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.
⚡ Code for machine Learning Pipeline with Scikit-learn ⚡
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.
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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