🔗 GitHub Repo Link: https://github.com/Waqas56jb/CustomerChurnPrediction
I am excited to present my Customer Churn Prediction project, where I developed a neural network model to predict customer churn. This project highlights the application of machine learning techniques in identifying customers who are likely to leave, which is essential for enhancing customer retention strategies.
- 🐍 Python: The core programming language used for implementing the neural network.
- 🔢 TensorFlow: An advanced open-source library for numerical computation and machine learning.
- 📊 Keras: A high-level neural networks API that runs on top of TensorFlow.
- 📉 Matplotlib: A library for creating visualizations of model performance and training metrics.
- 📊 Data Preprocessing: Techniques for cleaning and preparing customer data for model training.
- 🏗️ Model Building: Designing a neural network architecture for churn prediction.
- 🏋️♂️ Training & Validation: Training the model and assessing its performance using validation data.
- 📈 TensorBoard: Monitoring and visualizing training metrics in real-time.
- 🧪 Model Evaluation: Evaluating the model’s accuracy and effectiveness in predicting churn.
The model achieved an accuracy of 80%, showcasing its effectiveness in predicting customer churn. This result demonstrates the model’s capability to identify at-risk customers and supports proactive retention strategies.
Check out the video walkthrough where I explain the code and methodology used in this project. This repository showcases my expertise in machine learning and my dedication to solving practical problems with advanced technologies.
🔗 GitHub Repo Link: https://github.com/Waqas56jb/BankChurnPrediction
I am pleased to share my Bank Churn Prediction project, where I developed a neural network model aimed at predicting customer churn in the banking sector. This project demonstrates the use of machine learning techniques to enhance customer retention in financial institutions.
- 🐍 Python: The primary language used for implementing the neural network model.
- 🔢 TensorFlow: A robust library for numerical computation and machine learning.
- 📊 Keras: A high-level API used for building and training neural networks.
- 📉 Matplotlib: Utilized for visualizing the performance and training metrics of the model.
- 📊 Data Preprocessing: Preparing and cleaning bank customer data for model training.
- 🏗️ Model Building: Creating a neural network model to predict bank customer churn.
- 🏋️♂️ Training & Validation: Training the model and validating its performance.
- 📈 TensorBoard: Real-time visualization and monitoring of training metrics.
- 🧪 Model Evaluation: Assessing model accuracy and robustness to ensure reliable predictions.
The neural network achieved an 80% accuracy rate in predicting bank customer churn, showcasing its effectiveness in identifying potential churners. This model provides valuable insights for improving customer retention strategies in the banking sector.
Explore the code walkthrough video where I detail each step of the project. This repository highlights my skills in machine learning and my commitment to applying innovative techniques to real-world challenges.
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