konkinit / churn_modeling

This project aims to predict churn on Telco industry dataset using ML models

ML App for Churn Prediction

This project aims to predict churn using datasetss from the Telco industry

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To Do

• Refine metadata stats
• Add accuracy plot in page 03
• Implement XGB modeel
• Implement Text mining

Getting Started

docker pull kidrissa/churnapp:latest

docker run -p 8501:8501 -d kidrissa/churnapp:latest

Project description

The project is set up in order to practice a tuto followed in an online course. Using a dataset from Telco industry the idea in the online course was to model churn by implementing an end-to-end data science project from data preprocessing, data modeling, model comparaison to putting champion model into production. The project was done using SAS viya, a low-code environment. Consequently I decide to reimplement the project using the first two C's of Cloud Native approach that is Code and Container. My aim is to build a streamlit app on which user can follow the project end-to-end by entering some hyperparams to his convenience.

Continuous integration

One continuous integration (CI) procedure with 2 jobs mainly is designed and launched at every push and merge to the main branch:

• Linting & Testing. Flake8 grants high quality of code while Pytest collects the test from the tests folder and executes them
• if Testing goes through, a Docker Image is built and pushed onto the docker hub

Dataset

The dataset used is

Modeling