jGtKEJar

1{
2 "cells": [
3  {
4   "cell_type": "markdown",
5   "metadata": {},
6   "source": [
7    "<h2><b>Travel Datathon 2019 Assessment</b></h2>\n",
8    "\n",
9    "Participants:<br>\n",
10    "    Hasan Alp Boz, Sabancı University<br>\n",
11    "    Mert Gürkan, Sabancı Üniversity<br>"
12   ]
13  },
14  {
15   "cell_type": "markdown",
16   "metadata": {},
17   "source": [
18    "<h4>Importing Packages and Initialization</h4>"
19   ]
20  },
21  {
22   "cell_type": "code",
23   "execution_count": 74,
24   "metadata": {},
25   "outputs": [],
26   "source": [
27    "# If you need to install, XGBoost to your environment\n",
28    "# !pip install xgboost"
29   ]
30  },
31  {
32   "cell_type": "code",
33   "execution_count": 33,
34   "metadata": {},
35   "outputs": [],
36   "source": [
37    "import pandas as pd\n",
38    "import matplotlib.pyplot as plt\n",
39    "import seaborn as sns\n",
40    "import numpy as np\n",
41    "from scipy import stats\n",
42    "from sklearn.model_selection import train_test_split\n",
43    "from sklearn.metrics import mean_squared_error\n",
44    "from sklearn.metrics import r2_score\n",
45    "\n",
46    "import os\n",
47    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
48    "\n",
49    "import xgboost as xgb\n",
50    "from sklearn.ensemble import RandomForestRegressor\n",
51    "\n",
52    "import warnings\n",
53    "warnings.filterwarnings('ignore')"
54   ]
55  },
56  {
57   "cell_type": "code",
58   "execution_count": 2,
59   "metadata": {},
60   "outputs": [],
61   "source": [
62    "# Please change the path of the .csv file to the location of it in your system\n",
63    "df = pd.read_csv(r\"C:\\Users\\Mert\\Desktop\\assessment\\assessment\\Assessment Data\\Assessment Train Data.csv\")"
64   ]
65  },
66  {
67   "cell_type": "markdown",
68   "metadata": {},
69   "source": [
70    "<h2>Data Exploration</h2>"
71   ]
72  },
73  {
74   "cell_type": "code",
75   "execution_count": 3,
76   "metadata": {},
77   "outputs": [
78    {
79     "data": {
80      "text/html": [
81       "<div>\n",
82       "<style scoped>\n",
83       "    .dataframe tbody tr th:only-of-type {\n",
84       "        vertical-align: middle;\n",
85       "    }\n",
86       "\n",
87       "    .dataframe tbody tr th {\n",
88       "        vertical-align: top;\n",
89       "    }\n",
90       "\n",
91       "    .dataframe thead th {\n",
92       "        text-align: right;\n",
93       "    }\n",
94       "</style>\n",
95       "<table border=\"1\" class=\"dataframe\">\n",
96       "  <thead>\n",
97       "    <tr style=\"text-align: right;\">\n",
98       "      <th></th>\n",
99       "      <th>Departure_YMD_LMT</th>\n",
100       "      <th>Operation_YMD_LMT</th>\n",
101       "      <th>Departure_Airport</th>\n",
102       "      <th>Operation_Airport</th>\n",
103       "      <th>Terminal_Number</th>\n",
104       "      <th>Terminal_Name</th>\n",
105       "      <th>Operation_Initials</th>\n",
106       "      <th>Operation_Sonic_Code</th>\n",
107       "      <th>Operation_Channel</th>\n",
108       "      <th>Passenger_Title</th>\n",
109       "      <th>...</th>\n",
110       "      <th>SWC_FLY</th>\n",
111       "      <th>Cabin_Class</th>\n",
112       "      <th>SWC_FQTV_Member</th>\n",
113       "      <th>Passenger_Baggage_Count</th>\n",
114       "      <th>Passenger_Baggage_Weight</th>\n",
115       "      <th>SWC_Staff</th>\n",
116       "      <th>SWC_CIP_Passenger</th>\n",
117       "      <th>SWC_VIP_Passenger</th>\n",
118       "      <th>SWC_Has_Infant</th>\n",
119       "      <th>Operation_Count</th>\n",
120       "    </tr>\n",
121       "  </thead>\n",
122       "  <tbody>\n",
123       "    <tr>\n",
124       "      <th>0</th>\n",
125       "      <td>20190326</td>\n",
126       "      <td>20190326</td>\n",
127       "      <td>KDT</td>\n",
128       "      <td>KDT</td>\n",
129       "      <td>?</td>\n",
130       "      <td>032363</td>\n",
131       "      <td>KS</td>\n",
132       "      <td>?</td>\n",
133       "      <td>QC</td>\n",
134       "      <td>MISTER</td>\n",
135       "      <td>...</td>\n",
136       "      <td>1</td>\n",
137       "      <td>Y</td>\n",
138       "      <td>1</td>\n",
139       "      <td>0</td>\n",
140       "      <td>0</td>\n",
141       "      <td>0</td>\n",
142       "      <td>1</td>\n",
143       "      <td>0</td>\n",
144       "      <td>0</td>\n",
145       "      <td>1</td>\n",
146       "    </tr>\n",
147       "    <tr>\n",
148       "      <th>1</th>\n",
149       "      <td>20180321</td>\n",
150       "      <td>20180321</td>\n",
151       "      <td>KDT</td>\n",
152       "      <td>KDT</td>\n",
153       "      <td>?</td>\n",
154       "      <td>03F960</td>\n",
155       "      <td>KS</td>\n",
156       "      <td>?</td>\n",
157       "      <td>QC</td>\n",
158       "      <td>MISTER</td>\n",
159       "      <td>...</td>\n",
160       "      <td>1</td>\n",
161       "      <td>Y</td>\n",
162       "      <td>1</td>\n",
163       "      <td>0</td>\n",
164       "      <td>0</td>\n",
165       "      <td>0</td>\n",
166       "      <td>1</td>\n",
167       "      <td>0</td>\n",
168       "      <td>0</td>\n",
169       "      <td>1</td>\n",
170       "    </tr>\n",
171       "    <tr>\n",
172       "      <th>2</th>\n",
173       "      <td>20190322</td>\n",
174       "      <td>20190322</td>\n",
175       "      <td>KDT</td>\n",
176       "      <td>KDT</td>\n",
177       "      <td>?</td>\n",
178       "      <td>03AA2P</td>\n",
179       "      <td>KS</td>\n",
180       "      <td>Y011161</td>\n",
181       "      <td>QC</td>\n",
182       "      <td>MISTER</td>\n",
183       "      <td>...</td>\n",
184       "      <td>1</td>\n",
185       "      <td>Y</td>\n",
186       "      <td>0</td>\n",
187       "      <td>0</td>\n",
188       "      <td>0</td>\n",
189       "      <td>0</td>\n",
190       "      <td>0</td>\n",
191       "      <td>0</td>\n",
192       "      <td>0</td>\n",
193       "      <td>1</td>\n",
194       "    </tr>\n",
195       "    <tr>\n",
196       "      <th>3</th>\n",
197       "      <td>20180515</td>\n",
198       "      <td>20180515</td>\n",
199       "      <td>KDT</td>\n",
200       "      <td>KDT</td>\n",
201       "      <td>?</td>\n",
202       "      <td>0353D0</td>\n",
203       "      <td>MK</td>\n",
204       "      <td>?</td>\n",
205       "      <td>TS</td>\n",
206       "      <td>MISTER</td>\n",
207       "      <td>...</td>\n",
208       "      <td>1</td>\n",
209       "      <td>Y</td>\n",
210       "      <td>1</td>\n",
211       "      <td>0</td>\n",
212       "      <td>0</td>\n",
213       "      <td>0</td>\n",
214       "      <td>1</td>\n",
215       "      <td>0</td>\n",
216       "      <td>0</td>\n",
217       "      <td>2</td>\n",
218       "    </tr>\n",
219       "    <tr>\n",
220       "      <th>4</th>\n",
221       "      <td>20190420</td>\n",
222       "      <td>20190420</td>\n",
223       "      <td>KDT</td>\n",
224       "      <td>KDT</td>\n",
225       "      <td>?</td>\n",
226       "      <td>03F902</td>\n",
227       "      <td>KS</td>\n",
228       "      <td>Y013437</td>\n",
229       "      <td>QC</td>\n",
230       "      <td>MISTER</td>\n",
231       "      <td>...</td>\n",
232       "      <td>1</td>\n",
233       "      <td>Y</td>\n",
234       "      <td>0</td>\n",
235       "      <td>0</td>\n",
236       "      <td>0</td>\n",
237       "      <td>1</td>\n",
238       "      <td>0</td>\n",
239       "      <td>0</td>\n",
240       "      <td>0</td>\n",
241       "      <td>1</td>\n",
242       "    </tr>\n",
243       "  </tbody>\n",
244       "</table>\n",
245       "<p>5 rows × 23 columns</p>\n",
246       "</div>"
247      ],
248      "text/plain": [
249       "   Departure_YMD_LMT  Operation_YMD_LMT Departure_Airport Operation_Airport  \\\n",
250       "0           20190326           20190326               KDT               KDT   \n",
251       "1           20180321           20180321               KDT               KDT   \n",
252       "2           20190322           20190322               KDT               KDT   \n",
253       "3           20180515           20180515               KDT               KDT   \n",
254       "4           20190420           20190420               KDT               KDT   \n",
255       "\n",
256       "  Terminal_Number Terminal_Name Operation_Initials Operation_Sonic_Code  \\\n",
257       "0               ?        032363                 KS                    ?   \n",
258       "1               ?        03F960                 KS                    ?   \n",
259       "2               ?        03AA2P                 KS              Y011161   \n",
260       "3               ?        0353D0                 MK                    ?   \n",
261       "4               ?        03F902                 KS              Y013437   \n",
262       "\n",
263       "  Operation_Channel Passenger_Title       ...        SWC_FLY Cabin_Class  \\\n",
264       "0                QC          MISTER       ...              1           Y   \n",
265       "1                QC          MISTER       ...              1           Y   \n",
266       "2                QC          MISTER       ...              1           Y   \n",
267       "3                TS          MISTER       ...              1           Y   \n",
268       "4                QC          MISTER       ...              1           Y   \n",
269       "\n",
270       "  SWC_FQTV_Member  Passenger_Baggage_Count Passenger_Baggage_Weight  \\\n",
271       "0               1                        0                        0   \n",
272       "1               1                        0                        0   \n",
273       "2               0                        0                        0   \n",
274       "3               1                        0                        0   \n",
275       "4               0                        0                        0   \n",
276       "\n",
277       "   SWC_Staff  SWC_CIP_Passenger  SWC_VIP_Passenger  SWC_Has_Infant  \\\n",
278       "0          0                  1                  0               0   \n",
279       "1          0                  1                  0               0   \n",
280       "2          0                  0                  0               0   \n",
281       "3          0                  1                  0               0   \n",
282       "4          1                  0                  0               0   \n",
283       "\n",
284       "   Operation_Count  \n",
285       "0                1  \n",
286       "1                1  \n",
287       "2                1  \n",
288       "3                2  \n",
289       "4                1  \n",
290       "\n",
291       "[5 rows x 23 columns]"
292      ]
293     },
294     "execution_count": 3,
295     "metadata": {},
296     "output_type": "execute_result"
297    }
298   ],
299   "source": [
300    "df.head()"
301   ]
302  },
303  {
304   "cell_type": "code",
305   "execution_count": 4,
306   "metadata": {},
307   "outputs": [
308    {
309     "data": {
310      "text/plain": [
311       "(808696, 23)"
312      ]
313     },
314     "execution_count": 4,
315     "metadata": {},
316     "output_type": "execute_result"
317    }
318   ],
319   "source": [
320    "df.shape"
321   ]
322  },
323  {
324   "cell_type": "markdown",
325   "metadata": {},
326   "source": [
327    "Training data provided consists of 808696 rows and 23 columns. Among those 23 columns, 22 of them are features and 1 is - denoted with Operation_Count - is target label.\n",
328    "\n",
329    "Data types of different features are listed below."
330   ]
331  },
332  {
333   "cell_type": "code",
334   "execution_count": 5,
335   "metadata": {},
336   "outputs": [
337    {
338     "data": {
339      "text/plain": [
340       "Departure_YMD_LMT             int64\n",
341       "Operation_YMD_LMT             int64\n",
342       "Departure_Airport            object\n",
343       "Operation_Airport            object\n",
344       "Terminal_Number              object\n",
345       "Terminal_Name                object\n",
346       "Operation_Initials           object\n",
347       "Operation_Sonic_Code         object\n",
348       "Operation_Channel            object\n",
349       "Passenger_Title              object\n",
350       "Passenger_Gender             object\n",
351       "Inbound_Departure_Airport    object\n",
352       "Outbound_Arrival_Airport     object\n",
353       "SWC_FLY                       int64\n",
354       "Cabin_Class                  object\n",
355       "SWC_FQTV_Member               int64\n",
356       "Passenger_Baggage_Count       int64\n",
357       "Passenger_Baggage_Weight      int64\n",
358       "SWC_Staff                     int64\n",
359       "SWC_CIP_Passenger             int64\n",
360       "SWC_VIP_Passenger             int64\n",
361       "SWC_Has_Infant                int64\n",
362       "Operation_Count               int64\n",
363       "dtype: object"
364      ]
365     },
366     "execution_count": 5,
367     "metadata": {},
368     "output_type": "execute_result"
369    }
370   ],
371   "source": [
372    "df.dtypes"
373   ]
374  },
375  {
376   "cell_type": "markdown",
377   "metadata": {},
378   "source": [
379    "<h2>Data Preprocessing</h2>"
380   ]
381  },
382  {
383   "cell_type": "markdown",
384   "metadata": {},
385   "source": [
386    "<h4>Helper Functions</h4>\n",
387    "\n",
388    "Functions below are used to generate new features from existing ones. You can also check their descriptions from the following list:\n",
389    "\n",
390    "- is_op_airport_different : Returns 0 if check-in operation is done in KDT airport, returns 1 if operation is performed in another airport\n",
391    "- map_operation_channels : Maps operations channels to their channel groups\n",
392    "- map_inbound_flights : Returns 1 if an inbound flight exists for the entry, returns 0 otherwise\n",
393    "- map_outbound_flights : Returns 1 if an outbound flight exists for the entry, returns 0 otherwise\n",
394    "- is_inbound_kdt : Returns 1 if inbound flight location is KDT, returns 0 otherwise\n",
395    "- is_outbound_kdt : Returns 1 if outbound flight location is KDT, returns 0 otherwise"
396   ]
397  },
398  {
399   "cell_type": "code",
400   "execution_count": 6,
401   "metadata": {},
402   "outputs": [],
403   "source": [
404    "def map_operation_channels(channel):\n",
405    "        if channel == 'JW' or channel == 'TW':\n",
406    "            return 'online'\n",
407    "        elif channel == 'TS' or channel == 'JM':\n",
408    "            return 'mobile'\n",
409    "        elif channel == 'TY' or channel == 'QC':\n",
410    "            return 'kontuar'\n",
411    "        elif channel == 'SC':\n",
412    "            return 'kiosk'\n",
413    "        else:\n",
414    "            return 'other'\n",
415    "\n",
416    "## This function is not being used becasue \n",
417    "## there is no KG code in dataframe for KGA personnel\n",
418    "## It seems there is a mistake in the pdf file sent to us.\n",
419    "def map_operation_personnel(initial):\n",
420    "        if initial == 'KG':\n",
421    "            # Returns 1 if operation is performed by\n",
422    "            # KGA personnel\n",
423    "            return 1\n",
424    "        else:\n",
425    "            return 0\n",
426    "\n",
427    "def map_inbound_flights(flight):\n",
428    "        if flight == '?':\n",
429    "            return 0\n",
430    "        else:\n",
431    "            return 1\n",
432    "\n",
433    "def map_outbound_flights(flight):\n",
434    "        if flight == '?':\n",
435    "            return 0\n",
436    "        else:\n",
437    "            return 1\n",
438    "\n",
439    "def is_inbound_kdt(flight):\n",
440    "        if flight == 'KDT':\n",
441    "            return 1\n",
442    "        else:\n",
443    "            return 0\n",
444    "\n",
445    "def is_outbound_kdt(flight):\n",
446    "        if flight == 'KDT':\n",
447    "            return 1\n",
448    "        else:\n",
449    "            return 0\n",
450    "        \n",
451    "def is_op_airport_different(port):\n",
452    "    if port != 'KDT':\n",
453    "        return 1\n",
454    "    else:\n",
455    "        return 0"
456   ]
457  },
458  {
459   "cell_type": "markdown",
460   "metadata": {},
461   "source": [
462    "Applying functions defined above to existing features to create binary features."
463   ]
464  },
465  {
466   "cell_type": "code",
467   "execution_count": 7,
468   "metadata": {},
469   "outputs": [],
470   "source": [
471    "#df['Performed_By_KG'] = df['Operation_Initials'].apply(map_operation_personnel)\n",
472    "df['Inbound_Exist'] = df['Inbound_Departure_Airport'].apply(map_inbound_flights)\n",
473    "df['Outbound_Exist'] = df['Outbound_Arrival_Airport'].apply(map_outbound_flights)\n",
474    "df['Is_Inbound_KDT'] = df['Inbound_Departure_Airport'].apply(is_inbound_kdt)\n",
475    "df['Is_Outbound_KDT'] = df['Outbound_Arrival_Airport'].apply(is_outbound_kdt)\n",
476    "df['Different_Operation_Airport'] = df['Operation_Airport'].apply(is_op_airport_different)\n",
477    "df['Mapped_Channels'] = df['Operation_Channel'].apply(map_operation_channels)"
478   ]
479  },
480  {
481   "cell_type": "markdown",
482   "metadata": {},
483   "source": [
484    "Below, cabin class information is mapped to 1s and 0s. Also, for passenger gender and operation channel features, new features are created with one-hot encoding."
485   ]
486  },
487  {
488   "cell_type": "code",
489   "execution_count": 8,
490   "metadata": {},
491   "outputs": [],
492   "source": [
493    "df['Cabin_Class'] = df['Cabin_Class'].map({'Y': 0, 'C': 1})\n",
494    "\n",
495    "onehot_gender = pd.get_dummies(df['Passenger_Gender'])\n",
496    "df = pd.concat([df, onehot_gender], axis=1)\n",
497    "\n",
498    "onehot_channels = pd.get_dummies(df['Mapped_Channels'])\n",
499    "df = pd.concat([df, onehot_channels], axis=1)"
500   ]
501  },
502  {
503   "cell_type": "markdown",
504   "metadata": {},
505   "source": [
506    "Time difference between flight dates of passengers and their check-in date is stored in a feature called 'checkin_fly_difference'."
507   ]
508  },
509  {
510   "cell_type": "code",
511   "execution_count": 9,
512   "metadata": {},
513   "outputs": [
514    {
515     "data": {
516      "text/plain": [
517       "<matplotlib.axes._subplots.AxesSubplot at 0x18718f6e710>"
518      ]
519     },
520     "execution_count": 9,
521     "metadata": {},
522     "output_type": "execute_result"
523    },
524    {
525     "data": {
526      "image/png": "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\n",
527      "text/plain": [
528       "<Figure size 432x288 with 1 Axes>"
529      ]
530     },
531     "metadata": {
532      "needs_background": "light"
533     },
534     "output_type": "display_data"
535    }
536   ],
537   "source": [
538    "df['Departure_YMD_LMT'] = pd.to_datetime(df['Departure_YMD_LMT'], format='%Y%m%d')\n",
539    "df['Operation_YMD_LMT'] = pd.to_datetime(df['Operation_YMD_LMT'], format='%Y%m%d')\n",
540    "df['checkin_fly_difference'] = df['Departure_YMD_LMT'] - df['Operation_YMD_LMT']\n",
541    "df['checkin_fly_difference'].plot(kind=\"box\")"
542   ]
543  },
544  {
545   "cell_type": "markdown",
546   "metadata": {},
547   "source": [
548    "Features that do not provide additional information - because they are replaced with new features - are dropped."
549   ]
550  },
551  {
552   "cell_type": "code",
553   "execution_count": 10,
554   "metadata": {},
555   "outputs": [],
556   "source": [
557    "processed_df = df.drop(['Departure_YMD_LMT', 'Operation_YMD_LMT','Departure_Airport', 'Operation_Airport', 'Terminal_Number', 'Terminal_Name',\n",
558    "                        'Operation_Initials', 'Operation_Sonic_Code', 'Operation_Channel','Passenger_Title', \n",
559    "                        'Passenger_Gender', 'Inbound_Departure_Airport','Outbound_Arrival_Airport'], axis=1)"
560   ]
561  },
562  {
563   "cell_type": "markdown",
564   "metadata": {},
565   "source": [
566    "After dropping unnecessary columns, features left are following:"
567   ]
568  },
569  {
570   "cell_type": "code",
571   "execution_count": 11,
572   "metadata": {},
573   "outputs": [
574    {
575     "data": {
576      "text/plain": [
577       "SWC_FLY                                  int64\n",
578       "Cabin_Class                            float64\n",
579       "SWC_FQTV_Member                          int64\n",
580       "Passenger_Baggage_Count                  int64\n",
581       "Passenger_Baggage_Weight                 int64\n",
582       "SWC_Staff                                int64\n",
583       "SWC_CIP_Passenger                        int64\n",
584       "SWC_VIP_Passenger                        int64\n",
585       "SWC_Has_Infant                           int64\n",
586       "Operation_Count                          int64\n",
587       "Inbound_Exist                            int64\n",
588       "Outbound_Exist                           int64\n",
589       "Is_Inbound_KDT                           int64\n",
590       "Is_Outbound_KDT                          int64\n",
591       "Different_Operation_Airport              int64\n",
592       "Mapped_Channels                         object\n",
593       "?                                        uint8\n",
594       "C                                        uint8\n",
595       "C/INF                                    uint8\n",
596       "F                                        uint8\n",
597       "F/INF                                    uint8\n",
598       "M                                        uint8\n",
599       "M/INF                                    uint8\n",
600       "kiosk                                    uint8\n",
601       "kontuar                                  uint8\n",
602       "mobile                                   uint8\n",
603       "online                                   uint8\n",
604       "other                                    uint8\n",
605       "checkin_fly_difference         timedelta64[ns]\n",
606       "dtype: object"
607      ]
608     },
609     "execution_count": 11,
610     "metadata": {},
611     "output_type": "execute_result"
612    }
613   ],
614   "source": [
615    "processed_df.dtypes"
616   ]
617  },
618  {
619   "cell_type": "markdown",
620   "metadata": {},
621   "source": [
622    "Checking if features include null values;"
623   ]
624  },
625  {
626   "cell_type": "code",
627   "execution_count": 12,
628   "metadata": {},
629   "outputs": [
630    {
631     "data": {
632      "text/plain": [
633       "SWC_FLY                            0\n",
634       "Cabin_Class                    27571\n",
635       "SWC_FQTV_Member                    0\n",
636       "Passenger_Baggage_Count            0\n",
637       "Passenger_Baggage_Weight           0\n",
638       "SWC_Staff                          0\n",
639       "SWC_CIP_Passenger                  0\n",
640       "SWC_VIP_Passenger                  0\n",
641       "SWC_Has_Infant                     0\n",
642       "Operation_Count                    0\n",
643       "Inbound_Exist                      0\n",
644       "Outbound_Exist                     0\n",
645       "Is_Inbound_KDT                     0\n",
646       "Is_Outbound_KDT                    0\n",
647       "Different_Operation_Airport        0\n",
648       "Mapped_Channels                    0\n",
649       "?                                  0\n",
650       "C                                  0\n",
651       "C/INF                              0\n",
652       "F                                  0\n",
653       "F/INF                              0\n",
654       "M                                  0\n",
655       "M/INF                              0\n",
656       "kiosk                              0\n",
657       "kontuar                            0\n",
658       "mobile                             0\n",
659       "online                             0\n",
660       "other                              0\n",
661       "checkin_fly_difference             0\n",
662       "dtype: int64"
663      ]
664     },
665     "execution_count": 12,
666     "metadata": {},
667     "output_type": "execute_result"
668    }
669   ],
670   "source": [
671    "processed_df.isna().sum()"
672   ]
673  },
674  {
675   "cell_type": "markdown",
676   "metadata": {},
677   "source": [
678    "Only Cabin_Class feature includes some null values. Since we do not want to fill this feature with random values, we can drop entries with cabin information having NaN."
679   ]
680  },
681  {
682   "cell_type": "code",
683   "execution_count": 13,
684   "metadata": {},
685   "outputs": [],
686   "source": [
687    "processed_df = processed_df[pd.notnull(processed_df['Cabin_Class'])]"
688   ]
689  },
690  {
691   "cell_type": "code",
692   "execution_count": 14,
693   "metadata": {},
694   "outputs": [],
695   "source": [
696    "processed_df = processed_df.drop(['Mapped_Channels'], axis = 1)\n",
697    "# Converting timedelta typed feature to int\n",
698    "processed_df['checkin_fly_difference'] = processed_df['checkin_fly_difference'].dt.days"
699   ]
700  },
701  {
702   "cell_type": "markdown",
703   "metadata": {},
704   "source": [
705    "<h2>Applying Machine Learning Models on Validation Set</h2>"
706   ]
707  },
708  {
709   "cell_type": "markdown",
710   "metadata": {},
711   "source": [
712    "<h4><b>XGBoost Regressor Model</b></h4>"
713   ]
714  },
715  {
716   "cell_type": "code",
717   "execution_count": 15,
718   "metadata": {},
719   "outputs": [],
720   "source": [
721    "y = processed_df['Operation_Count']\n",
722    "X = processed_df.drop(['Operation_Count'], axis = 1)\n",
723    "\n",
724    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state=42)"
725   ]
726  },
727  {
728   "cell_type": "code",
729   "execution_count": 16,
730   "metadata": {},
731   "outputs": [
732    {
733     "name": "stdout",
734     "output_type": "stream",
735     "text": [
736      "[19:36:06] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
737     ]
738    },
739    {
740     "data": {
741      "text/plain": [
742       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
743       "       colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
744       "       importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
745       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
746       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
747       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
748       "       silent=None, subsample=1, verbosity=1)"
749      ]
750     },
751     "execution_count": 16,
752     "metadata": {},
753     "output_type": "execute_result"
754    }
755   ],
756   "source": [
757    "xgbmodel = xgb.XGBRegressor()\n",
758    "xgbmodel.fit(X_train, y_train)"
759   ]
760  },
761  {
762   "cell_type": "code",
763   "execution_count": 17,
764   "metadata": {},
765   "outputs": [
766    {
767     "data": {
768      "text/plain": [
769       "4.225014413957816"
770      ]
771     },
772     "execution_count": 17,
773     "metadata": {},
774     "output_type": "execute_result"
775    }
776   ],
777   "source": [
778    "pred = xgbmodel.predict(X_test)\n",
779    "mean_squared_error(y_test, pred)"
780   ]
781  },
782  {
783   "cell_type": "code",
784   "execution_count": 18,
785   "metadata": {},
786   "outputs": [
787    {
788     "data": {
789      "text/plain": [
790       "0.17922021632556528"
791      ]
792     },
793     "execution_count": 18,
794     "metadata": {},
795     "output_type": "execute_result"
796    }
797   ],
798   "source": [
799    "r2_score(y_test, pred)"
800   ]
801  },
802  {
803   "cell_type": "code",
804   "execution_count": 19,
805   "metadata": {},
806   "outputs": [
807    {
808     "data": {
809      "image/png": 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\n",
810      "text/plain": [
811       "<Figure size 864x864 with 1 Axes>"
812      ]
813     },
814     "metadata": {
815      "needs_background": "light"
816     },
817     "output_type": "display_data"
818    }
819   ],
820   "source": [
821    "feature_importances = pd.Series(xgbmodel.feature_importances_, index=X.columns).sort_values(ascending=False)\n",
822    "\n",
823    "plt.figure(figsize=(12, 12))\n",
824    "sns.barplot(x=feature_importances, y=feature_importances.index)\n",
825    "\n",
826    "# Add labels to our graph  \n",
827    "plt.xlabel('Feature Importance Score')\n",
828    "plt.ylabel('Features')\n",
829    "plt.title(\"Feature Importance Rankings\")\n",
830    "plt.show()"
831   ]
832  },
833  {
834   "cell_type": "markdown",
835   "metadata": {},
836   "source": [
837    "<h4>K-Fold Cross Validation</h4>\n",
838    "\n",
839    "10-fold cross validation for XGBoost model is below."
840   ]
841  },
842  {
843   "cell_type": "code",
844   "execution_count": 20,
845   "metadata": {},
846   "outputs": [
847    {
848     "name": "stdout",
849     "output_type": "stream",
850     "text": [
851      "[19:36:58] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
852      "[19:37:57] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
853      "[19:38:55] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
854      "[19:39:54] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
855      "[19:40:53] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
856      "[19:41:51] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
857      "[19:42:51] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
858      "[19:43:49] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
859      "[19:44:48] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
860      "[19:45:47] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
861     ]
862    },
863    {
864     "data": {
865      "text/plain": [
866       "array([0.18317994, 0.18209709, 0.17805348, 0.17923736, 0.17389156,\n",
867       "       0.18128156, 0.16611453, 0.18291924, 0.17375828, 0.1774409 ])"
868      ]
869     },
870     "execution_count": 20,
871     "metadata": {},
872     "output_type": "execute_result"
873    }
874   ],
875   "source": [
876    "from sklearn.model_selection import KFold\n",
877    "from sklearn.model_selection import cross_val_score\n",
878    "\n",
879    "kfold = KFold(n_splits = 10, random_state=7)\n",
880    "xgbresults = cross_val_score(xgbmodel, X, y, cv=kfold)\n",
881    "xgbresults"
882   ]
883  },
884  {
885   "cell_type": "markdown",
886   "metadata": {},
887   "source": [
888    "As seen from the cross-validation scores, R^2 score of XGBoost do not change much. All of the scores after each iteration are very close to R^2 score of initial fit of the model."
889   ]
890  },
891  {
892   "cell_type": "markdown",
893   "metadata": {},
894   "source": [
895    "<h4><b>Random Forest Regressor Model</b></h4>"
896   ]
897  },
898  {
899   "cell_type": "code",
900   "execution_count": 21,
901   "metadata": {},
902   "outputs": [
903    {
904     "data": {
905      "text/plain": [
906       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
907       "           max_features='auto', max_leaf_nodes=None,\n",
908       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
909       "           min_samples_leaf=1, min_samples_split=2,\n",
910       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
911       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
912      ]
913     },
914     "execution_count": 21,
915     "metadata": {},
916     "output_type": "execute_result"
917    }
918   ],
919   "source": [
920    "rfmodel = RandomForestRegressor(criterion='mse')\n",
921    "rfmodel.fit(X_train, y_train)"
922   ]
923  },
924  {
925   "cell_type": "code",
926   "execution_count": 22,
927   "metadata": {},
928   "outputs": [
929    {
930     "data": {
931      "text/plain": [
932       "3.7485005941444633"
933      ]
934     },
935     "execution_count": 22,
936     "metadata": {},
937     "output_type": "execute_result"
938    }
939   ],
940   "source": [
941    "rf_pred = rfmodel.predict(X_test)\n",
942    "mean_squared_error(y_test, rf_pred)"
943   ]
944  },
945  {
946   "cell_type": "code",
947   "execution_count": 23,
948   "metadata": {},
949   "outputs": [
950    {
951     "data": {
952      "text/plain": [
953       "0.27179100345760343"
954      ]
955     },
956     "execution_count": 23,
957     "metadata": {},
958     "output_type": "execute_result"
959    }
960   ],
961   "source": [
962    "r2_score(y_test, rf_pred)"
963   ]
964  },
965  {
966   "cell_type": "code",
967   "execution_count": 24,
968   "metadata": {},
969   "outputs": [
970    {
971     "data": {
972      "image/png": 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\n",
973      "text/plain": [
974       "<Figure size 864x864 with 1 Axes>"
975      ]
976     },
977     "metadata": {
978      "needs_background": "light"
979     },
980     "output_type": "display_data"
981    }
982   ],
983   "source": [
984    "feature_importances = pd.Series(rfmodel.feature_importances_, index=X.columns).sort_values(ascending=False)\n",
985    "\n",
986    "plt.figure(figsize=(12, 12))\n",
987    "sns.barplot(x=feature_importances, y=feature_importances.index)\n",
988    "\n",
989    "# Add labels to our graph  \n",
990    "plt.xlabel('Feature Importance Score')\n",
991    "plt.ylabel('Features')\n",
992    "plt.title(\"Feature Importance Rankings\")\n",
993    "plt.show()"
994   ]
995  },
996  {
997   "cell_type": "markdown",
998   "metadata": {},
999   "source": [
1000    "<h4>K-Fold Cross Validation</h4>\n",
1001    "\n",
1002    "10-fold cross validation for Random Forest Regressor model is below."
1003   ]
1004  },
1005  {
1006   "cell_type": "code",
1007   "execution_count": 25,
1008   "metadata": {},
1009   "outputs": [
1010    {
1011     "data": {
1012      "text/plain": [
1013       "array([0.28349469, 0.2705083 , 0.26981818, 0.26610356, 0.25349095,\n",
1014       "       0.27755084, 0.24296661, 0.26295131, 0.27446709, 0.25949676])"
1015      ]
1016     },
1017     "execution_count": 25,
1018     "metadata": {},
1019     "output_type": "execute_result"
1020    }
1021   ],
1022   "source": [
1023    "kfold = KFold(n_splits = 10, random_state=7)\n",
1024    "rf_results = cross_val_score(rfmodel, X, y, cv=kfold)\n",
1025    "rf_results"
1026   ]
1027  },
1028  {
1029   "cell_type": "markdown",
1030   "metadata": {},
1031   "source": [
1032    "Similar to cross-validation results of XGBoost, R^2 scores of Random Forest Regressor also do not change significantly. For both models, we can consider initial scores as scores for these models."
1033   ]
1034  },
1035  {
1036   "cell_type": "markdown",
1037   "metadata": {},
1038   "source": [
1039    "<h2>Insights From Machine Learning Models</h2>"
1040   ]
1041  },
1042  {
1043   "cell_type": "markdown",
1044   "metadata": {},
1045   "source": [
1046    "1. For both models, it can be seend that most important features are very similar. Most important features can be listed as Passanger_Baggage_Count, Inbound_Exist, Outbound_Exist, kontuar and SWC_CIP_Passenger. Among those features, Inbound_Exist and Outbound_Exist are ones created by us. These being important features for regression operations can be interpreted as presence or absence of inbound and outbound flights do affect number of times check-in operations are being made. Also, from the importance graphs above, it can be seen that check-in operation being made from kontuar also affects number of times check-in operations are attempted.\n",
1047    "\n",
1048    "2. When two models compared, Random Forest Regressor model seem to perform better for prediction operation counts in validation set. This may be due to XGBoost model overfitting to training data after boosting iterations. Parameters of both models can be tuned to further analyze their performances.\n",
1049    "\n",
1050    "3. With current parameters, for predicting operation counts in test file, Random Forest Regressor should be used, since its evaluation metrics are better."
1051   ]
1052  },
1053  {
1054   "cell_type": "markdown",
1055   "metadata": {},
1056   "source": [
1057    "<h4>Conducting Hypothesis Testing from Insights</h4>"
1058   ]
1059  },
1060  {
1061   "cell_type": "markdown",
1062   "metadata": {},
1063   "source": [
1064    "<b>1.</b> For first hypothesis test, we will analyzse whether check-in operation is being performed from kontuar cause statistically significant difference on number of operations made compared to other forms of check-in channels. "
1065   ]
1066  },
1067  {
1068   "cell_type": "code",
1069   "execution_count": 26,
1070   "metadata": {},
1071   "outputs": [],
1072   "source": [
1073    "kontuar_ops = processed_df[processed_df['kontuar'] == 1]['Operation_Count']\n",
1074    "other_ops = processed_df[processed_df['kontuar'] == 0]['Operation_Count']"
1075   ]
1076  },
1077  {
1078   "cell_type": "code",
1079   "execution_count": 27,
1080   "metadata": {},
1081   "outputs": [
1082    {
1083     "name": "stdout",
1084     "output_type": "stream",
1085     "text": [
1086      "Mean value of operation count when check-in is performed in kontuar: 1.31\n"
1087     ]
1088    }
1089   ],
1090   "source": [
1091    "print(\"Mean value of operation count when check-in is performed in kontuar: {:.2f}\".format(kontuar_ops.mean()))"
1092   ]
1093  },
1094  {
1095   "cell_type": "code",
1096   "execution_count": 28,
1097   "metadata": {},
1098   "outputs": [
1099    {
1100     "name": "stdout",
1101     "output_type": "stream",
1102     "text": [
1103      "Mean value of operation count when check-in is NOT performed in kontuar: 1.76\n"
1104     ]
1105    }
1106   ],
1107   "source": [
1108    "print(\"Mean value of operation count when check-in is NOT performed in kontuar: {:.2f}\".format(other_ops.mean()))"
1109   ]
1110  },
1111  {
1112   "cell_type": "code",
1113   "execution_count": 38,
1114   "metadata": {},
1115   "outputs": [
1116    {
1117     "data": {
1118      "image/png": 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\n",
1119      "text/plain": [
1120       "<Figure size 1008x432 with 3 Axes>"
1121      ]
1122     },
1123     "metadata": {
1124      "needs_background": "light"
1125     },
1126     "output_type": "display_data"
1127    }
1128   ],
1129   "source": [
1130    "fig, ax = plt.subplots(1, 3, figsize=(14,6))  # a figure with 1 row and 3 columns\n",
1131    "                                              # ax variable stores a list with 3 elements\n",
1132    "                                              # each element in ax correspons to chart\n",
1133    "        \n",
1134    "kontuar_ops.plot(kind=\"hist\", ax=ax[0], bins=40, label=\"k\", color=\"c\")\n",
1135    "ax[0].set_title(\"From Kontuar\")\n",
1136    "\n",
1137    "other_ops.plot(kind=\"hist\", ax=ax[1], bins=40, label=\"o\", color=\"m\")\n",
1138    "ax[1].set_title(\"From Other Channels\")\n",
1139    "\n",
1140    "sns.kdeplot(kontuar_ops, shade=True, label=\"kontuar operations\", ax=ax[2], color=\"c\")\n",
1141    "sns.kdeplot(other_ops, shade=True, label=\"non-kontuar operations\", ax=ax[2], color=\"m\")\n",
1142    "ax[2].set_title(\"Comparison with KDE\")\n",
1143    "\n",
1144    "plt.suptitle(\"Operation Count Distributions\")\n",
1145    "plt.show()"
1146   ]
1147  },
1148  {
1149   "cell_type": "code",
1150   "execution_count": 34,
1151   "metadata": {},
1152   "outputs": [
1153    {
1154     "data": {
1155      "text/plain": [
1156       "Ttest_indResult(statistic=-88.80864853163777, pvalue=0.0)"
1157      ]
1158     },
1159     "execution_count": 34,
1160     "metadata": {},
1161     "output_type": "execute_result"
1162    }
1163   ],
1164   "source": [
1165    "# One tail t-test between 2 distributions\n",
1166    "stats.ttest_ind(kontuar_ops, other_ops, equal_var=False)"
1167   ]
1168  },
1169  {
1170   "cell_type": "markdown",
1171   "metadata": {},
1172   "source": [
1173    "t-test between 2 distribution gives us p-value of 0.0. Interpreting this value as 0.001 and considering threshold value for significance is 0.05, this result tell us that difference between two distributions are statistically significant. Additionally, after checking means of two distributions, we can conclude that doing check-in in kontuar is likely to be completed in less number of operations compared to doing it in other ways. "
1174   ]
1175  },
1176  {
1177   "cell_type": "markdown",
1178   "metadata": {},
1179   "source": [
1180    "<b>2.</b> For the second hypothesis test, we will compare two distributions of operations counts for different values Inbound_Exist feature. By doing that, we will test if existance/absence of inbound flight actually affect number of operations performed for check-ins of passengers."
1181   ]
1182  },
1183  {
1184   "cell_type": "code",
1185   "execution_count": 35,
1186   "metadata": {},
1187   "outputs": [],
1188   "source": [
1189    "inbound_present = processed_df[processed_df['Inbound_Exist'] == 1]['Operation_Count']\n",
1190    "inbound_absent = processed_df[processed_df['Inbound_Exist'] == 0]['Operation_Count']"
1191   ]
1192  },
1193  {
1194   "cell_type": "code",
1195   "execution_count": 36,
1196   "metadata": {},
1197   "outputs": [
1198    {
1199     "name": "stdout",
1200     "output_type": "stream",
1201     "text": [
1202      "Mean value of operation count when there is an inbound flight: 1.24\n"
1203     ]
1204    }
1205   ],
1206   "source": [
1207    "print(\"Mean value of operation count when there is an inbound flight: {:.2f}\".format(inbound_present.mean()))"
1208   ]
1209  },
1210  {
1211   "cell_type": "code",
1212   "execution_count": 37,
1213   "metadata": {},
1214   "outputs": [
1215    {
1216     "name": "stdout",
1217     "output_type": "stream",
1218     "text": [
1219      "Mean value of operation count when there is not an inbound flight: 1.57\n"
1220     ]
1221    }
1222   ],
1223   "source": [
1224    "print(\"Mean value of operation count when there is not an inbound flight: {:.2f}\".format(inbound_absent.mean()))"
1225   ]
1226  },
1227  {
1228   "cell_type": "code",
1229   "execution_count": 39,
1230   "metadata": {},
1231   "outputs": [
1232    {
1233     "data": {
1234      "image/png": 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\n",
1235      "text/plain": [
1236       "<Figure size 1008x432 with 3 Axes>"
1237      ]
1238     },
1239     "metadata": {
1240      "needs_background": "light"
1241     },
1242     "output_type": "display_data"
1243    }
1244   ],
1245   "source": [
1246    "fig, ax = plt.subplots(1, 3, figsize=(14,6))  # a figure with 1 row and 3 columns\n",
1247    "                                              # ax variable stores a list with 3 elements\n",
1248    "                                              # each element in ax correspons to chart\n",
1249    "        \n",
1250    "inbound_present.plot(kind=\"hist\", ax=ax[0], bins=40, label=\"inb\", color=\"b\")\n",
1251    "ax[0].set_title(\"Inbound Present\")\n",
1252    "\n",
1253    "inbound_absent.plot(kind=\"hist\", ax=ax[1], bins=40, label=\"no\", color=\"r\")\n",
1254    "ax[1].set_title(\"Inbound Absent\")\n",
1255    "\n",
1256    "sns.kdeplot(inbound_present, shade=True, label=\"inbound flights\", ax=ax[2], color=\"b\")\n",
1257    "sns.kdeplot(inbound_absent, shade=True, label=\"no inbound flights\", ax=ax[2], color=\"r\")\n",
1258    "ax[2].set_title(\"Comparison with KDE\")\n",
1259    "\n",
1260    "plt.suptitle(\"Operation Count Distributions\")\n",
1261    "plt.show()"
1262   ]
1263  },
1264  {
1265   "cell_type": "code",
1266   "execution_count": 40,
1267   "metadata": {},
1268   "outputs": [
1269    {
1270     "data": {
1271      "text/plain": [
1272       "Ttest_indResult(statistic=-89.01460439966546, pvalue=0.0)"
1273      ]
1274     },
1275     "execution_count": 40,
1276     "metadata": {},
1277     "output_type": "execute_result"
1278    }
1279   ],
1280   "source": [
1281    "# One tail t-test between 2 distributions\n",
1282    "stats.ttest_ind(inbound_present, inbound_absent, equal_var=False)"
1283   ]
1284  },
1285  {
1286   "cell_type": "markdown",
1287   "metadata": {},
1288   "source": [
1289    "Again, t-test result is 0.0. Interpreting this value as 0.001 and considering threshold value for significance is 0.05, this result tell us that difference between two distributions are statistically significant. If we combine this result with observation of means of distributions, we can conclude that presence of inbound flights results less number of operations for check-in. However, it should be noted that size of the distribution where inbound flight is present is much lower that where inbound flight is not present."
1290   ]
1291  },
1292  {
1293   "cell_type": "markdown",
1294   "metadata": {},
1295   "source": [
1296    "<h2>Predicting Labels in Test Set<h2>"
1297   ]
1298  },
1299  {
1300   "cell_type": "markdown",
1301   "metadata": {},
1302   "source": [
1303    "As done in training file, test file should be preprocessed as well. Below, same operations performed on training csv file are also performed on training data.\n"
1304   ]
1305  },
1306  {
1307   "cell_type": "code",
1308   "execution_count": 47,
1309   "metadata": {},
1310   "outputs": [],
1311   "source": [
1312    "test_df = pd.read_csv(r\"C:\\Users\\Mert\\Desktop\\assessment\\assessment\\Assessment Data\\Assessment Result File.csv\")"
1313   ]
1314  },
1315  {
1316   "cell_type": "code",
1317   "execution_count": 48,
1318   "metadata": {},
1319   "outputs": [
1320    {
1321     "data": {
1322      "text/html": [
1323       "<div>\n",
1324       "<style scoped>\n",
1325       "    .dataframe tbody tr th:only-of-type {\n",
1326       "        vertical-align: middle;\n",
1327       "    }\n",
1328       "\n",
1329       "    .dataframe tbody tr th {\n",
1330       "        vertical-align: top;\n",
1331       "    }\n",
1332       "\n",
1333       "    .dataframe thead th {\n",
1334       "        text-align: right;\n",
1335       "    }\n",
1336       "</style>\n",
1337       "<table border=\"1\" class=\"dataframe\">\n",
1338       "  <thead>\n",
1339       "    <tr style=\"text-align: right;\">\n",
1340       "      <th></th>\n",
1341       "      <th>Departure_YMD_LMT</th>\n",
1342       "      <th>Operation_YMD_LMT</th>\n",
1343       "      <th>Departure_Airport</th>\n",
1344       "      <th>Operation_Airport</th>\n",
1345       "      <th>Terminal_Number</th>\n",
1346       "      <th>Terminal_Name</th>\n",
1347       "      <th>Operation_Initials</th>\n",
1348       "      <th>Operation_Sonic_Code</th>\n",
1349       "      <th>Operation_Channel</th>\n",
1350       "      <th>Passenger_Title</th>\n",
1351       "      <th>...</th>\n",
1352       "      <th>SWC_FLY</th>\n",
1353       "      <th>Cabin_Class</th>\n",
1354       "      <th>SWC_FQTV_Member</th>\n",
1355       "      <th>Passenger_Baggage_Count</th>\n",
1356       "      <th>Passenger_Baggage_Weight</th>\n",
1357       "      <th>SWC_Staff</th>\n",
1358       "      <th>SWC_CIP_Passenger</th>\n",
1359       "      <th>SWC_VIP_Passenger</th>\n",
1360       "      <th>SWC_Has_Infant</th>\n",
1361       "      <th>Operation_Count</th>\n",
1362       "    </tr>\n",
1363       "  </thead>\n",
1364       "  <tbody>\n",
1365       "    <tr>\n",
1366       "      <th>0</th>\n",
1367       "      <td>20190608</td>\n",
1368       "      <td>20190607</td>\n",
1369       "      <td>KDT</td>\n",
1370       "      <td>KDT</td>\n",
1371       "      <td>?</td>\n",
1372       "      <td>03539F</td>\n",
1373       "      <td>MK</td>\n",
1374       "      <td>?</td>\n",
1375       "      <td>TS</td>\n",
1376       "      <td>MISS</td>\n",
1377       "      <td>...</td>\n",
1378       "      <td>1</td>\n",
1379       "      <td>Y</td>\n",
1380       "      <td>1</td>\n",
1381       "      <td>1</td>\n",
1382       "      <td>17</td>\n",
1383       "      <td>0</td>\n",
1384       "      <td>0</td>\n",
1385       "      <td>0</td>\n",
1386       "      <td>0</td>\n",
1387       "      <td></td>\n",
1388       "    </tr>\n",
1389       "    <tr>\n",
1390       "      <th>1</th>\n",
1391       "      <td>20190609</td>\n",
1392       "      <td>20190609</td>\n",
1393       "      <td>KDT</td>\n",
1394       "      <td>KDT</td>\n",
1395       "      <td>?</td>\n",
1396       "      <td>03F39C</td>\n",
1397       "      <td>KS</td>\n",
1398       "      <td>Y013171</td>\n",
1399       "      <td>QC</td>\n",
1400       "      <td>MISTER</td>\n",
1401       "      <td>...</td>\n",
1402       "      <td>1</td>\n",
1403       "      <td>Y</td>\n",
1404       "      <td>0</td>\n",
1405       "      <td>1</td>\n",
1406       "      <td>7</td>\n",
1407       "      <td>0</td>\n",
1408       "      <td>0</td>\n",
1409       "      <td>0</td>\n",
1410       "      <td>0</td>\n",
1411       "      <td></td>\n",
1412       "    </tr>\n",
1413       "    <tr>\n",
1414       "      <th>2</th>\n",
1415       "      <td>20190612</td>\n",
1416       "      <td>20190612</td>\n",
1417       "      <td>KDT</td>\n",
1418       "      <td>KDT</td>\n",
1419       "      <td>?</td>\n",
1420       "      <td>032A72</td>\n",
1421       "      <td>KS</td>\n",
1422       "      <td>Y013347</td>\n",
1423       "      <td>QC</td>\n",
1424       "      <td>MISTER</td>\n",
1425       "      <td>...</td>\n",
1426       "      <td>1</td>\n",
1427       "      <td>Y</td>\n",
1428       "      <td>1</td>\n",
1429       "      <td>0</td>\n",
1430       "      <td>0</td>\n",
1431       "      <td>0</td>\n",
1432       "      <td>0</td>\n",
1433       "      <td>0</td>\n",
1434       "      <td>0</td>\n",
1435       "      <td></td>\n",
1436       "    </tr>\n",
1437       "    <tr>\n",
1438       "      <th>3</th>\n",
1439       "      <td>20190613</td>\n",
1440       "      <td>20190612</td>\n",
1441       "      <td>KDT</td>\n",
1442       "      <td>KDT</td>\n",
1443       "      <td>?</td>\n",
1444       "      <td>0302F9</td>\n",
1445       "      <td>Q7</td>\n",
1446       "      <td>?</td>\n",
1447       "      <td>TW</td>\n",
1448       "      <td>MISTER</td>\n",
1449       "      <td>...</td>\n",
1450       "      <td>1</td>\n",
1451       "      <td>Y</td>\n",
1452       "      <td>1</td>\n",
1453       "      <td>0</td>\n",
1454       "      <td>0</td>\n",
1455       "      <td>0</td>\n",
1456       "      <td>1</td>\n",
1457       "      <td>0</td>\n",
1458       "      <td>0</td>\n",
1459       "      <td></td>\n",
1460       "    </tr>\n",
1461       "    <tr>\n",
1462       "      <th>4</th>\n",
1463       "      <td>20190602</td>\n",
1464       "      <td>20190601</td>\n",
1465       "      <td>KDT</td>\n",
1466       "      <td>KDT</td>\n",
1467       "      <td>?</td>\n",
1468       "      <td>03023B</td>\n",
1469       "      <td>MK</td>\n",
1470       "      <td>?</td>\n",
1471       "      <td>TW</td>\n",
1472       "      <td>MISS</td>\n",
1473       "      <td>...</td>\n",
1474       "      <td>1</td>\n",
1475       "      <td>Y</td>\n",
1476       "      <td>0</td>\n",
1477       "      <td>1</td>\n",
1478       "      <td>18</td>\n",
1479       "      <td>0</td>\n",
1480       "      <td>0</td>\n",
1481       "      <td>0</td>\n",
1482       "      <td>0</td>\n",
1483       "      <td></td>\n",
1484       "    </tr>\n",
1485       "  </tbody>\n",
1486       "</table>\n",
1487       "<p>5 rows × 23 columns</p>\n",
1488       "</div>"
1489      ],
1490      "text/plain": [
1491       "   Departure_YMD_LMT  Operation_YMD_LMT Departure_Airport Operation_Airport  \\\n",
1492       "0           20190608           20190607               KDT               KDT   \n",
1493       "1           20190609           20190609               KDT               KDT   \n",
1494       "2           20190612           20190612               KDT               KDT   \n",
1495       "3           20190613           20190612               KDT               KDT   \n",
1496       "4           20190602           20190601               KDT               KDT   \n",
1497       "\n",
1498       "  Terminal_Number Terminal_Name Operation_Initials Operation_Sonic_Code  \\\n",
1499       "0               ?        03539F                 MK                    ?   \n",
1500       "1               ?        03F39C                 KS              Y013171   \n",
1501       "2               ?        032A72                 KS              Y013347   \n",
1502       "3               ?        0302F9                 Q7                    ?   \n",
1503       "4               ?        03023B                 MK                    ?   \n",
1504       "\n",
1505       "  Operation_Channel Passenger_Title       ...        SWC_FLY Cabin_Class  \\\n",
1506       "0                TS            MISS       ...              1           Y   \n",
1507       "1                QC          MISTER       ...              1           Y   \n",
1508       "2                QC          MISTER       ...              1           Y   \n",
1509       "3                TW          MISTER       ...              1           Y   \n",
1510       "4                TW            MISS       ...              1           Y   \n",
1511       "\n",
1512       "  SWC_FQTV_Member  Passenger_Baggage_Count Passenger_Baggage_Weight  \\\n",
1513       "0               1                        1                       17   \n",
1514       "1               0                        1                        7   \n",
1515       "2               1                        0                        0   \n",
1516       "3               1                        0                        0   \n",
1517       "4               0                        1                       18   \n",
1518       "\n",
1519       "   SWC_Staff  SWC_CIP_Passenger  SWC_VIP_Passenger  SWC_Has_Infant  \\\n",
1520       "0          0                  0                  0               0   \n",
1521       "1          0                  0                  0               0   \n",
1522       "2          0                  0                  0               0   \n",
1523       "3          0                  1                  0               0   \n",
1524       "4          0                  0                  0               0   \n",
1525       "\n",
1526       "   Operation_Count  \n",
1527       "0                   \n",
1528       "1                   \n",
1529       "2                   \n",
1530       "3                   \n",
1531       "4                   \n",
1532       "\n",
1533       "[5 rows x 23 columns]"
1534      ]
1535     },
1536     "execution_count": 48,
1537     "metadata": {},
1538     "output_type": "execute_result"
1539    }
1540   ],
1541   "source": [
1542    "test_df.head()"
1543   ]
1544  },
1545  {
1546   "cell_type": "code",
1547   "execution_count": 49,
1548   "metadata": {},
1549   "outputs": [
1550    {
1551     "data": {
1552      "text/plain": [
1553       "Departure_YMD_LMT             int64\n",
1554       "Operation_YMD_LMT             int64\n",
1555       "Departure_Airport            object\n",
1556       "Operation_Airport            object\n",
1557       "Terminal_Number              object\n",
1558       "Terminal_Name                object\n",
1559       "Operation_Initials           object\n",
1560       "Operation_Sonic_Code         object\n",
1561       "Operation_Channel            object\n",
1562       "Passenger_Title              object\n",
1563       "Passenger_Gender             object\n",
1564       "Inbound_Departure_Airport    object\n",
1565       "Outbound_Arrival_Airport     object\n",
1566       "SWC_FLY                       int64\n",
1567       "Cabin_Class                  object\n",
1568       "SWC_FQTV_Member               int64\n",
1569       "Passenger_Baggage_Count       int64\n",
1570       "Passenger_Baggage_Weight      int64\n",
1571       "SWC_Staff                     int64\n",
1572       "SWC_CIP_Passenger             int64\n",
1573       "SWC_VIP_Passenger             int64\n",
1574       "SWC_Has_Infant                int64\n",
1575       "Operation_Count              object\n",
1576       "dtype: object"
1577      ]
1578     },
1579     "execution_count": 49,
1580     "metadata": {},
1581     "output_type": "execute_result"
1582    }
1583   ],
1584   "source": [
1585    "test_df.dtypes"
1586   ]
1587  },
1588  {
1589   "cell_type": "code",
1590   "execution_count": 50,
1591   "metadata": {},
1592   "outputs": [],
1593   "source": [
1594    "# Performing all preprocessing in this cell\n",
1595    "\n",
1596    "#test_df['Performed_By_KG'] = test_df['Operation_Initials'].apply(map_operation_personnel)\n",
1597    "test_df['Inbound_Exist'] = test_df['Inbound_Departure_Airport'].apply(map_inbound_flights)\n",
1598    "test_df['Outbound_Exist'] = test_df['Outbound_Arrival_Airport'].apply(map_outbound_flights)\n",
1599    "test_df['Is_Inbound_KDT'] = test_df['Inbound_Departure_Airport'].apply(is_inbound_kdt)\n",
1600    "test_df['Is_Outbound_KDT'] = test_df['Outbound_Arrival_Airport'].apply(is_outbound_kdt)\n",
1601    "test_df['Different_Operation_Airport'] = test_df['Operation_Airport'].apply(is_op_airport_different)\n",
1602    "test_df['Mapped_Channels'] = test_df['Operation_Channel'].apply(map_operation_channels)\n",
1603    "\n",
1604    "test_df['Cabin_Class'] = test_df['Cabin_Class'].map({'Y': 0, 'C': 1})\n",
1605    "\n",
1606    "onehot_gender = pd.get_dummies(test_df['Passenger_Gender'])\n",
1607    "test_df = pd.concat([test_df, onehot_gender], axis=1)\n",
1608    "\n",
1609    "onehot_channels = pd.get_dummies(test_df['Mapped_Channels'])\n",
1610    "test_df = pd.concat([test_df, onehot_channels], axis=1)\n",
1611    "\n",
1612    "test_df['Departure_YMD_LMT'] = pd.to_datetime(test_df['Departure_YMD_LMT'], format='%Y%m%d')\n",
1613    "test_df['Operation_YMD_LMT'] = pd.to_datetime(test_df['Operation_YMD_LMT'], format='%Y%m%d')\n",
1614    "test_df['checkin_fly_difference'] = test_df['Departure_YMD_LMT'] - test_df['Operation_YMD_LMT']\n",
1615    "\n",
1616    "processed_test_df = test_df.drop(['Departure_YMD_LMT', 'Operation_YMD_LMT','Departure_Airport', 'Operation_Airport', 'Terminal_Number', 'Terminal_Name',\n",
1617    "                        'Operation_Initials', 'Operation_Sonic_Code', 'Operation_Channel','Passenger_Title', \n",
1618    "                        'Passenger_Gender', 'Inbound_Departure_Airport','Outbound_Arrival_Airport'], axis=1)"
1619   ]
1620  },
1621  {
1622   "cell_type": "code",
1623   "execution_count": 52,
1624   "metadata": {},
1625   "outputs": [
1626    {
1627     "data": {
1628      "text/html": [
1629       "<div>\n",
1630       "<style scoped>\n",
1631       "    .dataframe tbody tr th:only-of-type {\n",
1632       "        vertical-align: middle;\n",
1633       "    }\n",
1634       "\n",
1635       "    .dataframe tbody tr th {\n",
1636       "        vertical-align: top;\n",
1637       "    }\n",
1638       "\n",
1639       "    .dataframe thead th {\n",
1640       "        text-align: right;\n",
1641       "    }\n",
1642       "</style>\n",
1643       "<table border=\"1\" class=\"dataframe\">\n",
1644       "  <thead>\n",
1645       "    <tr style=\"text-align: right;\">\n",
1646       "      <th></th>\n",
1647       "      <th>SWC_FLY</th>\n",
1648       "      <th>Cabin_Class</th>\n",
1649       "      <th>SWC_FQTV_Member</th>\n",
1650       "      <th>Passenger_Baggage_Count</th>\n",
1651       "      <th>Passenger_Baggage_Weight</th>\n",
1652       "      <th>SWC_Staff</th>\n",
1653       "      <th>SWC_CIP_Passenger</th>\n",
1654       "      <th>SWC_VIP_Passenger</th>\n",
1655       "      <th>SWC_Has_Infant</th>\n",
1656       "      <th>Operation_Count</th>\n",
1657       "      <th>...</th>\n",
1658       "      <th>?</th>\n",
1659       "      <th>C</th>\n",
1660       "      <th>F</th>\n",
1661       "      <th>M</th>\n",
1662       "      <th>kiosk</th>\n",
1663       "      <th>kontuar</th>\n",
1664       "      <th>mobile</th>\n",
1665       "      <th>online</th>\n",
1666       "      <th>other</th>\n",
1667       "      <th>checkin_fly_difference</th>\n",
1668       "    </tr>\n",
1669       "  </thead>\n",
1670       "  <tbody>\n",
1671       "    <tr>\n",
1672       "      <th>0</th>\n",
1673       "      <td>1</td>\n",
1674       "      <td>0</td>\n",
1675       "      <td>1</td>\n",
1676       "      <td>1</td>\n",
1677       "      <td>17</td>\n",
1678       "      <td>0</td>\n",
1679       "      <td>0</td>\n",
1680       "      <td>0</td>\n",
1681       "      <td>0</td>\n",
1682       "      <td></td>\n",
1683       "      <td>...</td>\n",
1684       "      <td>0</td>\n",
1685       "      <td>0</td>\n",
1686       "      <td>1</td>\n",
1687       "      <td>0</td>\n",
1688       "      <td>0</td>\n",
1689       "      <td>0</td>\n",
1690       "      <td>1</td>\n",
1691       "      <td>0</td>\n",
1692       "      <td>0</td>\n",
1693       "      <td>1 days</td>\n",
1694       "    </tr>\n",
1695       "    <tr>\n",
1696       "      <th>1</th>\n",
1697       "      <td>1</td>\n",
1698       "      <td>0</td>\n",
1699       "      <td>0</td>\n",
1700       "      <td>1</td>\n",
1701       "      <td>7</td>\n",
1702       "      <td>0</td>\n",
1703       "      <td>0</td>\n",
1704       "      <td>0</td>\n",
1705       "      <td>0</td>\n",
1706       "      <td></td>\n",
1707       "      <td>...</td>\n",
1708       "      <td>0</td>\n",
1709       "      <td>0</td>\n",
1710       "      <td>0</td>\n",
1711       "      <td>1</td>\n",
1712       "      <td>0</td>\n",
1713       "      <td>1</td>\n",
1714       "      <td>0</td>\n",
1715       "      <td>0</td>\n",
1716       "      <td>0</td>\n",
1717       "      <td>0 days</td>\n",
1718       "    </tr>\n",
1719       "    <tr>\n",
1720       "      <th>2</th>\n",
1721       "      <td>1</td>\n",
1722       "      <td>0</td>\n",
1723       "      <td>1</td>\n",
1724       "      <td>0</td>\n",
1725       "      <td>0</td>\n",
1726       "      <td>0</td>\n",
1727       "      <td>0</td>\n",
1728       "      <td>0</td>\n",
1729       "      <td>0</td>\n",
1730       "      <td></td>\n",
1731       "      <td>...</td>\n",
1732       "      <td>0</td>\n",
1733       "      <td>0</td>\n",
1734       "      <td>0</td>\n",
1735       "      <td>1</td>\n",
1736       "      <td>0</td>\n",
1737       "      <td>1</td>\n",
1738       "      <td>0</td>\n",
1739       "      <td>0</td>\n",
1740       "      <td>0</td>\n",
1741       "      <td>0 days</td>\n",
1742       "    </tr>\n",
1743       "    <tr>\n",
1744       "      <th>3</th>\n",
1745       "      <td>1</td>\n",
1746       "      <td>0</td>\n",
1747       "      <td>1</td>\n",
1748       "      <td>0</td>\n",
1749       "      <td>0</td>\n",
1750       "      <td>0</td>\n",
1751       "      <td>1</td>\n",
1752       "      <td>0</td>\n",
1753       "      <td>0</td>\n",
1754       "      <td></td>\n",
1755       "      <td>...</td>\n",
1756       "      <td>0</td>\n",
1757       "      <td>0</td>\n",
1758       "      <td>0</td>\n",
1759       "      <td>1</td>\n",
1760       "      <td>0</td>\n",
1761       "      <td>0</td>\n",
1762       "      <td>0</td>\n",
1763       "      <td>1</td>\n",
1764       "      <td>0</td>\n",
1765       "      <td>1 days</td>\n",
1766       "    </tr>\n",
1767       "    <tr>\n",
1768       "      <th>4</th>\n",
1769       "      <td>1</td>\n",
1770       "      <td>0</td>\n",
1771       "      <td>0</td>\n",
1772       "      <td>1</td>\n",
1773       "      <td>18</td>\n",
1774       "      <td>0</td>\n",
1775       "      <td>0</td>\n",
1776       "      <td>0</td>\n",
1777       "      <td>0</td>\n",
1778       "      <td></td>\n",
1779       "      <td>...</td>\n",
1780       "      <td>0</td>\n",
1781       "      <td>0</td>\n",
1782       "      <td>1</td>\n",
1783       "      <td>0</td>\n",
1784       "      <td>0</td>\n",
1785       "      <td>0</td>\n",
1786       "      <td>0</td>\n",
1787       "      <td>1</td>\n",
1788       "      <td>0</td>\n",
1789       "      <td>1 days</td>\n",
1790       "    </tr>\n",
1791       "  </tbody>\n",
1792       "</table>\n",
1793       "<p>5 rows × 26 columns</p>\n",
1794       "</div>"
1795      ],
1796      "text/plain": [
1797       "   SWC_FLY  Cabin_Class  SWC_FQTV_Member  Passenger_Baggage_Count  \\\n",
1798       "0        1            0                1                        1   \n",
1799       "1        1            0                0                        1   \n",
1800       "2        1            0                1                        0   \n",
1801       "3        1            0                1                        0   \n",
1802       "4        1            0                0                        1   \n",
1803       "\n",
1804       "   Passenger_Baggage_Weight  SWC_Staff  SWC_CIP_Passenger  SWC_VIP_Passenger  \\\n",
1805       "0                        17          0                  0                  0   \n",
1806       "1                         7          0                  0                  0   \n",
1807       "2                         0          0                  0                  0   \n",
1808       "3                         0          0                  1                  0   \n",
1809       "4                        18          0                  0                  0   \n",
1810       "\n",
1811       "   SWC_Has_Infant Operation_Count           ...            ?  C  F  M  kiosk  \\\n",
1812       "0               0                           ...            0  0  1  0      0   \n",
1813       "1               0                           ...            0  0  0  1      0   \n",
1814       "2               0                           ...            0  0  0  1      0   \n",
1815       "3               0                           ...            0  0  0  1      0   \n",
1816       "4               0                           ...            0  0  1  0      0   \n",
1817       "\n",
1818       "  kontuar  mobile  online  other  checkin_fly_difference  \n",
1819       "0       0       1       0      0                  1 days  \n",
1820       "1       1       0       0      0                  0 days  \n",
1821       "2       1       0       0      0                  0 days  \n",
1822       "3       0       0       1      0                  1 days  \n",
1823       "4       0       0       1      0                  1 days  \n",
1824       "\n",
1825       "[5 rows x 26 columns]"
1826      ]
1827     },
1828     "execution_count": 52,
1829     "metadata": {},
1830     "output_type": "execute_result"
1831    }
1832   ],
1833   "source": [
1834    "processed_test_df.head()"
1835   ]
1836  },
1837  {
1838   "cell_type": "code",
1839   "execution_count": 53,
1840   "metadata": {},
1841   "outputs": [],
1842   "source": [
1843    "processed_test_df = processed_test_df.drop(['Mapped_Channels'], axis = 1)\n",
1844    "# Converting timedelta typed feature to int\n",
1845    "processed_test_df['checkin_fly_difference'] = processed_test_df['checkin_fly_difference'].dt.days"
1846   ]
1847  },
1848  {
1849   "cell_type": "code",
1850   "execution_count": 57,
1851   "metadata": {},
1852   "outputs": [
1853    {
1854     "data": {
1855      "text/plain": [
1856       "SWC_FLY                         int64\n",
1857       "Cabin_Class                     int64\n",
1858       "SWC_FQTV_Member                 int64\n",
1859       "Passenger_Baggage_Count         int64\n",
1860       "Passenger_Baggage_Weight        int64\n",
1861       "SWC_Staff                       int64\n",
1862       "SWC_CIP_Passenger               int64\n",
1863       "SWC_VIP_Passenger               int64\n",
1864       "SWC_Has_Infant                  int64\n",
1865       "Operation_Count                object\n",
1866       "Inbound_Exist                   int64\n",
1867       "Outbound_Exist                  int64\n",
1868       "Is_Inbound_KDT                  int64\n",
1869       "Is_Outbound_KDT                 int64\n",
1870       "Different_Operation_Airport     int64\n",
1871       "?                               uint8\n",
1872       "C                               uint8\n",
1873       "F                               uint8\n",
1874       "M                               uint8\n",
1875       "kiosk                           uint8\n",
1876       "kontuar                         uint8\n",
1877       "mobile                          uint8\n",
1878       "online                          uint8\n",
1879       "other                           uint8\n",
1880       "checkin_fly_difference          int64\n",
1881       "dtype: object"
1882      ]
1883     },
1884     "execution_count": 57,
1885     "metadata": {},
1886     "output_type": "execute_result"
1887    }
1888   ],
1889   "source": [
1890    "processed_test_df.dtypes"
1891   ]
1892  },
1893  {
1894   "cell_type": "markdown",
1895   "metadata": {},
1896   "source": [
1897    "It seems some of the one-hot encoded features are missing from processed_test_df. Adding those in the cell below."
1898   ]
1899  },
1900  {
1901   "cell_type": "code",
1902   "execution_count": 59,
1903   "metadata": {},
1904   "outputs": [],
1905   "source": [
1906    "processed_test_df['C/INF'] = 0\n",
1907    "processed_test_df['M/INF'] = 0\n",
1908    "processed_test_df['F/INF'] = 0"
1909   ]
1910  },
1911  {
1912   "cell_type": "code",
1913   "execution_count": 60,
1914   "metadata": {},
1915   "outputs": [
1916    {
1917     "data": {
1918      "text/html": [
1919       "<div>\n",
1920       "<style scoped>\n",
1921       "    .dataframe tbody tr th:only-of-type {\n",
1922       "        vertical-align: middle;\n",
1923       "    }\n",
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1930       "        text-align: right;\n",
1931       "    }\n",
1932       "</style>\n",
1933       "<table border=\"1\" class=\"dataframe\">\n",
1934       "  <thead>\n",
1935       "    <tr style=\"text-align: right;\">\n",
1936       "      <th></th>\n",
1937       "      <th>SWC_FLY</th>\n",
1938       "      <th>Cabin_Class</th>\n",
1939       "      <th>SWC_FQTV_Member</th>\n",
1940       "      <th>Passenger_Baggage_Count</th>\n",
1941       "      <th>Passenger_Baggage_Weight</th>\n",
1942       "      <th>SWC_Staff</th>\n",
1943       "      <th>SWC_CIP_Passenger</th>\n",
1944       "      <th>SWC_VIP_Passenger</th>\n",
1945       "      <th>SWC_Has_Infant</th>\n",
1946       "      <th>Operation_Count</th>\n",
1947       "      <th>...</th>\n",
1948       "      <th>M</th>\n",
1949       "      <th>kiosk</th>\n",
1950       "      <th>kontuar</th>\n",
1951       "      <th>mobile</th>\n",
1952       "      <th>online</th>\n",
1953       "      <th>other</th>\n",
1954       "      <th>checkin_fly_difference</th>\n",
1955       "      <th>C/INF</th>\n",
1956       "      <th>M/INF</th>\n",
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2020       "      <td></td>\n",
2021       "      <td>...</td>\n",
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2037       "      <td>1</td>\n",
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2044       "      <td></td>\n",
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2058       "      <th>4</th>\n",
2059       "      <td>1</td>\n",
2060       "      <td>0</td>\n",
2061       "      <td>0</td>\n",
2062       "      <td>1</td>\n",
2063       "      <td>18</td>\n",
2064       "      <td>0</td>\n",
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2067       "      <td>0</td>\n",
2068       "      <td></td>\n",
2069       "      <td>...</td>\n",
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2071       "      <td>0</td>\n",
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2076       "      <td>1</td>\n",
2077       "      <td>0</td>\n",
2078       "      <td>0</td>\n",
2079       "      <td>0</td>\n",
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2081       "    <tr>\n",
2082       "      <th>5</th>\n",
2083       "      <td>1</td>\n",
2084       "      <td>0</td>\n",
2085       "      <td>0</td>\n",
2086       "      <td>2</td>\n",
2087       "      <td>27</td>\n",
2088       "      <td>0</td>\n",
2089       "      <td>0</td>\n",
2090       "      <td>0</td>\n",
2091       "      <td>0</td>\n",
2092       "      <td></td>\n",
2093       "      <td>...</td>\n",
2094       "      <td>1</td>\n",
2095       "      <td>0</td>\n",
2096       "      <td>1</td>\n",
2097       "      <td>0</td>\n",
2098       "      <td>0</td>\n",
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2100       "      <td>0</td>\n",
2101       "      <td>0</td>\n",
2102       "      <td>0</td>\n",
2103       "      <td>0</td>\n",
2104       "    </tr>\n",
2105       "    <tr>\n",
2106       "      <th>6</th>\n",
2107       "      <td>0</td>\n",
2108       "      <td>1</td>\n",
2109       "      <td>1</td>\n",
2110       "      <td>0</td>\n",
2111       "      <td>0</td>\n",
2112       "      <td>0</td>\n",
2113       "      <td>0</td>\n",
2114       "      <td>0</td>\n",
2115       "      <td>0</td>\n",
2116       "      <td></td>\n",
2117       "      <td>...</td>\n",
2118       "      <td>1</td>\n",
2119       "      <td>0</td>\n",
2120       "      <td>0</td>\n",
2121       "      <td>1</td>\n",
2122       "      <td>0</td>\n",
2123       "      <td>0</td>\n",
2124       "      <td>1</td>\n",
2125       "      <td>0</td>\n",
2126       "      <td>0</td>\n",
2127       "      <td>0</td>\n",
2128       "    </tr>\n",
2129       "    <tr>\n",
2130       "      <th>7</th>\n",
2131       "      <td>0</td>\n",
2132       "      <td>0</td>\n",
2133       "      <td>1</td>\n",
2134       "      <td>0</td>\n",
2135       "      <td>0</td>\n",
2136       "      <td>0</td>\n",
2137       "      <td>0</td>\n",
2138       "      <td>0</td>\n",
2139       "      <td>0</td>\n",
2140       "      <td></td>\n",
2141       "      <td>...</td>\n",
2142       "      <td>0</td>\n",
2143       "      <td>1</td>\n",
2144       "      <td>0</td>\n",
2145       "      <td>0</td>\n",
2146       "      <td>0</td>\n",
2147       "      <td>0</td>\n",
2148       "      <td>0</td>\n",
2149       "      <td>0</td>\n",
2150       "      <td>0</td>\n",
2151       "      <td>0</td>\n",
2152       "    </tr>\n",
2153       "    <tr>\n",
2154       "      <th>8</th>\n",
2155       "      <td>1</td>\n",
2156       "      <td>0</td>\n",
2157       "      <td>0</td>\n",
2158       "      <td>0</td>\n",
2159       "      <td>0</td>\n",
2160       "      <td>0</td>\n",
2161       "      <td>0</td>\n",
2162       "      <td>0</td>\n",
2163       "      <td>0</td>\n",
2164       "      <td></td>\n",
2165       "      <td>...</td>\n",
2166       "      <td>0</td>\n",
2167       "      <td>0</td>\n",
2168       "      <td>1</td>\n",
2169       "      <td>0</td>\n",
2170       "      <td>0</td>\n",
2171       "      <td>0</td>\n",
2172       "      <td>0</td>\n",
2173       "      <td>0</td>\n",
2174       "      <td>0</td>\n",
2175       "      <td>0</td>\n",
2176       "    </tr>\n",
2177       "    <tr>\n",
2178       "      <th>9</th>\n",
2179       "      <td>1</td>\n",
2180       "      <td>1</td>\n",
2181       "      <td>1</td>\n",
2182       "      <td>1</td>\n",
2183       "      <td>10</td>\n",
2184       "      <td>0</td>\n",
2185       "      <td>1</td>\n",
2186       "      <td>0</td>\n",
2187       "      <td>0</td>\n",
2188       "      <td></td>\n",
2189       "      <td>...</td>\n",
2190       "      <td>0</td>\n",
2191       "      <td>0</td>\n",
2192       "      <td>1</td>\n",
2193       "      <td>0</td>\n",
2194       "      <td>0</td>\n",
2195       "      <td>0</td>\n",
2196       "      <td>0</td>\n",
2197       "      <td>0</td>\n",
2198       "      <td>0</td>\n",
2199       "      <td>0</td>\n",
2200       "    </tr>\n",
2201       "  </tbody>\n",
2202       "</table>\n",
2203       "<p>10 rows × 28 columns</p>\n",
2204       "</div>"
2205      ],
2206      "text/plain": [
2207       "   SWC_FLY  Cabin_Class  SWC_FQTV_Member  Passenger_Baggage_Count  \\\n",
2208       "0        1            0                1                        1   \n",
2209       "1        1            0                0                        1   \n",
2210       "2        1            0                1                        0   \n",
2211       "3        1            0                1                        0   \n",
2212       "4        1            0                0                        1   \n",
2213       "5        1            0                0                        2   \n",
2214       "6        0            1                1                        0   \n",
2215       "7        0            0                1                        0   \n",
2216       "8        1            0                0                        0   \n",
2217       "9        1            1                1                        1   \n",
2218       "\n",
2219       "   Passenger_Baggage_Weight  SWC_Staff  SWC_CIP_Passenger  SWC_VIP_Passenger  \\\n",
2220       "0                        17          0                  0                  0   \n",
2221       "1                         7          0                  0                  0   \n",
2222       "2                         0          0                  0                  0   \n",
2223       "3                         0          0                  1                  0   \n",
2224       "4                        18          0                  0                  0   \n",
2225       "5                        27          0                  0                  0   \n",
2226       "6                         0          0                  0                  0   \n",
2227       "7                         0          0                  0                  0   \n",
2228       "8                         0          0                  0                  0   \n",
2229       "9                        10          0                  1                  0   \n",
2230       "\n",
2231       "   SWC_Has_Infant Operation_Count  ...    M  kiosk  kontuar  mobile  online  \\\n",
2232       "0               0                  ...    0      0        0       1       0   \n",
2233       "1               0                  ...    1      0        1       0       0   \n",
2234       "2               0                  ...    1      0        1       0       0   \n",
2235       "3               0                  ...    1      0        0       0       1   \n",
2236       "4               0                  ...    0      0        0       0       1   \n",
2237       "5               0                  ...    1      0        1       0       0   \n",
2238       "6               0                  ...    1      0        0       1       0   \n",
2239       "7               0                  ...    0      1        0       0       0   \n",
2240       "8               0                  ...    0      0        1       0       0   \n",
2241       "9               0                  ...    0      0        1       0       0   \n",
2242       "\n",
2243       "   other  checkin_fly_difference  C/INF  M/INF  F/INF  \n",
2244       "0      0                       1      0      0      0  \n",
2245       "1      0                       0      0      0      0  \n",
2246       "2      0                       0      0      0      0  \n",
2247       "3      0                       1      0      0      0  \n",
2248       "4      0                       1      0      0      0  \n",
2249       "5      0                       0      0      0      0  \n",
2250       "6      0                       1      0      0      0  \n",
2251       "7      0                       0      0      0      0  \n",
2252       "8      0                       0      0      0      0  \n",
2253       "9      0                       0      0      0      0  \n",
2254       "\n",
2255       "[10 rows x 28 columns]"
2256      ]
2257     },
2258     "execution_count": 60,
2259     "metadata": {},
2260     "output_type": "execute_result"
2261    }
2262   ],
2263   "source": [
2264    "processed_test_df.head(10)"
2265   ]
2266  },
2267  {
2268   "cell_type": "markdown",
2269   "metadata": {},
2270   "source": [
2271    "Using Random Forest Model, which was better performing compared to XGBoost, on test data"
2272   ]
2273  },
2274  {
2275   "cell_type": "code",
2276   "execution_count": 61,
2277   "metadata": {},
2278   "outputs": [],
2279   "source": [
2280    "X_test_data = processed_test_df.drop(['Operation_Count'], axis = 1)"
2281   ]
2282  },
2283  {
2284   "cell_type": "code",
2285   "execution_count": 62,
2286   "metadata": {},
2287   "outputs": [],
2288   "source": [
2289    "final_predictions = rfmodel.predict(X_test_data)"
2290   ]
2291  },
2292  {
2293   "cell_type": "markdown",
2294   "metadata": {},
2295   "source": [
2296    "Since final predictions should be integers, we rounded float type prediction values coming from Random Forest Regressor to nearest integer value."
2297   ]
2298  },
2299  {
2300   "cell_type": "code",
2301   "execution_count": 67,
2302   "metadata": {},
2303   "outputs": [],
2304   "source": [
2305    "rounded_list = []\n",
2306    "for pred in final_predictions:\n",
2307    "    pred = int(round(pred))\n",
2308    "    rounded_list.append(pred)"
2309   ]
2310  },
2311  {
2312   "cell_type": "code",
2313   "execution_count": 68,
2314   "metadata": {},
2315   "outputs": [],
2316   "source": [
2317    "processed_test_df['Operation_Count'] = rounded_list"
2318   ]
2319  },
2320  {
2321   "cell_type": "code",
2322   "execution_count": 69,
2323   "metadata": {},
2324   "outputs": [
2325    {
2326     "data": {
2327      "text/html": [
2328       "<div>\n",
2329       "<style scoped>\n",
2330       "    .dataframe tbody tr th:only-of-type {\n",
2331       "        vertical-align: middle;\n",
2332       "    }\n",
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2341       "</style>\n",
2342       "<table border=\"1\" class=\"dataframe\">\n",
2343       "  <thead>\n",
2344       "    <tr style=\"text-align: right;\">\n",
2345       "      <th></th>\n",
2346       "      <th>SWC_FLY</th>\n",
2347       "      <th>Cabin_Class</th>\n",
2348       "      <th>SWC_FQTV_Member</th>\n",
2349       "      <th>Passenger_Baggage_Count</th>\n",
2350       "      <th>Passenger_Baggage_Weight</th>\n",
2351       "      <th>SWC_Staff</th>\n",
2352       "      <th>SWC_CIP_Passenger</th>\n",
2353       "      <th>SWC_VIP_Passenger</th>\n",
2354       "      <th>SWC_Has_Infant</th>\n",
2355       "      <th>Operation_Count</th>\n",
2356       "      <th>...</th>\n",
2357       "      <th>M</th>\n",
2358       "      <th>kiosk</th>\n",
2359       "      <th>kontuar</th>\n",
2360       "      <th>mobile</th>\n",
2361       "      <th>online</th>\n",
2362       "      <th>other</th>\n",
2363       "      <th>checkin_fly_difference</th>\n",
2364       "      <th>C/INF</th>\n",
2365       "      <th>M/INF</th>\n",
2366       "      <th>F/INF</th>\n",
2367       "    </tr>\n",
2368       "  </thead>\n",
2369       "  <tbody>\n",
2370       "    <tr>\n",
2371       "      <th>0</th>\n",
2372       "      <td>1</td>\n",
2373       "      <td>0</td>\n",
2374       "      <td>1</td>\n",
2375       "      <td>1</td>\n",
2376       "      <td>17</td>\n",
2377       "      <td>0</td>\n",
2378       "      <td>0</td>\n",
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2381       "      <td>1</td>\n",
2382       "      <td>...</td>\n",
2383       "      <td>0</td>\n",
2384       "      <td>0</td>\n",
2385       "      <td>0</td>\n",
2386       "      <td>1</td>\n",
2387       "      <td>0</td>\n",
2388       "      <td>0</td>\n",
2389       "      <td>1</td>\n",
2390       "      <td>0</td>\n",
2391       "      <td>0</td>\n",
2392       "      <td>0</td>\n",
2393       "    </tr>\n",
2394       "    <tr>\n",
2395       "      <th>1</th>\n",
2396       "      <td>1</td>\n",
2397       "      <td>0</td>\n",
2398       "      <td>0</td>\n",
2399       "      <td>1</td>\n",
2400       "      <td>7</td>\n",
2401       "      <td>0</td>\n",
2402       "      <td>0</td>\n",
2403       "      <td>0</td>\n",
2404       "      <td>0</td>\n",
2405       "      <td>1</td>\n",
2406       "      <td>...</td>\n",
2407       "      <td>1</td>\n",
2408       "      <td>0</td>\n",
2409       "      <td>1</td>\n",
2410       "      <td>0</td>\n",
2411       "      <td>0</td>\n",
2412       "      <td>0</td>\n",
2413       "      <td>0</td>\n",
2414       "      <td>0</td>\n",
2415       "      <td>0</td>\n",
2416       "      <td>0</td>\n",
2417       "    </tr>\n",
2418       "    <tr>\n",
2419       "      <th>2</th>\n",
2420       "      <td>1</td>\n",
2421       "      <td>0</td>\n",
2422       "      <td>1</td>\n",
2423       "      <td>0</td>\n",
2424       "      <td>0</td>\n",
2425       "      <td>0</td>\n",
2426       "      <td>0</td>\n",
2427       "      <td>0</td>\n",
2428       "      <td>0</td>\n",
2429       "      <td>1</td>\n",
2430       "      <td>...</td>\n",
2431       "      <td>1</td>\n",
2432       "      <td>0</td>\n",
2433       "      <td>1</td>\n",
2434       "      <td>0</td>\n",
2435       "      <td>0</td>\n",
2436       "      <td>0</td>\n",
2437       "      <td>0</td>\n",
2438       "      <td>0</td>\n",
2439       "      <td>0</td>\n",
2440       "      <td>0</td>\n",
2441       "    </tr>\n",
2442       "    <tr>\n",
2443       "      <th>3</th>\n",
2444       "      <td>1</td>\n",
2445       "      <td>0</td>\n",
2446       "      <td>1</td>\n",
2447       "      <td>0</td>\n",
2448       "      <td>0</td>\n",
2449       "      <td>0</td>\n",
2450       "      <td>1</td>\n",
2451       "      <td>0</td>\n",
2452       "      <td>0</td>\n",
2453       "      <td>1</td>\n",
2454       "      <td>...</td>\n",
2455       "      <td>1</td>\n",
2456       "      <td>0</td>\n",
2457       "      <td>0</td>\n",
2458       "      <td>0</td>\n",
2459       "      <td>1</td>\n",
2460       "      <td>0</td>\n",
2461       "      <td>1</td>\n",
2462       "      <td>0</td>\n",
2463       "      <td>0</td>\n",
2464       "      <td>0</td>\n",
2465       "    </tr>\n",
2466       "    <tr>\n",
2467       "      <th>4</th>\n",
2468       "      <td>1</td>\n",
2469       "      <td>0</td>\n",
2470       "      <td>0</td>\n",
2471       "      <td>1</td>\n",
2472       "      <td>18</td>\n",
2473       "      <td>0</td>\n",
2474       "      <td>0</td>\n",
2475       "      <td>0</td>\n",
2476       "      <td>0</td>\n",
2477       "      <td>1</td>\n",
2478       "      <td>...</td>\n",
2479       "      <td>0</td>\n",
2480       "      <td>0</td>\n",
2481       "      <td>0</td>\n",
2482       "      <td>0</td>\n",
2483       "      <td>1</td>\n",
2484       "      <td>0</td>\n",
2485       "      <td>1</td>\n",
2486       "      <td>0</td>\n",
2487       "      <td>0</td>\n",
2488       "      <td>0</td>\n",
2489       "    </tr>\n",
2490       "    <tr>\n",
2491       "      <th>5</th>\n",
2492       "      <td>1</td>\n",
2493       "      <td>0</td>\n",
2494       "      <td>0</td>\n",
2495       "      <td>2</td>\n",
2496       "      <td>27</td>\n",
2497       "      <td>0</td>\n",
2498       "      <td>0</td>\n",
2499       "      <td>0</td>\n",
2500       "      <td>0</td>\n",
2501       "      <td>1</td>\n",
2502       "      <td>...</td>\n",
2503       "      <td>1</td>\n",
2504       "      <td>0</td>\n",
2505       "      <td>1</td>\n",
2506       "      <td>0</td>\n",
2507       "      <td>0</td>\n",
2508       "      <td>0</td>\n",
2509       "      <td>0</td>\n",
2510       "      <td>0</td>\n",
2511       "      <td>0</td>\n",
2512       "      <td>0</td>\n",
2513       "    </tr>\n",
2514       "    <tr>\n",
2515       "      <th>6</th>\n",
2516       "      <td>0</td>\n",
2517       "      <td>1</td>\n",
2518       "      <td>1</td>\n",
2519       "      <td>0</td>\n",
2520       "      <td>0</td>\n",
2521       "      <td>0</td>\n",
2522       "      <td>0</td>\n",
2523       "      <td>0</td>\n",
2524       "      <td>0</td>\n",
2525       "      <td>1</td>\n",
2526       "      <td>...</td>\n",
2527       "      <td>1</td>\n",
2528       "      <td>0</td>\n",
2529       "      <td>0</td>\n",
2530       "      <td>1</td>\n",
2531       "      <td>0</td>\n",
2532       "      <td>0</td>\n",
2533       "      <td>1</td>\n",
2534       "      <td>0</td>\n",
2535       "      <td>0</td>\n",
2536       "      <td>0</td>\n",
2537       "    </tr>\n",
2538       "    <tr>\n",
2539       "      <th>7</th>\n",
2540       "      <td>0</td>\n",
2541       "      <td>0</td>\n",
2542       "      <td>1</td>\n",
2543       "      <td>0</td>\n",
2544       "      <td>0</td>\n",
2545       "      <td>0</td>\n",
2546       "      <td>0</td>\n",
2547       "      <td>0</td>\n",
2548       "      <td>0</td>\n",
2549       "      <td>1</td>\n",
2550       "      <td>...</td>\n",
2551       "      <td>0</td>\n",
2552       "      <td>1</td>\n",
2553       "      <td>0</td>\n",
2554       "      <td>0</td>\n",
2555       "      <td>0</td>\n",
2556       "      <td>0</td>\n",
2557       "      <td>0</td>\n",
2558       "      <td>0</td>\n",
2559       "      <td>0</td>\n",
2560       "      <td>0</td>\n",
2561       "    </tr>\n",
2562       "    <tr>\n",
2563       "      <th>8</th>\n",
2564       "      <td>1</td>\n",
2565       "      <td>0</td>\n",
2566       "      <td>0</td>\n",
2567       "      <td>0</td>\n",
2568       "      <td>0</td>\n",
2569       "      <td>0</td>\n",
2570       "      <td>0</td>\n",
2571       "      <td>0</td>\n",
2572       "      <td>0</td>\n",
2573       "      <td>6</td>\n",
2574       "      <td>...</td>\n",
2575       "      <td>0</td>\n",
2576       "      <td>0</td>\n",
2577       "      <td>1</td>\n",
2578       "      <td>0</td>\n",
2579       "      <td>0</td>\n",
2580       "      <td>0</td>\n",
2581       "      <td>0</td>\n",
2582       "      <td>0</td>\n",
2583       "      <td>0</td>\n",
2584       "      <td>0</td>\n",
2585       "    </tr>\n",
2586       "    <tr>\n",
2587       "      <th>9</th>\n",
2588       "      <td>1</td>\n",
2589       "      <td>1</td>\n",
2590       "      <td>1</td>\n",
2591       "      <td>1</td>\n",
2592       "      <td>10</td>\n",
2593       "      <td>0</td>\n",
2594       "      <td>1</td>\n",
2595       "      <td>0</td>\n",
2596       "      <td>0</td>\n",
2597       "      <td>1</td>\n",
2598       "      <td>...</td>\n",
2599       "      <td>0</td>\n",
2600       "      <td>0</td>\n",
2601       "      <td>1</td>\n",
2602       "      <td>0</td>\n",
2603       "      <td>0</td>\n",
2604       "      <td>0</td>\n",
2605       "      <td>0</td>\n",
2606       "      <td>0</td>\n",
2607       "      <td>0</td>\n",
2608       "      <td>0</td>\n",
2609       "    </tr>\n",
2610       "    <tr>\n",
2611       "      <th>10</th>\n",
2612       "      <td>1</td>\n",
2613       "      <td>0</td>\n",
2614       "      <td>0</td>\n",
2615       "      <td>3</td>\n",
2616       "      <td>34</td>\n",
2617       "      <td>0</td>\n",
2618       "      <td>0</td>\n",
2619       "      <td>0</td>\n",
2620       "      <td>0</td>\n",
2621       "      <td>1</td>\n",
2622       "      <td>...</td>\n",
2623       "      <td>0</td>\n",
2624       "      <td>0</td>\n",
2625       "      <td>1</td>\n",
2626       "      <td>0</td>\n",
2627       "      <td>0</td>\n",
2628       "      <td>0</td>\n",
2629       "      <td>0</td>\n",
2630       "      <td>0</td>\n",
2631       "      <td>0</td>\n",
2632       "      <td>0</td>\n",
2633       "    </tr>\n",
2634       "    <tr>\n",
2635       "      <th>11</th>\n",
2636       "      <td>1</td>\n",
2637       "      <td>0</td>\n",
2638       "      <td>0</td>\n",
2639       "      <td>2</td>\n",
2640       "      <td>30</td>\n",
2641       "      <td>0</td>\n",
2642       "      <td>0</td>\n",
2643       "      <td>0</td>\n",
2644       "      <td>0</td>\n",
2645       "      <td>1</td>\n",
2646       "      <td>...</td>\n",
2647       "      <td>0</td>\n",
2648       "      <td>0</td>\n",
2649       "      <td>1</td>\n",
2650       "      <td>0</td>\n",
2651       "      <td>0</td>\n",
2652       "      <td>0</td>\n",
2653       "      <td>0</td>\n",
2654       "      <td>0</td>\n",
2655       "      <td>0</td>\n",
2656       "      <td>0</td>\n",
2657       "    </tr>\n",
2658       "    <tr>\n",
2659       "      <th>12</th>\n",
2660       "      <td>1</td>\n",
2661       "      <td>0</td>\n",
2662       "      <td>1</td>\n",
2663       "      <td>1</td>\n",
2664       "      <td>9</td>\n",
2665       "      <td>0</td>\n",
2666       "      <td>0</td>\n",
2667       "      <td>0</td>\n",
2668       "      <td>0</td>\n",
2669       "      <td>1</td>\n",
2670       "      <td>...</td>\n",
2671       "      <td>0</td>\n",
2672       "      <td>0</td>\n",
2673       "      <td>0</td>\n",
2674       "      <td>0</td>\n",
2675       "      <td>1</td>\n",
2676       "      <td>0</td>\n",
2677       "      <td>1</td>\n",
2678       "      <td>0</td>\n",
2679       "      <td>0</td>\n",
2680       "      <td>0</td>\n",
2681       "    </tr>\n",
2682       "    <tr>\n",
2683       "      <th>13</th>\n",
2684       "      <td>1</td>\n",
2685       "      <td>0</td>\n",
2686       "      <td>1</td>\n",
2687       "      <td>1</td>\n",
2688       "      <td>7</td>\n",
2689       "      <td>0</td>\n",
2690       "      <td>1</td>\n",
2691       "      <td>0</td>\n",
2692       "      <td>0</td>\n",
2693       "      <td>1</td>\n",
2694       "      <td>...</td>\n",
2695       "      <td>0</td>\n",
2696       "      <td>0</td>\n",
2697       "      <td>0</td>\n",
2698       "      <td>0</td>\n",
2699       "      <td>1</td>\n",
2700       "      <td>0</td>\n",
2701       "      <td>1</td>\n",
2702       "      <td>0</td>\n",
2703       "      <td>0</td>\n",
2704       "      <td>0</td>\n",
2705       "    </tr>\n",
2706       "    <tr>\n",
2707       "      <th>14</th>\n",
2708       "      <td>1</td>\n",
2709       "      <td>0</td>\n",
2710       "      <td>1</td>\n",
2711       "      <td>0</td>\n",
2712       "      <td>0</td>\n",
2713       "      <td>0</td>\n",
2714       "      <td>0</td>\n",
2715       "      <td>0</td>\n",
2716       "      <td>0</td>\n",
2717       "      <td>2</td>\n",
2718       "      <td>...</td>\n",
2719       "      <td>1</td>\n",
2720       "      <td>0</td>\n",
2721       "      <td>0</td>\n",
2722       "      <td>1</td>\n",
2723       "      <td>0</td>\n",
2724       "      <td>0</td>\n",
2725       "      <td>1</td>\n",
2726       "      <td>0</td>\n",
2727       "      <td>0</td>\n",
2728       "      <td>0</td>\n",
2729       "    </tr>\n",
2730       "    <tr>\n",
2731       "      <th>15</th>\n",
2732       "      <td>1</td>\n",
2733       "      <td>0</td>\n",
2734       "      <td>0</td>\n",
2735       "      <td>1</td>\n",
2736       "      <td>14</td>\n",
2737       "      <td>0</td>\n",
2738       "      <td>0</td>\n",
2739       "      <td>0</td>\n",
2740       "      <td>0</td>\n",
2741       "      <td>1</td>\n",
2742       "      <td>...</td>\n",
2743       "      <td>1</td>\n",
2744       "      <td>1</td>\n",
2745       "      <td>0</td>\n",
2746       "      <td>0</td>\n",
2747       "      <td>0</td>\n",
2748       "      <td>0</td>\n",
2749       "      <td>0</td>\n",
2750       "      <td>0</td>\n",
2751       "      <td>0</td>\n",
2752       "      <td>0</td>\n",
2753       "    </tr>\n",
2754       "    <tr>\n",
2755       "      <th>16</th>\n",
2756       "      <td>1</td>\n",
2757       "      <td>0</td>\n",
2758       "      <td>1</td>\n",
2759       "      <td>2</td>\n",
2760       "      <td>26</td>\n",
2761       "      <td>0</td>\n",
2762       "      <td>0</td>\n",
2763       "      <td>0</td>\n",
2764       "      <td>0</td>\n",
2765       "      <td>1</td>\n",
2766       "      <td>...</td>\n",
2767       "      <td>0</td>\n",
2768       "      <td>0</td>\n",
2769       "      <td>0</td>\n",
2770       "      <td>1</td>\n",
2771       "      <td>0</td>\n",
2772       "      <td>0</td>\n",
2773       "      <td>0</td>\n",
2774       "      <td>0</td>\n",
2775       "      <td>0</td>\n",
2776       "      <td>0</td>\n",
2777       "    </tr>\n",
2778       "    <tr>\n",
2779       "      <th>17</th>\n",
2780       "      <td>1</td>\n",
2781       "      <td>0</td>\n",
2782       "      <td>0</td>\n",
2783       "      <td>0</td>\n",
2784       "      <td>0</td>\n",
2785       "      <td>0</td>\n",
2786       "      <td>0</td>\n",
2787       "      <td>0</td>\n",
2788       "      <td>0</td>\n",
2789       "      <td>1</td>\n",
2790       "      <td>...</td>\n",
2791       "      <td>1</td>\n",
2792       "      <td>0</td>\n",
2793       "      <td>1</td>\n",
2794       "      <td>0</td>\n",
2795       "      <td>0</td>\n",
2796       "      <td>0</td>\n",
2797       "      <td>0</td>\n",
2798       "      <td>0</td>\n",
2799       "      <td>0</td>\n",
2800       "      <td>0</td>\n",
2801       "    </tr>\n",
2802       "    <tr>\n",
2803       "      <th>18</th>\n",
2804       "      <td>1</td>\n",
2805       "      <td>0</td>\n",
2806       "      <td>0</td>\n",
2807       "      <td>0</td>\n",
2808       "      <td>0</td>\n",
2809       "      <td>0</td>\n",
2810       "      <td>0</td>\n",
2811       "      <td>0</td>\n",
2812       "      <td>0</td>\n",
2813       "      <td>6</td>\n",
2814       "      <td>...</td>\n",
2815       "      <td>0</td>\n",
2816       "      <td>0</td>\n",
2817       "      <td>1</td>\n",
2818       "      <td>0</td>\n",
2819       "      <td>0</td>\n",
2820       "      <td>0</td>\n",
2821       "      <td>0</td>\n",
2822       "      <td>0</td>\n",
2823       "      <td>0</td>\n",
2824       "      <td>0</td>\n",
2825       "    </tr>\n",
2826       "    <tr>\n",
2827       "      <th>19</th>\n",
2828       "      <td>1</td>\n",
2829       "      <td>0</td>\n",
2830       "      <td>1</td>\n",
2831       "      <td>6</td>\n",
2832       "      <td>22</td>\n",
2833       "      <td>0</td>\n",
2834       "      <td>0</td>\n",
2835       "      <td>0</td>\n",
2836       "      <td>0</td>\n",
2837       "      <td>1</td>\n",
2838       "      <td>...</td>\n",
2839       "      <td>1</td>\n",
2840       "      <td>0</td>\n",
2841       "      <td>1</td>\n",
2842       "      <td>0</td>\n",
2843       "      <td>0</td>\n",
2844       "      <td>0</td>\n",
2845       "      <td>0</td>\n",
2846       "      <td>0</td>\n",
2847       "      <td>0</td>\n",
2848       "      <td>0</td>\n",
2849       "    </tr>\n",
2850       "    <tr>\n",
2851       "      <th>20</th>\n",
2852       "      <td>1</td>\n",
2853       "      <td>0</td>\n",
2854       "      <td>1</td>\n",
2855       "      <td>0</td>\n",
2856       "      <td>0</td>\n",
2857       "      <td>0</td>\n",
2858       "      <td>1</td>\n",
2859       "      <td>0</td>\n",
2860       "      <td>0</td>\n",
2861       "      <td>1</td>\n",
2862       "      <td>...</td>\n",
2863       "      <td>1</td>\n",
2864       "      <td>0</td>\n",
2865       "      <td>1</td>\n",
2866       "      <td>0</td>\n",
2867       "      <td>0</td>\n",
2868       "      <td>0</td>\n",
2869       "      <td>0</td>\n",
2870       "      <td>0</td>\n",
2871       "      <td>0</td>\n",
2872       "      <td>0</td>\n",
2873       "    </tr>\n",
2874       "    <tr>\n",
2875       "      <th>21</th>\n",
2876       "      <td>1</td>\n",
2877       "      <td>0</td>\n",
2878       "      <td>0</td>\n",
2879       "      <td>0</td>\n",
2880       "      <td>0</td>\n",
2881       "      <td>0</td>\n",
2882       "      <td>0</td>\n",
2883       "      <td>0</td>\n",
2884       "      <td>0</td>\n",
2885       "      <td>1</td>\n",
2886       "      <td>...</td>\n",
2887       "      <td>1</td>\n",
2888       "      <td>0</td>\n",
2889       "      <td>1</td>\n",
2890       "      <td>0</td>\n",
2891       "      <td>0</td>\n",
2892       "      <td>0</td>\n",
2893       "      <td>1</td>\n",
2894       "      <td>0</td>\n",
2895       "      <td>0</td>\n",
2896       "      <td>0</td>\n",
2897       "    </tr>\n",
2898       "    <tr>\n",
2899       "      <th>22</th>\n",
2900       "      <td>1</td>\n",
2901       "      <td>0</td>\n",
2902       "      <td>0</td>\n",
2903       "      <td>2</td>\n",
2904       "      <td>14</td>\n",
2905       "      <td>0</td>\n",
2906       "      <td>0</td>\n",
2907       "      <td>0</td>\n",
2908       "      <td>0</td>\n",
2909       "      <td>1</td>\n",
2910       "      <td>...</td>\n",
2911       "      <td>1</td>\n",
2912       "      <td>1</td>\n",
2913       "      <td>0</td>\n",
2914       "      <td>0</td>\n",
2915       "      <td>0</td>\n",
2916       "      <td>0</td>\n",
2917       "      <td>0</td>\n",
2918       "      <td>0</td>\n",
2919       "      <td>0</td>\n",
2920       "      <td>0</td>\n",
2921       "    </tr>\n",
2922       "    <tr>\n",
2923       "      <th>23</th>\n",
2924       "      <td>1</td>\n",
2925       "      <td>0</td>\n",
2926       "      <td>0</td>\n",
2927       "      <td>0</td>\n",
2928       "      <td>0</td>\n",
2929       "      <td>0</td>\n",
2930       "      <td>0</td>\n",
2931       "      <td>0</td>\n",
2932       "      <td>0</td>\n",
2933       "      <td>3</td>\n",
2934       "      <td>...</td>\n",
2935       "      <td>1</td>\n",
2936       "      <td>0</td>\n",
2937       "      <td>0</td>\n",
2938       "      <td>1</td>\n",
2939       "      <td>0</td>\n",
2940       "      <td>0</td>\n",
2941       "      <td>0</td>\n",
2942       "      <td>0</td>\n",
2943       "      <td>0</td>\n",
2944       "      <td>0</td>\n",
2945       "    </tr>\n",
2946       "    <tr>\n",
2947       "      <th>24</th>\n",
2948       "      <td>1</td>\n",
2949       "      <td>0</td>\n",
2950       "      <td>1</td>\n",
2951       "      <td>0</td>\n",
2952       "      <td>0</td>\n",
2953       "      <td>0</td>\n",
2954       "      <td>1</td>\n",
2955       "      <td>0</td>\n",
2956       "      <td>0</td>\n",
2957       "      <td>1</td>\n",
2958       "      <td>...</td>\n",
2959       "      <td>1</td>\n",
2960       "      <td>0</td>\n",
2961       "      <td>0</td>\n",
2962       "      <td>0</td>\n",
2963       "      <td>1</td>\n",
2964       "      <td>0</td>\n",
2965       "      <td>1</td>\n",
2966       "      <td>0</td>\n",
2967       "      <td>0</td>\n",
2968       "      <td>0</td>\n",
2969       "    </tr>\n",
2970       "  </tbody>\n",
2971       "</table>\n",
2972       "<p>25 rows × 28 columns</p>\n",
2973       "</div>"
2974      ],
2975      "text/plain": [
2976       "    SWC_FLY  Cabin_Class  SWC_FQTV_Member  Passenger_Baggage_Count  \\\n",
2977       "0         1            0                1                        1   \n",
2978       "1         1            0                0                        1   \n",
2979       "2         1            0                1                        0   \n",
2980       "3         1            0                1                        0   \n",
2981       "4         1            0                0                        1   \n",
2982       "5         1            0                0                        2   \n",
2983       "6         0            1                1                        0   \n",
2984       "7         0            0                1                        0   \n",
2985       "8         1            0                0                        0   \n",
2986       "9         1            1                1                        1   \n",
2987       "10        1            0                0                        3   \n",
2988       "11        1            0                0                        2   \n",
2989       "12        1            0                1                        1   \n",
2990       "13        1            0                1                        1   \n",
2991       "14        1            0                1                        0   \n",
2992       "15        1            0                0                        1   \n",
2993       "16        1            0                1                        2   \n",
2994       "17        1            0                0                        0   \n",
2995       "18        1            0                0                        0   \n",
2996       "19        1            0                1                        6   \n",
2997       "20        1            0                1                        0   \n",
2998       "21        1            0                0                        0   \n",
2999       "22        1            0                0                        2   \n",
3000       "23        1            0                0                        0   \n",
3001       "24        1            0                1                        0   \n",
3002       "\n",
3003       "    Passenger_Baggage_Weight  SWC_Staff  SWC_CIP_Passenger  SWC_VIP_Passenger  \\\n",
3004       "0                         17          0                  0                  0   \n",
3005       "1                          7          0                  0                  0   \n",
3006       "2                          0          0                  0                  0   \n",
3007       "3                          0          0                  1                  0   \n",
3008       "4                         18          0                  0                  0   \n",
3009       "5                         27          0                  0                  0   \n",
3010       "6                          0          0                  0                  0   \n",
3011       "7                          0          0                  0                  0   \n",
3012       "8                          0          0                  0                  0   \n",
3013       "9                         10          0                  1                  0   \n",
3014       "10                        34          0                  0                  0   \n",
3015       "11                        30          0                  0                  0   \n",
3016       "12                         9          0                  0                  0   \n",
3017       "13                         7          0                  1                  0   \n",
3018       "14                         0          0                  0                  0   \n",
3019       "15                        14          0                  0                  0   \n",
3020       "16                        26          0                  0                  0   \n",
3021       "17                         0          0                  0                  0   \n",
3022       "18                         0          0                  0                  0   \n",
3023       "19                        22          0                  0                  0   \n",
3024       "20                         0          0                  1                  0   \n",
3025       "21                         0          0                  0                  0   \n",
3026       "22                        14          0                  0                  0   \n",
3027       "23                         0          0                  0                  0   \n",
3028       "24                         0          0                  1                  0   \n",
3029       "\n",
3030       "    SWC_Has_Infant  Operation_Count  ...    M  kiosk  kontuar  mobile  online  \\\n",
3031       "0                0                1  ...    0      0        0       1       0   \n",
3032       "1                0                1  ...    1      0        1       0       0   \n",
3033       "2                0                1  ...    1      0        1       0       0   \n",
3034       "3                0                1  ...    1      0        0       0       1   \n",
3035       "4                0                1  ...    0      0        0       0       1   \n",
3036       "5                0                1  ...    1      0        1       0       0   \n",
3037       "6                0                1  ...    1      0        0       1       0   \n",
3038       "7                0                1  ...    0      1        0       0       0   \n",
3039       "8                0                6  ...    0      0        1       0       0   \n",
3040       "9                0                1  ...    0      0        1       0       0   \n",
3041       "10               0                1  ...    0      0        1       0       0   \n",
3042       "11               0                1  ...    0      0        1       0       0   \n",
3043       "12               0                1  ...    0      0        0       0       1   \n",
3044       "13               0                1  ...    0      0        0       0       1   \n",
3045       "14               0                2  ...    1      0        0       1       0   \n",
3046       "15               0                1  ...    1      1        0       0       0   \n",
3047       "16               0                1  ...    0      0        0       1       0   \n",
3048       "17               0                1  ...    1      0        1       0       0   \n",
3049       "18               0                6  ...    0      0        1       0       0   \n",
3050       "19               0                1  ...    1      0        1       0       0   \n",
3051       "20               0                1  ...    1      0        1       0       0   \n",
3052       "21               0                1  ...    1      0        1       0       0   \n",
3053       "22               0                1  ...    1      1        0       0       0   \n",
3054       "23               0                3  ...    1      0        0       1       0   \n",
3055       "24               0                1  ...    1      0        0       0       1   \n",
3056       "\n",
3057       "    other  checkin_fly_difference  C/INF  M/INF  F/INF  \n",
3058       "0       0                       1      0      0      0  \n",
3059       "1       0                       0      0      0      0  \n",
3060       "2       0                       0      0      0      0  \n",
3061       "3       0                       1      0      0      0  \n",
3062       "4       0                       1      0      0      0  \n",
3063       "5       0                       0      0      0      0  \n",
3064       "6       0                       1      0      0      0  \n",
3065       "7       0                       0      0      0      0  \n",
3066       "8       0                       0      0      0      0  \n",
3067       "9       0                       0      0      0      0  \n",
3068       "10      0                       0      0      0      0  \n",
3069       "11      0                       0      0      0      0  \n",
3070       "12      0                       1      0      0      0  \n",
3071       "13      0                       1      0      0      0  \n",
3072       "14      0                       1      0      0      0  \n",
3073       "15      0                       0      0      0      0  \n",
3074       "16      0                       0      0      0      0  \n",
3075       "17      0                       0      0      0      0  \n",
3076       "18      0                       0      0      0      0  \n",
3077       "19      0                       0      0      0      0  \n",
3078       "20      0                       0      0      0      0  \n",
3079       "21      0                       1      0      0      0  \n",
3080       "22      0                       0      0      0      0  \n",
3081       "23      0                       0      0      0      0  \n",
3082       "24      0                       1      0      0      0  \n",
3083       "\n",
3084       "[25 rows x 28 columns]"
3085      ]
3086     },
3087     "execution_count": 69,
3088     "metadata": {},
3089     "output_type": "execute_result"
3090    }
3091   ],
3092   "source": [
3093    "processed_test_df.head(25)"
3094   ]
3095  },
3096  {
3097   "cell_type": "code",
3098   "execution_count": 71,
3099   "metadata": {},
3100   "outputs": [],
3101   "source": [
3102    "### Printing to Excel ###\n",
3103    "# If you wish you can print results to excel by uncommenting and executing this cell.\n",
3104    "# processed_test_df.to_excel(\"Enter filename here\")"
3105   ]
3106  },
3107  {
3108   "cell_type": "markdown",
3109   "metadata": {},
3110   "source": [
3111    "<b> Thanks for checking out our work :) </b>"
3112   ]
3113  }
3114 ],
3115 "metadata": {
3116  "kernelspec": {
3117   "display_name": "Python 3",
3118   "language": "python",
3119   "name": "python3"
3120  },
3121  "language_info": {
3122   "codemirror_mode": {
3123    "name": "ipython",
3124    "version": 3
3125   },
3126   "file_extension": ".py",
3127   "mimetype": "text/x-python",
3128   "name": "python",
3129   "nbconvert_exporter": "python",
3130   "pygments_lexer": "ipython3",
3131   "version": "3.7.1"
3132  }
3133 },
3134 "nbformat": 4,
3135 "nbformat_minor": 2
3136}