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

binary-classification binary-classifiers data-science jupyter-notebook machine-learning pandas python scikit-learn sklearn stacking-ensemble streamlit streamlit-webapp

Last synced: about 1 month ago
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This streamlit app predicts the churn rate using Gradient Boosting models (XGBoost, Catboost, LightGBM) on IBM Customer Churn Dataset

• Host: GitHub
• URL: https://github.com/ahmedshahriar/telco-customer-churn-prediction-streamlit-app
• Owner: ahmedshahriar
• License: apache-2.0
• Created: 2021-10-22T14:00:59.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-03-08T21:57:41.000Z (10 months ago)
• Last Synced: 2024-03-08T22:39:33.728Z (10 months ago)
• Topics: binary-classification, binary-classifiers, data-science, jupyter-notebook, machine-learning, pandas, python, scikit-learn, sklearn, stacking-ensemble, streamlit, streamlit-webapp
• Language: Python
• Homepage: https://share.streamlit.io/ahmedshahriar/Telco-Customer-Churn-Prediction-Streamlit-App/main/app.py
• Size: 1.32 MB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Telco Customer Churn Prediction Streamlit App

[![Live in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io//ahmedshahriar/Telco-Customer-Churn-Prediction-Streamlit-App/main/app.py)

![Telco Customer Churn Prediction Streamlit](https://user-images.githubusercontent.com/40615350/142819900-60053284-5266-4a66-87a3-cddcb2f0d929.gif "Telco Customer Churn Prediction Streamlit")

**This app was featured in [Streamlit Weekly Roundup](https://discuss.streamlit.io/t/weekly-roundup-streamlit-as-a-powerpoint-google-trends-excel-file-updates-and-more/19045#finance-and-business-9)**

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 

* [Kaggle Dataset URL](https://www.kaggle.com/blastchar/telco-customer-churn)

* [GitHub Dataset URL](https://github.com/IBM/telco-customer-churn-on-icp4d/tree/master/data)

### GitHub Project Repository 

* [Customer-Churn-Prediction](https://github.com/ahmedshahriar/Customer-Churn-Prediction)

## View The Project

* View the Project in Jupyter Notebook Html : [![Open in HTML](https://img.shields.io/badge/Html-Open%20Notebook-blue?logo=HTML5)](https://nbviewer.org/github/ahmedshahriar/Customer-Churn-Prediction/blob/main/Telco-Customer-Churn-Prediction.html)

* Open The GitHub Project in Binder : [![Open in Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ahmedshahriar/Customer-Churn-Prediction/main)

  

### View this notebook on kaggle

1. [Churn Prediction I : EDA+Statistical Analysis](https://www.kaggle.com/ahmedshahriarsakib/churn-prediction-i-eda-statistical-analysis)

2. [Churn Prediction II : Triple Boost Stacking+  Optuna](https://www.kaggle.com/ahmedshahriarsakib/churn-prediction-ii-triple-boost-stacking-optuna)