Question $1$: Provide python code for below analysis and explain how the analysis can improve the upfront price precision

The key aspect of ride$-$hailing $($ex$.$ Uber, Ola$)$ is upfront pricing, which works in 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 more than $20\%.$ For example, suppose you want to make a ride that upfront price predicts a cost of $$5.$ If the metered price is between $$4$ and $$6,$ you will end up paying $$5.$ You will end up paying something else if the metered price is anything less than $$4$ or more than $$6.$

No customer likes surprises $($especially when it comes to money!$),$ that$$s why a company always strives to improve our upfront pricing precision for our customers$$ smooth journeys.

In the attached file you will find sample data. Your task is to analyze this data and identify the top $1$ to $2$ opportunities that can help us improve upfront pricing precision. The expected output is a business PDF report $(4$ pages maximum$).$

Assume that both business and technical people will read your results.

Variables in the data:

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

a$.$ upfront, prediction: prediction happened before the ride

b$.$ 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

us$\_$indicator: whether a ride happens in US

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

Question $2$: Case Study

Blinkit is about to launch a store in a city of your choice. This store will be open for $18$ hours every day, seven days a week, and is expected to handle $2000$ orders daily. The store's size will be $2000$ square feet, with specific dimensions of $50$ feet by $40$ feet, resembling the attached image. Some points to keep in mind:

$$ The orders will be assigned to a delivery partner $($driver$)$ only if they are available at the store.

$$ Given that this is a new store, the challenge at hand is to organize and manage inventory within the store.

Create an assessment report $($can be a spreadsheet, doc, ppt$,$ pdf$)$ including the following:

How many delivery partners $($drivers$)$ would be necessary to fulfill the orders without compromising Blinkit's unique selling proposition $($USP$)$ and customer experience?

How many workers would be required inside the store to pick and pack the orders, taking into account an average of $5$ items per order?