https://github.com/amarnarayandwivedi/ann_project_churn_prediction
A simple End to End Churn Prediction Application Using ANN
https://github.com/amarnarayandwivedi/ann_project_churn_prediction
ann feature-engineering python tensorflow
Last synced: 7 months ago
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A simple End to End Churn Prediction Application Using ANN
- Host: GitHub
- URL: https://github.com/amarnarayandwivedi/ann_project_churn_prediction
- Owner: AmarNarayanDwivedi
- License: gpl-3.0
- Created: 2025-01-07T16:51:58.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-08T12:19:23.000Z (9 months ago)
- Last Synced: 2025-01-20T09:13:39.495Z (9 months ago)
- Topics: ann, feature-engineering, python, tensorflow
- Language: Jupyter Notebook
- Homepage: https://ann-project-churn-prediction-amar.streamlit.app/
- Size: 402 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Churn Prediction Using Artificial Neural Networks (ANN)
## Overview
This project uses Artificial Neural Networks (ANN) to predict customer churn based on a dataset containing customer attributes. The goal is to create a predictive model that identifies whether a customer will leave the company (churn) or remain a customer. By identifying at-risk customers, businesses can take proactive steps to retain them.
The model is built using Keras and TensorFlow libraries, and it is trained on a customer dataset with several features, including demographic information, services subscribed to, and usage patterns.
---
## Project Structure
```bash
ANN_Project_Churn_Prediction/
│── Churn_Modelling.csv # Customer data CSV file
│── model.h5 # Trained model saved in HDF5 format
│── experiments.ipynb # Jupyter notebook with analysis and model training
│── app.py # Code for Streamlit Application
├── requirements.txt # Python dependencies
└── README.md # Project documentation
```
# Churn Prediction Using Artificial Neural Networks (ANN)
## Features of the Project
### 1. Data Preprocessing:
- Clean and transform the raw customer data for model training.
- Handle missing values, categorical variables, and scale numerical features.
### 2. Model Training:
- A neural network model is created using Keras and TensorFlow.
- The model is trained using customer features to predict churn.
- Hyperparameter tuning and evaluation of different architectures.
### 3. Model Evaluation:
- Evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score.
- Confusion matrix and ROC curve visualizations.
### 4. Churn Prediction:
- Predict whether a given customer will churn based on their attributes.
---
## Requirements
To run this project, you'll need Python 3.6 or higher. Install the necessary dependencies using the provided `requirements.txt`.
```bash
pip install -r requirements.txt
```
### Dependencies include:
- **tensorflow**: Deep learning framework used for building the ANN model.
- **pandas**: For data manipulation and cleaning.
- **numpy**: For numerical operations.
- **scikit-learn**: For data preprocessing and evaluation.
- **matplotlib** and **seaborn**: For visualizations.
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/AmarNarayanDwivedi/ANN_Project_Churn_Prediction.git
```
2. **Navigate to the project directory:**
```bash
cd ANN_Project_Churn_Prediction
```
3. **Install dependencies:**
```bash
pip install -r requirements.txt
```