ETX2250留学生代写、代做Data Visualisation、代做c/c++，Java，Python程序语言

日期：2020-01-31 11:06

ETX2250/ETF5922 Data Visualisation and

Analytics

Total of 100 points equating 15% of the assessment in

ETX2250/ETF5922

Business Case

In late 2013, the taxi company Yourcabs.com in Bangalore, India was

facing a problem with the drivers using their platform-not all

drivers were showing up for their scheduled calls. Drivers would

cancel their acceptance of a call, and, if the cancellation did not

occur with adequate notice, the customer would be delayed or even

left high and dry.

Bangalore is a key tech center in India, and technology was

transforming the taxi industry. Yourcabs.com featured an online

booking system (though customers could phone in as well) and

presented itself as a taxi booking portal. The Uber ride sharing

service started its Bangalore operations in mid-2014.

Yourcabs.com had collected data on its bookings from 2011 to 2013,

and posted a contest on Kaggle, in coordination with the Indian

School of Business, to see what it could learn about the problem of

cab cancellations.

The data presented for this case are a randomly selected subset of

the original data, with 10,000 rows, one row for each booking. There

are 17 input variables, including user (customer) ID, vehicle model,

whether the booking was made online or via a mobile app, type of

travel, type of booking package, geographic information, and the

date and time of the scheduled trip. The target variable of interest

is the binary indicator of whether a ride was canceled. The overall

cancellation rate is between 7% and 8%.

Assignment:

1. How can a predictive model based on these data be used by

Yourcabs? Describe how you would approach this problem in

practice. 150 – 200 words (10 points)

2. Explore, prepare, and transform the data to facilitate

predictive modeling. Provide the code of your final results

how you created your predictor variables and print first 6

rows as a table. (30 points)

Here are some hints:

? In exploratory modeling, it is useful to move fairly

soon to at least an initial model without solving all

data preparation issues. One example is the GPS

information-other geographic information is available,

so you could defer the challenge of how to

interpret/use the GPS information.

? How will you deal with missing data, such as cases

where NULL is indicated?

? Think about what useful information might be held

within the date and time fields (the booking timestamp

and the trip timestamp).

? Think also about the categorical variables, and how to

deal with them. Should we turn them all into dummies?

Use only some?

3. Fit a classification tree: Does it provide information on how

the predictor variables relate to cancellations? The data-set

is imbalanced, make use of the upSample function to balance

the classes. Prune the tree to not have more than 3-4

predictors. Provide the code and plot the pruned tree as a

figure (30 points). Describe the predictor variables, do they

make sense? 150-200 words (10 points)

4. Report the predictive performance of your model in terms of

error rates (the confusion matrix). How well does the model

perform overall? Which plot do you choose? Can the model be

used in practice? 100 words max (10 points)

5. Examine the predictive performance of your model in terms of

ranking(lift). Which plot do you choose? How well does the

model perform? Can the model be used in practice? 100 words

max (10 points)

Submission

A pdf document should be submitted. The code should be nicely

formatted and be in a fix size font, e.g “Courier New”. There is

no need to include code for loading libraries. Maximum length of

6 A4 pages.