The "Churn Prediction" project analyzes customer data to identify factors leading to churn 📉🤔. Using machine learning algorithms, it predicts which customers are likely to leave, enabling businesses to implement targeted retention strategies and improve customer satisfaction.

This project develops a machine learning model to predict customer churn for a California-based telecom company using data from 7043 customers. Our goal is to enhance customer retention strategies through detailed data analysis and feature engineering.

This project focuses on predicting customer churn using machine learning algorithms. By analyzing historical customer data, the model aims to identify patterns that indicate a customer is likely to stop using a service, enabling businesses to take proactive measures to retain valuable customers.

Churn prediction using Random Forest and Decision Tree Classifiers.

This repository contains the data, code, and documentation for a project to analyze and predict churn in PowerCo's SME customer segment. The project includes data exploration, cleaning, and transformation, as well as the development and evaluation of a machine learning model to predict churn based on price sensitivity and other relevant factors.