🛠 Contribute : Found a typo? Or any other change in the description that you would like to see ? Please consider sending us a pull request in the public repo of the challenge here.
Another one of your beautiful power naps got wrecked by a promotional call? Tired of it yet? Well sometimes the only way to get rid of a problem is to solve it! So put on your smart hats and get rid of your problems once and for all!
Given the history and information of a
predict if the client will
term depsit service.
Understand with code! Here is getting started code for you.😄
The dataset basically describes the marketing campaign of a portuguese banking institution. The campaign was based on phone calls and the dataset describes the clients who were contacted, the description also includes the
details of previous contacts made to the client as telesales typically need multiple contacts to convince a client.
The final aim of the calls was to convince the client to
subscribe to a term deposit. So for each call made to a client you have
20 attributes describing the client and corresponding to a description, there is a final outcome of the call , whether or not the client
subscribe to a term deposit. For details about input variable visit here!.
Following files are available in the
32928samples) This csv file contains the attributes describing information along with the binary value denoting whether or not the client will avail the term deposit service.
8238samples) File that will be used for actual evaluation for the leaderboard score but does not have the binary value denoting whether or not the client will avail the term deposit service.
- Prepare a CSV containing header as
yand predicted value as digit
norespectively denoting whether or not the client will avail the term deposit service.
- Name of the above file should be
- Sample submission format available at
sample_submission.csvin the resorces section.
Make your first submission here 🚀 !!
🖊 Evaluation Criteria
During evaluation F1 score will be used to test the efficiency of the model where,
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- [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
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