The dataset stores information — 2008 to 2015 — of a marketing sales operation (telemarketing) implemented by a Portuguese bank’s marketing team to attract customers to subscribe term deposits, classifying the results as ‘yes’ and ‘no’ into a binary categorical variable.
Until that time, the strategy was to reach the maximum number of clients, indiscriminately, and try to sell them the financial product over the phone. However, that approach, besides spending many resources was also very uncomfortable for many clients disturbed by this type of action.
To determine the costs of the campaign, the marketing team has concluded:
We’re facing a binary classification problem. The goal is to train the best machine learning model that should be able to predict the optimal number of candidates to be targeted in order to reduce to the minimum costs and maximize efficiency.
The project divides into three categories:
In this article, I’ll be focusing only on the first section, the **Exploratory Data Analysis **(EDA).
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