Repositório para o #alurachallengedatascience1
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Updated
May 30, 2022 - Jupyter Notebook
Repositório para o #alurachallengedatascience1
Customer churn prediction for telecom dataset
Machine-Learning-1
Used Random Forest model to predict customers likely to churn and recommended discount and pricing strategies to improve customers retention.
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.
Employee Churn Analysis, Feature Importance and Prediction Using Ensembling Model
This Project is on the Customer Churn Prediction for a Particular bank in Europe. This Project is being developed for the DS 5220, Supervised Machine Learning under Dr. David Brady
Challenge de Data Science da Alura - Alura-Voz
This repository presents a machine learning classification project focused on predicting customer churn in the telecommunications industry.
Projeto realizado durante o primeiro challenge de data science da Alura.
Churn Modelling using XGBoost
Churn prediction based on bank customers
Churn-modelling using Logistic Regression
Redução da taxa de evasão de clientes (Churn Rate)
Churn prediction for banking customers using logistic regression and decision trees, implemented from scratch in R.
⚡ Code for machine Learning Pipeline with Scikit-learn ⚡
Analyze IBM Telco Customer data to offer valuable insights for data-driven decision-making on customer retention to reduce churn
Graduation Project Repository - Bogazici University IE 492 - Spring 2024
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