https://github.com/laavanjan/ann-classification-churn
https://github.com/laavanjan/ann-classification-churn
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/laavanjan/ann-classification-churn
- Owner: laavanjan
- License: gpl-3.0
- Created: 2025-02-05T14:47:53.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-05T15:38:47.000Z (8 months ago)
- Last Synced: 2025-02-05T16:23:57.590Z (8 months ago)
- Language: Jupyter Notebook
- Size: 351 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚀 ANN Classification Churn
✨ **A deep learning-based classification model to predict customer churn using Artificial Neural Networks (ANN).** ✨
## 📌 Overview
This project leverages an Artificial Neural Network (ANN) to classify customer churn based on historical data. The model is built using TensorFlow and Scikit-Learn and is deployed via Streamlit for easy interaction. 🎯
🔗 **GitHub Repository:** [ANN Classification Churn](https://github.com/laavanjan/ANN-classification-churn) 🔥
🌐 **Live Streamlit App:** [View Here](https://ann-classification-churn-7umnppseyrpddza3tkekc4.streamlit.app/) 🎉
---
## 📂 Features 🚀
✅ Preprocessed dataset for accurate predictions
✅ Fully connected ANN with optimized hyperparameters
✅ Model evaluation with performance metrics 📊
✅ Interactive UI via Streamlit for real-time classification 🎨
✅ TensorBoard integration for visualization 🖥️
---
## ⚙️ Installation & Setup 🔧
### 1️⃣ Clone the repository 💻
```bash
git clone https://github.com/laavanjan/ANN-classification-churn.git
cd ANN-classification-churn
```
### 2️⃣ Create and activate a Conda environment 🐍
```bash
conda create --name ann_env python=3.11 -y
conda activate ann_env
```
### 3️⃣ Install dependencies 📦
```bash
pip install -r requirements.txt
```
Alternatively, install manually:
```bash
pip install tensorflow==2.15.0 pandas numpy scikit-learn tensorboard matplotlib streamlit ipykernel
```
---
## 🚀 Usage 🎯
### Running the Model 🏃♂️
```bash
python app.py # Modify as needed
```
### Launching **Streamlit** UI 🌍
```bash
streamlit run app.py
```
Then open `http://localhost:8501/` in your browser. 🌐
---
## 📊 Model Training & Evaluation 🏋️♂️
- 📈 The model is trained on labeled customer data.
- 🔄 Uses backpropagation and optimizer tuning for improved accuracy.
- 🎯 Evaluated using precision, recall, and F1-score metrics.
- 🖥️ TensorBoard is used for tracking training performance.
To launch TensorBoard:
```bash
tensorboard --logdir=logs
```
---
## 📷 Screenshots 🖼️
---
## 🤝 Contributing 🤲
💡 Contributions are welcome! Feel free to fork the repo, create a new branch, and submit a pull request. 🚀
---
## 📝 License 📜
This project is licensed under the GPL License. ⚖️
---
## 📧 Contact 📩
For any queries, reach out via GitHub Issues or email at `your_email@example.com`. 📬
---
### ⭐ Don't forget to **star** the repo if you find it useful! 🌟