Customer Churn Modelling using Multi-Layer Perceptron

This repository contains a customer churn prediction model trained using the Multi-Layer Perceptron (MLP) to identify customers that will churn in a particular telecommunication company

An Introduction to Artificial Intelligence assignment that uses a Deep Learning Multi-Layer Perceptron model to predict the churn rate of a company's customers

This project develops a deep learning model to predict customer churn for a telecom company. It uses EDA and a Multi-Layer Perceptron model, and includes a platform for users to predict churn with new data.

This repository contains a machine learning model for predicting customer churn based on a set of input features. The model is implemented using a Multi-Layer Perceptron (MLP) architecture with hyperparameters optimized through GridSearchCV.

This repository contains a machine learning model for predicting customer churn based on a set of input features. The model is implemented using a Multi-Layer Perceptron (MLP) architecture with hyperparameters optimized through GridSearchCV.

Customer churn prediction in telecom using Machine Learning and Deep Learning. Implements Decision Tree and Multi-Layer Perceptron (MLP) models on the Telco dataset to classify customers likely to leave. Includes data preprocessing, model training, evaluation, and performance comparison.