📊 Client Churn Prediction

This project focuses on predicting customer churn using a synthetic dataset and a logistic regression model. The goal is to identify clients who are likely to leave a service based on their usage patterns and profile data.

🚀 Features

The dataset includes the following features:

• tenure: Time with the company (in months)
• monthly_charges: Monthly billing amount
• total_charges: Total amount billed to date
• num_support_calls: Number of customer support calls
• uses_internet: Binary flag indicating internet usage (0 = No, 1 = Yes)
• contract_type: Type of contract
  • 0 = Month-to-month
  • 1 = One-year
  • 2 = Two-year

🧠 Model

The script trains a Logistic Regression model and evaluates its performance using:

• Accuracy
• Confusion matrix

The trained model is saved as churn_model.pkl.

🛠 How to Run

Ensure you have the required dependencies installed (see below), then run the script:

python churn_model.py