Hi i need help that will guide with what I have

The key aspect of ride-hailing is upfront pricing, which works the following way. First, it predicts the price for a ride based on predicted distance and time. This price is what you see on the screen of the phone before ordering a ride. Second, if the metered price based on actual distance and time differs a lot from the predicted one, the upfront price switches to the metered price. 'A lot' means by more than 20%. For example, suppose you want to make a ride that upfront price predicts to cost 5 euros. If the metered price is between 4 and 6 euros - the rider pays 5 euros, otherwise the metered price.

We would like to improve the upfront pricing precision. Kindly analyze the data and identify top opportunities for that. Could you name the top one opportunity?

THE DATA SET IS VERY LARGE SO I COPIED THE FIRST 20 ROWS

Sample data SET in the table below

order_id_new	order_try_id_new	calc_created	metered_price	upfront_price	distance	duration	gps_confidence	entered_by	b_state	dest_change_number	prediction_price_type	predicted_distance	predicted_duration	change_reason_pricing	ticket_id_new	device_token	rider_app_version	order_state	order_try_state	driver_app_version	driver_device_uid_new	device_name	eu_indicator	overpaid_ride_ticket	fraud_score
22	22	2020-02-02 3:37:31	4.04	10	2839	700	1	client	finished	1	upfront	13384	1091		1376		CI.4.17	finished	finished	DA.4.37	1596	Xiaomi Redmi 6	1	0	-1383
618	618	2020-02-08 2:26:19	6.09	3.6	5698	493	1	client	finished	1	upfront	2286	360		2035		CA.5.43	finished	finished	DA.4.39	1578	Samsung SM-G965F	1	0
657	657	2020-02-08 11:50:35	4.32	3.5	4426	695	1	client	finished	1	upfront	4101	433		2222		CA.5.43	finished	finished	DA.4.37	951	Samsung SM-A530F	1	0	-166
313	313	2020-02-05 6:34:54	72871.72		49748	1400	0	client	finished	2	upfront_destination_changed	3017	600	client_destination_changed	1788		CA.5.23	finished	finished	DA.4.37	1587	TECNO-Y6	0	1
1176	1176	2020-02-13 17:31:24	20032.5	19500	10273	5067	1	client	finished	1	upfront	14236	2778		2710		CA.5.04	finished	finished	DA.4.37	433	Itel W5504	0	0
1209	1209	2020-02-14 1:27:01	6.11	6.3	4924	513	1	client	finished	1	upfront	4882	562		2732		CA.5.04	finished	finished	DA.4.39	1591	HUAWEI WAS-LX1	1	0
761	761	2020-02-09 6:51:20	20753.2	10500	10459	1874	1	client	finished	1	upfront	4892	698		2173		CI.4.17	finished	finished	DA.4.19	982	TECNO MOBILE LIMITED TECNO KA7O	0	0
1662	1662	2020-02-17 18:24:45	2.61	6.5	2020	412	1	client	finished	1	upfront	8545	888		3229		CI.4.18	finished	finished	DA.4.37	1701	Samsung SM-N950F	1	0	-2350
1904	1904	2020-02-20 16:38:34	13600.5		9540	1917	1	client	finished	1	prediction	11018	1914		3557		CA.5.40	finished	finished	DA.4.31	605	TECNO K7	0	0
1999	1999	2020-02-21 11:02:38	4.14	2.5	3845	720	1	client	finished	1	upfront	1883	298		3631		CI.4.18	finished	finished	DA.4.39	1960	Samsung SM-G930F	1	0	-5181
2366	2366	2020-02-24 8:51:31	8.04	8.1	9977	1257	1	client	finished	1	upfront	9891	1282		4157		CA.5.44	finished	finished	DA.4.39	1339	HUAWEI BLA-L29	1	0	-652
2803	2803	2020-02-29 1:44:16	18.8		17107	1552	1	client	finished	2	upfront_destination_changed	11054	962	client_destination_changed	4608		CI.4.17	finished	finished	DA.4.37	1695	LENOVO Lenovo TB-7304F	1	0	-100
3313	3313	2020-03-06 0:29:03	31.23		54757	6581	1	driver	finished	3	upfront_destination_changed	13878	1381	driver_destination_changed	342		CI.4.19	finished	finished	DA.4.39	1459	Samsung SM-G950F	1	0	-58
3299	3299	2020-03-05 18:52:48	7.65	6.3	13355	1335	1	client	finished	1	upfront	11823	888		282		CA.5.32	finished	finished	DI.3.37	214	iPhone9,3	1	0	-44
3675	3675	2020-03-09 10:50:57	8.53		11456	1205	1	driver	finished	2	upfront_destination_changed	11482	1179	driver_destination_changed	700		CI.4.19	finished	finished	DA.4.42	629	Samsung SM-J610FN	1	0	-498
3638	3638	2020-03-08 21:01:19	13643.28		10365	1533	1	client	finished	1	prediction	9088	1175		736		CI.4.19	finished	finished	DA.4.39	1968	TECNO MOBILE LIMITED TECNO CC7	0	0
3797	3797	2020-03-10 16:32:01	8.49	8.7	9276	1555	1	client	finished	1	upfront	8781	1733		854		CA.5.46	finished	finished	DA.4.42	1889	Samsung SM-J730F	1	0
3406	3406	2020-03-06 20:24:49	4.6	4.5	5278	662	1	client	finished	1	upfront	5632	546		397		CA.5.46	finished	finished	DA.4.42	1392	Samsung SM-A500FU	1	0
73	73	2020-02-02 14:46:22	15.67		22396	2237	1	client	finished	2	upfront_destination_changed	9713	802	client_destination_changed	1436		CA.5.40	finished	finished	DA.4.37	2110	Samsung SM-G965F	1	0	-160

Variables in the file:

order_id_new, order_try_id_new - id of an order

calc_created- time when the order was created

metered_price, distance, duration- actual price, distance and duration of a ride

upfront_price- promised to the rider price, based on predicted duration (predicted_duration) and distance (predicted_distance)

distance - ride distance

duration - ride duration

gps_confidence- indicator for good GPS connection (1 - good one, 0 - bad one)

entered_by- who entered the address

b_state- state of a ride (finished implies that the ride was actually done)

dest_change_number- number of destination changes by a rider and a driver. It includes the original input of the destination by a rider. That is why the minimum value of it is 1

predicted distance - predicted duration of a ride based on the pickup and dropoff points entered by the rider requesting a car

predicted duration - predicted duration of a ride based on the pickup and dropoff points entered by the rider requesting a car

prediction_price_type- internal variable for the type of prediction:

upfront, prediction - prediction happened before the ride

upfront_destination_changed - prediction happened after rider changed destination during the ride

change_reason_pricing - indicates whose action triggered a change in the price prediction. If it is empty, it means that either nobody changed the destination or that the change has not affected the predicted price

ticket_id_new - id for customer support ticket

device_token, device_token_new - id for a device_token (empty for all the fields)

rider_app_version - app version of rider phone

driver_app_version- app version of driver phone

driver_device_uid_new - id for UID of a phone device

device_name- the name of the phone

eu_indicator- whether a ride happens in EU

overpaid_ride_ticket- indicator for a rider complaining about the overpaid ride

fraud_score- fraud score of a rider. The higher it is the more likely the rider will cheat.

Part II: Business Research

Please create an assessment for food delivery (courier delivery of food from restaurants) launch in a city of your choice. The output should be a spreadsheet including the following:

A top-down estimation of market size

Unit economics with profitability per order