IFood Marketing-Data Analysis, Segmentation and classification

Introduction

iFood is the lead food delivery app in Brazil, present in over a thousand cities and keeping a high customer engagement is key for growing and consolidating the company’s position as the market leader. We aim to understand the data, find business opportunities & insights and to propose any data driven action to optimize the campaigns results & generate value to the company.

Methodology

The project is split into 3 parts: Exploratory analysis, classification modelling and customer segmentation. In exploratory analysis, we will conduct detailed analysis to uncover trends and patterns in the data that will inform future business insights and decisions. In classification modelling we aim to build a classification model that can predict whether or not a customer will respond positively to future marketing campaigns. For the third and final part, we will segment the customers into distinct groups using Clustering Algorithms, based on customer profiles to enable focused future marketing campaigns.

To achieve these goals, we'll leverage the python libraries listed below:

• Pandas and NumPy: For efficient data manipulation and numerical operations.

• Seaborn and Matplotlib: To visualize the data, uncover trends, and gain valuable insights.

• RandomForest Classifier & Catboost classifier: to create robust classification models for predicting customer response to marketing campaigns

• KMeans Clustering: to create k distinct clusters for the customers based on their profile