Projeto realizado durante o primeiro challenge de data science da Alura.

Churn prediction based on bank customers

Churn-modelling using Logistic Regression

Redução da taxa de evasão de clientes (Churn Rate)

Employee Churn Analysis, Feature Importance and Prediction Using Ensembling Model

Analyze IBM Telco Customer data to offer valuable insights for data-driven decision-making on customer retention to reduce churn

LP3_Sem7_Computer_Engineering

This is a complete Project that revolves around churn modeling and it contains every aspect from data cleaning down to model deployment. The data of a bank was used in this implementation. An Artificial Neural Network was trained and used to predict the probability that a given customer would leave the bank(With 87% Test accuracy) and for deploy…

Customer abandonment modeling in big data

Bank churn data to carry out Exploratory data analysis and Logistic regression

Customer Churn Analysis using R & RStudio

We conduct a comprehensive data analysis and model evaluation for a churn prediction problem

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

A comprehensive project predicting customer churn for a telecommunications company using Logistic Regression, Decision Trees, and Random Forest models. Includes data preprocessing, feature engineering, model evaluation, and result visualization to provide actionable insights for customer retention.

Data analysis using python by exploring and processing diverse datasets followed by data visualization. Linear regression , Multilinear regressions used for predictive modeling and outlier detection for identifying and handling data inconsistencies to improve model accuracy and reliability.

Optimizing customer retention with FFNN-based churn prediction model

Verwendung von Tidymodel zur Vorhersage der Kundenabwanderung. | Using Tidymodels to predict customer churn.