Welcome to the repository for our Capstone project for FourthBrain. This project aims to develop and implement novel approaches to modeling the impact of treatment on individual customers in a marketing context. Our team is composed of experienced data scientists and machine learning experts who are passionate about solving real-world problems with cutting-edge technology.
In marketing, it's important to understand the impact of a treatment (such as a promotional campaign) on customer behavior. Conventional machine learning models, such as supervised learning algorithms, are not well-suited for this task because they assume that all customers are equally affected by the treatment. In reality, the impact of a treatment can vary greatly from one customer to another, based on factors such as past behavior and demographic characteristics.
Uplift modeling is a specialized approach to marketing analysis that aims to estimate the individual impact of a treatment on customers. The goal of uplift modeling is to identify the customers who are most likely to be influenced by the treatment and to quantify the size of that impact. Our team is developing and implementing new uplift modeling algorithms and evaluating their performance on real-world datasets.
The repository is structured as follows:
dat: contains the datasets that we will use for training and evaluating our models.
nb: contains the notebooks and source code for our Uplift machine learning models and any supporting functions.
reports: contains presentations and technical reports that document our progress and findings.
app: contains the app.py files for deployment.
To get started with our project, clone the repository and install the required dependencies. The dependencies are listed in the requirements.txt file.
Our team consists of the following members: