'),\n",
       "  (0.1741295486781566, 'DepTime'),\n",
       "  (0.11037105991977182, 'CarrierDelay'),\n",
       "  (0.07033981629030445, 'CRSArrTime'),\n",
       "  (0.06572726642682851, 'CRSDepTime'),\n",
       "  (0.06551164428501575, 'NASDelay'),\n",
       "  (0.05764509896215839, 'LateAircraftDelay'),\n",
       "  (0.056551714960842776, 'DayofMonth'),\n",
       "  (0.03105602602667109, 'Dest'),\n",
       "  (0.0303209246923872, 'Destination_City'),\n",
       "  (0.027785606926793948, 'Origin'),\n",
       "  (0.027206996331785586, 'Origin_City'),\n",
       "  (0.0258108335656627, 'Destination_State'),\n",
       "  (0.02425325845150263, 'Operating_Airline '),\n",
       "  (0.024061749956408595, 'Origin_State'),\n",
       "  (0.023389023233077857, 'DayOfWeek'),\n",
       "  (0.007997789488050558, 'WeatherDelay'),\n",
       "  (0.0007236009076534593, 'Diverted'),\n",
       "  (0.00017611247431002723, 'SecurityDelay'),\n",
       "  (0.0, 'Month')])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl5 = [\n",
    "    'Month',\n",
    "    'DayofMonth',\n",
    "    'DayOfWeek',\n",
    "    'Operating_Airline ',\n",
    "    'Origin',\n",
    "    'Dest',\n",
    "    'Diverted',\n",
    "    'CRSArrTime',\n",
    "    'CRSDepTime',\n",
    "    'ArrTime',\n",
    "    'DepTime',\n",
    "    'Origin_City',\n",
    "    'Origin_State',\n",
    "    'Destination_City',\n",
    "    'Destination_State',\n",
    "    'CarrierDelay',\n",
    "    'WeatherDelay',\n",
    "    'NASDelay',\n",
    "    'SecurityDelay',\n",
    "    'LateAircraftDelay'\n",
    "]\n",
    "\n",
    "rfmodel_complete(encoder_df,fl5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix\n",
      "\n",
      "           On Time (Pred)  Delayed (Pred)  Cancelled (Pred)\n",
      "On Time             58546           13804              2095\n",
      "Delayed             20409           35215              1857\n",
      "Cancelled            3287            2096              3626\n",
      "\n",
      "Accuracy Score : 0.6910064923546315\n",
      "\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.79      0.75     74445\n",
      "           1       0.69      0.61      0.65     57481\n",
      "           2       0.48      0.40      0.44      9009\n",
      "\n",
      "    accuracy                           0.69    140935\n",
      "   macro avg       0.63      0.60      0.61    140935\n",
      "weighted avg       0.69      0.69      0.69    140935\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " 'Feature Importances',\n",
       " [(0.28006377653279035, 'Dest'),\n",
       "  (0.2637839061917631, 'Origin'),\n",
       "  (0.14073936771103956, 'CarrierDelay'),\n",
       "  (0.08368189815610687, 'NASDelay'),\n",
       "  (0.07495047933884628, 'LateAircraftDelay'),\n",
       "  (0.06582473788378508, 'DayofMonth'),\n",
       "  (0.060493716354469616, 'Operating_Airline '),\n",
       "  (0.018828014889863822, 'DayOfWeek'),\n",
       "  (0.011124722395746798, 'WeatherDelay'),\n",
       "  (0.0005093805455884666, 'SecurityDelay')])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl6 = [\n",
    "    'DayofMonth',\n",
    "    'DayOfWeek',\n",
    "    'Operating_Airline ',\n",
    "    'Origin',\n",
    "    'Dest',\n",
    "    'CarrierDelay',\n",
    "    'WeatherDelay',\n",
    "    'NASDelay',\n",
    "    'SecurityDelay',\n",
    "    'LateAircraftDelay'\n",
    "]\n",
    "\n",
    "rfmodel_complete(encoder_df,fl6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix\n",
      "\n",
      "           On Time (Pred)  Delayed (Pred)  Cancelled (Pred)\n",
      "On Time             67021            6673               751\n",
      "Delayed             23524           33100               857\n",
      "Cancelled            4150            1644              3215\n",
      "\n",
      "Accuracy Score : 0.733217440664136\n",
      "\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.90      0.79     74445\n",
      "           1       0.80      0.58      0.67     57481\n",
      "           2       0.67      0.36      0.46      9009\n",
      "\n",
      "    accuracy                           0.73    140935\n",
      "   macro avg       0.72      0.61      0.64    140935\n",
      "weighted avg       0.74      0.73      0.72    140935\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " 'Feature Importances',\n",
       " [(0.16307116911330602, 'CRSArrTime'),\n",
       "  (0.15498432232244133, 'CRSDepTime'),\n",
       "  (0.13075293523700315, 'Origin'),\n",
       "  (0.13015502929281805, 'Dest'),\n",
       "  (0.11383790949973349, 'CarrierDelay'),\n",
       "  (0.0851816218136928, 'DayofMonth'),\n",
       "  (0.06721102876875835, 'NASDelay'),\n",
       "  (0.060440174008858406, 'LateAircraftDelay'),\n",
       "  (0.05762559068337515, 'Operating_Airline '),\n",
       "  (0.027690107276919833, 'DayOfWeek'),\n",
       "  (0.008711996303257622, 'WeatherDelay'),\n",
       "  (0.00033811567983584826, 'SecurityDelay')])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl7 = [\n",
    "    'CRSArrTime',\n",
    "    'CRSDepTime',\n",
    "    'DayofMonth',\n",
    "    'DayOfWeek',\n",
    "    'Operating_Airline ',\n",
    "    'Origin',\n",
    "    'Dest',\n",
    "    'CarrierDelay',\n",
    "    'WeatherDelay',\n",
    "    'NASDelay',\n",
    "    'SecurityDelay',\n",
    "    'LateAircraftDelay'\n",
    "]\n",
    "\n",
    "rfmodel_complete(encoder_df,fl7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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