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This repository hosts a logistic regression model for telecom customer churn prediction. Trained on historical data, it analyzes customer attributes like account weeks, contract renewal status, and data plan usage to forecast churn likelihood. Its insights aid telecom companies in proactively retaining customers and mitigating churn rates.

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DDILLOUD/Telecom-Churn-Prediction-Model

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Telecom Churn Prediction Model

This repository contains a logistic regression model for predicting telecom customer churn.

Purpose

The purpose of this model is to predict whether a telecom customer is likely to churn or not, based on various features such as account weeks, contract renewal status, data plan usage, customer service calls, etc.

Model Type

The model uses logistic regression for binary classification.

Dependencies

  • Python 3.x
  • scikit-learn
  • pandas
  • numpy

Usage

To use the model, follow these steps:

  1. Clone the repository to your local machine.
  2. Install the required dependencies using pip install -r requirements.txt.
  3. Run the telecom_prediction_model.py script to load the model, preprocess data, and make predictions.

About

This repository hosts a logistic regression model for telecom customer churn prediction. Trained on historical data, it analyzes customer attributes like account weeks, contract renewal status, and data plan usage to forecast churn likelihood. Its insights aid telecom companies in proactively retaining customers and mitigating churn rates.

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