https://github.com/hchandeepa/customer_churn_prediction_using_ann

In this project I build a customer churn prediction model using artificial neural network or ANN.
https://github.com/hchandeepa/customer_churn_prediction_using_ann

artficial-neural-network deep-learning jupyter-notebook python

Last synced: 5 days ago
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In this project I build a customer churn prediction model using artificial neural network or ANN.

• Host: GitHub
• URL: https://github.com/hchandeepa/customer_churn_prediction_using_ann
• Owner: HChandeepa
• Created: 2024-08-11T10:17:29.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-11T17:24:44.000Z (3 months ago)
• Last Synced: 2024-09-13T16:20:02.541Z (2 months ago)
• Topics: artficial-neural-network, deep-learning, jupyter-notebook, python
• Language: Jupyter Notebook
• Homepage:
• Size: 241 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer Churn Prediction Model

This project focuses on building a customer churn prediction model using an Artificial Neural Network (ANN). Customer churn is a critical metric for understanding why and how customers are leaving a business, and predicting this can help in implementing strategies to retain them.

## Project Overview

### Model Development:

We use the Telecom Customer Churn dataset from Kaggle to build a deep learning model for predicting customer churn. The model is developed using an Artificial Neural Network (ANN) to classify whether a customer is likely to churn or not.

### Model Evaluation:

We evaluate the model's performance using key metrics such as precision, recall, and accuracy. Additionally, we utilize a confusion matrix and a classification report to get deeper insights into the model's effectiveness and to understand its prediction capabilities.

Dataset

The dataset used in this project is the Telecom Customer Churn dataset, which can be accessed via the following link:

https://www.kaggle.com/datasets/blastchar/telco-customer-churn