Repositório para o #alurachallengedatascience1

Machine-Learning-1

Customer churn prediction for telecom dataset

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.

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

Churn Modelling using XGBoost

⚡ Code for machine Learning Pipeline with Scikit-learn ⚡

Repositório destinado a documentar o desafio de Data Science da Alura #alurachallengedatascience1

Churn_Modelling Using Deep Learning (Implemented ANN)

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.

This repository presents a machine learning classification project focused on predicting customer churn in the telecommunications industry.

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.

Данный проект выполнен в процессе обучения в Яндекс Практикум по программе Специалист Data Science +. Проект посвящен прогнозированию оттока клиентов банка на основе исторических данных.

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

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

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

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.