Client Churn Prediction Model by Muhammad BilalClient Churn Prediction Model by Muhammad Bilal

Client Churn Prediction Model

Muhammad Bilal

Completed work

Data Analyst

Data Scientist

Python

Data

📊 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.

Note: This project was built as a personal learning project with assistance from AI tools like ChatGPT to generate initial code structure, documentation, and feature ideas. All content was reviewed and refined by me to ensure understanding and performance.

🚀 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

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Completed work

Posted May 24, 2025

Developed a logistic regression model to predict customer churn.

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Timeline

Oct 1, 2024 - Nov 1, 2024

Clients

Self project