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https://github.com/ahmedshahriar/telco-customer-churn-prediction-streamlit-app

This streamlit app predicts the churn rate using Gradient Boosting models (XGBoost, Catboost, LightGBM) on IBM Customer Churn Dataset

https://github.com/ahmedshahriar/telco-customer-churn-prediction-streamlit-app

Science Score: 13.0%

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    CITATION.cff file
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    Scientific vocabulary similarity
    Low similarity (7.8%) to scientific vocabulary

Keywords

binary-classification binary-classifiers data-science jupyter-notebook machine-learning pandas python scikit-learn sklearn stacking-ensemble streamlit streamlit-webapp
Last synced: 5 months ago · JSON representation

Repository

This streamlit app predicts the churn rate using Gradient Boosting models (XGBoost, Catboost, LightGBM) on IBM Customer Churn Dataset

Basic Info
Statistics
  • Stars: 3
  • Watchers: 2
  • Forks: 3
  • Open Issues: 0
  • Releases: 0
Topics
binary-classification binary-classifiers data-science jupyter-notebook machine-learning pandas python scikit-learn sklearn stacking-ensemble streamlit streamlit-webapp
Created over 4 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License

README.md

Telco Customer Churn Prediction Streamlit App

Live in Streamlit

Telco Customer Churn Prediction Streamlit

This app was featured in Streamlit Weekly Roundup

Install packages pip install requirements.txt

Requires pandas==1.3.3 numpy~=1.21.2 matplotlib==3.4.3 streamlit==0.88.0 xgboost==0.90 catboost==1.0.0 lightgbm==2.2.3 scikit-learn==1.0.1

To run this app streamlit run app.py

Dataset Source

GitHub Project Repository

View The Project

  • View the Project in Jupyter Notebook Html : Open in HTML

  • Open The GitHub Project in Binder : Open in Binder

View this notebook on kaggle

  1. Churn Prediction I : EDA+Statistical Analysis
  2. Churn Prediction II : Triple Boost Stacking+ Optuna

Owner

  • Name: Ahmed Shahriar Sakib
  • Login: ahmedshahriar
  • Kind: user
  • Location: Ontario, Canada
  • Company: @criticalml-uw

Software Engineer, an expert in web scraping & automation, data analytics, and machine learning. Kaggle Master.

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Dependencies

requirements.txt pypi
  • catboost ==1.0.0
  • lightgbm ==2.2.3
  • matplotlib ==3.4.3
  • numpy *
  • pandas ==1.3.3
  • scikit-learn ==1.0.1
  • streamlit ==0.88.0
  • xgboost ==0.90