About the Data

There are 19 variables (including the target variable) in the train dataset. Below are some of the listed variables and their description:

age: age of the customer

job_type: what job does customer do

marital_status: if the customer is married or not

education_level: education level of the customer

default_or_not: if the customer was able to repay in time or not

balance_amt: balance amount of the customer in the bank

hoursehold_insurance: if the customer has bought household insurance or not

car_loan: if the customer has car loan

communication: mode of communication

last_contact_day: when the customer was last contacted (day)

last_contact_month: when the customer was last contacted (month)

no_of_contacts: How many times was the customer contacted in the current campaign

days_passed: days passed since the last contact (-1 means the data is missing)

prev_attempts: how many times was the customer previously contacted (i.e. in the campaigns befor this)

outcome: what was the outcome of the last contact

call_start: call start time

call_end: call end time

car_insurance: if the customer bought car insurance (the target variable, 1 - boutght, 0 - not bought)

How to download and read the dataset?

In the 'Data' section of the challenge page check the 'Resources' subsection. For reference, see the screenshot below (the red box):

Click on the dataset that you want to download.
After you have downloaded the dataset, you can read the dataset in your jupyter notebook using the read_csv() function. Provide the appropriate path of the data located on your machine. For example, if the data is located in the same location where you are executing your jupyter notebook, you can read the dataset as:
train_data = pd.read_csv('Train data.csv')

Saving Prediction File & Sample Submission

You can find more details on how to save a prediction file here: https://discuss.dphi.tech/t/how-to-submit-predictions/548

Sample submission: You should submit a CSV file with a header row and the sample submission can be found below:

prediction

1

1

1

0

1

0

0

.

.

.

Etc.

Note that the header name should be ‘prediction’ else it will throw an evaluation error.

Notebook Submission

All the participants are required to submit their notebook for this assignment. A template notebook for the same can be found here: https://dphi.tech/notebooks/1401/manish_kc_06/template-notebook-getting-started-with-data-science-bootcamp-final-assignmentipynb

Steps to follow to submit your notebook:

Go to the Notebook section on this challenge page
Click on the 'Upload Notebook' button on top left corner as shown in the image below:
Make sure to make the visibility of your notebook as 'public' while uploading the notebook.
Provide Notebook Title, Summary, add some tags, go to next, chose a file and click on Upload.

Acknowledgment

Thanks goes to the Decision Science and Systems Chair of Technical University of Munich (TUM) for getting the data set from a real world company and making it available to be shared publicly.

Getting Started With Data Science Bootcamp: Final Assignment

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