	num_passengers	sales_channel	trip_type	purchase_lead	length_of_stay	flight_hour	flight_day	route	booking_origin	wants_extra_baggage	wants_preferred_seat	wants_in_flight_meals	flight_duration	booking_complete
0	1	Internet	RoundTrip	21	12	6	Tue	AKLHGH	Australia	0	0	0	7.21	1
1	2	Internet	RoundTrip	262	19	7	Sat	AKLDEL	New Zealand	1	0	0	5.52	0
2	1	Internet	RoundTrip	112	20	3	Sat	AKLDEL	New Zealand	0	0	0	5.52	0
3	2	Internet	RoundTrip	243	22	17	Wed	AKLDEL	India	1	1	0	5.52	0
4	1	Internet	RoundTrip	96	31	4	Sat	AKLDEL	New Zealand	0	0	1	5.52	0

",{"_149":61,"_150":171,"_54":172},{"_152":153,"_154":155},{"_157":173},"idle","timeout_triggered",[176],"c366e341-13e7-41b0-b9d4-215e9cfacac9",{"_40":176,"_45":178,"_71":120,"_41":187},{"_40":176,"_47":179,"_32":181,"_54":182,"_61":62,"_63":14,"_64":65,"_51":185,"_69":70},{"_49":102,"_51":180},{},1689437191.09521,{"_56":57,"_58":183},[184],"The dataset appears to contain information about passenger bookings for British Airways flights. Here's a brief description of the columns:\n\n1. `num_passengers`: The number of passengers in the booking.\n2. `sales_channel`: The channel through which the ticket was purchased (e.g., Internet).\n3. `trip_type`: The type of trip (e.g., RoundTrip).\n4. `purchase_lead`: The number of days between the booking and the flight.\n5. `length_of_stay`: The length of the stay in days.\n6. `flight_hour`: The hour of the flight.\n7. `flight_day`: The day of the week the flight is on.\n8. `route`: The route of the flight.\n9. `booking_origin`: The origin of the booking.\n10. `wants_extra_baggage`: Whether the passenger wants extra baggage (1 if yes, 0 if no).\n11. `wants_preferred_seat`: Whether the passenger wants a preferred seat (1 if yes, 0 if no).\n12. `wants_in_flight_meals`: Whether the passenger wants in-flight meals (1 if yes, 0 if no).\n13. `flight_duration`: The duration of the flight in hours.\n14. `booking_complete`: Whether the booking is complete (1 if yes, 0 if no).\n\nBased on this information, there are many analyses that we can perform. Here are a few ideas:\n\n1. **Booking trends**: Analyze booking trends based on `sales_channel`, `trip_type`, `flight_day`, `route`, and `booking_origin`. Look at how these factors influence the number of passengers (`num_passengers`) and the completion of the booking (`booking_complete`).\n\n2. **Lead time analysis**: Analyze the `purchase_lead` to understand how far in advance people tend to book their flights. This could be further broken down by factors like `trip_type`, `route`, and `booking_origin`.\n\n3. **Preferences analysis**: Analyze the data related to passenger preferences, such as `wants_extra_baggage`, `wants_preferred_seat`, and `wants_in_flight_meals`. Understand how these preferences vary with other factors like `sales_channel`, `trip_type`, `route`, and `booking_origin`.\n\n4. **Flight schedule analysis**: Analyze `flight_hour` and `flight_day` to understand the most popular times for flights. This could also be analyzed in conjunction with `route` and `booking_origin`.\n\n5. **Stay duration analysis**: Analyze `length_of_stay` to understand how long passengers typically stay at their destination. This could also be broken down by `route` and `booking_origin`.\n\n6. **Flight duration analysis**: Analyze `flight_duration` in relation to other variables such as `route`, `booking_origin`, and passenger preferences.\n\n7. **Multivariate analysis**: Perform multivariate analysis to understand the relationship between different variables in the dataset.\n\n8. **Predictive modeling**: Build a predictive model to forecast future bookings or to predict whether a booking will be completed based on the other variables in the dataset.\n\nLet's start by performing some basic descriptive analysis of the data to get a better understanding of its characteristics.",{"_111":186,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[188],"4687e684-60d5-4470-86e7-bbdc77f3da6b",{"_40":188,"_45":190,"_71":176,"_41":198},{"_40":188,"_47":191,"_32":193,"_54":194,"_61":62,"_63":14,"_64":65,"_51":196,"_69":118},{"_49":102,"_51":192},{},1689437191.095511,{"_56":106,"_107":108,"_57":195},"# Basic descriptive analysis\ndata.describe(include='all')",{"_111":197,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[199],"998e4ea6-f28e-43bc-b624-34a594285951",{"_40":199,"_45":201,"_71":188,"_41":228},{"_40":199,"_47":202,"_32":204,"_34":205,"_54":206,"_61":62,"_64":65,"_51":208,"_69":70},{"_49":124,"_87":118,"_51":203},{},1689437191.095802,1689437174.7221537,{"_56":129,"_57":207}," num_passengers sales_channel trip_type purchase_lead \\\ncount 50002.000000 50002 50002 50002.000000 \nunique NaN 2 3 NaN \ntop NaN Internet RoundTrip NaN \nfreq NaN 44383 49498 NaN \nmean 1.591256 NaN NaN 84.940582 \nstd 1.020167 NaN NaN 90.450548 \nmin 1.000000 NaN NaN 0.000000 \n25% 1.000000 NaN NaN 21.000000 \n50% 1.000000 NaN NaN 51.000000 \n75% 2.000000 NaN NaN 115.000000 \nmax 9.000000 NaN NaN 867.000000 \n\n length_of_stay flight_hour flight_day route booking_origin \\\ncount 50002.000000 50002.000000 50002 50002 50002 \nunique NaN NaN 7 799 104 \ntop NaN NaN Mon AKLKUL Australia \nfreq NaN NaN 8102 2680 17873 \nmean 23.044778 9.066277 NaN NaN NaN \nstd 33.887171 5.412569 NaN NaN NaN \nmin 0.000000 0.000000 NaN NaN NaN \n25% 5.000000 5.000000 NaN NaN NaN \n50% 17.000000 9.000000 NaN NaN NaN \n75% 28.000000 13.000000 NaN NaN NaN \nmax 778.000000 23.000000 NaN NaN NaN \n\n wants_extra_baggage wants_preferred_seat wants_in_flight_meals \\\ncount 50002.000000 50002.000000 50002.000000 \nunique NaN NaN NaN \ntop NaN NaN NaN \nfreq NaN NaN NaN \nmean 0.668773 0.296968 0.427143 \nstd 0.470659 0.456927 0.494668 \nmin 0.000000 0.000000 0.000000 \n25% 0.000000 0.000000 0.000000 \n50% 1.000000 0.000000 0.000000 \n75% 1.000000 1.000000 1.000000 \nmax 1.000000 1.000000 1.000000 \n\n flight_duration booking_complete \ncount 50002.000000 50002.000000 \nunique NaN NaN \ntop NaN NaN \nfreq NaN NaN \nmean 7.277524 0.149574 \nstd 1.496854 0.356657 \nmin 4.670000 0.000000 \n25% 5.620000 0.000000 \n50% 7.570000 0.000000 \n75% 8.830000 0.000000 \nmax 9.500000 1.000000 ",{"_132":209,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_61":134,"_135":210,"_137":211,"_34":212,"_106":195,"_140":212,"_141":207,"_142":-5,"_143":-5,"_144":213,"_146":214,"_174":-5},"afed5621-704c-4556-8204-cabe46b5f10e",1689437174.6025054,1689437174.7217743,[],[215,218,220,225],{"_149":61,"_150":216,"_54":217},{"_152":153,"_154":155},{"_157":158},{"_149":160,"_150":219},{"_152":153,"_154":155},{"_150":221,"_149":164,"_54":222},{"_152":153,"_154":155},{"_28":223},{"_167":207,"_168":224},"

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	num_passengers	sales_channel	trip_type	purchase_lead	length_of_stay	flight_hour	flight_day	route	booking_origin	wants_extra_baggage	wants_preferred_seat	wants_in_flight_meals	flight_duration	booking_complete
count	50002.000000	50002	50002	50002.000000	50002.000000	50002.000000	50002	50002	50002	50002.000000	50002.000000	50002.000000	50002.000000	50002.000000
unique	NaN	2	3	NaN	NaN	NaN	7	799	104	NaN	NaN	NaN	NaN	NaN
top	NaN	Internet	RoundTrip	NaN	NaN	NaN	Mon	AKLKUL	Australia	NaN	NaN	NaN	NaN	NaN
freq	NaN	44383	49498	NaN	NaN	NaN	8102	2680	17873	NaN	NaN	NaN	NaN	NaN
mean	1.591256	NaN	NaN	84.940582	23.044778	9.066277	NaN	NaN	NaN	0.668773	0.296968	0.427143	7.277524	0.149574
std	1.020167	NaN	NaN	90.450548	33.887171	5.412569	NaN	NaN	NaN	0.470659	0.456927	0.494668	1.496854	0.356657
min	1.000000	NaN	NaN	0.000000	0.000000	0.000000	NaN	NaN	NaN	0.000000	0.000000	0.000000	4.670000	0.000000
25%	1.000000	NaN	NaN	21.000000	5.000000	5.000000	NaN	NaN	NaN	0.000000	0.000000	0.000000	5.620000	0.000000
50%	1.000000	NaN	NaN	51.000000	17.000000	9.000000	NaN	NaN	NaN	1.000000	0.000000	0.000000	7.570000	0.000000
75%	2.000000	NaN	NaN	115.000000	28.000000	13.000000	NaN	NaN	NaN	1.000000	1.000000	1.000000	8.830000	0.000000
max	9.000000	NaN	NaN	867.000000	778.000000	23.000000	NaN	NaN	NaN	1.000000	1.000000	1.000000	9.500000	1.000000

",{"_149":61,"_150":226,"_54":227},{"_152":153,"_154":155},{"_157":173},[229],"a0f5151d-87fb-4a27-9807-c46ef48853a8",{"_40":229,"_45":231,"_71":199,"_41":241},{"_40":229,"_47":232,"_32":234,"_54":235,"_61":62,"_63":12,"_64":65,"_51":238,"_69":70},{"_49":102,"_51":233},{},1689437191.096066,{"_56":57,"_58":236},[237],"Based on the descriptive statistics of the data:\n\n- The average number of passengers per booking (`num_passengers`) is approximately 1.6, with a maximum of 9 passengers in a single booking.\n\n- The majority of bookings are made via the Internet, with a small number made over the Phone.\n\n- Most trips are Round Trips, with a smaller number of One Way and Multi City trips.\n\n- The average lead time for purchases (`purchase_lead`) is approximately 85 days, with some passengers booking as much as 867 days (over two years) in advance.\n\n- The average length of stay is around 23 days, with a maximum stay of 778 days.\n\n- Flights occur at all hours of the day, with an average flight time around 9am. Monday is the most common flight day.\n\n- The most common flight route is `AKLKUL` (presumably Auckland to Kuala Lumpur), with the most common booking origin being Australia.\n\n- A large number of passengers want extra baggage (67% of bookings), while fewer want preferred seating (30%) or in-flight meals (43%).\n\n- The average flight duration is approximately 7.3 hours, with durations ranging from 4.67 to 9.5 hours.\n\n- Only about 15% of bookings are marked as complete.\n\nGiven these findings, it would be interesting to further explore the distribution of these variables and their interrelationships, as well as investigate why only 15% of bookings are completed. This could involve analyzing factors influencing booking completion, studying the effect of sales channels on booking completion, or exploring the relationship between booking lead time and completion. We could also analyze passenger preferences (extra baggage, preferred seating, in-flight meals) and how these affect booking completion.",{"_111":239,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":240},"<|diff_marker|>",[242],"aaa2173f-9aca-4d3e-b016-2c63621d4078",{"_40":242,"_45":244,"_71":229,"_41":252},{"_40":242,"_47":245,"_32":247,"_54":248,"_61":62,"_64":65,"_51":251,"_69":70},{"_49":77,"_51":246},{},1689437246.269702,{"_56":57,"_58":249},[250],"yes let's do some visualisations. ",{"_94":95,"_96":-5,"_67":24},[253],"25d2f25b-1be7-4fd7-a996-b7f9bb32aa9a",{"_40":253,"_45":255,"_71":242,"_41":263},{"_40":253,"_47":256,"_32":258,"_54":259,"_61":62,"_63":14,"_64":65,"_51":261,"_69":118},{"_49":102,"_51":257},{},1689437275.347827,{"_56":106,"_107":108,"_57":260},"import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Set up the matplotlib figure\nf, axes = plt.subplots(2, 2, figsize=(15, 10))\n\n# Plot a histogram of the number of passengers\nsns.histplot(data=data, x=\"num_passengers\", ax=axes[0, 0], bins=10, kde=False)\naxes[0, 0].set_title('Number of Passengers')\n\n# Plot a histogram of the purchase lead time\nsns.histplot(data=data, x=\"purchase_lead\", ax=axes[0, 1], bins=50, kde=False)\naxes[0, 1].set_title('Purchase Lead Time')\n\n# Plot a histogram of the length of stay\nsns.histplot(data=data, x=\"length_of_stay\", ax=axes[1, 0], bins=50, kde=False)\naxes[1, 0].set_title('Length of Stay')\n\n# Plot a histogram of the flight duration\nsns.histplot(data=data, x=\"flight_duration\", ax=axes[1, 1], bins=50, kde=False)\naxes[1, 1].set_title('Flight Duration')\n\nplt.tight_layout()\nplt.show()",{"_111":262,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[264],"da44d0b3-b20d-4e39-8e50-a7e6e5ef499e",{"_40":264,"_45":266,"_71":253,"_41":305},{"_40":264,"_47":267,"_32":269,"_34":270,"_54":271,"_61":62,"_64":65,"_51":273,"_69":70},{"_49":124,"_87":118,"_51":268},{},1689437275.348027,1689437263.3363173,{"_56":129,"_57":272},"\n<>",{"_132":274,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_61":134,"_135":275,"_137":276,"_34":277,"_106":260,"_140":277,"_141":-5,"_142":-5,"_143":-5,"_144":278,"_146":288,"_174":-5},"106c3e3d-11b1-4425-ad46-5295605d019f",1689437259.629881,1689437263.3358684,[279],{"_96":280,"_281":282,"_283":284,"_285":-5,"_286":287},"image","time",1689437263.3143334,"sender","server","image_payload","image_url","file-service://bd79ed16-3cde-4e76-9fe8-a47d548f49b6?shared_conversation_id=ff1c2d5d-80ef-461a-bc69-12326ee340ba",[289,292,294,302],{"_149":61,"_150":290,"_54":291},{"_152":153,"_154":155},{"_157":158},{"_149":160,"_150":293},{"_152":153,"_154":155},{"_149":295,"_150":296,"_54":297},"display_data",{"_152":153,"_154":155},{"_28":298},{"_167":299,"_300":301},"

","image/vnd.openai.fileservice.png","file-service://bd79ed16-3cde-4e76-9fe8-a47d548f49b6",{"_149":61,"_150":303,"_54":304},{"_152":153,"_154":155},{"_157":173},[306],"40adf214-435f-4f4f-824a-d1f6d58dff7c",{"_40":306,"_45":308,"_71":264,"_41":317},{"_40":306,"_47":309,"_32":311,"_54":312,"_61":62,"_63":12,"_64":65,"_51":315,"_69":70},{"_49":102,"_51":310},{},1689437275.348183,{"_56":57,"_58":313},[314],"Here are the visualizations of several variables in the dataset:\n\n1. **Number of Passengers**: Most bookings are for one passenger, with fewer bookings for two or more passengers. There are very few bookings for more than four passengers.\n\n2. **Purchase Lead Time**: There is a wide range in the number of days between booking and flying. While many bookings are made within 100 days of the flight, there are some bookings made well over 200 days in advance. \n\n3. **Length of Stay**: The majority of stays are short, with most being less than 50 days. However, there are a few very long stays, some lasting over 600 days.\n\n4. **Flight Duration**: Flight durations are spread across a wide range. There are peaks around 5-6 hours and 7-8 hours, and the majority of flights are less than 9 hours long.\n\nNext, we could look at the categorical variables such as `sales_channel`, `trip_type`, and `flight_day`, as well as the binary variables such as `wants_extra_baggage`, `wants_preferred_seat`, and `wants_in_flight_meals`. We could also look at `booking_complete` to see how many bookings are completed. Would you like to proceed with these visualizations?",{"_111":316,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":240},[318],"aaa2adb5-0578-44bf-9f9b-a33d401ed4a1",{"_40":318,"_45":320,"_71":306,"_41":328},{"_40":318,"_47":321,"_32":323,"_54":324,"_61":62,"_64":65,"_51":327,"_69":70},{"_49":77,"_51":322},{},1689437307.426587,{"_56":57,"_58":325},[326],"yes",{"_94":95,"_96":-5,"_67":24},[329],"e83886fb-5aee-4d0d-a458-2adfe4354189",{"_40":329,"_45":331,"_71":318,"_41":339},{"_40":329,"_47":332,"_32":334,"_54":335,"_61":62,"_63":14,"_64":65,"_51":337,"_69":118},{"_49":102,"_51":333},{},1689437333.692916,{"_56":106,"_107":108,"_57":336},"# Set up the matplotlib figure\nf, axes = plt.subplots(3, 2, figsize=(15, 15))\n\n# Plot a countplot of the sales channel\nsns.countplot(data=data, x=\"sales_channel\", ax=axes[0, 0])\naxes[0, 0].set_title('Sales Channel')\n\n# Plot a countplot of the trip type\nsns.countplot(data=data, x=\"trip_type\", ax=axes[0, 1])\naxes[0, 1].set_title('Trip Type')\n\n# Plot a countplot of the flight day\nsns.countplot(data=data, x=\"flight_day\", ax=axes[1, 0])\naxes[1, 0].set_title('Flight Day')\n\n# Plot a countplot of the wants extra baggage\nsns.countplot(data=data, x=\"wants_extra_baggage\", ax=axes[1, 1])\naxes[1, 1].set_title('Wants Extra Baggage')\n\n# Plot a countplot of the wants preferred seat\nsns.countplot(data=data, x=\"wants_preferred_seat\", ax=axes[2, 0])\naxes[2, 0].set_title('Wants Preferred Seat')\n\n# Plot a countplot of the wants in-flight meals\nsns.countplot(data=data, x=\"wants_in_flight_meals\", ax=axes[2, 1])\naxes[2, 1].set_title('Wants In-flight Meals')\n\nplt.tight_layout()\nplt.show()",{"_111":338,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[340],"7d8834ea-79ef-412f-979a-dfd8f3bedb66",{"_40":340,"_45":342,"_71":329,"_41":372},{"_40":340,"_47":343,"_32":345,"_34":346,"_54":347,"_61":62,"_64":65,"_51":348,"_69":70},{"_49":124,"_87":118,"_51":344},{},1689437333.693156,1689437324.9615955,{"_56":129,"_57":272},{"_132":349,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_61":134,"_135":350,"_137":351,"_34":352,"_106":336,"_140":352,"_141":-5,"_142":-5,"_143":-5,"_144":353,"_146":357,"_174":-5},"4ba7a93f-40dd-481a-a713-097f5ffea9f1",1689437322.2814405,1689437324.9611645,[354],{"_96":280,"_281":355,"_283":284,"_285":-5,"_286":356},1689437324.9518533,"file-service://3093194e-5563-4f67-b8d5-e3e310d41c55?shared_conversation_id=ff1c2d5d-80ef-461a-bc69-12326ee340ba",[358,361,363,369],{"_149":61,"_150":359,"_54":360},{"_152":153,"_154":155},{"_157":158},{"_149":160,"_150":362},{"_152":153,"_154":155},{"_149":295,"_150":364,"_54":365},{"_152":153,"_154":155},{"_28":366},{"_167":367,"_300":368},"

","file-service://3093194e-5563-4f67-b8d5-e3e310d41c55",{"_149":61,"_150":370,"_54":371},{"_152":153,"_154":155},{"_157":173},[373],"1f0faac4-0433-422c-ad94-2ce12d64f750",{"_40":373,"_45":375,"_71":340,"_41":384},{"_40":373,"_47":376,"_32":378,"_54":379,"_61":62,"_63":12,"_64":65,"_51":382,"_69":70},{"_49":102,"_51":377},{},1689437333.693339,{"_56":57,"_58":380},[381],"Here are the visualizations of several categorical and binary variables in the dataset:\n\n1. **Sales Channel**: The vast majority of bookings are made through the Internet, with a smaller number made through the Phone.\n\n2. **Trip Type**: Most bookings are for Round Trips, with a smaller number for One Way and Multi City trips.\n\n3. **Flight Day**: Flights are fairly evenly spread across the days of the week, with the least flights on Sunday and the most on Monday.\n\n4. **Wants Extra Baggage**: A large number of bookings include a request for extra baggage.\n\n5. **Wants Preferred Seat**: Fewer bookings include a request for preferred seating.\n\n6. **Wants In-flight Meals**: Around half of the bookings include a request for in-flight meals.\n\nNext, we could examine the correlation between these variables and `booking_complete` to better understand which factors contribute to a booking being completed. Would you like to proceed with this analysis?",{"_111":383,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":240},[385],"aaa2b13c-9002-4810-b1a1-a59bc5d71ae6",{"_40":385,"_45":387,"_71":373,"_41":395},{"_40":385,"_47":388,"_32":390,"_54":391,"_61":62,"_64":65,"_51":394,"_69":70},{"_49":77,"_51":389},{},1689437396.567394,{"_56":57,"_58":392},[393],"show me a correlation matrix on dimension you think are important",{"_94":95,"_96":-5,"_67":24},[396],"ad355b59-8746-4c8a-af46-76c7e6916e42",{"_40":396,"_45":398,"_71":385,"_41":406},{"_40":396,"_47":399,"_32":401,"_54":402,"_61":62,"_63":14,"_64":65,"_51":404,"_69":118},{"_49":102,"_51":400},{},1689437434.368871,{"_56":106,"_107":108,"_57":403},"# Creating a subset of the data for the correlation matrix\ndata_subset = data[['num_passengers', 'purchase_lead', 'length_of_stay', 'flight_hour', \n 'wants_extra_baggage', 'wants_preferred_seat', 'wants_in_flight_meals', \n 'flight_duration', 'booking_complete']]\n\n# Calculate the correlation matrix\ncorr = data_subset.corr()\n\n# Generate a mask for the upper triangle\nmask = np.triu(np.ones_like(corr, dtype=bool))\n\n# Set up the matplotlib figure\nf, ax = plt.subplots(figsize=(11, 9))\n\n# Generate a custom diverging colormap\ncmap = sns.diverging_palette(230, 20, as_cmap=True)\n\n# Draw the heatmap with the mask and correct aspect ratio\nsns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0, annot=True,\n square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n\nplt.title('Correlation Matrix')\nplt.show()",{"_111":405,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[407],"d4c7dcf4-4a50-4ec5-9a98-027573041aca",{"_40":407,"_45":409,"_71":396,"_41":453},{"_40":407,"_47":410,"_32":412,"_34":413,"_54":414,"_61":62,"_64":65,"_51":416,"_69":70},{"_49":124,"_87":118,"_51":411},{},1689437434.369077,1689437409.6470287,{"_56":129,"_57":415},"---------------------------------------------------------------------------\nNameError Traceback (most recent call last)\nCell In[5], line 10\n 7 corr = data_subset.corr()\n 9 # Generate a mask for the upper triangle\n---> 10 mask = np.triu(np.ones_like(corr, dtype=bool))\n 12 # Set up the matplotlib figure\n 13 f, ax = plt.subplots(figsize=(11, 9))\n\nNameError: name 'np' is not defined\n",{"_132":417,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_61":418,"_135":419,"_137":420,"_34":421,"_106":403,"_140":421,"_141":-5,"_142":422,"_143":-5,"_144":435,"_146":436,"_174":-5},"failed_with_in_kernel_exception","37aecc19-78dc-4187-aa0e-3490ff3fed75",1689437409.2424982,1689437409.646664,{"_87":423,"_424":425,"_430":431,"_433":434},"NameError","traceback",[426,427,428,429],"---------------------------------------------------------------------------\n","NameError Traceback (most recent call last)\n","Cell In[5], line 10\n 7 corr = data_subset.corr()\n 9 # Generate a mask for the upper triangle\n---> 10 mask = np.triu(np.ones_like(corr, dtype=bool))\n 12 # Set up the matplotlib figure\n 13 f, ax = plt.subplots(figsize=(11, 9))\n\n","NameError: name 'np' is not defined\n","args",[432],"name 'np' is not defined","notes",[],[],[437,440,442],{"_149":61,"_150":438,"_54":439},{"_152":153,"_154":155},{"_157":158},{"_149":160,"_150":441},{"_152":153,"_154":155},{"_149":443,"_150":444,"_54":445},"error",{"_152":153,"_154":155},{"_424":446,"_451":423,"_452":432},[447,448,449,450],"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[5], line 10\u001b[0m\n\u001b[1;32m 7\u001b[0m corr \u001b[38;5;241m=\u001b[39m data_subset\u001b[38;5;241m.\u001b[39mcorr()\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Generate a mask for the upper triangle\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m mask \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mtriu(np\u001b[38;5;241m.\u001b[39mones_like(corr, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m))\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# Set up the matplotlib figure\u001b[39;00m\n\u001b[1;32m 13\u001b[0m f, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m11\u001b[39m, \u001b[38;5;241m9\u001b[39m))\n","\u001b[0;31mNameError\u001b[0m: name 'np' is not defined","ename","evalue",[454],"8c1901c5-0998-4262-8a6e-9a0d537b3008",{"_40":454,"_45":456,"_71":407,"_41":465},{"_40":454,"_47":457,"_32":459,"_54":460,"_61":62,"_63":14,"_64":65,"_51":463,"_69":70},{"_49":102,"_51":458},{},1689437434.369244,{"_56":57,"_58":461},[462],"My apologies, there was an error due to missing the import statement for the NumPy library. Let me correct that and generate the correlation matrix again.",{"_111":464,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[466],"3df029cf-1acb-4263-bd02-afb41fcf2cc5",{"_40":466,"_45":468,"_71":454,"_41":476},{"_40":466,"_47":469,"_32":471,"_54":472,"_61":62,"_63":14,"_64":65,"_51":474,"_69":118},{"_49":102,"_51":470},{},1689437434.369396,{"_56":106,"_107":108,"_57":473},"import numpy as np\n\n# Generate a mask for the upper triangle\nmask = np.triu(np.ones_like(corr, dtype=bool))\n\n# Set up the matplotlib figure\nf, ax = plt.subplots(figsize=(11, 9))\n\n# Generate a custom diverging colormap\ncmap = sns.diverging_palette(230, 20, as_cmap=True)\n\n# Draw the heatmap with the mask and correct aspect ratio\nsns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0, annot=True,\n square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n\nplt.title('Correlation Matrix')\nplt.show()",{"_111":475,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[477],"cbc08332-c263-4c37-9f19-d1f19f40896d",{"_40":477,"_45":479,"_71":466,"_41":509},{"_40":477,"_47":480,"_32":482,"_34":483,"_54":484,"_61":62,"_64":65,"_51":485,"_69":70},{"_49":124,"_87":118,"_51":481},{},1689437434.369551,1689437420.158054,{"_56":129,"_57":272},{"_132":486,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_61":134,"_135":487,"_137":488,"_34":489,"_106":473,"_140":489,"_141":-5,"_142":-5,"_143":-5,"_144":490,"_146":494,"_174":-5},"21d76401-b30d-40a1-b2cf-430c6373e7f2",1689437419.0920424,1689437420.1573737,[491],{"_96":280,"_281":492,"_283":284,"_285":-5,"_286":493},1689437420.1452014,"file-service://50733578-9fec-449f-aa70-e5c3e7aac2da?shared_conversation_id=ff1c2d5d-80ef-461a-bc69-12326ee340ba",[495,498,500,506],{"_149":61,"_150":496,"_54":497},{"_152":153,"_154":155},{"_157":158},{"_149":160,"_150":499},{"_152":153,"_154":155},{"_149":295,"_150":501,"_54":502},{"_152":153,"_154":155},{"_28":503},{"_167":504,"_300":505},"

","file-service://50733578-9fec-449f-aa70-e5c3e7aac2da",{"_149":61,"_150":507,"_54":508},{"_152":153,"_154":155},{"_157":173},[510],"36f5f992-5c37-4ee0-bd7b-214bbabb6496",{"_40":510,"_45":512,"_71":477,"_41":521},{"_40":510,"_47":513,"_32":515,"_54":516,"_61":62,"_63":12,"_64":65,"_51":519,"_69":70},{"_49":102,"_51":514},{},1689437434.369698,{"_56":57,"_58":517},[518],"Here's the correlation matrix for the selected variables:\n\n- `num_passengers` has a weak positive correlation with `wants_extra_baggage`, suggesting that bookings with more passengers are slightly more likely to request extra baggage.\n\n- `purchase_lead` has a weak negative correlation with `booking_complete`, indicating that bookings made further in advance are slightly less likely to be completed.\n\n- `length_of_stay` has a weak positive correlation with `wants_in_flight_meals`, suggesting that passengers staying longer are slightly more likely to request in-flight meals.\n\n- `flight_hour` has a weak negative correlation with `wants_extra_baggage`, suggesting that flights at later hours are slightly less likely to have requests for extra baggage.\n\n- `wants_extra_baggage`, `wants_preferred_seat`, and `wants_in_flight_meals` have weak positive correlations with `booking_complete`, indicating that bookings with these requests are slightly more likely to be completed.\n\n- `flight_duration` has a weak negative correlation with `booking_complete`, indicating that bookings for longer flights are slightly less likely to be completed.\n\nThese correlations are all quite weak, suggesting that no single variable has a strong direct influence on whether a booking is completed. However, it's worth noting that these are only linear correlations. There may be non-linear relationships or interactions between variables that this analysis doesn't capture. \n\nWe could proceed with a more complex analysis, such as a multivariate analysis or a machine learning model, to better understand these relationships. Would you like to proceed with that?",{"_111":520,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":240},[522],"aaa2c0bb-b2b3-453f-94af-67c9476dccd7",{"_40":522,"_45":524,"_71":510,"_41":532},{"_40":522,"_47":525,"_32":527,"_54":528,"_61":62,"_64":65,"_51":531,"_69":70},{"_49":77,"_51":526},{},1689437440.142515,{"_56":57,"_58":529},[530],"please do a comprehensive booking channel analysis, give the answers in tables and graphs and write out in bullet points the key business insights from this analysis.",{"_94":95,"_96":-5,"_67":24},[533],"e0b3caf7-b81a-44ec-b1c5-a1f2a726488d",{"_40":533,"_45":535,"_71":522,"_41":543},{"_40":533,"_47":536,"_32":538,"_54":539,"_61":62,"_63":14,"_64":65,"_51":541,"_69":118},{"_49":102,"_51":537},{},1689437476.876881,{"_56":106,"_107":108,"_57":540},"# Group the data by sales_channel\ngrouped_data = data.groupby('sales_channel').agg({\n 'num_passengers': 'sum',\n 'booking_complete': 'mean',\n 'wants_extra_baggage': 'mean',\n 'wants_preferred_seat': 'mean',\n 'wants_in_flight_meals': 'mean',\n 'flight_duration': 'mean',\n}).reset_index()\n\n# Calculate percentages for binary variables\ngrouped_data['booking_complete'] = grouped_data['booking_complete'] * 100\ngrouped_data['wants_extra_baggage'] = grouped_data['wants_extra_baggage'] * 100\ngrouped_data['wants_preferred_seat'] = grouped_data['wants_preferred_seat'] * 100\ngrouped_data['wants_in_flight_meals'] = grouped_data['wants_in_flight_meals'] * 100\n\ngrouped_data",{"_111":542,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[544],"9eabff84-c9a2-4004-b871-aa070e1059fd",{"_40":544,"_45":546,"_71":533,"_41":573},{"_40":544,"_47":547,"_32":549,"_34":550,"_54":551,"_61":62,"_64":65,"_51":553,"_69":70},{"_49":124,"_87":118,"_51":548},{},1689437476.877089,1689437450.0433009,{"_56":129,"_57":552}," sales_channel num_passengers booking_complete wants_extra_baggage \\\n0 Internet 70663 15.478900 67.784963 \n1 Mobile 8903 10.838227 59.708133 \n\n wants_preferred_seat wants_in_flight_meals flight_duration \n0 29.159813 43.104342 7.298131 \n1 33.938423 39.633387 7.114759 ",{"_132":554,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_61":134,"_135":555,"_137":556,"_34":557,"_106":540,"_140":557,"_141":552,"_142":-5,"_143":-5,"_144":558,"_146":559,"_174":-5},"b0c84387-f384-4499-a50c-1a73ce0d3334",1689437449.9075,1689437450.0428984,[],[560,563,565,570],{"_149":61,"_150":561,"_54":562},{"_152":153,"_154":155},{"_157":158},{"_149":160,"_150":564},{"_152":153,"_154":155},{"_150":566,"_149":164,"_54":567},{"_152":153,"_154":155},{"_28":568},{"_167":552,"_168":569},"

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	sales_channel	num_passengers	booking_complete	wants_extra_baggage	wants_preferred_seat	wants_in_flight_meals	flight_duration
0	Internet	70663	15.478900	67.784963	29.159813	43.104342	7.298131
1	Mobile	8903	10.838227	59.708133	33.938423	39.633387	7.114759

",{"_149":61,"_150":571,"_54":572},{"_152":153,"_154":155},{"_157":173},[574],"eee51e4f-71ee-43e0-bf18-188ce8e652ac",{"_40":574,"_45":576,"_71":544,"_41":584},{"_40":574,"_47":577,"_32":579,"_54":580,"_61":62,"_63":14,"_64":65,"_51":582,"_69":118},{"_49":102,"_51":578},{},1689437476.87725,{"_56":106,"_107":108,"_57":581},"# Set up the matplotlib figure\nf, axes = plt.subplots(2, 2, figsize=(15, 10))\n\n# Plot a barplot of the number of passengers\nsns.barplot(data=grouped_data, x=\"sales_channel\", y=\"num_passengers\", ax=axes[0, 0])\naxes[0, 0].set_title('Total Number of Passengers')\n\n# Plot a barplot of the booking completion rate\nsns.barplot(data=grouped_data, x=\"sales_channel\", y=\"booking_complete\", ax=axes[0, 1])\naxes[0, 1].set_title('Booking Completion Rate (%)')\n\n# Plot a barplot of the wants extra baggage rate\nsns.barplot(data=grouped_data, x=\"sales_channel\", y=\"wants_extra_baggage\", ax=axes[1, 0])\naxes[1, 0].set_title('Wants Extra Baggage Rate (%)')\n\n# Plot a barplot of the wants in-flight meals rate\nsns.barplot(data=grouped_data, x=\"sales_channel\", y=\"wants_in_flight_meals\", ax=axes[1, 1])\naxes[1, 1].set_title('Wants In-flight Meals Rate (%)')\n\nplt.tight_layout()\nplt.show()",{"_111":583,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[585],"6274ce3d-1971-4a65-bf1f-aa4d1b30ed71",{"_40":585,"_45":587,"_71":574,"_41":616},{"_40":585,"_47":588,"_32":590,"_34":591,"_54":592,"_61":62,"_64":65,"_51":593,"_69":70},{"_49":124,"_87":118,"_51":589},{},1689437476.877413,1689437463.376488,{"_56":129,"_57":272},{"_132":594,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_61":134,"_135":595,"_137":596,"_34":597,"_106":581,"_140":597,"_141":-5,"_142":-5,"_143":-5,"_144":598,"_146":602,"_174":-5},"50779cf9-edd0-483d-a447-24f9ce10f380",1689437462.2553644,1689437463.3759503,[599],{"_96":280,"_281":600,"_283":284,"_285":-5,"_286":601},1689437463.3638232,"file-service://6e6e2cf7-8760-4dff-9c71-16b637f7f558?shared_conversation_id=ff1c2d5d-80ef-461a-bc69-12326ee340ba",[603,606,608,613],{"_149":61,"_150":604,"_54":605},{"_152":153,"_154":155},{"_157":158},{"_149":160,"_150":607},{"_152":153,"_154":155},{"_149":295,"_150":609,"_54":610},{"_152":153,"_154":155},{"_28":611},{"_167":299,"_300":612},"file-service://6e6e2cf7-8760-4dff-9c71-16b637f7f558",{"_149":61,"_150":614,"_54":615},{"_152":153,"_154":155},{"_157":173},[617],"aa318801-14f9-4e8e-b07d-fe96e4286aa5",{"_40":617,"_45":619,"_71":585,"_41":628},{"_40":617,"_47":620,"_32":622,"_54":623,"_61":62,"_63":12,"_64":65,"_51":626,"_69":70},{"_49":102,"_51":621},{},1689437476.877635,{"_56":57,"_58":624},[625],"Here are the results of the sales channel analysis:\n\n- The **Total Number of Passengers** is significantly higher for bookings made through the Internet compared to the Mobile channel.\n\n- The **Booking Completion Rate** is slightly higher for bookings made through the Internet compared to the Mobile channel.\n\n- The rate of passengers who **Want Extra Baggage** is higher for bookings made through the Internet compared to the Mobile channel.\n\n- The rate of passengers who **Want In-flight Meals** is slightly higher for bookings made through the Internet compared to the Mobile channel.\n\nKey business insights from this analysis:\n\n1. The Internet is a more popular booking channel than Mobile, accounting for a significantly larger number of passengers.\n\n2. Bookings made through the Internet have a slightly higher completion rate compared to those made through Mobile. This suggests that the Internet booking process may be more user-friendly or efficient.\n\n3. Passengers who book through the Internet are more likely to request extra baggage and in-flight meals compared to those who book through Mobile. This could indicate that these options are more visible or easier to select during the Internet booking process, or it could reflect differences in the types of passengers who choose each booking channel.\n\nNext steps could include further analysis of the differences between these booking channels, such as the types of routes or trip types typically booked through each channel, or the lead times for bookings made through each channel. Would you like to proceed with this analysis?",{"_111":627,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":240},[629],"aaa2cd00-bd6d-4fd6-8777-32849ba86674",{"_40":629,"_45":631,"_71":617,"_41":639},{"_40":629,"_47":632,"_32":634,"_54":635,"_61":62,"_64":65,"_51":638,"_69":70},{"_49":77,"_51":633},{},1689437528.169005,{"_56":57,"_58":636},[637],"please give us an analysis on passenger preferences based on booking channels and tell us if there is any correlations or interesting insights",{"_94":95,"_96":-5,"_67":24},[640],"739274f1-2030-4d46-974b-27b9b587819d",{"_40":640,"_45":642,"_71":629,"_41":650},{"_40":640,"_47":643,"_32":645,"_54":646,"_61":62,"_63":14,"_64":65,"_51":648,"_69":118},{"_49":102,"_51":644},{},1689437569.614664,{"_56":106,"_107":108,"_57":647},"# Group the data by sales_channel\ngrouped_pref_data = data.groupby('sales_channel').agg({\n 'wants_extra_baggage': 'mean',\n 'wants_preferred_seat': 'mean',\n 'wants_in_flight_meals': 'mean',\n}).reset_index()\n\n# Calculate percentages for binary variables\ngrouped_pref_data['wants_extra_baggage'] = grouped_pref_data['wants_extra_baggage'] * 100\ngrouped_pref_data['wants_preferred_seat'] = grouped_pref_data['wants_preferred_seat'] * 100\ngrouped_pref_data['wants_in_flight_meals'] = grouped_pref_data['wants_in_flight_meals'] * 100\n\ngrouped_pref_data",{"_111":649,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[651],"76f2ed30-b5dc-45f4-840e-91b08bc0df90",{"_40":651,"_45":653,"_71":640,"_41":680},{"_40":651,"_47":654,"_32":656,"_34":657,"_54":658,"_61":62,"_64":65,"_51":660,"_69":70},{"_49":124,"_87":118,"_51":655},{},1689437569.614892,1689437538.2698672,{"_56":129,"_57":659}," sales_channel wants_extra_baggage wants_preferred_seat \\\n0 Internet 67.784963 29.159813 \n1 Mobile 59.708133 33.938423 \n\n wants_in_flight_meals \n0 43.104342 \n1 39.633387 ",{"_132":661,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_61":134,"_135":662,"_137":663,"_34":664,"_106":647,"_140":664,"_141":659,"_142":-5,"_143":-5,"_144":665,"_146":666,"_174":-5},"77423598-be11-46fb-9834-4b933498bbe6",1689437538.0396125,1689437538.269414,[],[667,670,672,677],{"_149":61,"_150":668,"_54":669},{"_152":153,"_154":155},{"_157":158},{"_149":160,"_150":671},{"_152":153,"_154":155},{"_150":673,"_149":164,"_54":674},{"_152":153,"_154":155},{"_28":675},{"_167":659,"_168":676},"

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	sales_channel	wants_extra_baggage	wants_preferred_seat	wants_in_flight_meals
0	Internet	67.784963	29.159813	43.104342
1	Mobile	59.708133	33.938423	39.633387

",{"_149":61,"_150":678,"_54":679},{"_152":153,"_154":155},{"_157":173},[681],"116b8010-6d19-4782-930e-3a4eb727e5b3",{"_40":681,"_45":683,"_71":651,"_41":691},{"_40":681,"_47":684,"_32":686,"_54":687,"_61":62,"_63":14,"_64":65,"_51":689,"_69":118},{"_49":102,"_51":685},{},1689437569.615083,{"_56":106,"_107":108,"_57":688},"# Set up the matplotlib figure\nf, axes = plt.subplots(1, 3, figsize=(20, 6))\n\n# Plot a barplot of the wants extra baggage rate\nsns.barplot(data=grouped_pref_data, x=\"sales_channel\", y=\"wants_extra_baggage\", ax=axes[0])\naxes[0].set_title('Wants Extra Baggage Rate (%)')\n\n# Plot a barplot of the wants preferred seat rate\nsns.barplot(data=grouped_pref_data, x=\"sales_channel\", y=\"wants_preferred_seat\", ax=axes[1])\naxes[1].set_title('Wants Preferred Seat Rate (%)')\n\n# Plot a barplot of the wants in-flight meals rate\nsns.barplot(data=grouped_pref_data, x=\"sales_channel\", y=\"wants_in_flight_meals\", ax=axes[2])\naxes[2].set_title('Wants In-flight Meals Rate (%)')\n\nplt.tight_layout()\nplt.show()",{"_111":690,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_27":113,"_113":114},[692],"921e4459-adb5-44a6-944a-78759962448f",{"_40":692,"_45":694,"_71":681,"_41":724},{"_40":692,"_47":695,"_32":697,"_34":698,"_54":699,"_61":62,"_64":65,"_51":700,"_69":70},{"_49":124,"_87":118,"_51":696},{},1689437569.615262,1689437548.965538,{"_56":129,"_57":272},{"_132":701,"_115":12,"_96":-5,"_116":117,"_94":95,"_67":24},{"_61":134,"_135":702,"_137":703,"_34":704,"_106":688,"_140":704,"_141":-5,"_142":-5,"_143":-5,"_144":705,"_146":709,"_174":-5},"59a2002b-b6d2-4dea-8c47-ba6b51b456a0",1689437548.131249,1689437548.9649997,[706],{"_96":280,"_281":707,"_283":284,"_285":-5,"_286":708},1689437548.9498022,"file-service://bd785242-a0d9-47a0-80d1-d05e3a2c38bb?shared_conversation_id=ff1c2d5d-80ef-461a-bc69-12326ee340ba",[710,713,715,721],{"_149":61,"_150":711,"_54":712},{"_152":153,"_154":155},{"_157":158},{"_149":160,"_150":714},{"_152":153,"_154":155},{"_149":295,"_150":716,"_54":717},{"_152":153,"_154":155},{"_28":718},{"_167":719,"_300":720},"

","file-service://bd785242-a0d9-47a0-80d1-d05e3a2c38bb",{"_149":61,"_150":722,"_54":723},{"_152":153,"_154":155},{"_157":173},[725],"e647d1d9-651a-4ef0-802b-16f3b51d9cdb",{"_40":725,"_45":727,"_71":692,"_41":736},{"_40":725,"_47":728,"_32":730,"_54":731,"_61":62,"_63":12,"_64":65,"_51":734,"_69":70},{"_49":102,"_51":729},{},1689437569.615435,{"_56":57,"_58":732},[733],"The passenger preferences based on booking channels are as follows:\n\n- **Wants Extra Baggage**: The rate of passengers requesting extra baggage is higher for bookings made through the Internet compared to the Mobile channel.\n\n- **Wants Preferred Seat**: Interestingly, the rate of passengers requesting preferred seating is higher for bookings made through Mobile compared to the Internet channel.\n\n- **Wants In-flight Meals**: The rate of passengers requesting in-flight meals is slightly higher for bookings made through the Internet compared to the Mobile channel.\n\nKey business insights from this analysis:\n\n1. Passengers who book through the Internet are more likely to request extra baggage and in-flight meals compared to those who book through Mobile. This could indicate that these options are more visible or easier to select during the Internet booking process.\n\n2. However, passengers who book through Mobile are more likely to request preferred seating. This could suggest that the Mobile booking process makes it easier to select preferred seating, or it could reflect differences in the types of passengers who choose each booking channel.\n\n3. Overall, these findings suggest that the booking channel can influence passenger preferences, possibly due to differences in the booking process or the types of passengers who choose each channel. These insights could be used to improve the booking process and better cater to passenger preferences.\n\n4. It may be beneficial to further investigate the reasons behind these differences, such as by conducting user research or analyzing additional data on the booking process and passenger characteristics. \n\n5. These insights can also be used to tailor marketing and upselling strategies for each channel. 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