This project was part of Becode AI Bootcamp
App deployed on Streamlit and Heroku
Description
Installation
Usage
This application aims at predicting churning customers from a bank. The original dataset as well as a description of the project can be found on Kaggle
A particular feature of this otherwise extremely clean dataset is that it is highly unbalanced; only 16.07% of customers are flagged as "attrited".
The streamlit application is divided into 2 sections:
- Exploratory Data Visualization. The goal of this section is to display
important features of the dataset, and to be able to visualize data points
in a 3D scatter plot with the binary label (attrited / existing).
- Prediction. At the top of this section one can find KPIs regarding the chosen model from the sidebar, including the parameters of the trained model. Users can also display a more advanced dashboard with single predictions from the testing set, and the SHapley Additive exPlanation (SHAP) relative to this particular model's decision.
- Clone the repository:
git clone https://github.com/CorentinChanet/churn-prediction
- Install the required libraries:
pip install -r requirements.txt
To start the program on your local machine:
streamlit run streamlit_app.py