Churn analysis library

Repositório para o #alurachallengedatascience1

Customer churn prediction for telecom dataset

A machine learning project that predicts customer churn using the Telco dataset.

Churn Modelling with Bank Customer Prediction using ANN: Utilizing Artificial Neural Networks for predicting customer churn in banking scenarios.

Machine-Learning-1

- The project is based on a bank dataset where we analyzed each feature. To understand why customers are leaving.

A churn model is a mathematical representation of how churn impacts your business. Churn calculations are built on existing data (the number of customers who left your service during a given time period). A predictive churn model extrapolates on this data to show future potential churn rates.

Churn Modelling using XGBoost

Challenge de Data Science da Alura - Alura-Voz

Used Random Forest model to predict customers likely to churn and recommended discount and pricing strategies to improve customers retention.

Churn_Modelling Using Deep Learning (Implemented ANN)

Churn Modelling - unusual rate at which customers leaving the company, we need to figure out why? we need to understand the problem? We actually need to create a demographic segmentation model to tell the bank/company which customers are at high risk of leaving.

Churn prediction for banking customers using logistic regression and decision trees, implemented from scratch in R.

End-to-end customer churn analysis using SQL, Python, EDA, and machine learning with business-focused insights and predictive modeling.

Employee Churn Analysis, Feature Importance and Prediction Using Ensembling Model

This repository aims to find out whether or not customers who churn from telecommunication companies. And looking for modeling solutions to get predictions of future churn.

⚡ Code for machine Learning Pipeline with Scikit-learn ⚡

Built a logistic regression based predictive model to identify customers at high risk of churn and identify the main indicators of churn.