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Churn Prediction in Telecom Industry

This repository contains code for performing churn prediction in the telecom industry using logistic regression. The goal of this project is to predict whether a customer is likely to churn or cancel their subscription based on various features and usage patterns.

Dataset

The dataset used for this project consists of customer information, including demographics, usage patterns, and the churn label. The dataset is stored in the file churn_dataset.csv

Dependencies

This project requires the following dependencies:

  • Python (version 3.11)
  • scikit-learn (version 1.2.2)
  • pandas (version 2.0.1)
  • numpy (version 1.22.0)

Results

The logistic regression model achieved an accuracy of 0.7957 in predicting churn on the test set. The model was trained on X% of the data and tested on Y% of the data.