Bank Marketing Campaign: costs prediction – EDA (I). A Data Science approach to predict the best candidates to be targeted for a marketing campaign.
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).
Suppose you are looking to book a flight ticket for a trip of yours. Now, you will not go directly to a specific site and book the first ticket that you see.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.
Analysis, Price Modeling and Prediction: AirBnB Data for Seattle. A detailed overview of AirBnB’s Seattle data analysis using Data Engineering & Machine Learning techniques.
A Data Science approach to predict the best candidates to be targeted for a marketing campaign. 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.