The dataset stores information — 2008 to 2015 — of a marketing sales’ operation (telemarketing) implemented by a portuguese bank’s marketing team in order 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, in addition of spending more resources was also very uncomfortable for many clients disturbed by this type of action.
In order to determine the costs of the campaign, the marketing team has reached to a conclusion:
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 is divided into 3 categories:
In this article, I’ll be focusing only on the first section, the **Exploratory Data Analysis **(EDA).
The metric used for evaluation is the **total costs **since the objective is to determine minimum costs of the campaign.
You will find the entire code of this project here.
The ‘bank_marketing_campaign.csv’ dataset can be downloaded here.
#predictive-analytics #exploratory-data-analysis #machine-learning #data-analysis #visualization