Machine Learning: costs prediction of a Marketing Campaign

Machine Learning: costs prediction of a Marketing Campaign

Bank Marketing Campaign: costs prediction – EDA (I). A Data Science approach to predict the best candidates to be targeted for a marketing campaign.

About the project

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:

  • For each customer identified as a good candidate and therefore defined as a target but doesn’t subscribe the deposit, the bank had a cost of 500 EUR.
  • For each customer identified as a bad candidate and excluded from the target but would subscribe the product, the bank had a cost of 2.000 EUR.

Machine Learning problem and objectives

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.

Project structure

The project divides into three categories:

  1. EDA: Exploratory Data Analysis
  2. Data Wrangling: Cleaning and Feature Engineering
  3. Machine Learning: Predictive Modelling

In this article, I’ll be focusing only on the first section, the *Exploratory Data Analysis *(EDA).

predictive-analytics exploratory-data-analysis machine-learning visualization data analysis

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Machine Learning: costs prediction of a Marketing Campaign

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.