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

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.

Graduation Project Repository - Bogazici University IE 492 - Spring 2024

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

LP3_Sem7_Computer_Engineering

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

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

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

Optimizing customer retention with FFNN-based churn prediction model

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.

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

Churn_Modelling Using Deep Learning (Implemented ANN)

Churn-modelling using Logistic Regression

⚡ Code for machine Learning Pipeline with Scikit-learn ⚡

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

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

Churn Modelling using XGBoost

Customer abandonment modeling in big data