In order to optimize the resources available, any marketing campaign must focus its efforts in those potential customers that are more likely to accept the proposed offer. In this case, a Portuguese banking institution called it's users to offer them a new product, and they registered whether the outcome of the call was positive. A proper analysis on this data can drastically increase the acceptance rates by targeting customers that are more interested on the offer and not wasting resources on customers that are not.
This dataset contains 11,162 records that belong to telephone calls done to the clients of the bank. The target variable deposit tells whether the call was successful and the client subscribed to a term deposit, and there are 16 explanatory variables to predict the outcome of the call. These variables contain personal information about the user (such as age, job, family status, education), other products that they signed for, and detailed information about the current and previous marketing campaigns.
The objective is to train a ML model that returns the probability of a customer to accept the offered product. This is a binary classification task, therefore F1-score is a good metric to evaluate the performance of this dataset as itweights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously.
Although this dataset can make a huge difference on the banking institution's performance, it has some problems that complicate its usage. Luckily, Synthesized can solve these problems in a fast and intuitive way.
This dataset is publicly available in the UCI dataset repository as "Bank Marketing".