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https://github.com/durai0706/telecom_churn_prediction_site
Customer churn, where customers leave a service provider for a competitor, poses significant challenges for telecom companies. This project develops a predictive model using a dataset of 100,000 records with 100 variables, aiming to identify likely churners and provide actionable insights to enhance retention strategies
https://github.com/durai0706/telecom_churn_prediction_site
dj flask h ju py streamlit
Last synced: 2 days ago
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Customer churn, where customers leave a service provider for a competitor, poses significant challenges for telecom companies. This project develops a predictive model using a dataset of 100,000 records with 100 variables, aiming to identify likely churners and provide actionable insights to enhance retention strategies
- Host: GitHub
- URL: https://github.com/durai0706/telecom_churn_prediction_site
- Owner: DURAI0706
- Created: 2024-06-27T09:35:09.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-07-01T14:43:39.000Z (3 months ago)
- Last Synced: 2024-09-28T17:01:18.942Z (5 days ago)
- Topics: dj, flask, h, ju, py, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 10.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Customer Churn Prediction
## Project Overview
Customer churn, the rate at which customers leave a service provider for a competitor, is a significant challenge for telecom companies. High churn rates lead to substantial revenue losses, increased customer acquisition costs, and reduced market share. This project aims to develop a predictive model that accurately identifies customers likely to churn, enabling telecom companies to implement proactive retention strategies, improve customer satisfaction, and enhance long-term loyalty.
## Objective
The objective of this project is to develop a machine learning model that predicts customer churn and provides actionable insights to help telecom companies prioritize retention efforts and mitigate churn.
## Dataset
The dataset used in this project contains approximately 100,000 records and 100 variables, including customer demographics, usage patterns, service plans, billing information, and more. The target variable is `churn`, indicating whether a customer has left the service provider.
## Deployment
The model has been deployed using three different frameworks:
- **Streamlit:** [Streamlit App](https://telecom-hmb5wxajocib8bnamx8pra.streamlit.app/)
- **Flask:** [Flask App](https://telecom-79j7.onrender.com/)
- **Django:** [Django App](https://telecom-1-ibrd.onrender.com/)
