Source Code: GitHub
Background (Not Finished)
This case is used for hiring Data Analysts for the iFood Brain team. The analysis was to predict whether a customer would accept the marketing campaign proposed by iFood in their grocery stored. Based on the customers profile, you must predict if they would accept one of the multiple marketing campaigns. The idea is to make a model from the data collected by iFood to better predict which customers would accept the campaign for marketing.
Project Summary
In this workflow, there is a good demonstration of exploratory data analysis with higher dimensional datasets, as well as feature selection.
The model used to predict success of marketing campaigns was a XGBoost, which is a gradient boosting model. BorutaPy was used for feature selection, which is a XX framework for choosing features.
The overall predicted accuracy of the model was ~87% and the F-Score was 0.68. Below is the confusion matrix produced by the output. https://github.com/alexjimenez99/education-workflows
