Repositorio del proyecto de predicción del problema Telco Customer Churn (kaggle)

The primary objective of this project was to develop a predictive model to accurately forecast customer churn within the telecommunications industry. Utilizing historical customer data, I applied machine learning techniques to identify and analyze relevant features that influence churn behavior

This project aims to build a predictive model that helps identify customers at risk of churning (leaving the service) in a telecommunications company. By leveraging machine learning techniques, we can help businesses take preemptive actions to retain valuable customers and reduce churn rates.

This project aims to optimize bank client retention by predicting customer churn using machine learning models, with LightGBM being the most effective. SHAP analysis identified key churn factors, and the model's financial impact showed a potential benefit of $159,157.

This repository contains the final project of a 400-hour Data Analytics bootcamp at Le Wagon. The project focuses on analyzing subscriber churn for HomeSwap, a platform where members exchange homes for travel. The goal is to identify churn factors and provide actionable insights through a business intelligence dashboard.

Analyzed the top reasons and characteristics of churners and recommended potential actions to be taken to retain customers

Analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Customer churn prediction

The analysis identifies key bottlenecks, variability in processes, and provides actionable recommendations to optimize operations.

A Power BI project with the churn analysis of a multinational bank's customer data.

MLOps project Training and Deployment of Churn prediction model

Churn Analysis Hands On Project

This project aims to predict customer churn using machine learning algorithms. The project includes data preprocessing, feature engineering, and model evaluation.

Churn prediction aims to identify customers who are likely to cancel/switch their accounts based on their characteristics and behavior patterns. This helps banks prioritize retention efforts.

Customer Churn Analysis in Telecom Industry

This Churn Analysis evaluates the financial impact on total revenue by offering controlled discounts to targeted customers for retention.

Customer Churn Analysis explores factors influencing customer attrition using various data visualizations and statistical tests.

I analyzed a dataset of telco customers' activity and how it relates to churn. This repository is designed for data cleaning and exploratory data analysis (EDA), which will be expanded into feature engineering and modelling.

PWC Job Simulation

Customer Churn Analysis (Telco Dataset)