churn

Project Overview

This project focuses on identifying customers who are likely to cancel their subscriptions or stop using a service. By leveraging Machine Learning, the goal is to help businesses understand the reasons behind customer attrition and take proactive steps to improve retention rates.

?️ Key Features

Data Cleaning & Preprocessing: Handling missing values, encoding categorical variables, and feature scaling.

Exploratory Data Analysis (EDA): Visualizing trends to identify the main "churn drivers" (e.g., contract type, monthly charges, or tenure).

Machine Learning Modeling: Implementing and comparing various classification models like Logistic Regression, Random Forest, and XGBoost.

Evaluation: Using metrics such as Accuracy, Precision, Recall, and F1-Score to ensure the model's reliability.