https://github.com/thee-unruly/predicting-customer-churn-with-neural-networks-a-deep-learning-approach

The Customer Churn ML model aims to forecast customer attrition using Neural Networks, enabling businesses to proactively retain customers. By leveraging advanced analytics, it empowers companies to identify patterns, enhance customer satisfaction, and make informed decisions, ultimately fostering sustainable growth and profitability.
https://github.com/thee-unruly/predicting-customer-churn-with-neural-networks-a-deep-learning-approach

artificial-neural-networks customer-churn-prediction machine-learning

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The Customer Churn ML model aims to forecast customer attrition using Neural Networks, enabling businesses to proactively retain customers. By leveraging advanced analytics, it empowers companies to identify patterns, enhance customer satisfaction, and make informed decisions, ultimately fostering sustainable growth and profitability.

• Host: GitHub
• URL: https://github.com/thee-unruly/predicting-customer-churn-with-neural-networks-a-deep-learning-approach
• Owner: Thee-Unruly
• Created: 2023-12-09T21:36:01.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-09T21:40:54.000Z (over 1 year ago)
• Last Synced: 2025-01-07T22:10:06.611Z (5 months ago)
• Topics: artificial-neural-networks, customer-churn-prediction, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 279 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
**Project Title:** Predicting Customer Churn with Neural Networks: A Deep Learning Approach

**Project Overview:**

In this project, we aim to develop a machine learning model based on artificial neural networks to predict customer churn in a company. Customer churn, or the loss of customers, is a critical concern for businesses, and predicting it accurately can help companies take proactive measures to retain valuable customers. 

**Objectives:**

1. **Data Preprocessing:**

   - Cleanse and preprocess the dataset, handling missing values and encoding categorical variables.

   - Normalize numerical features to bring them to a standard scale.

2. **Feature Engineering:**

   - Explore and identify relevant features that contribute to customer churn prediction.

   - Create new features or transformations that might enhance model performance.

3. **Neural Network Model:**

   - Design and implement an artificial neural network (ANN) architecture for predicting customer churn.

   - Experiment with various neural network configurations, layers, and activation functions to optimize model performance.

4. **Training and Validation:**

   - Split the dataset into training and validation sets to train and evaluate the model.

   - Utilize techniques such as cross-validation to ensure the robustness of the model.

5. **Model Evaluation:**

   - Assess the model's performance using metrics such as accuracy, precision, recall, and F1 score.

   - Utilize a confusion matrix to gain insights into the true positive, true negative, false positive, and false negative predictions.

6. **Hyperparameter Tuning:**

   - Optimize the hyperparameters of the neural network to enhance its predictive capabilities.

7. **Interpretability and Visualization:**

   - Visualize the neural network architecture and key performance indicators.

   - Interpret and communicate the results, highlighting the factors contributing to customer churn.

**Importance:**

Predicting customer churn is crucial for businesses to:

- **Retain Customers:** Identifying potential churners allows businesses to take proactive measures, such as targeted marketing or personalized incentives, to retain valuable customers.

  

- **Resource Allocation:** Companies can allocate resources more efficiently by focusing efforts on customers with a higher likelihood of churning.

- **Financial Impact:** Reducing customer churn can have a direct positive impact on the company's revenue and profitability.

- **Enhanced Customer Experience:** Understanding the factors leading to churn enables companies to address underlying issues and improve overall customer satisfaction.

By leveraging artificial neural networks, this project aims to provide a powerful and accurate tool for businesses to predict and mitigate customer churn, ultimately contributing to the company's long-term success.