Telecom Churn Case Study: Logistic Regression

Problem Statement:

In this project, we aim to predict whether a particular customer will switch to another telecom provider or not, a process referred to as churning and not churning in telecom terminology.

Objectives:

• Predict Churn: Develop a logistic regression model using 21 predictor variables to predict customer churn.
• Identify Key Factors: Determine the variables that significantly influence a customer's decision to churn.
• Model Evaluation: Assess the performance of the logistic regression model to ensure its accuracy and reliability in predicting customer churn.

Dataset:

The dataset includes 21 predictor variables related to customer demographics, usage patterns, and other relevant factors, along with the target variable indicating whether the customer churned or not.

Key Components:

Code: Scripts for data preprocessing, analysis, and model building.

Data: The dataset used for the analysis.

Documentation: Detailed explanation of the steps