Repositório para o #alurachallengedatascience1

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.

⚡ Code for machine Learning Pipeline with Scikit-learn ⚡

Churn Modelling using XGBoost

Customer churn prediction for telecom dataset

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

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.

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

Graduation Project Repository - Bogazici University IE 492 - Spring 2024

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

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

Churn_Modelling Using Deep Learning (Implemented ANN)

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